DEVELOPMENT OF A NUTRITION SCREENING TOOL FOR THE PREDICTION OF BIRTH OUTCOMES OF WOMEN ATTENDING THE ANTENATAL CLINIC AT PELONOMI HOSPITAL Elizabeth Margaretha Jordaan 2006012054 DEVELOPMENT OF A NUTRITION SCREENING TOOL FOR THE PREDICTION OF BIRTH OUTCOMES OF WOMEN ATTENDING THE ANTENATAL CLINIC AT PELONOMI HOSPITAL Elizabeth Margaretha Jordaan - 2006012054 Thesis submitted in accordance with the academic requirements for the degree D Dietetics in the Faculty of Health Sciences Department of Nutrition and Dietetics University of the Free State Bloemfontein South Africa 2021 Promoter: Prof CM Walsh Co-promoter: Prof G Joubert DECLARATION I, Elizabeth Margaretha Jordaan, declare that the thesis that I herewith submit for the Doctoral Degree in Dietetics at the University of the Free State, is my independent work, and that I have not previously submitted it for a qualification at another institution of higher education. EM Jordaan November 2020 i ACKNOWLEDGEMENTS I would like to express my sincere gratitude to the following without whom this study would not have been possible:  My Heavenly Father for providing me with the opportunity to study further and to complete my work;  Prof Corinna Walsh, my promotor, for her continuous guidance, encouragement, mentorship and support;  Prof Gina Joubert, my co-promotor, for her guidance regarding the statistical analysis of the data, as well as her valuable input and guidance;  Ms Liska Robb, my co-investigator, for her assistance with the data analysis and constant support;  Staff from the Department of Nutrition and Dietetics for their support and encouragement;  Dr Jennifer Osei-Ngounda for overseeing and ensuring the quality of data collection as well as Ms Michelle du Plooy, Ms Desiré Greyvensteyn, Ms Khanyi Khumalo and Ms Natasha De Figueiredo, the fieldworkers, for their assistance with the data collection;  The participants for their willingness to take part in this study;  The dietitians at Pelonomi Hospital, Ms Nicolien Wydeman, Ms Carika Weideman, Ms Carine Luwes and Ms Elza Hunter for assisting with the collection of the data from the Road to Health Booklets; and  My husband, family and friends for their prayers and support. ii SUMMARY In countries with limited resources, poor birth outcomes significantly contribute to morbidity and mortality and hold short- and long-term consequences for both the mother and her offspring. Optimal nutrition during pregnancy may lead to improved birth outcomes. Nutrition screening during pregnancy may identify women who are at risk of poor birth outcomes, including premature birth and growth retardation (stunting or wasting). This quantitative, cohort analytical study aimed to develop a nutrition screening tool to identify women at risk of poor birth outcomes among pregnant women attending the high-risk antenatal clinic at Pelonomi Hospital, Bloemfontein. During the first phase, questionnaires were completed for 682 pregnant women in their second or third trimester using structured interviews after which each participant was weighed and measured according to standard techniques. During the interview, information related to socio-demographic and household information, reported health and lifestyle, pregnancy history, household food security (using the Household Food Insecurity Access Scale) and individual dietary intake (using a quantitative food frequency questionnaire and a 24-hour recall) was obtained. Participants were asked to return to Pelonomi Hospital after the delivery to provide the information recorded in the Road to Health Booklet at their babies' birth. A total of 331 mothers returned and, together with their 347 babies, were included in the second phase of the study. Associations between socio-demographic, reported health and lifestyle, and nutrition information and the following individual birth outcomes were investigated, namely method of delivery, gestational age at delivery, birth length-for-age and birth weight-for- length, as well as overall poor birth outcome (defined as prematurity, or birth length-for-age below the -2 SD, or birth weight-for-length below the -2 SD). Separate theme-specific (socio- demographic, reported health and lifestyle and nutrition) logistic regressions with backward selection (p<0.05) were used to select significant independent factors associated with overall birth outcome. Variables with a p-value of < 0.15 on univariate analysis were considered for inclusion in the model. Variables found to be significant in the theme-specific logistic regressions were considered for inclusion in the final logistic regression, which identified variables to be included in the screening tool. iii The median age of participants was 31.9 years (interquartile range 26.8–36.5 years). Most women had access to basic amenities such as a flush toilet and/or their own tap, inside or outside the house. A concerning percentage of women continued to smoke (30.0%), use snuff or chew tobacco (40.3%), or use alcohol (12.1%) during pregnancy. A large percentage of participants were exposed to various forms of stress during their pregnancy, including not being able to find a job for more than six months (themselves or a close family member) (70.9%) and having so much debt that they did not know how they were going to repay it (36.0%). A high prevalence of overweight and obesity as well as food insecurity was observed. About half of participants returned for phase two of the study. Significant differences were observed in terms of socio-demographic indicators and nutrient intake between women who provided their babies' birth information (responders) compared to those who did not (non- responders). Generally, responders were better off, indicating that responders may not have been representative of the population and may be indicative of non-response bias. Most babies were born full-term and by means of caesarean section, with almost one in ten being part of a twin pregnancy. Low birth weight (<2.5 kg) was evident in 14.4%. According to the World Health Organization’s (WHO) Z-scores, 12.6% of neonates were underweight, 18.9% were stunted and 14.5% were wasted at birth. Just over a third of neonates were exposed to Human Immunodeficiency Virus (HIV). Overall, 37.1% of neonates experienced overall poor birth outcome. Several social determinants of health were significantly associated with at least one of the defined birth outcomes. Significant associations between various reported health and lifestyle factors and the individual birth outcomes were also observed, most notably, premature delivery. Significant associations between individual poor birth outcomes and nutrient intakes seem to indicate that improved birth outcomes are associated with improved nutrient intake. The proposed tool included all variables identified in the final logistic regression model of predictors of overall birth outcome namely ownership of a stove, participant’s highest level of education, participant’s employment status, being in real danger of being killed by criminals in the past six months (themselves or a close family member), being diagnosed with or treated for high blood pressure during the current pregnancy, number of babies expected and gestational body mass index (replaced by current body mass index for ease of application in the screening tool). Experiencing weight loss of more than 3 kg during the current pregnancy iv was added to the tool. A score of two or more was considered as indicative of an overall poor birth outcome since this cut-off gave the best combination of sensitivity and specificity namely. 68.8% and 70.5% respectively, while the positive predictive value was 58.1%, and the negative predictive value was 79.1%. Pregnant women should be educated on the importance of regularly attending antenatal follow-up visits, focusing on the risks associated with poor lifestyle choices during pregnancy and the benefits of following a healthy diet and lifestyle. Pregnant women who regularly attend antenatal visits may be more likely to be screened and referred for specialised nutrition care at an early stage when such interventions can still make a difference to birth outcomes. Keywords: pregnancy, socio-demography, reported health, food security, nutrient intake, responders, non-responders, premature, stunting, wasting v CONTENTS LIST OF TABLES .......................................................................................................................... xi LIST OF FIGURES ...................................................................................................................... xiv LIST OF APPENDICES ................................................................................................................ xv LIST OF ABBREVIATIONS ......................................................................................................... xvi CHAPTER 1 – ORIENTATION AND MOTIVATION ........................................................................ 1 1.1 INTRODUCTION ....................................................................................................................... 1 1.2 PROBLEM STATEMENT ............................................................................................................ 6 1.3 AIM AND OBJECTIVES ............................................................................................................. 7 1.4 STRUCTURE OF THIS THESIS .................................................................................................... 8 1.5 REFERENCES ............................................................................................................................ 9 CHAPTER 2 – LITERATURE REVIEW .......................................................................................... 13 2.1 INTRODUCTION ..................................................................................................................... 13 2.2 THE DEVELOPMENTAL ORIGINS OF HEALTH AND DISEASE HYPOTHESIS ............................. 13 2.3 THE MISMATCH HYPOTHESIS ................................................................................................ 15 2.4 SOCIAL DETERMINANTS OF HEALTH ..................................................................................... 16 2.5 ENVIRONMENTS THAT SHAPE HEALTH AND DEVELOPMENT ............................................... 18 2.5.1 Family environment ...................................................................................................... 18 2.5.2 Community environment .............................................................................................. 21 2.5.3 Physical environment .................................................................................................... 21 2.5.4 Individual-level factors .................................................................................................. 24 2.6 THE DEVELOPMENT OF A NUTRITION SCREENING TOOL ..................................................... 45 2.6.1 Basic requirements of screening tools .......................................................................... 52 2.6.2 Steps in the development of a screening tool .............................................................. 52 2.7 CONCLUSION ......................................................................................................................... 53 2.8 REFERENCES .......................................................................................................................... 54 CHAPTER 3 METHODOLOGY .................................................................................................... 77 3.1 INTRODUCTION ..................................................................................................................... 77 3.2 STUDY DESIGN....................................................................................................................... 77 3.3 SETTING, POPULATION AND SAMPLING ............................................................................... 77 vi 3.3.1 Study setting ................................................................................................................. 77 3.3.2 Study population ........................................................................................................... 78 3.3.3 Sampling ........................................................................................................................ 79 3.4 MEASUREMENTS ................................................................................................................... 79 3.4.1 Information collected and operational definitions ....................................................... 79 3.4.2 Techniques and study procedures ................................................................................ 88 3.4.3 Validity and reliability ................................................................................................... 91 3.4.4 Pilot study ..................................................................................................................... 92 3.4.5 Data analysis ................................................................................................................. 93 3.5 STATISTICAL ANALYSIS .......................................................................................................... 93 3.5.1 Technique for developing the nutrition screening tool ................................................ 95 3.6 ETHICAL ASPECTS .................................................................................................................. 96 3.7 SUMMARY ............................................................................................................................. 97 3.8 REFERENCES .......................................................................................................................... 97 CHAPTER 4 – CHARACTERISTICS OF WOMEN ATTENDING THE ANTENATAL CLINIC AT PELONOMI HOSPITAL, BLOEMFONTEIN: A COMPARISON OF WOMEN WITH KNOWN BIRTH OUTCOMES AND WOMEN WITH UNKNOWN BIRTH OUTCOMES ......................................... 100 4.1 ABSTRACT ............................................................................................................................ 100 4.2 INTRODUCTION ................................................................................................................... 102 4.3 METHODS ............................................................................................................................ 104 4.3.1 Study design and site description ............................................................................... 104 4.3.2 Sampling ...................................................................................................................... 104 4.3.3 Study procedures ........................................................................................................ 105 4.3.4 Statistical analysis ....................................................................................................... 107 4.4 RESULTS............................................................................................................................... 108 4.5 DISCUSSION ......................................................................................................................... 123 4.6 CONCLUSION AND RECOMMENDATIONS ........................................................................... 133 4.7 ACKNOWLEDGEMENTS ....................................................................................................... 134 4.8 REFERENCES ........................................................................................................................ 134 CHAPTER 5 - BIRTH OUTCOMES OF NEONATES BORN TO MOTHERS WHO RECEIVED ANTENATAL CARE AT PELONOMI HOSPITAL, BLOEMFONTEIN ............................................. 146 5.1 ABSTRACT ............................................................................................................................ 146 5.2 INTRODUCTION ................................................................................................................... 147 5.3 METHODS ............................................................................................................................ 149 vii 5.3.1 Study setting, study design, population and sampling ............................................... 149 5.3.2 Study procedures ........................................................................................................ 151 5.3.3 Statistical analysis ....................................................................................................... 151 5.4 RESULTS............................................................................................................................... 152 5.5 DISCUSSION ......................................................................................................................... 154 5.6 CONCLUSION AND RECOMMENDATIONS ........................................................................... 161 5.7 ACKNOWLEDGEMENTS ....................................................................................................... 162 5.8 REFERENCES ........................................................................................................................ 162 CHAPTER 6 - ASSOCIATIONS BETWEEN INDICATORS OF SOCIO-DEMOGRAPHY AND BIRTH OUTCOMES OF PREGNANT WOMEN ATTENDING THE ANTENATAL CLINIC AT PELONOMI HOSPITAL, BLOEMFONTEIN ................................................................................................... 171 6.1 ABSTRACT ............................................................................................................................ 171 6.2 INTRODUCTION ................................................................................................................... 172 6.3 MATERIALS AND METHODS ................................................................................................ 173 6.3.1 Study design, setting and participants ........................................................................ 173 6.3.2 Outcomes measures ................................................................................................... 174 6.3.3 Exposure measurements ............................................................................................ 175 6.3.4 Statistical analysis ....................................................................................................... 175 6.3.5 Ethical considerations ................................................................................................. 174 6.4 RESULTS............................................................................................................................... 176 6.5 DISCUSSION ......................................................................................................................... 190 6.6 CONCLUSION ....................................................................................................................... 195 6.7 ACKNOWLEDGEMENTS ....................................................................................................... 196 6.8 REFERENCES ........................................................................................................................ 196 CHAPTER 7 - ASSOCIATIONS BETWEEN REPORTED HEALTH AND LIFESTYLE AND BIRTH OUTCOMES OF PREGNANT WOMEN ATTENDING THE ANTENATAL CLINIC AT PELONOMI HOSPITAL, BLOEMFONTEIN ................................................................................................... 201 7.1 ABSTRACT ............................................................................................................................ 201 7.2 INTRODUCTION ................................................................................................................... 202 7.3 MATERIALS AND METHODS ................................................................................................ 204 7.3.1 Study design and participants ..................................................................................... 204 7.3.2 Outcomes measures ................................................................................................... 204 7.3.3 Exposure measurements and techniques ................................................................... 205 7.3.4 Statistical analysis ....................................................................................................... 206 viii 7.3.5 Ethical considerations ................................................................................................. 206 7.4 RESULTS............................................................................................................................... 207 7.5 DISCUSSION ......................................................................................................................... 228 7.6 CONCLUSION AND RECOMMENDATIONS ........................................................................... 234 7.7 ACKNOWLEDGEMENTS ....................................................................................................... 235 7.8 REFERENCES ........................................................................................................................ 235 CHAPTER 8 - HOUSEHOLD FOOD SECURITY AND NUTRIENT INTAKE AND THEIR ASSOCIATION WITH BIRTH OUTCOMES OF PREGNANT WOMEN ATTENDING THE ANTENATAL CLINIC AT PELONOMI HOSPITAL, BLOEMFONTEIN ................................................................................ 242 8.1 ABSTRACT ............................................................................................................................ 242 8.2 INTRODUCTION ................................................................................................................... 244 8.3 MATERIALS AND METHODS ................................................................................................ 246 8.3.1 Study design and participants ..................................................................................... 246 8.3.2 Outcomes measures ................................................................................................... 246 8.3.3 Exposure measurements ............................................................................................ 247 8.3.4 Statistical analysis ....................................................................................................... 248 8.3.5 Ethical considerations ................................................................................................. 249 8.4 RESULTS............................................................................................................................... 249 8.5 DISCUSSION ......................................................................................................................... 267 8.6 CONCLUSION ....................................................................................................................... 273 8.7 ACKNOWLEDGEMENTS ....................................................................................................... 273 8.8 REFERENCES ........................................................................................................................ 274 CHAPTER 9 – DEVELOPMENT OF A NUTRITION SCREENING TOOL FOR THE PREDICTION OF BIRTH OUTCOMES OF WOMEN ATTENDING THE ANTENATAL CLINIC AT PELONOMI HOSPITAL................................................................................................................................ 274 9.1 ABSTRACT ............................................................................................................................ 279 9.2 INTRODUCTION ................................................................................................................... 280 9.3 METHODS ............................................................................................................................ 281 9.3.1 Sample description and data collection ...................................................................... 281 9.3.2 Development of the nutrition screening tool ............................................................. 283 9.4 RESULTS............................................................................................................................... 284 9.5 DISCUSSION ......................................................................................................................... 289 9.6 CONCLUSION AND RECOMMENDATIONS ........................................................................... 292 9.7 ACKNOWLEDGEMENTS ....................................................................................................... 292 ix 9.8 REFERENCES ........................................................................................................................ 292 CHAPTER 10 - CONCLUSIONS AND RECOMMENDATIONS..................................................... 298 10.1 INTRODUCTION ................................................................................................................... 298 10.2 CONCLUSIONS ..................................................................................................................... 298 10.2.1 CHARACTERISTICS OF WOMEN ATTENDING THE ANTENATAL CLINIC AT PELONOMI REGIONAL HOSPITAL, BLOEMFONTEIN: A COMPARISON OF WOMEN WITH KNOWN BIRTH OUTCOMES AND WOMEN WITH UNKNOWN BIRTH OUTCOMES .................... 298 10.2.2 BIRTH OUTCOMES OF NEONATES BORN TO MOTHERS WHO RECEIVED ANTENATAL CARE AT PELONOMI REGIONAL HOSPITAL, BLOEMFONTEIN ..................................... 299 10.2.3 ASSOCIATIONS BETWEEN INDICATORS OF SOCIO-DEMOGRAPHY AND BIRTH OUTCOMES OF PREGNANT WOMEN ATTENDING THE ANTENATAL CLINIC AT PELONOMI REGIONAL HOSPITAL, BLOEMFONTEIN .................................................... 299 10.2.4 ASSOCIATIONS BETWEEN REPORTED HEALTH AND LIFESTYLE AND BIRTH OUTCOMES OF PREGNANT WOMEN ATTENDING THE ANTENATAL CLINIC AT PELONOMI REGIONAL HOSPITAL, BLOEMFONTEIN ........................................................................................ 300 10.2.5 HOUSEHOLD FOOD SECURITY AND NUTRIENT INTAKE AND THEIR ASSOCIATION WITH BIRTH OUTCOMES OF PREGNANT WOMEN ATTENDING THE ANTENATAL CLINIC AT PELONOMI HOSPITAL, BLOEMFONTEIN ..................................................................... 300 10.2.6 DEVELOPMENT OF A NUTRITION SCRERNING TOOL FOR THE PREDICTION OF BIRTH OUTCOMESOF PREGNANT WOMEN ATTEDING THE ANTENATAL CLINIC AT PELONOMI HOSPITAL ..................................................................................................................... 301 10.3 STUDY LIMITATIONS............................................................................................................ 301 10.4 RECOMMENDATIONS.......................................................................................................... 302 10.4.1 Recommendations concerning nutrition education and intervention ....................... 302 10.4.2 Recommendations for further research ..................................................................... 304 10.5 REFERENCES ........................................................................................................................ 304 1 x 2 LIST OF TABLES Table 2.1 Recommendations for gestational weight gain by body mass index (BMI) before conception (Rasmussen et al., 2009) ....................................................................................... 27 Table 2.2 Acceptable macronutrient distribution ranges (AMDR) for omega-6 and omega-3 fatty acids (IOM, 2006†; FAO/WHO/UNU, 2002‡) ................................................................... 31 Table 2.3 Reference Intakes for vitamins and minerals (amount per day) (IOM, 2006; Allen et al., 2006) .................................................................................................................................. 33 Table 2.4: Summary of studies on the development or testing of screening tools for pregnant women ..................................................................................................................................... 46 Table 3.1: Information collected during the two study phases ............................................... 80 Table 3.2: Sleeping rooms required for Equivalent Persons (EPs) in the same house (Coetzee et al., 1988:354) ....................................................................................................................... 80 Table 3.3: GBMI Classification (Cruz et al., 2007:686) ............................................................. 81 Table 3.4: Household food insecurity access calculation and interpretation (Coates et al., 2007) ........................................................................................................................................ 82 Table 3.5: Dietary Reference Intakes and Recommended Nutrient Intakes for macronutrients (FAO/WHO/UNU, 2002; WHO/FAO; IOM, 2006) ..................................................................... 84 Table 3.6: Reference Intakes for vitamins and minerals (amount per day) (IOM, 2006; Allen et al., 2006; Gibson & Ferguson, 2008) ........................................................................................ 85 Table 3.7: Food groups included in the Women’s Dietary Diversity Score (FAO, 2011) .......... 86 Table 3.8: Interpretation of World Health Organization Z-scores (WHO, 2008:14) ................. 87 Table 4.1: Information related to age and pregnancy stage .................................................. 108 Table 4.2: Socio-demographic information ............................................................................ 109 Table 4.3: Overview of reported health and lifestyle ............................................................ 113 Table 4.4: Information relating to social support and stress ................................................. 117 Table 4.5: Gestational BMI ..................................................................................................... 119 xi Table 4.6: Household food security status ............................................................................. 119 Table 4.7: Median nutrient intake .......................................................................................... 121 Table 5.1: Interpretation of World Health Organization Z-Scores (WHO, 2008) .................... 150 Table 5.2: Summary of gestational age and birth anthropometry ........................................ 152 Table 5.3: Results for birth weight, birth length and birth head circumference ................... 153 Table 5.4: Birth information obtained from Road to Health Booklets ................................... 154 Table 5.5: Comparison of birth outcomes to other studies ................................................... 158 Table 6.1: Interpretation of World Health Organization Z-scores (WHO, 2008:14) ............... 175 Table 6.2: Associations between socio-demographic variables, delivery method and prematurity ............................................................................................................................ 178 Table 6.3: Associations between socio-demography, length-for-age and weight-for-length at birth ........................................................................................................................................ 183 Table 6.4: Associations between socio-demographic variables and overall birth outcome.. 187 Table 6.5: Odds ratios of socio-demographic factors associated with overall poor birth outcome ................................................................................................................................. 190 Table 7.1: Associations between health and lifestyle questions and delivery method and prematurity ............................................................................................................................ 209 Table 7.2: Associations between health and lifestyle questions and length-for-age and weight-for-length ................................................................................................................... 216 Table 7.3: Associations between gestational body mass index and birth outcomes ............ 223 Table 7.4: Associations between reported health and lifestyle and overall birth outcome .. 224 Table 7.5: Odds ratios for reported health and lifestyle factors associated with overall birth outcome ................................................................................................................................. 228 Table 8.1: Food groups included in the WDDS (FAO, 2011) ................................................... 248 Table 8.2: Association between household food security status and birth outcomes .......... 250 Table 8.3: Nutrient intake and associations with delivery method and prematurity ............ 252 xii Table 8.4: Associations between length-for-age and weight-for-length at birth and energy, macro- and micronutrient intake of participants .................................................................. 260 Table 8.5: Associations between dietary diversity and overall birth outcome ...................... 266 Table 9.1: Variables considered for inclusion in the final model ........................................... 285 Table 9.2: Odds ratios of factors associated with overall poor birth outcome ...................... 286 Table 9.3: Predicted probabilities of experiencing overall poor birth outcome .................... 287 Table 9.4: Initial tick sheet of variables included in the screening tool ................................. 288 Table 9.5: Proposed nutrition screening tool ......................................................................... 289 xiii 3 LIST OF FIGURES Figure 1.1 Intergenerational cycle of growth failure (UNSCN, 2010) ........................................ 2 Figure 1.2 Framework on the relations between poverty, food insecurity, and other underlying and immediate causes to maternal and child undernutrition and its short-term and long-term consequences (Black et al., 2008) ...................................................................... 5 Figure 2.1 Conceptual framework of children surviving and thriving (Black et al., 2020) ...... 18 Figure 3.1: The development of a new screening tool (Jones, 2004) ...................................... 95 xiv 4 LIST OF APPENDICES Appendix A: Information document ...................................................................................... 306 Appendix B: Consent form ..................................................................................................... 312 Appendix C: Sociodemographic and household questionnaire ............................................. 315 Appendix D: Reported health, lifestyle pregnancy history and anthropometry questionnaire ................................................................................................................................................ 320 Appendix E: Household food security questionnaire ............................................................ 327 Appendix F: Dietary intake questionnaire.............................................................................. 330 Appendix G: Participant checklist .......................................................................................... 362 Appendix H: Researcher contact card .................................................................................... 363 Appendix I: SMS requesting mothers to provide the Road to Health Booklet of their babies ................................................................................................................................................ 364 Appendix J: Ethics clearance letter ........................................................................................ 365 xv 5 LIST OF ABBREVIATIONS AA Arachidonic acid AI Adequate Intake AIDS Acquired Immunodeficiency Syndrome AMDR Acceptable macronutrient distribution range BMI Body mass index CDC Centre for Disease Control and Prevention DAEK Dietary Assessment and Education Kit DHA Docosahexaenoic acid DNA Deoxyribonucleic acid DOHaD Developmental Origins of Health and Disease DRIs Dietary Reference Intakes EAR Estimated Average Requirement EP Equivalent person EPA Eicosapentaenoic acid FAO Food and Agriculture Organization g Grams HDR Household Density Ratio HIV Human Immunodeficiency Virus IOM Institute of Medicine IUGR Intrauterine growth restriction kCal Kilocalories Kg Kilogram MDT TB Multidrug-Resistant Tuberculosis MMS Multimedia Messaging Service NCDs Non-communicable diseases NuPED Nutrition during Pregnancy and Early Development PMTCT Prevention of mother-to-child transmission PUFAs Polyunsaturated fatty acids xvi QFFQ Quantitative food frequency questionnaire RDA Recommended Dietary Allowance RMR Resting Metabolic Rate RNA Ribonucleic acid RNI Recommended Nutrient Intake SADHS South African Demographic and Health Survey SA MRC South African Medical Research Council SD Standard deviation SMS Short Message Service TB Tuberculosis TE Total energy THUSA Transitions and Health during Urbanisation of South Africa UL Upper Tolerable Nutrient Intake Level UNICEF United Nations Children’s Fund UNU United Nations University US United States WHO World Health Organization xvii 1 CHAPTER 1 – ORIENTATION AND MOTIVATION 1.1 INTRODUCTION The period from conception to a child’s second birthday, also known as the first 1000 days of life, is a crucial time for growth and development (Adu-Afarwuah et al., 2017:18; Cusick & Georgief, 2017). The cornerstones of optimal health, growth and nervous system development across the lifespan are laid down during this period (Cusick & Georgieff, 2017). A fast-growing body of evidence supports the concept that the nutritional status of both mother and child during this critical period of development hold major consequences for health and development (Black et al., 2013a; Adu-Afarwuah et al., 2017:18), since poor nutritional status during this period may influence early developmental processes and pregnancy outcomes (Ramakrishnan et al., 2012:285; Symington et al., 2018). Maternal health before and throughout pregnancy may be influenced by various factors including genetics, malnutrition, acute and chronic disease, and environmental factors (Hampton, 2004; Jacob et al., 2017). Maternal health and diet may play a key role in programming the short- and long-term health of the offspring (Brenseke et al., 2013). The Developmental Origins of Health and Disease (DOHaD) hypothesis suggests that the environment inside the womb may affect foetal development during sensitive periods, and could potentially lead to changes in the foetus that influence the risk for specific diseases in adult life (Skogen & Overland, 2012:60). This hypothesis suggests that certain genes in the foetus may or may not be expressed, depending on the environment to which the mother and foetus are exposed during pregnancy (Hampton, 2004). Women from a lower socioeconomic background are more likely to experience negative environmental conditions and may not receive the health care they need (Lapillonne & Griffin, 2013:393). Inadequate dietary intake during pregnancy (in terms of both quantity and quality) may lead to poor maternal weight gain, which may increase the risk for premature delivery, low birth weight and congenital disabilities (Black et al., 2013a). Maternal malnutrition, including both undernutrition and overnutrition, thus hold important consequences for the offspring, in terms of survival, acute and chronic conditions including heart disease, diabetes and elevated blood pressure (Edelstein, 2015), healthy development and even future economic productivity (Black et al., 2013b:428). 1 Undernutrition prior to conception may increase the risk of complications (Ehrenberg et al., 2003:1726). Error! Reference source not found. indicates how growth failure may be t ransmitted from generation to generation, suggesting that adult women with a smaller build (particularly short in stature), which may have been the consequence of growth failure in utero and during childhood, are more prone to delivering low birth weight babies. These children, in turn, often suffer from growth failure during childhood (UNSCN, 2010). Various factors, including food insecurity and illness, contribute to the development of malnutrition. To maximise positive outcomes for both mother and baby, malnutrition before and during pregnancy and its underlying causes should be addressed as a matter of urgency (Edelstein, 2015). Figure 1.1 Intergenerational cycle of growth failure (UNSCN, 2010) The triple burden of malnutrition refers to undernutrition, micronutrient deficiency and overweight and obesity (Meenakshi, 2016). As low birth weight and other poor birth outcomes are more prevalent among babies from disadvantaged communities in developing countries, the need for improved care and counselling for those women who are at risk of malnutrition is obvious (Duquette et al., 2008:30). Careful monitoring of malnourished women is of utmost importance to ensure that their nutritional needs during pregnancy are met (Hauger et al., 2008:954). Maternal undernutrition, including chronic energy and micronutrient deficiencies, often persist in developing countries and remain important contributing factors to morbidity, mortality and poor birth outcomes such as low birth weight, neonatal mortality and later childhood malnutrition (Ahmed et al., 2012). On the other hand, overweight and obesity in the mother may significantly increase the risk of various adverse 2 outcomes or complications in both the mother and foetus during pregnancy, delivery and post-partum (Brenseke et al., 2013; Black et al., 2013b:427). Obese pregnant women have a greater risk to develop gestational diabetes and pre-eclampsia than their counterparts with a normal body mass index (BMI) (Black et al., 2013b:430). Maternal death, haemorrhage, caesarean delivery or infection during labour and delivery are associated with obesity in the mother, while the risks for neonatal or infant death, birth trauma and macrosomia are also increased (Flick et al., 2010:333; Black et al., 2013b:427). During the post-partum period, obese women may struggle to breastfeed and experience greater weight retention than normal-weight women (Black et al., 2013b:441). A history of gestational diabetes among obese women increases their risk of developing type 2 diabetes, metabolic syndrome and cardiovascular disease later in life (Ruager-Martin et al., 2010:715; Black et al., 2013b:430). In addition to being a common cause of premature birth and low birth weight, obesity is also a well-known contributor to oxidative stress. Oxidative stress during the critical development period, in turn, is strongly linked to adverse foetal growth and an elevated risk for the development of chronic diseases such as metabolic syndrome and type 2 diabetes in the offspring in later life (Brenseke et al., 2013). Maternal obesity in pregnancy may increase the risk for obesity during the childhood years that may continue into the adolescent and adult years, affecting the offspring’s health in later life (Black et al., 2013b:430). Pregnant women are particularly prone to developing micronutrient deficiencies due to the high nutrient demands of pregnancy (Zerfu & Ayele, 2013). Micronutrient deficiencies in the mother, particularly in iron, folate, vitamin A, zinc, iodine, calcium and vitamin D, are associated with adverse birth outcomes (Black et al., 2013b). Until recently, perinatal undernutrition and specific nutrient deficiencies were the main focus of this area of study; however, the global epidemic of overweight and obesity highlights the importance of examining perinatal overnutrition (Brenseke et al., 2013) as well as micronutrient deficiencies that are often prevalent amongst overweight and obese individuals (WHO, 2017). Various other factors may influence nutritional status (WHO, 2014). Figure 1.2 provides an overview of the complex interactions between underlying and immediate causes of maternal and child malnutrition and the short- and long-term consequences thereof (Black et al., 2008). Poor food security (WHO, 2014), isolation and emotional stress may also be directly or 3 indirectly associated with malnutrition and later life disease risk (Duquette et al., 2008:30; Standing Committee on Use of Emerging Science for Environmental Health Decisions, 2011). Alcohol consumption during pregnancy may contribute to cognitive impairment and neurodevelopmental disorders (Eustace et al., 2003), while it may also increase the risk of miscarriage, placenta abruption, low birth weight, cognitive impairment (Cox & Carney, 2017:272) as well as premature delivery (AIHW, 2016). Tobacco use, including direct and second-hand exposure to smoking, during pregnancy, poses a great risk to the health of the foetus and infant as contaminants in tobacco move through the placenta and breastmilk. Use of tobacco products also limits the amount of money available for proper nutrition in both the pregnant mother and her offspring (WHO, 2014). According to the World Health Organization (WHO, 2014), a greater proportion of people smoke in low- and middle-income countries, particularly girls and women of childbearing age. 4 Figure 1.2 Framework on the relations between poverty, food insecurity, and other underlying and immediate causes to maternal and child undernutrition and its short-term and long-term consequences (Black et al., 2008) It is clear that the environment that the foetus is exposed to during pregnancy has an important impact on both the short and long-term health of the baby. Maternal health and adequate nutrition before, during and after pregnancy are thus of utmost importance in ensuring healthy future generations. 5 1.2 PROBLEM STATEMENT Achieving a global reduction of 40% in stunting among children five years and younger, a 30% reduction in low birth weight, and no increase in childhood overweight, are some of the global nutrition targets set by the World Health Assembly for 2025 (WHO, 2012). The more recent 2020 Global Nutrition Report, published by the WHO, calls on all stakeholders to act now to meet the targets set for 2025. This report encourages stakeholders to work in coordination to address the barriers in the way of ending global malnutrition (over- as well as undernutrition) (GNR, 2020:14). Both maternal undernutrition and overnutrition, hold important consequences for survival, acute and chronic conditions, as well as healthy development (Black et al., 2013b:427). It is thus of concern that the South Africa Demographic and Health Survey (SADHS) conducted in 2016, found that the percentage of non-pregnant underweight women (BMI < 18.5 kg/m2) was much lower (4.9% amongst those aged 20-24 years, 2.1% amongst those aged 25-34 years and 4.9% amongst those aged 35-44 years) compared to the overweight or obese groups (52.8% amongst those aged 20-24 years, 66.6% amongst those aged 25-34 years and 77.5% amongst those aged 35-44 years) (NDoH et al., 2019:299). According to the Maternal and Child Nutrition Study Group (Black et al., 2013a), 800 000 neonatal deaths, accounting for approximately a quarter of all neonatal deaths worldwide, may be due to foetal growth restriction. Overall, 18 683 perinatal deaths were recorded in South Africa in 2016 of which 7.9% (1476) were in the Free State province. In addition, 1276 of the 18 683 (6.8%) perinatal deaths were due to disorders related to length of gestation and foetal growth (including short gestation and low birth weight), while 4484 (21.4%) were due to foetus and newborn affected by maternal factors and by complications of pregnancy, labour and delivery (Statistics South Africa, 2018). Low birth weight is defined as an infant weighing less than 2500g at birth (WHO, 2014). The South African Early Childhood review published in 2016, reported that 13% of infants born in public health facilities in South Africa in 2014 had a low birth weight (Hall et al., 2016). Low birth weight is strongly associated with perinatal, neonatal and post-natal mortality (Watkins et al., 2016) as well as the development of chronic diseases in adulthood (Edelstein, 2015; Baird et al., 2017). 6 Infants who experience growth restriction during the foetal period may further have a significantly increased risk of being stunted. Stunting, when combined with rapid weight gain during later stages of childhood, may be important contributing factors to obesity and non- communicable diseases during the adult years (Black et al., 2013a). The authors of a systematic review that determined the changes in stunting prevalence in South Africa over a 40-year period (1970 to 2013), concluded that stunting in South Africa remains at a significant level and prevalence has not declined in several years (Said-Mohammed et al., 2015:534). In order to ensure long-term health, early nutrition intervention is vital during the critical window of opportunity from pregnancy until the second birthday (Black et al., 2013a). The current study thus aimed to identify factors during pregnancy that are associated with poor birth outcomes in order to develop a nutrition screening tool to identify pregnant women who are at risk of delivering babies with poor birth outcomes, who are likely to benefit from more intensified nutrition care during pregnancy. The purpose of nutrition screening can be described as a method for predicting the likelihood of a better or worse outcome because of nutrition factors in order to direct nutritional management that may influence birth outcomes (Ferguson et al., 1999:458; Wenhold, 2017:5). Currently, few tools for detecting malnutrition and/or poor birth outcomes amongst pregnant women and their offspring exist, with no such screening tools available for the South African population. 1.3 AIM AND OBJECTIVES This study aimed to develop a nutrition screening tool for the prediction of birth outcomes of pregnant women, attending the high-risk antenatal clinic at Pelonomi Hospital, Bloemfontein, Free State. The following objectives were compiled in order to achieve the main aim: 1. In order to identify predictors of birth outcomes, the following were described 1. In the pregnant mother:  Socio-demographic status;  Reported health and lifestyle;  Dietary intake in order to determine nutrient intake; 7  Pregnancy history;  Anthropometric status; and,  Household Food Security. 2. In the neonate (as noted in the Road to Health Booklet of the baby):  Gestational age;  Delivery method;  Length-for-age at birth; and  Weight-for-length at birth. 2. To describe the population, the following were noted in the neonate:  Birth head circumference;  Immunisations; and,  Human immunodeficiency virus (HIV) exposure. 3. To determine and describe how the above-mentioned factors in the mother are associated with birth outcomes in the neonate. 4. To develop a nutrition screening tool based on the findings of the above-mentioned factors. 1.4 STRUCTURE OF THIS THESIS Chapter 1 orientates the reader towards the study and includes a brief motivation for conducting the study. Chapter 2 provides an overview of the literature regarding relevant information and variables researched in this study. Chapter 3 elaborates on the methodology that was applied, while chapters 4 to 9 are structured as a series of articles that were compiled based on the aims and objectives of the study. Chapter 10 provides a summary of the conclusions and recommendations for future interventions based on the research findings. 8 1.5 REFERENCES Adu-Afarwuah, S., Lartey, A. & Dewey, K.G. 2017. Meeting nutritional needs in the first 1000 days: a place for small-quantity lipid-based nutrient supplements. Annals of the New York Academy of Sciences, 1392:18–29, March. Ahmed, T., Deitchler, M. & Ballard, T. 2012. Global burden of maternal and child undernutrition and micronutrient deficiencies. Annals of Nutrition and Metabolism, 61(suppl 1):8–17, January. Australian Institute of Health and Welfare (AIHW). 2016. Child protection Australia 2014–15. Canberra: Australian Institute of Health and Welfare. Baird, J., Jacob, C., Barker, M., Fall, C.H., Hanson, M., Harvey, N.C., Inskip, H.M., Kumaran, K. & Cooper, C. 2017. Developmental Origins of Health and Disease: A lifecourse approach to the prevention of Non-Communicable Diseases. Healthcare, March 8. https://doi.org/10.3390/healthcare5010014 [18 January 2017]. Black, R.E., Alderman, H., Bhutta, Z.A., Gillespie, S., Haddad, L., Horton, S., Lartey, A., Mannar, V., Ruel, M., Victora, C.G., Walker, S.P. & Webb, P. 2013a. Maternal and child nutrition: Building momentum for impact. The Lancet, 382(9890):372–375, June 6. Black, R.E., Allen, L.H., Bhutta, Z.A., Caulfield, L.E., de Onis, M., Ezzati, M., Mathers, C. & Rivera, J. 2008. Maternal and child undernutrition: global and regional exposures and health consequences. The Lancet, 371:243–260, January. Black, M.M., Lutter, C.K. & Trude, A.C.B. 2020. All children surviving and thriving: re- envisioning UNICEF’s conceptual framework of malnutrition. The Lancet, 8(6):e766 ̶ e767, June. Black, R.E., Victora, C.G., Walker, S.P., Bhutta, Z.A., Christian, P., De Onis, M., Ezzati, M., Grantham-Mcgregor, S., Katz, J., Martorell, R. & Uauy, R. 2013b. Maternal and child undernutrition and overweight in low-income and middle-income countries. The Lancet, 382(9890):427–451, June 6. Brenseke, B., Prater, M.R., Bahamonde, J. & Gutierrez, J.C. 2013. Current thoughts on maternal nutrition and fetal programming of the metabolic syndrome. Journal of Pregnancy, February 14. https://doi.org/10.1155/2013/368461 [18 January 2017]. 9 Cox, J.T. & Carney, V.H. 2017. Nutrition for reproductive health and lactation. In Mahan, L.K. & Raymond, J.L. (eds). Krause’s food and the nutrition care process. 14th ed. St Louis: Elsevier. Cusick, S. & Georgieff, M.K. 2017. The first 1 000 days of life: The brain’s window of opportunity, Geneva: UNICEF. Duquette, M., Payette, H., Moutquin, J., Demmers, T. & Desrosiers-choquette, J. 2008. validation of a screening tool to identify the nutritionally at-risk pregnancy. Journal of Obstetrics and Gynaecology Canada, 30(1):29–37, January. Edelstein, S. 2015. Life cycle nutrition, 2nd ed. Burlington: Jones & Bartlett Learning. Ehrenberg, H., Dierker, L., Milluzzi, C. & Mercer, B. 2003. Low maternal weight, failure to thrive in pregnancy, and adverse pregnancy outomes. American Journal of Obstetrics and Gynecology, 189(6):1726–1730, December. Eustace, L.W., Kang, D.H. & Coombs, D. 2003. Fetal alcohol syndrome: A growing concern for health care professionals. Journal of Obstetric, Gynecologic and Neonatal Nursing, 32(2):215 ̶221, March. Ferguson, M., Capra, S., Bauer, J., & Banks, M. 1999. Development of a valid and reliable malnutrition screening tool for adult acute hospital patients. Nutrition, 15(6):458 ̶464, June. Flick, A.A., Brookfield, K.F., de la Torre, L., Tudela, C.M., Duthely, L. & Gonzalez-Quintero, V.H. 2010. Excessive weight gain among obese women and pregnancy outcomes. American Journal of Perinatology, 27:333 ̶338, April. Global Nutrition Report (GNR). 2020. Action on equity to end malnutrition. Bristol, UK: Development Initiatives. Hall, K., Sambu, W., Berry L., Giese, S., Almeleh, C. & Rosa, S. 2016. South African Early Childhood Review 2016, Cape Town: Children’s Institute, University of Cape Town and Ilifa Labantwana. Hampton, T. 2004. Fetal environment may have profound long-term consequences for health. Journal of the American Medical Association, 292:1285–1286, September. Hauger, M., Gibson, L., Vik, T. & Belizan, J. 2008. Prepregnancy weight and the risk of adverse pregnancy outcome. Acta Obstetricia et Gynecologica Scandinavica, 87(9):953–959, January. 10 Jacob, C.M., Baird, J., Barker, M., Cooper, C. & Hanson, M. 2017. The importance of a life course approach to health: Chronic disease risk from preconception through adolescence and adulthood. Geneva: World Health Organization. Lapillonne, A. & Griffin, I. 2013. Feeding preterm infants today for later metabolic and cardiovascular outcomes. Journal of Pediatrics, 132(Suppl. 3):939–940, March. Meenakshi, J.V. 2016. Trends and patterns in the triple burden of malnutrition in India. Agricultural Economics, November 29. https://doi.org/10.1111/agec.12304 [19 October 2020]. National Department of Health (NDoH), Statistics South Africa (Stats SA), South African Medical Research Council (SAMRC) & ICF. 2019. South Africa Demographic and Health Survey 2016. Pretoria, South Africa, and Rockville, Maryland, USA: NDoH, Stats SA, SAMRC, and ICF. Ramakrishnan, U., Grant, F., Goldenberg, T., Zongrone, A. & Martorell, R. 2012. Effect of women’s nutrition before and during early pregnancy on maternal and infant outcomes: A systematic review. Paediatric and Perinatal Epidemiology, 26(Suppl. 1):285–301, July. Ruager-Martin, R., Hyde, M.J. & Modi, N. 2010. Maternal obesity and infant outcomes. Early Human Development, 86:715 ̶ 722, November. Said-Mohamed, R., Micklesfield, L.K., Pettifor, J.M. & Norris, S.A. 2015. Has the prevalence of stunting in South African children changed in 40 years? A systematic review. BMC Public Health, 15:534 ̶541, June. Skogen, J.C. & Overland, S. 2012. The fetal origins of adult disease: a narrative review of the epidemiological literature. JRSM Short Reports, 3(8):59–59, August. Standing Committee on Use of Emerging Science for Environmental Health Decisions. 2011. Newsletter: Predicting later-life outcomes of early-life exposures. http://nas- sites.org/emergingscience/files/2011/05/inutero-newsletter-final11.pdf [22 February 2017]. Statistics South Africa. 2018. Perinatal deaths in South Africa. http://www.statssa.gov.za/publications/P03094/P030942016.pdf [19 October 2020]. Symington, E.A., Baumgartner, J., Zandberg, L., Malan, L., Zandberg, L., Ricci, C. & Smuts, C.M. 2018. Nutrition during pregnancy and early development (NuPED) in urban South Africa: a 11 study protocol for a prospective cohort. BMC Pregnancy and Childbirth, July 24. https://doi.org/10.1186/s12884-018-1943-6 [19 October 2020]. United Nations Standing Committee on Nutrition (UNSCN). 2010. 6th Report on the world nutrition situation: Nutrition progress report. Geneva: United Nations Standing Committee on Nutrition. Watkins, W.J., Kotecha, S.J., & Kotecha, S. 2016. All-cause mortality of low birth weight infants in infancy, childhood, and adolescence: Population study of England and Wales. PLoS Medicine, May 10. https://doi.org/10.1371/journal.pmed.1002018 [22 February 2017]. Wenhold, F.A.M. 2017. Nutrition screening: science behind simplicity. South African Journal of Clinical Nutrition, 30(3):5 ̶ 6, October. World Health Organization (WHO). 2012. Discussion paper. Proposed global targets for maternal, infant and young child nutrition. Geneva: World Health Organization. World Health Organization (WHO). 2014. Comprehensive implementation plan on maternal, infant and young child nutrition. Geneva: World Health Organization. World Health Organization (WHO). 2017. The double burden of malnutrition: Policy brief. WHO: Geneva. Zerfu, T.A. & Ayele, H.T. 2013. Micronutrients and pregnancy; effect of supplementation on pregnancy and pregnancy outcomes: a systematic review. Nutrition Journal, January 31. https://dx.doi.org/10.1186%2F1475-2891-12-20 [22 February 2017]. 12 2 CHAPTER 2 – LITERATURE REVIEW 2.1 INTRODUCTION Health may be affected by many factors including diet, environment and economics, all of which, through a complex interplay, affect normal development and optimal health during the entire lifespan (Hoffman et al., 2017:951). Three distinct periods in terms of development exist within the first 1000 days, namely, pregnancy and year one and year two of infancy (Moore et al., 2017) and interactions during these periods may hold life-long consequences for health (Brinkman et al., 2012; Woolfenden et al., 2013; Moore et al., 2017). According to Moore et al. (2017), three key concepts alter the understanding of how children grow and develop. These include the Developmental Origins of Health and Disease (DOHaD) hypothesis; social climate change and the mismatch hypothesis; as well as ecological impacts and social determinants of health and disease (Moore et al., 2017). In this literature review, the DOHaD hypothesis as well as those factors that have the potential to influence health and development during the first 1000 days, particularly focusing on the period of pregnancy, is reviewed. 2.2 THE DEVELOPMENTAL ORIGINS OF HEALTH AND DISEASE HYPOTHESIS The DOHaD hypothesis proposes that exposures to certain environmental factors during critical periods of growth and development (first 1000 days period) may significantly affect individuals' short- and long-term health (Barker, 2007). Changes within these environments, particularly during early life, induce changes in growth, organ structure, cell number, gene expression and metabolism in the foetus (Mandy & Nyirenda, 2018), which may influence behaviour, disease risk as well as mortality in the adult years (Lea et al., 2018). Worldwide, morbidity and mortality are significantly affected by obesity and non- communicable diseases (NCDs) including cardiovascular disease, cancer, respiratory disease, diabetes and musculoskeletal disorders (Baird et al., 2017). The DOHaD hypothesis focusses on how exposure to environmental factors such as stress, malnutrition and contaminants during periods of critical development impact the long-term health, including the prevalence of NCDs, and wellbeing of humans (Davies, 2014; Moore et al., 2017). Over- or undernutrition during pregnancy may lead to an increased risk of obesity later in life, since high maternal 13 body mass index (BMI) has been linked to greater gestational weight gain which may, in turn, lead to increased birth weight and fat mass at birth. Maternal gestational diabetes, for example, increases the risk of delivering a baby with a heavier birth weight, while also increasing the risk of overweight and obesity as well as diabetes later in life (Barouki et al., 2012). Over the past 30 years, research has demonstrated the potential effect of early environment on disease risk in adult life as well as in future generations (Baird et al., 2017), with experiences or exposures that occur in early life having the ability to shape certain characteristics in later life (Lea et al., 2018). The human body is able to adapt to various social and physical environments, a phenomenon known as developmental plasticity (Hanson & Gluckman, 2014). Although the human body can adjust throughout life, developmental plasticity peaks during the first 1000-day period (Barker, 2012). Each organ and system develops and matures at different times during pregnancy, except for the brain, liver and immune system that still mature after birth (Barker, 2012). Being able to adapt to the environment in which these critical periods of development for organs and systems occur is of utmost importance during the first 1000 days (Barker, 2012; Moore et al., 2017). Adaptation entails a process, called biological embedding, whereby the foetus reacts to certain stimuli, including nutrition or hormones, to adjust their phenotype to the environment in a manner that may hold consequences for later life (Moore et al., 2017). Epigenetics, the interaction between genes and the environment, is one of the main mechanisms involved in biological embedding (Burris et al., 2016). The early stages of pregnancy are sensitive periods during which the environment may influence how genes are expressed (Perera et al., 2020:177). Consequently, changes in an individual's epigenetics may affect growth, development, and disease risk in later life (Faulk & Dolinoy, 2011). This epigenetic system goes through critical adaptations during periods of developmental plasticity, and thus the epigenetic system is particularly sensitive to changes and/or undesirable exposures in the environment during the first 1000 days. Once fully developed, tissues or systems remain somewhat plastic, but may not be as sensitive to changes in the environment (Barouki et al., 2012). Following conception, epigenetic changes start to take place. Fusion of the egg and sperm to form the embryo results in the combination of genetic material. Age, as well as environmental 14 exposures of both parents, are reflected in the genes of the newly formed embryo (Lane et al., 2014). The embryo becomes extremely sensitive to stimuli from the reproductive tract of the mother during fertilisation and cell division (Leese et al., 2008). Since the embryo has a high degree of plasticity, it is able to respond to stimuli from the environment by adjusting its metabolism, gene expression as well as the rate of cell division (Lane et al., 2014). The maternal reproductive tract and the embryo, therefore, work together to produce a developmental path modified to meet the expected external environment (Hochberg et al., 2011). Epigenetic changes may occur throughout pregnancy as well as during the post-natal period (McDade, 2012). 2.3 THE MISMATCH HYPOTHESIS It has been proposed that social, physical and mental health problems may occur as a result of a mismatch between human evolutionary capabilities and modern environments (Kearns et al., 2007). Mismatch refers to an inconsistency between an individual’s phenotype and that which would equate to optimal responses of that individual in a particular environment (Gluckman et al., 2019). If a phenotype is induced during development but is exposed to a different environment thereafter, it is known as a developmental mismatch. Developmental mismatches may occur due to wider changes in the environment, for example, transitions from traditional to more Western diets and lifestyles (Gluckman et al., 2019). The body, therefore, makes certain adaptations based on the “predictions” concerning the type of environment it will be living in, however, the environment after birth then differs and does not match that which was “predicted” (Moore et al., 2017). Social and environmental changes may be associated with advantages such as increases in the availability of food and improvements in medicine, resulting in lower infant mortality. On the other hand, certain changes have also resulted in circumstances that the human body was not designed for, which may negatively impact both physical and mental health, also termed an “evolutionary mismatch” (Moore et al., 2017). If changes or challenges in the environment are severe, developmental plasticity may initiate immediate adaptive responses to maintain survival. If survival is not threatened by challenges within the environment, but an advantage in developing a phenotype that will be better adapted in later life exists, predictive adaptive 15 responses may be the result. Predictive adaptive responses may, however, have long-term adverse health consequences if a developmental mismatch exists (Gluckman et al., 2019). 2.4 SOCIAL DETERMINANTS OF HEALTH Social determinants may play a significant role in the first 1000 days since it is a period during which various vital skills and abilities develop (Hertzman, 2010). Social determinants often co- exist and can be transmitted across generations. Children not only acquire genetic and epigenetic traits from their parents but also inherit the environments that give rise to those traits (Moore et al., 2017). Health is expressively influenced by the social, economic and environmental circumstances into which an individual is born, grows, lives and ages (WHO, 2008; Braveman & Gottlieb, 2014). Social determinants that are key in shaping the health and wellbeing of individuals include income, wealth, educational attainment (Braveman & Gottlieb, 2014), employment status, poverty, geographic location, disability, gender and social connectivity, amongst others(Moore et al., 2017). The ‘group of people who live, eat and participate in daily, home-based activities together’, i.e. the family in which a child grows up, is the primary environmental influence that determines the development of children (Shonkoff & Philips, 2000; UNICEF, 2006). Social resources, such as parenting skills and level of education, cultural practices, relationships and the health of family members (Hertzman, 2010), as well as economic factors, including wealth, occupational status, availability of healthcare and dwelling conditions, may significantly affect the development of children (Braveman & Gottlieb, 2014; Hertzman, 2017). Outcomes of child development generally improve incrementally from lower to higher socioeconomic status (Braveman & Gottlieb, 2014; Hertzman, 2010). Associations between the socioeconomic status of families and low birth weight, cognitive test scores, difficulties in behaviour and socialisation as well as the risk of disengagement from school have been reported (Brooks-Gunn et al., 1997; Banerjee, 2016). Poverty during the first 1000 days is closely associated with poor health and wellbeing in later years (Goldfeld et al., 2014:31). 16 Poverty may influence brain development (Moore et al., 2017). Barch et al. (2016), hypothesised that the areas within the brain that are involved in learning and memory as well as regulating stress and emotions are not as “strongly” connected with other parts of the brain in children exposed to poverty. The effects of poverty on both physiologic and neurobiological development may lead to gaps in academic achievement as well as lifelong effects on physical and mental health (Blair & Raver, 2016:S30). Children born into poverty have a greater risk of experiencing poor health, developmental delays, lower achievement as well as behavioural and emotional problems (Johnson et al., 2016). During pregnancy, poorer women tend to seek antenatal care only after the first trimester (AIHW, 2015). In addition to consuming an inadequate diet, pregnant women who experience poverty may also be more prone to smoke, consume alcohol and use drugs (Moore et al., 2017). Nationally representative data collected as part of the South African Health and Nutrition Examination Survey (SANHANES-1) reported that 3.7% of South African women consumed alcohol while pregnant. Unemployment, amongst others, was also associated with alcohol use during pregnancy (Peltzer & Pengpid, 2019). The problem seems to be much worse in certain pockets of the population. A cross-sectional study conducted among mothers with five- to seven-week-old babies attending primary health care facilities in the Northern Cape reported that 26.1% of mothers smoked and 9.4% consumed alcohol while pregnant (Le Roux et al., 2020). Vythilingum et al. (2012:851) conducted a prospective self-report study among 323 women during their first antenatal visit in the East Metropole district in Cape Town in 2008 and found that 36.8% of pregnant women smoked, 20.2% used alcohol and 4.0% used substances. Poverty may also increase the chances of experiencing psychological stress such as domestic violence which has negative effects on both the pregnant woman and her unborn offspring through effects on hormonal regulation (Weinstock, 2005). Zar et al. (2019) report that psychological stress and exposure to violence was common among pregnant women attending two public health clinics in a poor peri-urban area (Drakenstein area) in South Africa from 2012 to 2015. Poverty and stress may increase the risk of foetal growth restriction and premature delivery (Weinstock, 2005). Premature delivery, in turn, increases the risk for depression, respiratory complications, jaundice in the neonate, epilepsy, cerebral palsy, visual disturbances and impairments in cognition (Moore et al., 2015). 17 Moore et al. (2017), summarised the effects of poverty on child development as follows: firstly, ‘it places significant psychological distress on a child’s caregiver and hence negatively impacts their caregiving capacity’, secondly, ‘families who live in poverty are less likely to be able to afford or access learning materials, and their standard of living is also more likely to be low’, and thirdly, ‘children are more likely to be exposed to ongoing traumatic experiences and the exposure to prolonged toxic stress may impact the developing brain and hormonal system with lifelong consequences.’ 2.5 ENVIRONMENTS THAT SHAPE HEALTH AND DEVELOPMENT Black et al. (2020) have proposed a revision of the UNICEF conceptual framework of malnutrition and death to not only reflect the importance of health and nutrition but also to acknowledge the role of nurturing care to ensure that children not only survive but also thrive (figure 3-1). The following section provides an overview of the different environments in which an individual functions, including the family, community and physical environments as well as individual-level factors, and how these may influence development in the first 1000 days of life. Figure 2.1 Conceptual framework of children surviving and thriving (Black et al., 2020) 2.5.1 Family environment Changes in the structure of families have increasingly become evident over the last few decades, with different family structures, such as single mother-headed families, emerging. 18 The quality of parenting, however, seems to be the most important factor in influencing how children develop their characteristics, as well as the social and physical environments in which children are raised (Moore et al., 2017). Children should ideally grow up in an environment that promotes calm, safety and protection (Black et al., 2020). If this is not the case, a child’s brain develops neural pathways associated with survival at the expense of those associated with future learning and growth (United States Department of Health and Human Services, 2011). Parenting styles during a child’s first two years, which still falls within the first 1000 days, may significantly influence children's attachment styles, which could further influence health and well-being in later years (Walfogel, 2006). Negative parenting characteristics, such as being excessively strict, portraying neglecting and/or controlling behaviour, and providing insufficient support, may cause behavioural and emotional problems later in life (Moore et al., 2017). Recent evidence highlights the distinctive role that fathers or male caregivers play in the early development of children (Yogman et al., 2016). Greater father involvement is closely associated with improved cognitive and social competence, ability to empathise, self-control and self-esteem, sibling interaction, as well as academic progress (Moore et al., 2017). In the South African context, the motherly role that grandmothers often play in raising their grandchildren should not also be acknowledged (Qhama, 2016). Experiencing stress within the family setting may damage the stress response of the human body and may result in the continuous production of stress hormones (Loman & Gunner, 2010). Stress during pregnancy may increase the risk for shorter gestation and low birth weight. Stress, in combination with poor nutrition, may adversely affect foetal neurocognitive development and memory function (Monk et al., 2013). Maternal stress has also been linked to poor birth outcomes, such as premature delivery, low birth weight, low gestational age, smaller head circumference and lower neurological scores at birth (Dole et al., 2003; Glynn et al., 2008). Stress during early life may affect physiology, behaviour and cognition in the adult years (Chaby, 2016). Changes in how the body responds to stress may further disturb physical health, which may increase the risk of developing diabetes, hypertension, cardiovascular disease, asthma (Swanson et al., 2009) as well as depression, anxiety and disruptive behaviours in the adult years (Alink et al., 2012). 19 One such stressor is domestic violence. Domestic violence may lead to an increased likelihood of poor lifelong outcomes (Humphreys, 2008). Pregnant women are more prone to experience increased violence against women, particularly in socio-economically deprived conditions (Bessa et al., 2014). An elevation in stress levels during pregnancy may increase cortisol levels which can cross the placenta and enter the foetal brain (Moore et al., 2017). Although the placenta has the ability to render part of the cortisol inactive, prolonged stress does affect the growing brain. Anxiety, particularly in early pregnancy, has been linked with low birth weight, premature delivery, as well as a smaller head circumference at birth, which may be an indication of decreased brain growth (Grigordiadis et al., 2018). During pregnancy, exposure to domestic violence may lead to poor emotional regulation and academic performance in the offspring, which may present as aggressive behaviour (Durand et al., 2011), behavioural problems in the infant (Flach et al., 2011), poor attachment to the mother (Quinlavin & Evans, 2005) and internalisation of problems (McFarlane et al., 2014). Food insecurity represents insufficient access to safe, nutritious, and sufficient food to meet a person’s basic needs (Carmichael et al., 2007; USDA, 2011) and is a significant source of stress within the family living in poverty. Food insecurity is more prevalent among the most vulnerable families (Ramalho et al., 2017) and has been linked to congenital disabilities (Carmichael et al., 2007; Ramalho et al., 2017); poor pre-pregnancy and pregnancy anthropometric nutritional status and pregnancy weight gain; maternal depression; complications including diabetes, hypertension and obesity; low birth weight; post-partum depression and suicide (Ramalho et al., 2017). Behavioural and other environmental factors within the family have been found to influence the eating behaviour of children (Jackson et al., 2015:9707). The family food environment may be an important factor in the development of the dietary habits of children (Hendrie et al., 2013). Family-level factors such as parent education and role modelling, family food rules and meal patterns, access to healthy foods, eating while watching television and the intake of fast foods may influence whether children consume healthy foods or not (Jackson et al., 2015:9707). Various kinds of families exist in South Africa. While nuclear families as the most common type, single-headed families are also common (SA Department of Social Development, 2013). Although underreporting occurs, gender-based violence is a major problem in South Africa (Vetten, 2014). 20 2.5.2 Community environment Social environments, including cultural beliefs, as well as physical environments within a community may influence health and wellbeing of individuals (Blau and Fingerman, 2009; Moore et al., 2017). Immediate social networks have been found to influence a person’s ideas, emotions, behaviours, relationships and health (United States Department of Health and Human Services, 2011). Pregnancy is characterised by significant psychological adjustment and pregnant women require considerable support (Moore et al., 2017). Inadequate support during this time may not only negatively affect psychological wellbeing in the mother, but her child as well (Dibaba et al., 2013). Inadequate support can further increase the risk of poor pregnancy outcomes (Elsenbruch et al., 2007:869). Sufficient social support during pregnancy has been found to reduce maternal stress, depression as well as risk-taking behaviours during pregnancy and thereafter (Rini et al., 2006). The level of support during pregnancy has furthermore been found to influence protective behaviours in the mother (Moore et al., 2017). Elsenbruch et al. (2007:869), report that pregnant women in Germany with low support during their first pregnancy were more likely to present with depressive symptoms, while also being more likely to smoke while pregnant (Elsenbruch et al., 2007:869). According to the Global Nutrition Report (2020), striking inequalities in location, age, sex, education and wealth exist between people living in urban and rural areas. These differences are even greater at community level where the most vulnerable groups are the most affected (GNR, 2020:13). Similarly, differences in living environments within communities may also have a strong influence on food intake (Kosaka et al., 2018). Inequalities in food systems are evident, with many people unable to access and afford a healthy diet (WHO, 2020a). Food outlets in urban areas differ from those in rural areas in both the quantity as well as the quality of available foods (Kosaka et al., 2018). 2.5.3 Physical environment Physical environments can influence development directly and indirectly (Moore et al., 2017). The effect of housing, as well as environmental toxins during the first 1000 days, are discussed as part of the physical environment. 21 2.5.3.1 Housing According to Maslow’s Hierarchy, access to stable and adequate shelter (housing) forms part of the basic physiologic needs (Maslow, 1943). Housing is ‘a setting in which an intricate and inter-related network of physical, social, economic and behavioural factors interplay to influence the health of occupants’ (Nkosi et al., 2019). Housing has the potential to provide a safe environment, autonomy and security (Wise, 2003). Home environments that do not support optimal growth and development during the first 1000 days have been associated with developmental challenges such as inadequate language development, behavioural issues, poor school readiness, aggressive, anxious and depressive behaviours as well as cognitive impairment (Evans et al., 2010). Long-term consequences may include lower chances of finishing high school, increased chance of teenage pregnancies, adult unemployment and poverty (Duncan et al., 2010). Income greatly affects the quality, type and size of housing. Consequently, lower-cost housing is often of lower quality and size (AIWH, 2010). Sufficient space is important in maintaining clean indoor air and may aid in reducing the risk of disease transmission (Nkosi et al., 2019). Another aspect frequently encountered in lower-cost housing is overcrowding (Blake et al., 2007). Overcrowding may persist throughout life and has the potential to affect future socioeconomic status and wellbeing (Solari & Mare, 2007). Having to live in crowded areas may have adverse effects on sense of autonomy, social behaviour, health, developmental outcomes as well as school behaviour in children, while also affecting parenting behaviour and result in conflict between parent and child as well as conflict between parents (Moore et al., 2017). Also, access to water and sanitation is a basic human right and plays a critical role in hygienic conditions of living (WHO, 2003). 2.5.3.2 Environmental toxins Environmental risks including pollution in the air, contamination of food and water as well as exposure to toxic substances in the environment are known to influence the development of organs and physiological systems during all critical life stages (Poore et al., 2017:e172). The brains of infants and young children are particularly sensitive to environmental toxins during the first 1000 days, and exposure to toxins could potentially play a role in the development 22 of disease and neurodevelopmental disorders (Heyer & Meredith, 2017). The placenta and foetal blood-brain barrier do not protect against all chemicals. The foetus can, therefore, be exposed to chemicals inside the womb. Since the foetus is much smaller in size than the mother and is still maturing in terms of growth and development, it is more vulnerable to chemicals and other toxins than the mother (Moore et al., 2017). Exposure to chemicals, particularly during critical periods of development, may contribute to premature birth and malformations (Burris et al., 2016). Even though the prevalence of morbidities like asthma and obesity are increasing in childhood, other conditions related to the environment may only present much later. Certain chemicals that are often part of daily life may have the potential for endocrine disruption which may influence reproductive and thyroid function as well as neurodevelopment and may lead to metabolic disorders, cancer and certain immune diseases (WHO, 2017). Daily environmental toxin exposure during pregnancy may result in the gradual accumulation of substances, called bioaccumulation. Bioaccumulation may expose the foetus to higher than expected concentrations of toxic substances and have added effects (Poore et al., 2017:e173). The risk of developing disease may be influenced by the combined effect of early life and later exposure to environmental toxins. The effect of household and ambient air pollution combined with second-hand smoke on systems as well as organ development during the prenatal period and the first year of life is one such an example (WHO, 2017). According to the WHO, 92.0% of the world population is exposed to unhealthy levels of ambient air pollution which is mainly caused by road traffic, industrial emissions and domestic fuel burning (WHO, 2016). Exposure to traffic-related air pollution while pregnant may increase the risk of premature birth, low birth weight, as well as heart malformations (Currie & Walker, 2011). The risk of delivering a child who suffers from autism was found to increase with high exposure to air pollution, particularly during the third trimester (Raz et al., 2015). The incomplete burning of coal, oil, gas, garbage and other organic substances, including tobacco, result in the formation of chemicals called polycyclic aromatic hydrocarbons which may contribute to damaging health effects (Moore et al., 2017; WHO, 2017). High prenatal exposure to these compounds has been linked to lower mental development (Ritz et al., 2002), while even relatively low exposure to polycyclic aromatic hydrocarbons may have adverse reproductive effects (Choi et al., 2006). 23 Other pollutants, such as carbon monoxide, have been found to increase the risk for premature delivery, asthma (the United States Environmental Protection Agency, 2019), heart malformation and valve defects in children (Ritz et al., 2002) and greater risk of respiratory-related death (Woodruff et al., 2008). 2.5.4 Individual-level factors The following section elaborates on individual-level factors that may affect pregnancy outcome and health. These factors include maternal nutritional status, substance use during pregnancy, multiple births and previous pregnancy history. 2.5.4.1 Maternal nutritional status It is well known that diet and nutritional status of pregnant mothers can affect the epigenetics of their offspring, with lifelong effects (Moore et al., 2017). Physically active women who follow a balanced diet, abstain from smoking and alcohol use and have a normal body mass index (BMI) when entering pregnancy are more likely to have a successful pregnancy outcome (Koletzko et al., 2014:96). Factors that affect nutritional needs during pregnancy include poor nutritional status, young and old maternal age, pregnant with more than one foetus, closely spaced births, consistent high levels of exercise, certain diseases and use of alcohol and other substances (NAS, 1990). i Maternal anthropometric status before and during pregnancy a Maternal stature Adult height is positively associated with birth weight as well as birth length. Foetal growth retardation may result from short stature in the mother and poor nutrition stores (Black et al., 2013:432). Growth failure during the period of early childhood may result in reduced adult stature unless catch-up growth occurs (Victoria et al., 2008:342). Growth failure, particularly stunting, is linked with increased mortality (Black et al., 2013:433). A baby born with low birth weight, who is stunted and underweight during infancy, and who experiences rapid weight gain after two-years of age, is likely to develop overweight or obesity in later life (Victoria et al., 2008:340). Short stature in pregnant women has further been linked to an increased risk of stillbirths, perinatal mortality, obstructed labour resulting in the need for caesarean 24 delivery and low Apgar scores (Stulp et al., 2011). The Apgar score is used to determine an infant’s condition at birth by assessing heart rate, respiratory effort, muscle tone, reflex, irritability and colour, awarding a score between zero and two for each of the items assessed. A total score between eight and ten indicates the best possible condition (Miller-Keane, 2003). b Maternal weight and weight status before pregnancy Maternal weight before conception, along with weight gain during pregnancy (discussed in the following section), can impact the short- and long-term health of the offspring (Moore et al., 2017). Maternal underweight as well as obesity, may have profound effects on foetal growth and pregnancy outcome (Kirchengast & Hartmann, 2018). Weight status prior to pregnancy, particularly BMI, has been strongly associated with health outcomes in both the mother and her offspring (Koletzko et al., 2019:96). Maternal obesity may increase the risk of delivering a large-for-gestational-age infant (Kirchengast & Hartmann, 2018). Mothers delivering infants weighing more than 4000 grams at birth (macrosomia) may have an increased need for artificial induction of labour, an increased chance of delivering via caesarean section, an increased risk of postpartum haemorrhage and experience prolonged birth. These mothers also have a greater risk of delivering a baby that suffers from birth asphyxia, newborn hypoglycaemia and hyperbilirubinaemia (Dietz et al., 2009). Postnatal risks have also been associated with maternal overweight and obesity. Jacota et al. (2016:321) studied data from the EDEN mother-child cohort that explored child development and health determinants during the pre- and early postnatal period among 2002 pregnant women from two French maternity departments between 2003 and 2006. The authors examined the associations between children’s BMI, fat mass and abdominal adiposity at 5–6 years and maternal pre-pregnancy average yearly weight change from age 20 along with gestational weight gain in 1069 mother-child pairs in this cohort. BMI and adiposity parameters in the children were positively associated with maternal pre-pregnancy weight. The authors did, however, conclude that a relationship between children’s BMI and adiposity rebound (the second rise in BMI occurring between 3 and 7 years) and overweight and obesity in the mother may only emerge when these children are older, hindering early prevention (Jacota et al., 2016:323). Similarly, Linares et al. (2016:1313) reported that high pre-pregnancy BMI was associated with earlier adiposity rebound among children of 594 mothers who were 25 enrolled in the Growth and Obesity Chilean Cohort study which assessed the association between early growth and development of adiposity and metabolic risk in low- to middle- income Chilean children. Blondon et al. (2016) estimated postpartum venous thromboembolism risk in 289 women in Washington State between 2003 and 2011. Maternal BMI was found to be an important risk factor for postpartum venous thromboembolism. The authors recommended that pre- pregnancy BMI be used along with gestational weight gain when determining the risk for postpartum venous thromboembolism at delivery (Blondon et al., 2016). On the other hand, maternal underweight seems to increase the risk of delivering a small-for- gestational-age infant (Kirchengast & Hartmann, 2018). Maternal undernutrition, including pre-pregnancy underweight and insufficient weight gain during pregnancy, increases the risk of spontaneous abortion, premature birth, foetal growth restriction, hypertensive disorders, metabolic syndrome and poor perinatal outcome (Triunfo & Lanzone, 2015). c Weight gain during pregnancy Guidelines on what is considered desirable weight gain during pregnancy have varied over time (Forsum, 2018). Pre-pregnancy weight, specifically pre-pregnancy BMI, is used to determine the recommended weight gain during pregnancy since optimal gestational weight gain is considered a function of a woman’s pre-pregnancy nutritional status. The Institute of Medicine (IOM), consequently, recommend that lean and underweight women should aim for greater weight gain during pregnancy than women with a normal body weight, while normal-weight women are advised to gain more weight than overweight and obese women (Table 2.1) (Rasmussen et al., 2009). 26 Table 2.1 Recommendations for gestational weight gain by body mass index (BMI) before conception (Rasmussen et al., 2009) BMI Category Gestational weight gain Gestation weight gain recommendation for recommendation for twin singleton pregnancy(kg) pregnancy (kg) Underweight 12.5 – 18.0 - (BMI < 18.5 kg/m2) Normal weight 11.5 – 16.0 16.8 – 24.5 (BMI = 18.5 – 24.9 kg/m2) Overweight 7.0 – 11.5 14.1 – 22.7 (BMI = 25.0 – 29.9 kg/m2) Obesity 2 5.0 – 9.0 11.4 – 19.1 (BMI ≥ 30.0 kg/m ) Despite extensive scientific evidence to support these guidelines, the IOM guidelines are not universally accepted. Various countries do not have gestational weight gain recommendations, while existing country-specific recommendations may differ from the IOM guidelines (Alavi et al., 2013). Excessive, as well as insufficient weight gain during pregnancy has been linked to poor pregnancy outcomes. Insufficient gestational weight gain is linked to small-for-gestational- age and premature delivery. In contrast, excessive weight gain has been linked to large-for- gestational-age, macrosomia, caesarean delivery, gestational diabetes, pre-eclampsia, postpartum weight retention and obesity in the offspring (Nohr et al., 2008). Excessive and insufficient weight gain during pregnancy adds to the complications already associated with being overweight or obese or underweight before conception (Forsum, 2018). ii Energy requirements during pregnancy Energy requirements during pregnancy make provision for the requirements of energy expended during rest, physical activity and tissue growth. Tissue growth includes the growth of the foetus as well as maternal tissues including breast tissue, fat mass, the uterus and placenta. Energy balance during pregnancy can thus be defined as energy intake equal to energy expenditure plus storage (Butte & King, 2005:1011; Most et al., 2019). To decrease the risk of negative outcomes in both the mother and her offspring, energy requirements are, therefore, not only focussed on weight maintenance, but also adequate weight gain. 27 Requirements for energy deposition are dependent on maternal pre-pregnancy body weight. In order to support optimal pregnancy outcomes, women with low body weight prior to pregnancy will require greater accumulation of fat mass than those who are obese. Differences in pre-pregnancy weight consequently affect energy requirements for a healthy pregnancy to a large extent. In contrast, energy deposited as foetal and placental tissues seems to be similar from most women, having a small effect on energy requirements. Different prediction equations are available to determine energy requirements for pregnancy (Most et al., 2019). Energy needs for healthy, normal-weight pregnant women who follow a moderately active lifestyle can be met with a slight increase in energy intake with a balance between macronutrients within the recommended guidelines (Marangoni et al., 2016). Energy requirements differ slightly between trimesters. On average, total energy expenditure per day remains constant during the first trimester, while an increase is seen during the second and third trimester. Resting Metabolic Rate (RMR) only increases slightly during the first trimester, while RMR can increase by 390 kilocalories (kCal) per day during the second and third trimester (Most et al., 2019). Most et al. (2019) found that physical activity seems to decline minimally during the first trimester and decreases slightly during the second and third trimester. Most et al. (2019) estimate energy requirements during the first trimester of pregnancy to be 50–150 kCal per day. The IOM and American College of Obstetricians and Gynaecologists recommend that pregnant women maintain their pre-pregnancy energy intake during the first trimester since the energy costs for weight gain during this time are low (Rasmussen et al., 2009; Kominiarek & Peaceman, 2017). During the second and third trimester, an estimated 340 kCal and 452 kCal respectively are required according to the IOM (IOM, 2009). Other recommendations for energy requirements during pregnancy are set at 200 ̶ 300 kCal per day above the needs of non-pregnant women for women expecting singletons (NAS, 1990). Limited data exist on requirements for twin pregnancies, therefore, Ghandi et al. (2018) aimed to determine the estimated energy requirements of healthy women carrying twins in Houston, Texas. Their findings indicate that an additional 700 kCal is required per day when carrying twins (Ghandi et al., 2018). 28 A comprehensive study published by Butte et al. in 2004, confirmed that additional energy needs are different for each trimester as well as for maternal preconception BMI (Butte et al., 2004:1078). While a deficiency of energy and micronutrients during pregnancy can be very damaging, overconsumption thereof may also be detrimental. Excessive energy and macronutrient intakes, particularly in overweight and obese pregnant women, have been associated with an increased risk of miscarriage, gestational diabetes and pre-eclampsia in the pregnant mother, while the risk of obesity and type 2 diabetes in the offspring is also increased (Maragoni et al., 2016). Although various recommendations in terms of energy intake during pregnancy are available, the most accurate means of determining whether a pregnant woman is consuming enough energy is by monitoring weight gain. iii Macronutrient requirements and intake during pregnancy The human body is capable of maintaining relatively constant tissue levels of most nutrients unless there is a severe deficiency of a particular nutrient. Four main homeostatic responses help to maintain tissue levels when dietary intake is low namely, using that which is available in body stores, increasing absorption of the specific nutrient, reducing excretion in the urine and slowing down the use or turnover of a nutrient. During pregnancy, certain physiologic changes may stimulate some of these physiologic responses, regardless of the pregnant woman’s nutritional status with the aim of increasing nutrient supply to meet the increased needs of pregnancy (NAS, 1990). If maternal weight gain during pregnancy is adequate, it is often assumed that maternal nutrition is also adequate. The increased requirements for nutrients to support appropriate foetal growth is, however, higher compared to the limited additional energy required. Maternal weight gain is, therefore, not necessarily a predictor of healthy outcomes during pregnancy, particularly for heavier women (Cox & Carney, 2017:262). a Protein requirements and intake Protein is probably the macronutrient that requires the most consideration during pregnancy since the need increases gradually to support protein synthesis for the maintenance of maternal tissues and foetal growth (Marangoni et al., 2016). Protein deficiency during pregnancy may, therefore, impair foetal growth. Protein and energy deficiencies frequently occur together, making it difficult to determine the individual effects. Protein needs increase 29 throughout pregnancy and are highest during the third trimester to support the synthesis of maternal as well as foetal tissues (Cox & Carney, 2017:251). The EAR (DRI) for protein during pregnancy is 0.88 grams per kilogram (g/kg) current body weight per day (IOM, 2006). According to the WHO/FAO system, protein requirements during pregnancy can be determined by adding 0.7 grams per day for the first trimester, 9.6 grams per day for the second trimester and 31.2 grams per day for the third trimester to the recommended 0.66 g/kg/day required for maintenance associated with increased weight during pregnancy (FAO/WHO/UNU, 2002). Adequacy of protein intake in the diet can also be determined using the acceptable macronutrient distribution range (AMDR) which ranges from 10–35% of the total energy (TE) intake (IOM, 2006) or 10–15% of TE intake (FAO/WHO/UNU, 2002) depending on whether the DRIs or WHO/FAO system are used. b Carbohydrate requirements and intake The requirements for carbohydrates increase slightly during pregnancy to help maintain appropriate blood glucose control and prevent ketosis. Carbohydrate choices need to be carefully considered during pregnancy in order to include all the required nutrients and to ensure that more unrefined carbohydrates are included (Cox & Carney, 2017:256). The EAR (DRI) for carbohydrates during pregnancy is set at 135 grams per day and 29 grams per day for fibre with an AMDR of 45–65% carbohydrates of TE per day (NAS, 2005). According to the Food and Agriculture Organization / World Health Organization / United Nations University (FAO/WHO/UNU), the AMDR for carbohydrates is 55–75% of TE per day, while more than 25 grams of fibre should be consumed per day, and intake from added sugar should be kept below 10% of TE (FAO/WHO/UNU, 2002). c Fat requirements and intake There is no DRI or RNI for total fat intake during pregnancy. Fat intake depends on the total energy intake for sufficient weight gain (Cox & Carney, 2017:256). Fat is a basic building material of body tissue and is essential for the formation of cell membranes and hormones (Innis & Friesen, 2008). The quality of fat in the diet of pregnant women, therefore, becomes more important rather than the quantity thereof, although the high energy density of fat makes it easier to consume excess energy. The relative proportion of polyunsaturated fatty 30 acids (PUFAs) should be improved rather than increasing total fats consumed (Maragoni et al., 2016). Essential omega-6 and omega-3 PUFAs play an important role in foetal and newborn neurodevelopment (Greenberg et al., 2008:162). These fatty acids also play an important role as precursors of hormone-like substances, eicosanoids. Omega-6 PUFAs (linoleic acid) give rise to pro-inflammatory eicosanoids while omega-3 PUFAs (linolenic acid) give rise to anti- inflammatory eicosanoids. The body requires a balance between omega-6 and omega-3 PUFAs to maintain optimal immune function (Connor, 2000; Kiecolt-Glaser et al., 2014:132). Docosahexaenoic acid (DHA, an omega-3 PUFA) is essential for brain and retinol development of the foetus during pregnancy since it is the major PUFA contained in the brain and retinal rods. Adequate DHA may also help to lower the risk of premature delivery and post-partum depression. The human body has a limited ability to synthesise long-chain PUFAs to DHA and eicosapentaenoic acid (EPA) which occur in high concentrations only in fatty fish living in cold seas (Maragoni et al., 2016). Therefore, these need to be consumed in sufficient quantities from the diet (Greenberg et al., 2008:163). The European Food Safety Authority recommends the consumption of 1 ̶ 2 up to 3 ̶4 servings of fish per week during pregnancy, without being associated with significant risk of contamination with heavy metals (EFSA Scientific Committee, 2015). Recommended intakes for omega-6 and omega-3 polyunsaturated fatty acid increase slightly during pregnancy (Cox & Carney, 2017:240). The AI for omega-6 is set at 13 grams per day while the AI for omega-3 is 1.4 gram per day. The AMDR for adults for omega-6 and omega-3 fatty acids are indicated in Table 2.2. Table 2.2 Acceptable macronutrient distribution ranges (AMDR) for omega-6 and omega-3 fatty acids (IOM, 2006†; FAO/WHO/UNU, 2002‡) Nutrient AMDR Omega-6 5 – 10% of TE per day† 5 – 8% TE per day‡ Omega-3 0.6 – 1.2% of TE per day† 1 – 2% of TE per day‡ DHA 200 – 1000 milligrams per day‡ DHA + EPA 300 – 2700 milligrams per day‡ AA < 800 milligrams per day‡ 31 iv Micronutrient requirements and intake All vitamins and minerals are important in ensuring optimal pregnancy outcome and the needs during pregnancy increase for most, with the degree of the increase varying by nutrient (Cox & Carney, 2017:256). The increased requirement for micronutrients is greater than that of macronutrients during pregnancy (Maragoni et al., 2016). Requirements may be met through the diet for most women, but for some, it may be necessary to start supplementing before conception (Cox & Carney, 2017:256). Table 2.3 indicates the EAR for both the DRI and the WHO/FAO reference systems for the different micronutrients during pregnancy. 32 Table 2.3 Reference Intakes for vitamins and minerals (amount per day) (IOM, 2006; Allen et al., 2006) Micronutrient Unit DRI WHO/FAO EAR Vitamin A μg 550 571 Vitamin C mg 70 46 Vitamin D μg 10 5 Vitamin E mg 12 6 Thiamine mg 1.2 1.2 Riboflavin mg 1.2 1.2 Niacin mg 14 14 Vitamin B6 mg 1.6 1.6 Folate μg 520 480 Vitamin B12 μg 2.2 2.2 Calcium mg 800 833 Copper μg 800 - Iodine μg 160 143 Iron mg 22 >40 Magnesium mg 290 (19-30 years of age) - 300 (31-50 years of age) Phosphorus mg 580 Selenium μg 49 23 Zinc mg 9.5 5.8 AI Vitamin K µg 90 - Pantothenic acid mg 6 - Biotin µg 30 - Choline mg 450 - Chromium µg 30 - Fluoride mg 3 - Manganese mg 2 - Potassium mg 4700 - Sodium mg 1500 - Chloride mg 2300 - In South Africa, legislation requiring the fortification of all wheat flour, wheat bread, maize meal and unsifted maize meal with vitamin A, thiamine, riboflavin, niacin, pyridoxine, folic acid, iron and zinc came into effect in 2003 (SA DoH, 2003). Between 35 and 65 parts per million iodine should also be added to food-grade salt or any other salt intended for use in or on food (SA DoH, 2007). 33 Currently, it is standard clinical practice to prophylactically supplement pregnant women in the South African public sector hospitals with iron, folate and calcium throughout pregnancy. As per the “Guidelines for maternity care in South Africa”, all pregnant women should receive supplements of ferrous sulphate (200 mg daily), calcium (1000 mg daily) and folic acid (5 mg daily) (SADoH, 2015). The World Health Organization (WHO) updated their recommendations on multiple micronutrient supplements during pregnancy and recommend the use of multiple micronutrient supplements that include both iron and folic acid and that multiple micronutrient supplements providing 30 mg of iron may be more acceptable than iron and folic acid supplements that contain higher doses of iron. Although all micronutrients are needed for optimal pregnancy outcome (Cox & Carney, 2017:256), those micronutrients that are of particular importance during pregnancy will be elaborated on, i.e. folate, vitamin A, vitamin D, calcium, iron and iodine. a Folate requirements and intake The requirements for folate increase during pregnancy to support maternal erythropoiesis, as well as foetal and placental growth (Guéant et al., 2013; Obeid et al., 2013). Folate also plays a vital role in various metabolic processes including the biosynthesis of deoxyribonucleic acid (DNA) and ribonucleic acid (RNA), methylation of homocysteine to methionine and amino acid metabolism (Maragoni et al., 2016). Low folate levels may increase the risk for miscarriage, low birth weight and premature birth. Early maternal folate deficiency has been linked to an increased risk for congenital malformations, neural tube defects, possibly orofacial clefts and congenital heart defects, amongst others (Guéant et al., 2013; Obeid et al., 2013). Since the neural tube closes by 28 days of pregnancy, a time when most women do not yet know that they are pregnant, the Centers for Disease Control and Prevention (CDC), recommend that all women of childbearing age take 400 µg of folic acid in the synthetic form (supplements and fortified foods) every day, regardless of dietary folate intake (CDC, 2014). The RDA for folate for non-pregnant women is 600 µg per day, while the UL for folate during pregnancy is 1000 µg (IOM, 2006). The current supplementation guideline of 5 mg (5000 µg) (SADoH, 2015) is well above the UL for pregnant women which may raise the concern of safety, particularly the the potential to mask and worsen neuropathy in individuals with a vitamin B12 deficiency (Dolin et al., 2018). 34 Evidence concerning the risks associated with high intakes of folic acid during pregnancy is limited and inconsistent (Dolin et al., 2018), however, increased risk of cleft palates, spontaneous abortion, impaired psychomotor development and childhood respiratory issues have been noted (Li et al., 2016). Practices and recommendations on supplementation need to be evaluated routinely as newer research becomes available. Supplementation of folic acid is one such a recommendation that needs re-evaluation, considering that studies have found that detectable levels of unmetabolised folic acid were found in both maternal and foetal blood samples among pregnant and non-pregnant women who consumed folic acid doses of 800 – 1000 µg per day (Kelly et al., 1997; Plumptre et al., 2015). Data on folate deficiency, including during pregnancy, in South Africa, is lacking. According to the South African National Food Consumption Survey published in 2005, folate status appeared to be adequate uniformly throughout the country when considering mean serum and red blood cell folate concentrations (Labadarios et al., 2005:263). b Vitamin A requirements and intake Vitamin A is a vital nutrient during periods when growth is rapid. It also plays a vital role in differentiation of cells, ocular development, functioning of the immune system, lung development and maturity and gene expression (Wu et al., 2012). Low vitamin A levels have been linked to intrauterine growth restriction (IUGR) as well as greater risk of mortality in both the mother and the neonate (Cox & Carney, 2017:257). Hovdenak and Haram (2012) found that improved vitamin A status in women with Human Immunodeficiency Virus (HIV) was associated with improved birth weight, possibly as a result of improved immunity. When considering vitamin A, it is important to note that preformed vitamin A is teratogenic and supplementation during pregnancy is usually not necessary (Hovdenak & Haram, 2012). Supplementation with beta-carotene is not associated with congenital disabilities (Cox & Carney, 2017:257). The 2005 South African National Food Consumption Survey indicated that two out of three children and one out of four women had poor vitamin A status (Labadarios et al., 2005:261). The SANHANES-1 of 2012 reported that females of reproductive age in South African had a 35 vitamin A deficiency prevalence of 13.3%, which is indicative of a moderate public health problem (Shisana et al., 2014:11). c Vitamin D requirements and intake Vitamin D is important for regulating cytokine metabolism and modulating the immune system during the first stage of pregnancy. Vitamin D, therefore, contributes to the implantation of the embryo as well as the regulation of the secretion of various hormones during pregnancy (Maragoni et al., 2016). Maternal supplementation of vitamin D during pregnancy has been found to lower the risk of pre-eclampsia, premature delivery, low birth weight (De-Regil et al., 2016) and gestational diabetes (Maragoni et al., 2016). Vitamin D deficiency remains common amongst pregnant women, even in countries with sunny climates (Maragoni et al., 2016). According to a systematic review and meta-analysis conducted by Mogire et al. (2020:e134), the prevalence of vitamin D deficiency is high among populations in Africa. Serum vitamin D concentrations seem to be lower in South Africa compared to sub-Saharan Africa, in urban areas compared to rural areas, in females compared to males and in newborns compared to their mothers (Mogire et al., 2020:e134). Velaphi et al. (2019:807) found that 15.9% of pregnant women and 32.8% of their offspring suffered from vitamin D deficiency in a prospective cohort study undertaken between March 2013 and November 2014 at Chris Hani Baragwanath Academic Hospital. d Calcium requirements and intake Calcium requirements during pregnancy are strongly influenced by hormonal factors (Cox & Carney, 2017:259). The rate of bone turnover is moderately increased by human placental lactogen. Oestrogen inhibits bone resorption, while accretion and absorption of calcium increases during pregnancy. The absorption of calcium across the gut doubles during pregnancy. Approximately 30 grams of calcium is accumulated during pregnancy, of which about 25 grams is used for the formation of the foetal skeleton. The remainder is stored in the mother’s skeleton to serve as a reserve for calcium needs during lactation (Olausson et al., 2012). Low calcium intake during pregnancy has been linked to greater risk of IUGR as well as pre- eclampsia (Hovdenak & Haram, 2012), while adequate intakes are associated with higher 36 birth weights, lower risk of premature delivery and improved blood pressure control (Maragoni et al., 2016). The daily calcium consumption of populations in most low- and middle-income countries is below the recommendations (Cormick et al., 2019:444). A study conducted by Napier et al. (2019) among 100 pregnant women attending a public health care clinic in KwaZulu Natal, South Africa found that 98.0% of the women consumed less than 100% of the DRIs for calcium. e Iron requirements and intake Iron deficiency is still the most prevalent single micronutrient deficiency in the world (WHO, 2020). An increase in red blood cell volume occurs during pregnancy, however, a large increase in plasma volume also occurs, resulting in a dilution of haemoglobin during pregnancy (Randall et al., 2019). Inadequate iron consumption during pregnancy may have a negative impact on haemoglobin production, which could affect the delivery of oxygen to the uterus, placenta and developing foetus (Lee & Okam, 2011). Systemic iron bioavailability is largely controlled by hepcidin, a peptide hormone that inhibits the absorption of iron from the intestines as well as the transfer of iron from the body stores. Hepcidin levels appear to be lower during pregnancy to ensure greater iron bioavailability to both the mother and foetus (Koening et al., 2014). Iron deficiency anaemia during pregnancy has been associated with IUGR, premature births, increased foetal and neonatal mortality and, in severe cases (at haemoglobin levels below 9 g/dL) with complications during delivery. Other studies have linked iron deficiency anaemia during pregnancy with increased foetal cortisol levels and oxidative damage to foetal red blood cells (Hovdenak & Haram, 2012). Iron deficiency during the early stages of pregnancy may also affect foetal brain development and the regulation of brain function. Neonatal iron deficiency may also develop if the mother is extremely iron deficient. Hypertension in the mother, which could negatively affect blood flow, coupled with maternal smoking and prematurity, can further increase the risk for neonatal iron deficiency. Maternal diabetes may also increase the risk of iron deficiency in the neonate as foetal demands increase as a result. These changes may lead to long-term neuro-behavioural impairments which may affect temperament, interactions with other individuals, learning and memory and may potentially result in genomic changes (Georgieff, 2011). 37 Iron deficiency may cause fatigue, dyspnoea, light-headedness, and poor exercise tolerance in the pregnant mother. The mother is also at risk of greater blood loss with atony of the uterus during delivery, while wound healing and immune function may be impaired. The risk for postpartum depression is increased, and poor maternal/infant interaction and impaired lactation may result (Murray-Kolb, 2011). Since many women enter pregnancy with insufficient iron stores to provide for the physiologic needs of pregnancy, iron supplements are often routinely prescribed (Lee & Okam, 2011). Symington et al. (2019), however, found that despite routine iron supplementation, the prevalence of anaemia, iron deficiency anaemia and iron deficiency erythropoiesis increased amongst pregnant women recruited at primary health care clinics in Johannesburg, South Africa. On the other hand, excessive consumption of iron during pregnancy has been linked to greater exposure to oxidative stress, lipid peroxidation, impairments in glucose metabolism and gestational hypertension (Krebs et al., 2015). According to the National Food Consumption Survey, one out of five women in South Africa had poor iron status (Labadarios et al., 2005:262). Findings from the SANHANES-1 survey reported that among women of reproductive age, 5.9% were iron deficient and 9.7% suffered from iron deficiency anaemia (Shisana et al., 2014:12). f Iodine requirements and intake Iodine is an important component of thyroid hormones and is vital for growth, formation and development of organs and tissues, while also playing a role in glucose, protein, lipid, calcium and phosphorus metabolism as well as thermogenesis (Maragoni et al., 2016). Iodine deficiency during pregnancy may lead to an increased the risk of spontaneous abortion, perinatal mortality, congenital disabilities and neurological disorders (Trumpff et al., 2015). The WHO considers iodine deficiency as the most important preventable cause of brain damage (WHO, 2004). Iodine is required to produce foetal thyroid hormones during pregnancy (Zimmerman, 2012:108). Conditions of only mild to moderate nutritional iodine deficiency have been associated with a much higher risk of the development of hypothyroidism. The most sensitive 38 period to iodine deficiency is from the second trimester of pregnancy up to the third year of extrauterine life (Maragoni et al., 2016). On the other hand, data on iodine excess during pregnancy are scarce. Programmes of universal salt iodisation have resulted in a marked improvement in iodine nutrition. Regions with previously severe iodine deficiency now present with mild to moderate prevalence of deficiency. While iodine supplementation during pregnancy is recommended in areas with mild to moderate deficiency, the long-term effects thereof are unknown (Pearce et al., 2016:918S). Women who use iodine supplements during pregnancy may experience digestive intolerance (Mousa et al., 2019). Foetal hypothyroidism has been reported in cases of high iodine load in the pregnant mother. These were, however, observed with exposure to very high levels of iodine associated with medical doses (Pearce et al., 2016:921S). A lack of data concerning iodine status amongst pregnant South African women exists (Smuts & Baumgartner, 2019:3). According to the National Food Consumption Survey of 2005, almost all (97%) households used salt containing a significant amount of iodine (>2 parts per million) and four out of 10 women had a urinary iodine concentration in the excessive category (Labadarios et al., 2005:267). Mabasa et al. (2019:76) assessed the iodine status of pregnant women at primary health care clinics and households from five municipalities of Mopani District in Limpopo province. Their findings showed that maternal iodine status was sufficient (Mabasa et al., 2019:76). 2.5.4.2 Substance use during pregnancy Substance use during pregnancy is an important public health concern associated with various harmful consequences for both the mother and her offspring (Forray, 2016). Alcohol, illicit drug and tobacco use during pregnancy and the associated risk will be discussed in the following section. i Alcohol consumption Exposing the foetus to alcohol is known to be one of the leading causes of cognitive impairment and neurodevelopmental disorders (Eustace et al., 2003), while it is also the most common preventable cause of congenital disabilities (Moore et al., 2017). The frequency of use increases the risk of congenital disabilities. Exposure to alcohol before and during 39 pregnancy may have several negative consequences for foetal brain development and growth (AIHW, 2016). Consumption of alcohol during pregnancy increases the risk of miscarriage, placenta abruption, low birth weight, cognitive impairment (Cox & Carney, 2017:272) as well as premature delivery (AIHW, 2016). Disorders associated with alcohol consumption during pregnancy that affect physical, learning, as well as behavioural outcomes are known as foetal alcohol spectrum disorders (FASD) (Abel, 2012). Alcohol consumption during pregnancy may lead to abnormalities in the formation of the face, deficits in executive function, problems related to memory, delays in speech and language, difficulty with attention, hyperactivity, internalising and externalising behavioural problems as well as social impairments. All of these may continue to be present to varying degrees throughout life (Coles et al., 1997; Jacobson & Jacobson, 2002; Kingsbury & Tudehope, 2006). Currently, total abstinence from alcohol consumption during pregnancy is recommended since no safe threshold has been identified (Cox & Carney, 2017:272). ii Illicit drug use Establishing the prevalence of drug use during pregnancy may be challenging due to the illicit nature thereof (Moore et al., 2017). Le Roux et al. (2020) found that although only 0.3% of pregnant women visiting primary healthcare clinics in the Northern Cape reported using recreational drugs during pregnancy, the risk for stunting increased significantly with the use thereof. The negative effects associated with illicit drug abuse during pregnancy can be substantial and depend on the type of drug used. Cannabis use during pregnancy has been associated with premature delivery, low birth weight, small-for-gestational-age, adverse growth of the foetal brain, reduced attention and functioning skills, and poorer academic achievement, amongst others (Forray, 2016). Premature rupture of membranes, placenta abruption (Forray, 2016), premature birth, low birth weight as well as small-for-gestational-age are some of the risks associated with cocaine use during pregnancy (Pereira et al., 2018). Methamphetamine use during pregnancy is also associated with foetal loss (including miscarriage and stillbirth) while the risk for pre- eclampsia and gestational hypertension in the pregnant mother increases (Forray, 2016; Dinger et al., 2017). The negative effects associated with methamphetamine use during 40 pregnancy may hold long-term neurodevelopmental and behavioural consequences (Dinger et al., 2017). Opioid use during pregnancy is linked to an increased risk of spontaneous abortion, premature delivery, placenta abruption, low birth weight, foetal death (Lind et al., 2017), respiratory problems, bleeding during the third trimester, toxaemia, and mortality in the mother (Forray, 2016). The risk for neonatal abstinence syndrome (postnatal withdrawal due to opiate exposure in the womb) and postnatal growth deficiency, microcephaly, neurobehavioural problems and sudden infant death syndrome is also greater in the offspring of mothers who use opioids during pregnancy (Forray, 2016; Lind et al., 2017). iii Tobacco use Smoking during pregnancy is commonly underreported (Moore et al., 2017), but are associated with potential long-term and serious consequences for child health. Tobacco use during pregnancy may have direct negative effects on birth outcomes including damage to the structure of the umbilical cord, miscarriage, increased risk of ectopic pregnancy, abruption of the placenta (Forray, 2016), low birth weight, premature delivery as well as increased infant mortality (Forray, 2016; Maragoni et al., 2016). Negative effects associated with smoking during pregnancy may also extend beyond early childhood as smoking seems to increase the risk of overweight in infants born to mothers who smoke (Oken et al., 2008). Currently, total abstinence from smoking is recommended during pregnancy (Maragoni et al., 2016). 2.5.4.3 Multiple gestations Multiple gestations result in significant physiologic changes, beyond the usual for carrying a single foetus, in the mother. This applies to increases in plasma volume, metabolic rate as well as increased insulin resistance. Infants born from a multiple gestation have a greater risk for premature delivery, usually accompanied by IUGR or low birth weight, compared to their singleton counterparts. Sufficient weight gain in the pregnant mother is of utmost importance in multiple gestations (Goodnight & Newman, 2009). Unfortunately, optimal nutrient requirements for women carrying more than one baby are still unknown but are higher than the requirements for singleton pregnancies (Cox & Carney, 2017:265). Careful and 41 comprehensive assessment of the pregnant mother to optimise dietary intake of micronutrients is advised (Maragoni et al., 2016). 2.5.4.4 Pregnancy history A previous history of carrying more than one foetus at a time, premature delivery and pre- eclampsia should be carefully assessed in all pregnant women to ensure optimal dietary and micronutrient intake to lower the risk of premature delivery, pre-eclampsia and IUGR in subsequent pregnancies (Berks et al., 2012). Some observations suggest that consuming a balanced diet with adequate fruits, vegetables, whole grains and fish during the early stages of pregnancy may lower the risk of premature delivery (Englund-Ögge et al., 2014; Timmermans et al., 2011). 2.5.5 Tools to assess dietary intake The notion of reference levels for nutrients was first introduced in the 1940s and was based on the average levels of intakes of nutrients required to live a healthy life (Gibson, 2005:198). The dietary requirement for a micronutrient can be defined as an intake level that meets specific criteria for adequacy, which minimises the risk for deficit or excess. These criteria make provision for a range of biological effects related to a range of nutrient intakes. The database to scientifically support the definition of nutritional needs across age ranges, gender as well as physiologic states is, however, limited for many nutrients (WHO & FAO, 2004). Various countries around the world have developed recommendations for nutrient intake. Three large scale efforts include the Dietary Reference Intakes (DRIs) of the National Academy of Science (NAS), formerly the IOM (NAS, 1998); the Dietary Reference Values (DRVs) of the European Safety Authority (Committee on Medical Aspects of Food Policy, 1991) and the vitamin and mineral requirements of the World Health Organization (WHO) and the Food and Agriculture Organization of the United Nations (FAO) (WHO & FAO, 2004). This literature review focuses on the reference values of the NAS and the WHO and FAO. The DRIs have been established to support optimal body functions and prevent chronic diseases, in order to maximise health and increase quality of life. The DRIs are a set of reference values, based on dietary intake of North American populations, which comprises the Estimated Average Requirement (EAR), Recommended Dietary Allowance (RDA) or 42 Adequate Intake (AI), and the Upper Tolerable Nutrient Intake Level (UL) for each nutrient as appropriate (NAS, 1998). The WHO in collaboration with the FAO have published similar sets of recommendations including the EAR, Recommended Nutrient Intake (RNI), Protective Nutrient Intake (PNI) as well as the UL, that are considered sufficient to maintain health in nearly all healthy people worldwide (WHO & FAO, 2004). The EAR refers to the average daily nutrient intake level that meets the requirements of 50% of “healthy” individuals of a particular life stage and gender group (WHO & FAO; 2004; NAS, 2006). The EAR is based on given criteria of adequacy with the establishment of the necessary corrections for physiological and dietary factors (WHO & FAO, 2004). It represents the mean requirements of the healthy reference population for a specific nutrient. Therefore, the EAR is used to assess the prevalence of nutrient inadequacy of groups, as the average intake of a population (such as a sample in a research project) is compared to the average intake of the reference group (NAS, 2006). The RDA in the IOM system (DRIs) and the RNI in the WHO/FAO reference system both represent the EAR plus two standard deviations and represent the average daily dietary intake level that is sufficient to meet the nutrient requirement of almost all (97 to 98%) healthy individuals in a specific life stage and gender group. The ability to set the RDA or an RNI is dependent on the ability to set an EAR (WHO & FAO, 2004; NAS, 2006). In the case where scientific evidence to calculate the EAR is lacking, an AI is set in the place of the RDA. The AI is intended to be used as a goal for nutrient intake in individuals (NAS, 2006). In the WHO/FAO reference system (2004), PNI was introduced for certain micronutrients and refers to an amount greater than the RNI that may be protective against a particular health or nutritional risk that is of public health relevance. PNIs are indicated as a daily value or as an amount to be consumed with a meal (WHO & FAO, 1998). In both reference systems, the UL refers to the highest level of daily nutrient intake that is likely to pose no risk of adverse health effects to almost all individuals within the general population. The risk for adverse effects increases as an intake increases above the UL. The UL is based on the total intake from food (including fortified products), water as well as supplements (WHO & FAO, 2004; NAS, 2006). 43 The EAR is used to evaluate the adequacy of nutrient intakes of population communities. Two approaches for using the EAR exist, namely the probability approach and the EAR cut-point method. The probability approach expresses the percentage of the population who are at risk for inadequate intakes. The probability approach calculates the prevalence of inadequate intake from the estimates of the percentage of people in a predefined range, who are at risk of inadequate intakes. Since no actual requirement of the individual within a particular group exits, the method provides an estimation of the prevalence of inadequate intake in the population investigated and therefore does not identify individuals who are at risk (Gibson, 2005:215). The probability approach considers the EAR as well as the data on the usual intake of food, and the expected correlation between intakes and requirements. The probability approach is used when the intake levels of a nutrient are very dependent on the rest of the diet that influences its bioavailability. The probability method is used to assess the adequacy of iron intake at different levels of bioavailability, usually at 5% and 10%. It is also recommended for zinc intake (Gibson & Ferguson, 2008). The WHO guidelines recommend the use of the EAR cut-point for assessing intake levels of most micronutrients The exact nutrient requirement distribution is not required when using this approach (Gibson, 2005:217). This method assumes that the intake of a nutrient below the EAR within a particular population indicates that proportion of the population with inadequate intake of the particular nutrient. This method is based on the assumptions that the variation in intake is greater than that of the requirement and that the distribution of nutrient requirements is symmetrical (Allen et al., 2006:156). In order to use the EAR cut- point method, the data on nutrient intakes within a group (e.g. study population), after adjustment for the with-in subject variability is used and the number of people with intakes below the EAR is counted. The percentage of the population with intakes below the EARs represents the population at risk. This EAR cut-point method is the preferred method for assessing the adequacy of intakes of vitamins A, B6, B12, C, E, thiamine, riboflavin, niacin and folate, as well as copper, iodine, magnesium, molybdenum, phosphorus, zinc and selenium (Gibson, 2005:218). 44 2.6 THE DEVELOPMENT OF A NUTRITION SCREENING TOOL Nutrition screening involves a process whereby parameters that are known to be associated with nutrition problems are identified with the aim of intervening to improve the outcome (Ferguson et al., 1999:458; Kondrup, 2003; Wenhold, 2017:5). The aim of nutrition screening is, amongst others, to identify those individuals who are already malnourished as well as those who are at risk of developing malnutrition and may thus benefit from nutrition support (Ferguson et al., 1999:458; Wenhold, 2017:5). Ideally, screening forms part of a systematic approach where all patients are screened on admission (Reber et al., 2019). Various screening tools have been developed to evaluate the nutritional status of patients at risk for poor clinical outcome associated with malnutrition in the hospital setting (van Bokhorst-de van der Schueren et al., 2014). While some tools are reported to be valid for all populations, ages and settings, others have been developed for specific target populations (van Bokhorst-de van der Schueren et al., 2014) such as hospitalised adults and children, patients with cancer or renal disease, amongst others (Green & Watson, 2004). Most nutrition screening tools currently available aim to determine nutritional status, i.e. risk of malnutrition (van Bokhorst-de van der Schueren et al., 2014), without considering the social determinants of health. Also, few screening tools are available specifically for the pregnant population. Table 2.4 provides an overview of studies that have reported on the development or testing of different tools for pregnant women. Some of the tools used in these studies have been developed to determine nutritional status, while others may be used to predict birth outcomes. None of these tools have been developed for or tested in South Africa. 45 Table 2.4: Summary of studies on the development or testing of screening tools for pregnant women Outcomes Authors, year Country Title Study aim Factors included in the tool Conclusion determined Killeen et al., Ireland Examining the use of To gain insights from To evaluate the Diet quality, weight, The FIGO Nutrition Checklist is suitable to be 2020 the FIGO Nutrition pregnant women and usefulness of the folic acid supplement use, used during routine antenatal practice in a Checklist in routine obstetricians on the checklist in regular sun exposure, and tertiary care setting. The checklist helps in antenatal practice: utility of the FIGO antenatal practice. haemoglobin level identifying women who are potentially at-risk multi-stakeholder Nutrition Checklist in during early pregnancy and facilitates feedback to antenatal practice. conversations related to optimum diet. implementation. Hrolfsdottir et Iceland Development of a To evaluate whether Gestational weight Maternal diet (variety and Simple dietary questions related to dietary al., 2019a dietary screening a short dietary gain adequacy) intake during early pregnancy may aid in the questionnaire to screening Birth weight identification of women who should be predict excessive questionnaire could prioritised for further dietary counselling and weight gain in be used as a support. pregnancy. predictor of excessive gestational weight gain in a cohort of Icelandic women. Hrolfsdottir et Iceland Can a Simple Dietary To determine Risk of gestational Maternal diet (adequacy) A simple dietary screening tool that is al., 2019b Screening in Early whether a short 40- diabetes mellitus administered during the first trimester may Pregnancy Identify item dietary help with the identification of dietary habits Dietary Habits screening associated with gestational diabetes. Such a Associated with questionnaire tool should be easy to use in a clinical setting. Gestational Diabetes? administered in the By providing simple individualised feedback, 1st trimester could improvements in diet may be achieved. identify dietary habits associated with gestational diabetes mellitus. 46 Outcomes Authors, year Country Title Study aim Factors included in the tool Conclusion determined Kennedy & Ireland Development of a To develop a novel Birth weight Maternal diet (quality) Higher maternal dietary quality was Turner, 2019 novel Periconceptual Periconceptual Small for associated with increased intrauterine foetal Nutrition Score (PENS) Nutrition Score gestational age growth. The PENS has the potential to be to examine the (PENS) to assess Head useful in the identification of pregnant relationship between maternal dietary circumference women (before or during pregnancy) who maternal dietary quality in early may benefit from dietary interventions that quality and fetal pregnancy and may optimise foetal growth. The PENS may growth. examine its also be useful in tracking maternal dietary relationship with quality during pregnancy. foetal growth Saeed et al., Pakistan Maternal To identify Low birth weight Maternal weight, height and mid- Country specific cut-off values for maternal 2019. Anthropometry as a appropriate maternal upper arm circumference anthropometric measurements were Tool to Screen anthropometric cut- identified. Cut-off for maternal height, Mothers at High Risk off values associated booking weight and weight gain were of Delivering Low with high risk of effective in screening mothers who are at Birth Weight delivering Low Birth high risk of delivering LBW neonates. Neonates: A Multi- Weight (LBW) Centered Study in neonates in Lahore, Lahore, Pakistan. Pakistan. Salunkhe et India Development of Risk To develop an Premature birth Occupation, number of meals per Identification of low-, moderate-, and high- al., 2019 Scoring Scale Tool for antenatal risk scoring day, hours of resting during day, risk of preterm births was possible at <8, 8, Prediction of Preterm system/scale for education, weight gain during and 9 and equal to ≥10 with high sensitivity at Birth. prediction of preterm pregnancy, number of antenatal lower cut-off and high specificity at upper births. care visits, and type of family cut-off. Nombo et al. Tanzania Gestational diabetes To develop a simple, Gestational Mid-upper arm circumference, This study found that MUAC, previous 2018 mellitus risk score: A non-invasive practical diabetes Previous stillbirth, and stillbirth, and family history of type 2 diabetes practical tool to tool to predict Family history of type 2 diabetes. significantly predict GDM development in this predict gestational undiagnosed Tanzanian population. However, the diabetes mellitus risk Gestational diabetes developed non-invasive practical tool to in Tanzania. mellitus (GDM) in predict undiagnosed GDM only identified 6 Tanzania out of 10 individuals at risk of developing GDM. Thus, this tool required further development. 47 Outcomes Authors, year Country Title Study aim Factors included in the tool Conclusion determined Hillesund et Norway Development of a To construct a diet Gestational weight Adherence to New Nordic Diet The NND score was able to determine diet al., 2014 New Nordic Diet score score for assessing gain recommendations quality. Adherence to a regional diet including and its association degree of adherence Foetal growth a large representation of fruits and with gestational to a healthy and (small, appropriate vegetables, whole grains, potatoes, fish, weight gain and fetal environmentally or large for game, milk and drinking water during growth - a study friendly New Nordic gestational age) pregnancy may help to ensure optimal weight performed in the Diet (NND) and to gain among normal-weight women during Norwegian Mother investigate its pregnancy while also improving general foetal and Child Cohort association with growth. Study (MoBa). adequacy of gestational weight gain and foetal growth in a large prospective birth cohort. Langstroth et England Implementation and To implement and Response of Maternal diet (variety and The response from the pilot of the nutritional al., 2013 evaluation of a evaluate the use of a midwives regarding frequency of consumption) screening tool by the midwives was positive. nutritional screening nutritional screening the ease of use of The midwives rated the tool easy to use with tool. tool for pregnant the tool. a clear and simple format. The nutritional women. screening tool was not statistically validated but the tool had 100% sensitivity and 66% specificity. Duquette et Canada Validation of a To assess the efficacy Nutritionally at-risk Non-dietary factors: age, income, A simplified screening tool was developed al., 2008 screening tool to of a screening tool to pregnancy type of work, parity, closely spaced based on the revisions of the screening tool identify nutritionally be used by nurses to pregnancy, previous LBW baby, to identify nutritionally at-risk pregnant at-risk pregnancy. determine which previous abortions, illnesses, pre- women. This tool was able to appropriately economically pregnancy weight, weight change, determine nutritional risk in pregnancy. The disadvantaged pregnancy complications, smoking, use of this tool by any health professional pregnant drinking, drug use, single or may therefore identify the majority of women are most multiple pregnancy, gestational nutritionally at-risk pregnant women who are likely to benefit from age, absent father, serious most likely to benefit from intensive a dietitian’s intensive emotional problem, and support. intervention by a dietitian. intervention. Dietary factors: interval between meals and consumption of food in 48 Outcomes Authors, year Country Title Study aim Factors included in the tool Conclusion determined six groups, from which protein intake can be estimated: (1) milk and yoghurt; (2) potatoes, bread, cereal products;(3) meat, poultry, fish, and liver; (4) eggs; (5) cheese; (6) legumes, nuts, and peanut butter Gueorguieva United A risk assessment To develop a risk- Very low birth Maternal age, education and race, Identification of women at high risk for VLBW et al., 2003 States of screening test for very assessment screening weight marital status, trimester prenatal would be improved using the model-based America low birth weight. tool for very low care began, pre-pregnancy weight screening tool developed in this paper. Public birth weight (VLBW) of 115 lbs or less, previous health policymakers should use statistical and to compare our pregnancy experience, illness methods in addition to expert opinion to empirically derived requiring continuous care, number improve existing risk assessment methods. tool to the of cigarettes per day, more than The actual value of an improved screening nonempirically one drink per day, safe place to live instrument is dependent on the availability of derived screening and enough food, moved more effective intervention programs. tool used than three times in the last year, by the State of transportation problems in the last Florida. year, sexually transmitted disease in the last six months, and unwanted pregnancy Berglund & Sweden The usefulness of To assess the Premature delivery Risk at booking: age under 20 or The relative risk for premature delivery was Lindmark, initial risk assessment usefulness of initial Pregnancy over 39 years at the estimated date correlated to obstetric risk but was 1999 as a predictor of risk status as complications of delivery; history of two or more moderately increased with initial risk factors pregnancy predictor for miscarriages; chronic maternal dis- only. The initial risk status is a poor predictor complications and pregnancy ease (diabetes, essential of pregnancy complications and cannot be premature delivery. complications and hypertension, kidney disease and used for individual planning of surveillance premature delivery. other more infrequent chronic during pregnancy on its own. Routine conditions); history of obstetric programmes should be structured to ensure complications including previous appropriate identification of current caesarean section; history of foetal complications, even amongst those women or perinatal complications; who are perceived as low risk. negative experience of childbirth. 49 Outcomes Authors, year Country Title Study aim Factors included in the tool Conclusion determined Complications during the current pregnancy: hypertension (BP ≥140/90); preeclampsia; third trimester haemorrhage; rupture of membranes before completed 37 weeks; premature labour; abnormal foetal growth (more than two s.d. above or below mean fundal height for the week of gestation); gestational diabetes; multiple pregnancies; anaemia (haemoglobin ≤ 90 g/l); foetal malformations and various other complications like thrombo- embolism, hepatosis, etc Mercer et al., United The preterm To develop a risk Spontaneous Demographic factors, The possibility of developing a graded risk 1996 States of prediction study: a assessment system premature delivery socioeconomic status, home and assessment system that includes factors that America clinical risk for the prediction of work environment, drug and are highly associated with spontaneous assessment system. spontaneous preterm alcohol use, and medical history, premature delivery in nulliparous and delivery using clinical information regarding symptoms, multiparous women exists, however, such a information available cultures, and treatments in the system does not identify most women who at 23 to 24 weeks' current pregnancy, subsequently have a spontaneous premature gestation and to anthropomorphic and cervical delivery. This system has value for determine the examinations. investigative purposes that can be used as predictive value of basis for the evaluation of new technologies such a system. designed to identify at-risk sub-populations. Michielutte et United A comparison of risk To identify and Term and Age, race, no previous live births, Comparison of all risk factors indicates that al., 1992 States of assessment models compare risk factors premature low smoking, weight under 100 lb, and different multivariate models are needed to America for term and preterm for term and preterm birth weight previous premature or low understand the epidemiology of premature low birthweight. low birthweights, and birthweight birth and term low birthweights. In terms of clinical also examines the value, a general risk assessment model that usefulness of combines all low birthweight births may be separate multivariate just as effective as using separate models. risk assessment 50 Outcomes Authors, year Country Title Study aim Factors included in the tool Conclusion determined systems for term and preterm low birthweights that could be used in the clinical setting. de Caunes et Guadeloupe Anamnestic Based on data Perinatal mortality Maternal demographic, The findings emphasise the importance of al., 1990 pregnancy risk obtained from the Low birth weight socioeconomic, obstetric history developing risk assessments for discrete assessment. 1984-1985 Premature delivery and risk characteristics recorded at pregnancy outcomes within specific Guadeloupean Intrauterine growth the first prenatal visit. populations. Perinatal Audit, a restriction pregnancy risk scoring system was developed using maternal demographic, socioeconomic, obstetric history and risk characteristics recorded at the first prenatal visit. Mueller- United Evaluation of risk To evaluate a risk Premature delivery Pre-pregnancy weight <45.5 kg, Because of the importance of previous Heubach & States of scoring in a preterm scoring system in a black race, single marital status, premature delivery, risk scoring of Guzick, 1989 America birth prevention study premature birth one preterm labour and delivery, primigravid patients is of limited value. of indigent patients. prevention study. preterm labour, and delivery ≥2 Kennedy, United A prenatal screening An obstetrical risk Obstetrical risk Age, parity, length of inter- Women who were classified as at high 1986 States of system for use in a and a nutritional risk Low birth weight conceptual periods, a pre-gravid obstetrical risk had a significantly higher America community-based screening system weight-for-height index, and prior incidence of LBW infants than those in the setting. were developed and history of miscarriages and low low-risk group. However, the nutritional risk implemented in birth weight infants. score had no predictive value in identifying Massachusetts to be women who likely to produce LBW infants. used for the identification of high- risk pregnant women. 51 Having a healthy pre-pregnancy BMI, gaining sufficient weight during pregnancy (Gondwe et al., 2018), being taller (>150 cm), having a previous successful pregnancy and adequate birth spacing may lower the odds of delivering a low birth weight baby, while severe household food insecurity (Bater et al., 2020), nutrient deficiencies (Hjertholm et al., 2017), younger maternal age and low gestational weight gain may increase the risk of delivering a low birth weight baby. Gestational weight gain has also been positively associated with the duration of gestation, length-for-age at birth, and head circumference-for-age at birth (Gondwe et al., 2018). 2.6.1 Basic requirements of screening tools Tools containing only a few simple questions have been shown to be able to accurately determine nutritional risk in a cost-effective and time-effective way (Ferguson et al., 1999:458; Wenhold:5, 2017). If no relevant or useful tool for a specific setting is available, the development of a new tool is justified (Jones, 2004:299). Criteria for developing a screening tool to identify nutritional risk include that it should be applicable for use in a heterogeneous patient population; make use of readily available data; be easy and quick to complete by non- professional staff; be non-invasive and inexpensive, and that it should be valid and reproducible (Ferguson et al., 1999:458; Susetyowati et al., 2014:158). 2.6.2 Steps in the development of a screening tool Jones (2004:299) suggests that a multivariate procedure be used when developing a screening or assessment tool. When developing a nutrition screening tool, random or convenience sampling should be used to identify participants to be recruited as a single cohort (Jones, 2004:299). Participants should not be selected only based on their nutritional status and should be selected from a clinical setting and from a point in the referral process where the tool will be used (Jones, 2004:299; Susetyowati et al., 2014:158). Ideally, the training of those who will be required to administer the tool as well as the time points when the tool will be applied should be defined (Jones, 2004:299). Compiling a list of questions and variables associated with the measured outcome in the intended subject population is usually the starting point in developing a new tool. The 52 availability and skill of those intended to use the tool should also be considered (Jones, 2004:299; Susetyowati et al., 2014:158). Once the risk variables have been identified, the relevance and completeness of the selection should be considered, i.e. content validity (Jones, 2004:300; Susetyowati et al., 2014:158). Once the questions and variables have been verified, they should be used to compile a questionnaire to be used in a pilot study before the revised version is used in the main study (Jones, 2004:300). Univariate analysis of the data determines the effect of each potential risk factor. The univariate analysis assesses of how important each variable is by itself (Jones, 2004:300; McCarthy et al., 2014:312). The relative importance of all the variables needs to be determined at once by using a multivariate technique. This is recommended since the goal in prediction is to find the smallest number of relevant variables, thus, to identify a subset of variables that may effectively predict the measured outcome (Jones, 2004:301). It is advised that a new screening tool be developed by applying a model-fitting approach since its performance can be assessed by determining how close the model’s predicted values compared to those values that have been observed (goodness of fit) (Jones, 2004:301; McCarthy et al., 2012:314). A reduced proforma is developed based on the results obtained from the multivariate analysis that contains those variables that have been identified as important predictors of malnutrition which forms the nutrition screening tool (Jones, 2004:301). 2.7 CONCLUSION It is evident that pregnancy is a period of rapid change. Pregnancy course and outcome may be influenced by various factors which often occur in combination. The DOHaD hypothesis proposes that exposure to certain environmental factors during the pre-conception and pregnancy period, amongst others, may have significant effects on both short- and long-term health of individuals (Barker, 2007). Factors that have the potential to influence growth and development include not only individual factors such as exposure to stress, substance use and diet, but also the family environment, community environment and physical environment in which the pregnant 53 woman lives and functions. 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Paediatric and Perinatal Epidemiology, 26:108–117, July. 76 3 CHAPTER 3 METHODOLOGY 3.1 INTRODUCTION In this chapter, the study design, setting, population and sample, information collected and operational definitions, techniques and study procedures, statistical analysis as well as ethical considerations are described. 3.2 STUDY DESIGN This study is a sub-study of a larger cohort study and comprised a quantitative, cohort analytical study to develop a nutrition screening tool for pregnant women, attending the antenatal clinic at Pelonomi Hospital, Bloemfontein, Free State. This study is quantitative due to the type of data that were collected and the methods of analysis that were used (Joseph et al., 2015). The cohort consisted of pregnant mothers and their newborn infants who were followed up after birth in order to obtain key information regarding birth outcomes. Since the period of this longitudinal cohort study ranged from 12 weeks of pregnancy to the birth of the baby, the methodology was divided into that which is relevant to firstly, the whole sample of pregnant participants that were included at baseline (phase one) and secondly, the sample of mothers from the baseline for whom the information of the offspring was also available (phase two). 3.3 SETTING, POPULATION AND SAMPLING The methodology is presented separately for the two phases. 3.3.1 Study setting This study was undertaken at the antenatal clinic at Pelonomi Regional Hospital in Bloemfontein, Free State and will from this point on be referred to as Pelonomi Hospital. This clinic is a high-risk clinic to which the following pregnant women are referred from surrounding areas and towns: pregnant women with known chronic medical conditions such as hypertension or diabetes; pregnant women who are diagnosed with diabetes or hypertension during pregnancy; mothers who had two previous caesarean sections; pregnant 77 women who previously gave birth to a stillborn baby; pregnant women who experienced previous neonatal death of an unknown cause; pregnant women with advanced maternal age of > 35 years; women who are pregnant with two or more babies; pregnant women with previous premature delivery; pregnant women with a BMI between 40 kg/m2 and 48 kg/m2 as well as pregnant women with a total number of confirmed pregnancies (gravida) of six or more. This clinic was chosen for convenience and because of the good relationship with the dietitians that made follow-up possible. 3.3.2 Study population 3.3.2.1 Phase one All pregnant women attending the antenatal clinic at Pelonomi Hospital from May 2018 to April 2019 were eligible to participate in the study. Based on the statistics of the ante-natal clinic at Pelonomi Hospital, approximately 700 women visited the antenatal clinic at Pelonomi Hospital per month during 2017. i Inclusion and exclusion criteria All pregnant women attending the antenatal clinic at Pelonomi Hospital, who were 18 years and older; at 12 weeks gestation and longer (which is the time that most pregnant women present at the clinic); who could speak English and/or Afrikaans and/or Sesotho and gave informed consent. ii Exclusion criteria Women who were pregnant with more than two babies were excluded (since nutritional requirements increase more substantially when carrying more than two foetuses and only few such pregnancies are encountered at the clinic). 3.3.2.2 Phase two The total sample of participants included in phase one of the study (n=682) formed the population for phase two. 78 3.3.3 Sampling 3.3.3.1 Phase one A convenience sample of the first 700 women who met the inclusion criteria, who were willing to participate after the study was explained to them using the information document (Appendix A) and provided informed consent (Appendix B), was included in the first phase. Considering that approximately 700 women attend the antenatal clinic every month, approximately three to five women were interviewed by three fieldworkers (two qualified dietitians and one social worker) per day throughout data collection. Each fieldworker interviewed a maximum of four participants per day to be practically feasible within the operating times of the clinic. The final sample for the first phase consisted of 682 women. 3.3.3.2 Phase two All mothers from the baseline who provided the Road to Health booklet of her offspring postpartum were included in the sample for phase two of the study. A total of 331 mothers and 347 babies were included in the second phase of this study. 3.4 MEASUREMENTS The information collected and operational definitions, techniques and study procedures, validity and reliability, pilot study, and data analysis are outlined below for both phases. 3.4.1 Information collected and operational definitions Table 3.1 provides an overview of the type of information obtained during the two phases. 79 Table 3.1: Information collected during the two study phases Phase one Phase two Information collected during structured Information obtained from the Road to interviews: Health Booklet:  Socio-demographic and household  Gestational age information  Method of delivery  Information related to reported  HIV exposure health and lifestyle  Immunisations received at birth  Pregnancy history  Presence of congenital disabilities  Individual dietary intake information  Anthropometric measurements  Household food security information  Anthropometric measurements obtained 3.4.1.1 Phase one For phase one, information related to socio-demographic information, reported health and lifestyle, pregnancy history, anthropometry, household food security status and individual dietary intake of the pregnant women were collected. i Socio-demographic information (Appendix C) Socio-demographic and household information included age; marital status; structure of the house; household income; household amenities; household density ratio (HDR); access to water and sanitation; level of education (woman and partner); employment status (woman and partner); cooking facilities and cooking fuel. Table 3.2 indicates the number of sleeping rooms required for Equivalent Persons (EPs) in the same house which is required to calculate HDR. Table 3.2: Sleeping rooms required for Equivalent Persons (EPs) in the same house (Coetzee et al., 1988:354) Equivalent Persons (EPs) Number of sleeping rooms required < 2.5 Eps 1 < 3.5 Eps 2 <5.0 Eps 3 One further sleeping room is required for each extra 2.5 EPs 80 The HDR is calculated as: (number of EPs in the dwelling ÷ ideal number of EPs for the number of sleeping rooms) x 100. A value of over 100% indicates overcrowding (Coetzee et al., 1988:354). ii Reported health and lifestyle (Appendix D) Information on social support (group membership, network of friends, family structure); tobacco and alcohol use patterns; medical history and medications and levels of stress and behaviours related to the control of stress was obtained as reported health and lifestyle information. Questions included in the reported health and lifestyle questionnaire were based on the questions included in the Birth to Twenty study questionnaire (University of Witwatersrand, 2017), a longitudinal study focussing on child and adolescent health and development in Africa. iii Pregnancy history (Appendix D) Pregnancy history considered information about previous pregnancies, including the number of children born alive. For the current pregnancy, information on pregnancy stage (in weeks), illness during pregnancy as well as other symptoms experienced during the current pregnancy were asked and were thus self-reported. Questions relating to the current pregnancy were based on questions included in the questionnaires of the Birth to Twenty study (University of Witwatersrand, 2017). iv Anthropometry Current weight and height of each participant were measured (Appendix D) and entered into an algorithm by Davies et al. (2013:117) along with gestation in weeks to calculate gestational body mass index (GBMI). GBMI was calculated by the developer of the algorithm (Davies et al., 2013) herself. GBMI was categorised as indicated in Table 3.3 (Cruz et al., 2007: 686). Table 3.3: GBMI Classification (Cruz et al., 2007:686) GBMI (kg/m2) Classification ≥ 10 to < 19.8 kg/m2 Underweight ≥ 19.8 to < 26.1 kg/m2 Normal weight ≥ 26.1 to < 29 kg/m2 Overweight ≥ 29 to < 50 kg/m2 Obese 81 Those women with a GBMI ≥ 50 kg/m2 were also included in the “obese” category. i Household food security (Appendix E) Food security was determined by means of the Household Food Insecurity Access Scale (HFIAS). This tool consists of nine questions and determines household food access during the previous four-week period. To determine the frequency of occurrence, a response of “yes” to any of the nine questions is followed by a follow-up question that asks whether it rarely occurred (once or twice), sometimes (three to ten times) or often (more than ten times) (Coates et al., 2007). The HFIAS category for each woman’s household was determined and interpreted according to the categories indicated in Table 3.4. Table 3.4: Household food insecurity access calculation and interpretation (Coates et al., 2007) Score Calculation of score Interpretation 1 (Q2.1=0 or Q2.1=1) and Q3=0 and Q4=0 and Food secure Q5=0 and Q6=0 and Q7=0 and Q8=0 and Q9=0 and Q10=0 2 (Q2.1=2 or Q2.1=3 or Q3.1=1 or Q3.1=2 or Mildly food insecure Q3.1=3 or Q4.1=1 or Q5.1=1) and Q6=0 and Q7=0 and Q8=0 and Q9=0 and Q10=0 3 (Q4.1=2 or Q4.1=3 or Q5.1=2 or Q5.1=3 or Moderately food insecure Q6.1=1 or Q6.1=2 or Q7.1=1 or Q7.1=2) and Q8=0 and Q9=0 and Q10=0 4 Q6.1=3 or Q7.1=3 or Q8.1=1 or Q8.1=2 or Q8.1=3 Severely food insecure or Q9.1=1 or Q9.1=2 or Q9.1=3 or Q10.1=1 or Q10.1=2 or Q10.1=3 ii Individual dietary intake information (Appendix F) Individual dietary intake was determined using a quantitative food frequency questionnaire (QFFQ) (Appendix F) that was used in the Nutrition during Pregnancy and Early Development (NuPED) cohort study (Symington et al., 2018:5). This QFFQ was validated for the population in the Transitions and Health during Urbanisation of South Africa (THUSA) study (MacIntyre et al., 2001:45) as well as for the Women’s Health Study in the Free State (Hattingh et al., 2007:28) and has proven reproducibility (MacIntyre et al., 2002:239; Hattingh et al., 2007:28; Wentzel-Viljoen et al., 2011:143). For the NuPED study, minor changes were made to the QFFQ used in the THUSA study to make provision for vernacular differences between the 82 different study populations (Symington et al., 2018:5). The QFFQ was used to determined dietary intake for the previous four weeks and included approximately 140 commonly consumed food items. Field workers were also able to add any additional items to the QFFQ that were not already listed. The 24-hour recall was also used to determine dietary intake from the time the participant woke up the previous day, until the same time on the day of the interview. The 24-hour recall was used to cross-check values that needed verifying for the QFFQ and to determine dietary diversity score. The researchers compiled a dietary intake estimation kit that included food photographs, product packaging, and commonly used eating utensils to assist participants in recalling food choices and portion sizes. The Dietary Assessment and Education Kit (DAEK) developed by the SA MRC was used to obtain food photographs of commonly consumed foods in South Africa. The DAEK also contains information related to food quantities (Steyn & Senekal, 2004). Photos of commonly consumed foods that were not available in the DAEK were also added. Commonly used eating utensils and household measuring tools of a known volume included items such as plates, bowls, mugs, cups and spoons. Each “kit” also contained a set of bean bags with known volumes, between 30 ml and 500 ml, to assist participants and researchers with the estimation of portion sizes. Information related to food preparation methods and brands were also collected. Energy, macronutrient and micronutrient intakes were then quantified by the SA MRC based on the SA MRC Food Composition Database. Since no dietary reference intakes exist specifically for the South African population, energy intake of the participants were compared to the estimated average requirement (EAR) for groups of the US Dietary Reference Intakes (DRI) (IOM, 2006), as well the EAR for groups of the World Health Organization / Food and Agriculture Organization of the United Nations (WHO/FAO) (2004) for groups in developing countries. Adequacy of protein, fat and carbohydrate intakes were also determined through the acceptable macronutrient distribution ranges (AMDR). Macronutrient requirement values are indicated in Table 3.5. . 83 Table 3.5: Dietary Reference Intakes and Recommended Nutrient Intakes for macronutrients (FAO/WHO/UNU, 2002; WHO/FAO; IOM, 2006) Variable Unit DRI WHO/FAO Energy kJ 11 521 2nd trimester 10 500 Singletons 11 991 3rd trimester 12 180 Twins Total protein g 0.88 g/kga 0.66 g/kg + 9.6 g 2nd trimester 0.66 g/kg + 31.2 g 3rd trimester 10 – 35% TEa 10 – 15% TE Total fat g 20 – 35% TE 15 – 30% TE Saturated fatty acids g - <10% TE Mono-unsaturated fatty acids g - By difference1 Poly-unsaturated fatty acids g - 6 – 10% TE Linoleic acid g 13 - α Linolenic acid g 1.4 - Omega-6 PUFAs g 5 – 10% TE 5 – 8% TE Omega-3 PUFAs g 0.6 – 1.2% TE 1 – 2% TE DHA mg 200-1000 DHA + EPA mg 300 - 2700 AA mg <800 Trans fatty acids g - <1% TE Cholesterol mg - <300 mg Total CHO g 135 g 55 – 75% TE 45 – 65% TE Added sugar g <25% TE <10% TE Total fibre g 29 >25 g Alcohol g 0 - 1This is calculated as total fat -- (saturated fatty acids + polyunsaturated fatty acids + trans fatty acids). Can be up to 15 – 20 %E, according to total fat intake. Requirements for micronutrients were also compared to the EAR for groups of the US DRIs, as well as the EAR of the WHO/FAO (2004) recommendations for groups in developing countries (Table 3.6). For assessment of iron intake, the EAR of the WHO/FAO using two levels (5 and 10%) of bioavailability, were also used to evaluate iron intake for each participant (Gibson & Ferguson, 2008). Since the women mostly consumed mixed diets containing animal protein, zinc requirements were determined based on a diet of moderate bioavailability (Allen et al., 2006:60). 84 Table 3.6: Reference Intakes for vitamins and minerals (amount per day) (IOM, 2006; Allen et al., 2006; Gibson & Ferguson, 2008) Micronutrient Unit DRI WHO/FAO EAR Vitamin A μg 550 571 Vitamin C mg 70 46 Vitamin D μg 10 5 Vitamin E mg 12 6 Thiamine mg 1.2 1.2 Riboflavin mg 1.2 1.2 Niacin mg 14 14 Vitamin B6 mg 1.6 1.6 Folate μg 520 480 Vitamin B12 μg 2.2 2.2 Calcium mg 800 833 Copper μg 800 - Iodine μg 160 143 Iron mg 22 >40 Magnesium mg 290 (19-30 years of age) - 300 (31-50 years of age) Phosphorus mg 580 Selenium μg 49 23 Zinc mg 9.5 5.8 AI Vitamin K µg 90 - Pantothenic acid mg 6 - Biotin µg 30 - Choline mg 450 - Chromium µg 30 - Fluoride mg 3 - Manganese mg 2 - Potassium mg 4700 - Sodium mg 1500 - Chloride mg 2300 - As part of the dietary intake, pregnant women were also asked about supplement use. Women were asked to report on the type of supplement(s) used, the number of capsules/pills used as a time, of how often they consumed the supplements (how many times per week) and when they first started using the supplements. Women were asked to report on supplements obtained at the clinic as well as those bought elsewhere. 85 iii Individual dietary diversity Nutrient adequacy was evaluated by determining the dietary diversity score. The dietary diversity score is determined by summarising the number of food groups obtained from the 24-hour recall into nine standardised food groups according to the standardised tool for individual dietary diversity developed by the FAO (FAO, 2011:8). Dietary diversity was determined as indicated in Table 3.7. Table 3.7: Food groups included in the Women’s Dietary Diversity Score (FAO, 2011) Food group number Food group 1, 2 Starchy staples1 4 Dark, leafy green vegetables 3, 6 (and red palm oil if applicable) Other vitamin A-rich fruit and vegetables2 5, 7 Other fruits and vegetables3 8 Organ meat 9, 11 Meat and fish4 10 Eggs 12 Legumes, nuts and seeds 13 Milk and milk products 1The starchy staples food group is a combination of cereals and white roots and tubers 2 The other vitamin A-rich fruit and vegetable group is a combination of vitamin A-rich vegetables and tubers and vitamin A-rich fruit 3 The other fruit and vegetable group is a combination of all other fruit and vegetables 4 The meat group is a combination of meat and fish Dietary diversity scores were interpreted as low if less or equal to three food groups were consumed, as medium if between four and five food groups were consumed, and as high if six or more food groups were consumed (FAO, 2011). Each food group determined as part of the dietary diversity score was also considered individually. 3.4.1.2 Phase two In the second phase of the study, information related to the birth of each neonate, including anthropometry at birth was obtained from the Road to Health Booklet. 86 i Information from the Road to Health Booklet Information obtained from the Road to Health Booklet included information relating to gestational age, method of delivery, Human Immunodeficiency Virus (HIV) exposure, immunisations received and presence of congenital disabilities. For the purpose of this study, premature birth referred to a gestational age of 28 weeks of gestation or later, but before 37 weeks, while extreme prematurity referred to a gestational age before 28 weeks of gestation (WHO, 2016). Gestational age was obtained from the Road to Health Booklets and were all ≤ 40 weeks. ii Anthropometry Birth weight, length and head circumference were obtained from the neonate’s Road to Health Booklet and interpreted using the WHO Z-scores (WHO, 2008) indicated in Table 3.8. Table 3.8: Interpretation of World Health Organization Z-scores (WHO, 2008:14) Z-score Growth indicators Height-for-age Weight-for-age Weight-for-height Above 3 SD Obese Above 2 SD Overweight Above 1 SD Possible risk for overweight 0 (median) Below -1 SD Below -2 SD Stunted Underweight Wasted Below -3 SD Severely stunted Severely underweight Severely wasted Birth weight was classified using the International Statistical Classification of Diseases and Related Health Problems of the WHO (2016). “Low birth weight” refers to babies that weigh less than 2500 grams at birth, “very low birth weight” refers to babies weighing less than 1500 grams but greater or equal to 1000 grams at birth. “Extremely low birth weight” refers to babies weighing less than 1000 grams at birth. Heavy for gestational age refers to a baby with a birth weight of 4000 grams or more. Appropriate for gestational age refers to a baby with a birth weight greater than 2500 grams and lighter than 4000 grams at birth (WHO, 2016). 87 iii Overall birth outcome Women with either premature delivery (<37 weeks) or who had a baby with birth length-for- age below the -2 SD on the WHO reference charts, or birth weight-for-length below the -2 SD on the WHO reference charts, were classified as having experienced overall poor birth outcome. Those women who delivered a full-term baby (37+ weeks) with a birth length-for- age and a birth weight-for-length above or equal to the -2 SD were classified as having experienced overall good birth outcome. Since it was not possible to determine whether method of delivery was spontaneous or planned, it was not included in the set of variables used to determine overall birth outcome. In the case where a mother delivered twins of which at least one had a poor outcome, the mother was considered to have a poor outcome. 3.4.2 Techniques and study procedures The techniques and procedures followed to obtain the data during the two phases are explained in this section. 3.4.2.1 Phase one After approval was obtained from the relevant authorities, data collection for the first phase took place from May 2018 to April 2019. Before the commencement of data collection, the researchers received comprehensive training during a two-day workshop on the collection of dietary intake data by means of a QFFQ and 24-hour recall by Dr Mieke Faber from the South African Medical Research Council (SA MRC), who is an expert in the field of dietary intake assessment. Fieldworkers were then trained by the researchers on how to complete the questionnaires as well as to conduct the QFFQ and 24-hour recall. The first phase involved collecting data from the women through a structured interview on weekdays between 08:00 and 16:00 at the antenatal clinic at Pelonomi Hospital. i Recruitment The main purpose of the study was explained to all the pregnant women in the waiting area of the clinic by the nursing staff after which each woman was approached individually by the fieldworkers to determine whether she would be willing to participate. Those who were 88 interested in participating were interviewed after their appointment at the clinic was completed. During the interview, the fieldworker introduced herself to the participant and explained the objectives and procedures of the study to her in more detail, explaining that the study involved two phases and that the researchers were seeking informed consent from the participant to provide the required information on that day as well as after the baby was born (from her baby’s Road to Health Booklet once he/she has been born). Each woman was provided with an information document (Appendix A) and after she had been given time to decide whether she would agree to participate, she was asked to provide written informed consent (Appendix B). ii Questionnaire and anthropometric measurements Participants were required to move between two stations, as each fieldworker completed the entire set of questionnaires at one station and obtained the anthropometric measurements at the second station. Each participant was also asked to provide the researcher with her name and telephone number, on a separate sheet which served as a checklist to ensure that all the necessary information had been obtained (Appendix G). This sheet was also linked to the participant’s questionnaires through the respondent number. The questionnaires were completed in a private area in the clinic, which took approximately 1 hour and 30 minutes to complete. To assess dietary practices, the interviewer asked probing questions while completing the questionnaires during the interview. Food photos and packaging (also covered in the training by Dr Faber) were used to assist participants in recalling portion sizes and food choices. The participant was asked about her activities during the relevant time and how these might have been associated with eating and drinking to help them recall all intakes. After the questionnaires were completed, participants were weighed and measured. All anthropometric measurements were taken in accordance with the International Society for the Advancement of Kinanthropometry (Stewart, 2011). Weight was measured using a digital electronic foot scale (Seca 876) and recorded to the nearest 0.1 kg. The measurer placed the scale on a flat, hard surface to ensure the scale was level. Participants wore minimal clothing 89 and no shoes. Participants were instructed to stand still and straight in the middle of the scale, without any support, with the bodyweight distributed evenly between both feet. The scale was checked and adjusted to zero before each measurement was taken (Stewart, 2011). Height was measured using a stadiometer (Seca 213). Participants wore minimal clothing when measured in order to see their posture clearly. Shoes, socks and hats were removed before measurements were taken. Participants were instructed to stand with their heels together, arms to their sides, legs straight, shoulders relaxed, and their head facing straight ahead in the Frankfurt horizontal plane (the top of the external ear canal and the top of the lower bone of the eye socket should be in a horizontal plane parallel to the floor). The measurer ensured that each participant’s heels, buttocks, scapulae, and back of the head were against the vertical surface of the stadiometer if at all possible. The participant was then instructed to inhale deeply, hold her breath, and maintain an erect posture, while the headboard was lowered on the highest point of the head with sufficient pressure to compress the hair. The measurement was recorded to the closest 0.1 cm at eye level with the headboard to prevent any errors caused by parallax (Stewart, 2011). iii Closure At the end of the interview, each participant was asked to bring her neonate’s Road to Health Booklet to the dietitians’ offices at Pelonomi Hospital as soon as possible after her baby had been born. Each participant received a hamper containing body lotion, soap and a face cloth to thank her for her time. Each woman was also given a card with the researcher’s contact details (Appendix H) in order for her to contact the researcher should she have any questions. 3.4.2.2 Phase two During the second phase of data collection, the women were contacted via short message service (SMS) after their expected due date, to remind them to take the Road to Health Booklet of their baby to the dietitians’ offices at Pelonomi Hospital (Appendix I). The contact details of each participant were noted on the checklist (Appendix G) that was completed during the first phase of data collection. The researcher monitored the due dates of all the mothers and sent out an SMS at the end of each month to each mother who had an estimated due date within that month. After the due dates of all the mothers had passed, the researcher 90 sent out an additional six rounds of SMS reminders to all those mothers with outstanding Road to Health Booklets. The final SMS was sent on 29 May 2020. Upon presenting the booklet to the dietitian, the dietitian made a copy of the necessary information in the booklet after which the woman was given R100.00 for transport. In an effort to obtain outstanding information, the researcher submitted an amendment to the protocol to the Health Sciences Research Ethics Committee at the beginning of March 2020 to request that mothers send photos of the relevant pages of the Road to Health Booklet to the researcher directly via multimedia messaging service (MMS) or Whatsapp messenger. Airtime to the value of R20.00 was loaded onto the number from which the photos were sent. Although no specific instructions were given to mothers in cases where an infant was stillborn or miscarried, one mother did inform the researcher of her baby being stillborn. Data obtained from the Road to Health Booklets were captured on an Excel spreadsheet by the researcher. 3.4.3 Validity and reliability Validity can be defined as the extent to which a measuring instrument measures what it is intended to measure (Leedy & Ormrod, 2013:90). Reliability is defined as the consistency with which a measuring instrument delivers a certain, consistent result when the unit being measured has not changed (Leedy & Ormrod, 2013:91). 3.4.3.1 Questionnaires To assure validity, all questions were related to the objectives of the study and questionnaires were developed based on issues discussed in the relevant literature. Questions referring to stress, emotional support, lifestyle and pregnancy history were based on questions used in the Birth to Twenty study (University of Witwatersrand, 2017), while the QFFQ that was used for the current study was also used in the NuPED cohort study (Symington et al., 2018:5). Fieldworkers were trained by qualified dietitians and researchers of the Department of Nutrition and Dietetics at the University of the Free State in the techniques and intricacies of collecting information from participants. To determine household food security, the HFIAS tool (Coates et al., 2007), which is a validated tool for determining household food security was used. 91 To increase reliability, food photos were used to assist participants in recalling portion sizes, while participants were also asked about their activities during the relevant time to help with recalling information related to dietary intake. 3.4.3.2 Anthropometry In ensuring validity, it is important to ensure the tools used for data collection are calibrated and that measurements are taken using standardised, established techniques (Stewart, 2011). In order to further ensure validity, each measurement was taken twice, and if the difference between the two measurements was more than 100 g, a third measurement was taken, and the average value calculated (Lee & Nieman, 2013:170). The scale was calibrated after every 20th participant measured. In order to ensure the reliability of the anthropometric measures of pregnant mothers, anthropometric measurements were obtained by fieldworkers trained in anthropometry. Fieldworkers used consistent, standardised techniques, as described in the literature (Stewart, 2011). Although the anthropometric data on the neonates were obtained from the Road to Health Booklet, the nursing staff at the maternity wards at Pelonomi Hospital as well as Mangaung University Community Partnership Programme (MUCPP) clinic (where some of the participants gave birth) receive regular training on the standardised techniques for measuring weight and height in neonate infants from the dietitians working at these facilities. 3.4.4 Pilot study 3.4.4.1 Phase one Two smaller studies performed by fourth-year dietetics students (UFSHSD2018/0150/1906 and UFSHSD2018/0152/1906) formed part of the pilot study. The first of these smaller studies aimed to describe the household food security of pregnant women attending the antenatal clinic at Pelonomi Hospital, while the second study focused on reported health, lifestyle and anthropometric status of the same women. These studies included 128 pregnant women attending the antenatal clinic at Pelonomi Hospital. Data obtained from the pilot study were 92 included in the final study since no changes were made to the questionnaires (only the numbering of some questions was altered). 3.4.4.2 Phase two In order to test the second phase of this study (gathering of information from the neonates’ Road to Health Booklets), all mothers in the two smaller studies who were in their third trimester were asked to bring their child’s Road to Health Booklet to the dietitians’ offices after their child had been born. These mothers were also given R100.00 for transport. Data collected as part of the pilot study were included in the main study. 3.4.5 Data analysis From the QFFQ, researchers (two doctoral students that are also registered dietitians) calculated a total gram amount for each of the food codes reported by the respondent for the previous 28-day period. The researchers consulted the Condensed Food Composition Tables of South Africa (Wolmarans et al., 2010) and the SA MRC Food Quantities Manual (SAFOODS, 2018) to perform the calculations. The researchers also drew up a coding list with conversion factors (ml to gram) and portion sizes of food items that were not listed in the original QFFQ to ensure consistent coding. The two researchers were in constant communication during the process and continuously discussed and updated the list. Where uncertainties on the QFFQ were experienced for certain factors, such as portion sizes, the 24- hour recall was consulted as a cross-check. Dietary intake data obtained from the QFFQ were summarised on an Excel file for each participant. Data were then analysed for daily nutrient intake by the Biostatistics Unit at the SA MRC, using the South African Food Composition Database. Unlikely values for nutrient intakes were flagged by assessing intake ranges of each nutrient separately, as this assisted in finding and correcting coding errors. 3.5 STATISTICAL ANALYSIS The researcher was responsible for entering all the data onto an Excel spreadsheet after which statistical analysis was performed by the Department of Biostatistics, Faculty of Health Sciences, University of the Free State, following the steps outlined above. 93 Data were analysed using SAS/STAT software, Version 9.4 of the SAS system for Windows, Copyright © 2010 SAS Institute Inc. Descriptive statistics, including frequencies and percentages (for categorical data) and medians and interquartile ranges (for numerical data), were calculated. Differences between groups were assessed by chi-squared tests (for categorical variables) or Fisher’s exact test (for categorical variables with sparse data) and Kruskall-Wallis tests (for numerical variables). Analysis of associations for various individual birth outcomes was done using babies as units of analysis, whereas analysis of overall birth outcome considered mothers as unit of analysis. Logistic regression with backward selection (p<0.05) was used to select significant independent factors associated with overall birth outcome. Variables with a p-value of < 0.15 on univariate analysis were considered for inclusion in the model. Separate logistic regressions were performed per theme (socio-demography, reported health and lifestyle and nutrition), whereafter a final logistic regression was performed considering all the variables which were found to be significant in the preceding theme-specific logistic regressions. 94 3.5.1 Technique for developing the nutrition screening tool Figure 3.1 provides a stepwise approach to developing a screening tool that was used as a guide. Design Write study protocol Implementation Collect data Analysis Perform univariate and multivariate analysis Tool Devise allocation rule Performance Evaluate goodness of fit, reliability and validity Figure 3.1: The development of a new screening tool (Jones, 2004) 3.5.1.1 Implementation Data collection occurred in an antenatal clinic, where the screening tool is intended to be used. To ensure content validity, it is important to consider all the relevant components of the issue that the screening tool is meant to solve (Kondrup et al., 2003). To make provision for this, all the questions included in the current study were based on a comprehensive literature review. 95 3.5.1.2 Analysis The next step in the development of the screening tool was to perform univariate analysis to assess the effect of each potential risk factor on adverse birth outcome (Jones, 2004:300). 3.5.1.3 Tool development The logistic regression equation was used to estimate the probability of adverse birth outcomes, based on the findings of the univariate analysis, to allocate participants into one of the two groups i.e. at risk of overall poor birth outcome and not at risk (low risk) (Jones, 2004:301). The nutrition screening tool was only developed, but the performance of the tool was not tested or validated. 3.6 ETHICAL ASPECTS Approval for this research was obtained from the Health Sciences Research Ethics Committee at the University of the Free State (UFS-HSD2018/0148/2905) (Appendix J) as well as the Free State Department of Health. An information document (Appendix A) was given to participants in their language of choice (English, Afrikaans or SeSotho) after which an informed consent form (Appendix B) was signed. All information concerning the participants’ privacy was respected. Confidentiality of the information was maintained by using codes in data analysis and results. Participation was voluntary and respondents were given the freedom to withdraw from the study at any time. As a way of thanking the participants for their time and willingness to participate, each participant received a hamper containing body lotion, soap and a face cloth. The hamper was not mentioned to the mother during the recruitment process, so as not to coerce anyone into participating. Each participant who brought her child’s Road to Health Booklet to the dietitians’ office at Pelonomi Hospital was given R100.00 to cover her transport costs, while those who sent photos received R20.00 airtime. 96 3.7 SUMMARY In this chapter, the selection of the study sample, procedures followed as well as methods and techniques used to conduct this study were described. A description of the statistical methods used and ethical aspects of the study were included. The methodology described in this chapter was selected and followed according to the objectives stated in chapter one to reach the aim of this study. 3.8 REFERENCES Allen, L., de Benoist, B., Dary, O., Hurrell, R., Horton, S., Lewis, J., Parvanta, C., Rahmani, M., Ruel, M. & Thompson, B. 2006. Guidelines on food fortification with micronutrients, Geneva: World Health Organization (WHO) Food and Agriculture Organization (FAO). Coates, J., Swindale, A. & Bilinsky, P. 2007. 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World Health Organization (WHO) and Food and Agriculture Organization of the United Nations (FAO). 2004. Vitamin and mineral requirements in human nutrition. 2nd edition. Geneva: World Health Organization. 99 4 CHAPTER 4 – CHARACTERISTICS OF WOMEN ATTENDING THE ANTENATAL CLINIC AT PELONOMI HOSPITAL, BLOEMFONTEIN: A COMPARISON OF WOMEN WITH KNOWN BIRTH OUTCOMES AND WOMEN WITH UNKNOWN BIRTH OUTCOMES 4.1 ABSTRACT Background and objectives: Pregnancy is a period characterised by physical and emotional changes that may be affected by various factors. The current study aimed to describe the socio-demographic, reported health and lifestyle, pregnancy history, gestational body mass index (GBMI), household food security status and nutrient intake of women attending the antenatal clinic at Pelonomi Hospital (phase one). In phase two of the study, participants were asked to return to the hospital after their baby’s birth to provide information about the newborn. This publication further focuses on the differences between those participants who provided the birth information of the newborn (responders) and those who did not (non- responders). Design: Phase one of the study comprised a cross-sectional design to describe pregnant women who attended the antenatal clinic at Pelonomi Hospital, while phase two comprised a longitudinal design to determine the birth outcomes of the babies. Subjects and response rate: All women attending the high-risk antenatal clinic at Pelonomi Hospital from May 2018 until April 2019 were invited to participate in the initial phase of this study. Outcome measures: In phase one, questionnaires were completed during a structured interview with trained fieldworkers to collect information about socio-demography, reported health and lifestyle and pregnancy history. The Household Food Insecurity Access Scale (HFIAS) was used to determine food security status, while a Quantitative Food Frequency Questionnaire (QFFQ) and 24-hour recall were used to determine dietary intake. Weight and height measurements were obtained to determine GBMI. Results: Overall, 682 pregnant women were included in the initial phase on this study, while 331 women responded to the request to return to the hospital after delivery with the Road to Health Booklets of their babies, resulting in a response rate of 48.5%. The median age of the women was 31.9 years (interquartile range 26.8–36.5 years) and median household 100 density ratio (HDR) was 85.7 (interquartile range 85.7–71.4). Median GBMI fell in the obese category (30.8 kg/m2; interquartile range 24.7–37.1 kg/m2). A concerning percentage of 30.0%, 40.3% and 12.1% of the women who had ever smoked, used snuff or chewed tobacco, or used alcohol respectively, were still doing so while pregnant. Most women (82.3%) reported having a number of people to turn to for help when facing major problems. Only a quarter of women (26.6%) were classified as food secure, the responder (women who provided their neonate’s birth information) and non-responder group (those who did not provide the birth information of their baby) did not differ significantly. Significant differences were, however, observed between responders compared to non-responders for HDR (p=0.0458), owning a refrigerator (p=0.0318), microwave (p=0.0173) and kettle (p=0.017), using electricity as main source of fuel for cooking (p=0.0396) as well as participant’s highest level of education (p=0.0457). In terms of basic indicators of socio-demographic status, the responder group was generally better off than the non-responder group. A significantly higher percentage of responders reported experiencing constipation (p=0.0001) and heartburn (p=0.0100) during the current pregnancy, as well as being diagnosed or treated for vaginal infection/discharge (p=0.0146) during the current pregnancy. Significantly more women in the non-responders group reported being diagnosed or treated for hypertension (p=0.0057). Significantly more women in the responder group had so much debt in the past six months that they did not know how they were going to repay the money (p=0.0158) compared to the non-responder group. In terms of nutrient intake, responders generally had a higher median intake of all macro- and micronutrients. Conclusions: A high prevalence of overweight and obesity, as well as food insecurity were observed in the sample included in the current study. Generally, women in the responder group were better off regarding basic indicators of socio-demographic status and nutrient intake than the non-responders. Individuals with a better socio-economic status may be more likely to exhibit health-seeking behaviour, including participating in surveys related to health and nutritional status. Keywords: pregnancy, socio-demography, reported health, food security, nutrient intake, responders, non-responders 101 4.2 INTRODUCTION The National Plan of Action for Children in South Africa (2012–2017) aimed to reduce maternal mortality to 100 or less per 100 000 live births by 2017 (Republic of South Africa, 2012). The latest “State of the World’s Children 2019” published by the United Nations Children’s Fund (UNICEF), indicated that 119 deaths of women per 100 000 live births were from pregnancy-related causes in South Africa in 2017 (UNICEF, 2019), while the South African Demographic and Health Survey (SADHS) of 2016 found that the pregnancy-related mortality ratio during the seven-year period before the SADHS was 536 deaths per 100 000 live births (NDoH et al., 2019). Reduction in maternal mortality is a global health priority (Say et al., 2014). The Global Strategy for Women’s, Children’s and Adolescent Health (2016–2030) (Every Woman Every Child, 2015) as well as the Sustainable Development Goals (SDG) (UN, 2015) aim to reduce maternal mortality to less than 70 per 10 000 live births by 2030. Current priority health interventions for reducing maternal mortality in South Africa focus on providing basic antenatal care; human immunodeficiency virus (HIV) testing during pregnancy with the initiation of antiretroviral therapy (ART) and provisioning of other prevention of mother-to-child transmission (PMTCT) services as required; improved access to care during labour; improved intrapartum care and post-natal care within six days of delivery (Republic of South Africa, 2012). Identifying and understanding the causes of maternal death is key in reducing the rate thereof as well as planning relevant policies and interventions (Say et al., 2015). Women face interconnected health challenges influenced by poverty, inequality and marginalisation (Every Woman Every Child, 2015). Poverty, along with lack of information, inadequate and poor-quality services as well as cultural beliefs, influence access to quality maternal health services. These barriers should be addressed at both health system and societal levels to improve maternal health and consequently morbidity and mortality (WHO, 2019) since poor uptake of maternal health services has been found to contribute to maternal deaths in South Africa (Scorgie et al., 2015). Underlying causes of maternal deaths include pregnancies with an abortive outcome, hypertension, obstetric haemorrhage, infection during pregnancy, other obstetric 102 complications and non-obstetric complications including Human Immunodeficiency Virus / Acquired Immune Deficiency Syndrome (HIV/AIDS), amongst others (Say et al., 2015; Filippi et al., 2016). No formal education, primary education only, secondary education only and hypertensive disorders were associated with an increased risk of death among pregnant women enrolled in the multi-national Global Network for Women’s and Children’s Health Research Maternal and Neonatal Health Registry (Bauserman et al., 2015). Various other risk factors such as anaemia, prolonged labour, HIV/AIDS, and obesity may contribute to maternal morbidity and affect mortality risk (Filippi et al., 2016). Poverty has the potential to negatively influence pregnancy (Scrogie et al., 2015). The negative impacts of poverty increase the risk of malnutrition, not only for the pregnant mother but also for her offspring. Inadequate nutrition before, during and after pregnancy, increases the risk of poor birth outcomes and long-term health consequences (Black et al., 2008). Non-response errors in data refer to the failure to collect data from a sample unit in a particular target population. Non-response may introduce bias in the study estimates (Okafor, 2010:91). Differences may be present between women who provide the information related to the birth outcomes of their child (responders) compared to those who do not (non- responders) which may influence the results in a cohort study (Corry et al., 2017). Individuals with poorer health seem to be more likely to avoid participating in surveys related to health (Cheung et al., 2017). Pregnancy is characterised by both physical and emotional changes that are impacted by various factors. This study, therefore, aimed to describe the socio-demographic, reported health and lifestyle, pregnancy history, gestational body mass index (GBMI), household food security status and nutrient intake of women attending the antenatal clinic at Pelonomi Hospital and to determine differences between those mothers who provided the birth information of their child and those who did not. 103 4.3 METHODS 4.3.1 Study design and site description The current study comprises a cross-sectional design to describe pregnant women who attended the high-risk antenatal clinic at Pelonomi Hospital. The main study comprised two phases. The first phase included pregnant women attending the antenatal clinic at Pelonomi Hospital while the second phase included those women and their babies from the first phase who responded to the request to return to provide their baby’s Road to Health Booklet to the dietitians at Pelonomi Hospital. The antenatal clinic at Pelonomi Hospital provides care to a variety of obstetric and gynaecological conditions including pregnant women with known chronic medical conditions such as hypertension or diabetes; pregnant women who are diagnosed with diabetes or hypertension during pregnancy; mothers who had two previous caesarean sections; pregnant women who previously gave birth to a stillborn baby; pregnant women who experienced previous neonatal death of an unknown cause; pregnant women with advanced maternal age of > 35 years; women who are pregnant with two or more babies; pregnant women with previous premature delivery; pregnant women with a BMI between 40 kg/m2 and 48 kg/m2 as well as pregnant women with a total number of confirmed pregnancies (gravida) of six or more. This clinic serves as a referral centre for the Central and Southern Free State. 4.3.2 Sampling All pregnant women attending the antenatal clinic at Pelonomi Hospital from May 2018 to April 2019 were eligible to participate in the study. Based on the statistics of the ante-natal clinic at Pelonomi Hospital, approximately 700 women visited the antenatal clinic at Pelonomi Hospital per month during 2017. A convenience sample of the first 700 women who met the inclusion criteria, and gave informed consent, were included in the first phase of the current study. The final sample for the initial phase consisted of 682 women. Some questionnaires had to be excluded since the women did not know how long they were pregnant for at the time of data collection. All pregnant women attending the antenatal clinic at Pelonomi Hospital who were 18 years and older; at 12 weeks gestation and longer (which is the time that most pregnant women 104 present at the clinic); who could speak English and/or Afrikaans and/or Sesotho and gave informed consent were included in the first phase of this study. Women who were pregnant with more than two babies were excluded from the initial phase of this study. For the follow-up, all women included in the initial phase were requested to return to the hospital with the newborn’s Road to Health Booklet for specific pages to be copied. The women could also send photos of the specific pages to the researcher via Whatsapp messenger or Multimedia Messaging System (MMS). Women received R100 for transport if they returned to the hospital, while those who sent the photos directly to the researcher received R20 airtime. A total of 331 women provided the Road to Health Booklets of 347 babies, which were included in the second phase of this study. 4.3.3 Study procedures Approval for this study was obtained from the Health Sciences Research Ethics Committee at the University of the Free State (UFS-HSD2018/0148/2905) as well as the Free State Department of Health. Data collection was undertaken during a structured interview with individual mothers by one of three trained fieldworkers. A pilot study was conducted on 128 women attending the antenatal clinic at Pelonomi Hospital during the first two weeks in May 2018. Since only changes in the numbering of the questionnaires were made, these women were included in the final sample. The entire set of questionnaires were completed at one station, which took approximately 1 hour and 30 minutes, after which anthropometric measurements were obtained at the second station by the same fieldworker. Each participant received a hamper to thank her for her time (this was not mentioned to the mother during the recruitment process, so as not to coerce anyone into participation). Questionnaires on socio-demographic background and household information; reported health and lifestyle, pregnancy history, household food security and individual dietary intake were completed for each participant. Questions on social support, stress and pregnancy history included in the reported health and lifestyle questionnaire were based on questions 105 included in the Birth to Twenty study (University of Witwatersrand, 2017), a longitudinal study focussing on child and adolescent health and development in Africa. The household density ratio (HDR) was calculated. Overcrowding may contribute to the spread of infectious diseases such as tuberculosis and can indicate poor household conditions and poverty. Each person > 10 years old living in the same house counts as one equivalent person (EP). Persons < 10 years old count as half an EP (Coetzee et al., 1988:354). The HDR is calculated as: (number of EPs in the dwelling ÷ ideal number of EPs for the number of sleeping rooms) x 100. A value of over 100% indicates overcrowding (Coetzee et al., 1988:354). All anthropometric measurements were taken in accordance with the International Society for the Advancement of Kinanthropometry (Stewart et al., 2011). Current weight and height measurements were obtained for each participant and included in an algorithm by Davies et al. (2013:117) to calculate gestational body mass index (GBMI). GBMI was categorised as underweight (≥ 10 to ≤ 19.8 kg/m2), normal weight (≥ 19.8 to ≤ 26.1 kg/m2), overweight (≥ 26.1 to ≤ 29 kg/m2) and obese (≥ 29 to ≤ 50 kg/m2) (Cruz et al., 2007: 686). Food security was determined using the Household Food Insecurity Access Scale (HFIAS) and categorised as food secure, mildly food insecure, moderately food insecure and severely food insecure (Coates, Swindale & Bilinsky, 2007). Trained fieldworkers collected dietary intake data using a quantitative food frequency questionnaire (QFFQ) and 24-hour recall. The QFFQ used in the current study was also used in the Nutrition during Pregnancy and Early Development (NuPED) cohort study (Symington et al., 2018: 5). This QFFQ was validated for the population in the Transitions and Health during Urbanisation of South Africa (THUSA) study (MacIntyre et al., 2001:45) as well as for the Women’s Health Study in the Free State (Hattingh et al., 2007:28) and has proven reproducibility (Wentzel-Viljoen et al., 2011:143; MacIntyre et al., 20012:239; Hattingh et al., 2007:28). For the NuPED study, minor changes were made to the QFFQ used in the THUSA study to make provision for vernacular differences between the different study populations (Symington et al., 2018:5). The QFFQ was used to determined dietary intake for the previous four weeks and included approximately 140 commonly consumed food items. For the current study, fieldworkers were also able to add any additional items (e.g. horse meat) mentioned by participants that were not already listed to the QFFQ. 106 A dietary intake estimation kit containing food photographs, product packaging, and commonly used eating utensils was used to help participants recall food choices and portion sizes. The total gram amount for each of the food codes reported by the respondent on the QFFQ was calculated for the previous 28-day period. The Condensed Food Composition Tables of South Africa (Wolmarans et al., 2010) and the South African Medical Research Council’s (SA MRC) Food Quantities Manual (SAFOODS, 2018) were consulted when performing the calculations. A coding list with conversion factors (ml to gram) and portion sizes of food items that were not listed in the original QFFQ was also drawn up to ensure consistent coding. Where uncertainties on the QFFQ were present for certain factors, such as for portion sizes, the 24-hour recall was consulted as a cross-check. Dietary intake data obtained from the QFFQ were summarised on an Excel file for each participant. The Biostatistics Department at the SA MRC checked and analysed the data for daily nutrient intake by using the South African Food Composition Database. Energy intake of the participants was compared to the estimated average requirement (EAR) of the US Dietary Reference Intakes (DRI) (IOM, 2006), as well the EAR of the World Health Organization / Food and Agriculture Organization of the United Nations (WHO/FAO) (2004) recommendations for populations in developing countries. Adequacy of protein, fat and carbohydrate intakes were also determined through the acceptable macronutrient distribution ranges (AMDR). Requirements for micronutrients were also compared to the EAR for groups of the US DRIs, as well the EAR of the WHO/FAO (2004) for populations in developing countries. The probability method was used to assess the adequacy of iron intake at 5% and 10% bioavailability (Gibson & Ferguson, 2008). Since the women in the current study mostly consumed a mixed diet containing animal protein, zinc requirements were determined based on a diet of moderate bioavailability (Allen et al., 2006:60). 4.3.4 Statistical analysis Descriptive statistics, including frequencies and percentages (for categorical data) and medians and percentiles (for continuous data), were calculated. Differences between groups 107 were assessed by p-values [chi-squared tests (for categorical variables) or Fisher’s exact test (for categorical variables with sparse data)]. A p-value of <0.05 was considered significant. 4.4 RESULTS Of the 682 participants included in the initial phase of this study, 331 provided the Road to Health Booklets of their newborns as requested (48.5% response rate). The total sample of participants in the current study had a median age of 31.9 years (interquartile range 26.8– 36.5 years) (Table 4.1) and a median self-reported gestation of 32.0 weeks (interquartile range 26.0–36.0 weeks) at the time of the interview. Participants had a median HDR of 85.7 (interquartile range 71.4–114.3) and GBMI of 30.8 kg/m2 (interquartile range 24.7–37.1 kg/m2). No significant differences were noted between the medians of the women who responded to the request to provide their baby’s Road to Health Booklet (responders) and those who did not (non-responders) with regard to age and pregnancy stage. Table 4.1: Information related to age and pregnancy stage Variable Total sample Responders Non-responders Median (n) IQR Median (n) IQR Median (n) IQR p-value# Age 31.9 (682) 26.8–46.1 32.2 (331) 27.4–36.7 31.3 (351) 26.2–36.2 0.1941 Pregnancy 32.0 (682) 26.0–36.0 32.0 (331) 26.0–36.0 31.0 (351) 25.0–35.0 0.0580 Stage #p-value for median difference between responder and non-responder groups using the Kruskall-Wallis test; p <0.05 considered statistically significant indicated with * Most participants were married or in a relationship (94.2%) and lived with the future father of their baby (71.9%) (Table 4.2). About three-quarters of participants lived in a brick house (72.4%), and almost half (47.4%) lived in households that were considered overcrowded. Only 29.5% of participants had a bathroom inside the house, while only 17.9% had a bathroom outside the house. However, 30.2% had a flush toilet inside the house, and 33.9% had their own flush toilet outside the house. Almost all the participants had an indoor kitchen or cooking area (99.0%) and access to electricity (90.9%), while just over half (51.5%) had access to indoor water. Most participants had access to basic storage and cooking facilities and mostly used electricity for cooking (87.7%). Just over half (54.8%) of participants reported grade 10 ̶12 as their highest level of education, while a similar trend was seen amongst their partners (58.0%). Half (52.5%) of the participants were unemployed, whereas more than a third of their partners were employed full-time 108 (67.9%). Most common sources of income were salaries and wages from formal employment (60.9%) and pension or state grants (61.7%). Overall, 13.1% indicated that their current monthly household income was R 1000 or less. Overall 18.5% indicated that they were currently receiving less per month than what they usually received during the previous six months.” A higher percentage of participants in the responder group lived in an overcrowded household (p=0.0458). Of those households that were overcrowded (323/681), significantly more households had children who were younger than 10 years of age (79.6%; 257/323) (p<0.0001). Significantly more responders had access to a refrigerator (p=0.0318), microwave (p=0.0173), kettle (p=0.017) and electricity as fuel for cooking (p=0.0396) than non- responders. Moreover, more responders than non-responders had an education level of grade 10 and higher (p=0.0457). Table 4.2: Socio-demographic information Variable Total sample Responders Non-responders p-value# n % n % n % MARITAL STATUS AND LIVING ARRANGEMENTS Marital status (N=677) (n=331) (n=351) Married, not married but in a relationship 638 94.2 315 95.1 326 93.4 0.3393 Not married and not in a relationship, 39 5.8 16 4.9 23 6.6 divorced/separated Living arrangement with future father of the baby (N=669) (n=329) (n=340) Living with partner 481 71.9 240 73.0 241 70.9 0.6406 Not living with partner 170 25.4 79 24.0 91 26.8 Other 18 2.7 10 3.0 8 2.4 HOUSEHOLD DEMOGRAPHICS Type of housing (N=682) (n=331) (n=351) Brick 494 72.4 250 75.5 244 69.5 Shack 159 23.3 72 21.8 87 24.8 0.0761 Flat 25 3.7 9 2.7 16 4.6 Other 4 0.6 0 0.0 4 1.1 Household density ratio (N=681) (n=331) (n=350) Overcrowded 323 47.4 170 51.4 153 43.7 0.0458* Not overcrowded 358 52.6 161 48.6 197 56.3 WATER AND SANITATION Bathroom inside the house (N=681) (n=331) (n=350) Yes 201 29.5 103 31.1 98 28.0 0.4009 No 480 70.5 228 68.9 252 72.0 109 Variable Total sample Responders Non-responders p-value# n % n % n % Bathroom outside the house (N=682) (n=331) (n=351) Yes 122 17.9 59 17.8 63 18.0 0.3726 No 560 82.1 272 82.2 288 82.0 Access to water (N=682) (n=331) (n=351) Indoor water 348 51.0 179 54.1 169 48.2 0.2753 Own tap outside the house 209 30.7 97 29.3 112 31.9 Share a tap with other households 125 18.3 55 16.6 70 19.9 Toilet system available (N=682) (n=331) (n=351) Flush toilet inside the house 206 30.2 105 31.7 101 28.8 Own flush toilet outside the house 231 33.9 115 34.7 116 33.1 0.6315 Share an outside toilet with other households 73 10.7 30 9.1 43 12.3 Bucket system 58 8.5 26 7.9 32 9.1 Pit toilet 114 16.7 55 16.6 59 16.8 COOKING AND STORAGE FACILITIES AVAILABLE Access to electricity (N=682) (n=331) (n=351) Yes 620 90.9 305 92.2 315 89.7 0.2756 No 62 9.1 26 7.9 36 10.3 Fuel mostly used for cooking (N=681) (n=331) (n=350) Electricity 597 87.7 299 90.3 298 85.1 0.0396* Other (gas, paraffin, wood, coal, etc.) 84 12.3 32 9.7 52 14.9 Variable Total sample Responders Non-responders p-value# n % n % n % Indoor kitchen or cooking area (N=682) (n=331) (n=351) Yes 675 99.0 327 98.8 348 99.2 0.7180 No 7 1.0 4 1.2 3 0.8 Own a refrigerator (N=681) (n=330) (n=351) 0.0318 Yes 571 287 87.0 284 80.9 No 110 43 13.0 67 19.1 Own a freezer (N=681) (n=330) (n=351) Yes 512 75.2 255 77.3 257 73.2 0.2210 No 169 24.8 75 22.7 94 26.8 Own a microwave (N=681) (n=330) (n=351) Yes 487 71.5 250 75.8 237 67.5 0.0173* No 194 28.5 80 24.2 114 32.5 Own a kettle (N=681) (n=330) (n=351) Yes 605 88.8 303 91.8 302 86.0 0.0167* No 76 11.2 27 8.2 49 14.0 Own a radio (N=681) (n=330) (n=351) Yes 550 80.8 270 81.8 280 79.8 0.4984 No 131 19.2 60 18.2 71 20.2 Own a television (N=681) (n=330) (n=351) Yes 600 88.1 296 89.7 304 86.6 0.2136 No 81 11.9 34 10.3 47 13.4 110 Variable Total sample Responders Non-responders p-value# n % n % n % EDUCATION, EMPLOYMENT AND INCOME Highest level of education of participant (N=682) (n=331) (n=351) None 1 0.2 0 0.0 1 0.3 Primary school 47 6.9 17 5.1 30 8.6 Grade 8-10 181 26.5 77 23.3 104 29.6 0.0457* Grade 10-12 374 54.8 194 58.6 180 51.3 Tertiary education 78 11.4 42 12.7 36 10.3 Don’t know 1 0.2 1 0.3 0 0.0 Partner’s highest level of education (N=669) (n=325) (n=344) None 5 0.8 2 0.6 3 0.9 Primary school 27 4.0 16 4.9 11 3.2 Grade 8-10 92 13.8 39 12.0 53 15.4 0.1527 Grade 10-12 388 58.0 192 59.1 196 57.0 Tertiary education 93 13.9 52 16.0 41 11.9 Don’t know 64 9.6 24 7.4 40 11.6 Employment status of participant (N=682) (n=331) (n=351) Full-time employed 141 20.7 69 20.9 72 20.5 Part-time employed 76 11.1 36 10.9 40 11.4 Unemployed 358 52.5 171 51.7 187 53.3 0.9718 Self-employed 54 7.9 27 8.2 27 7.7 Housewife by choice 34 5.0 17 5.1 17 4.8 Other 19 2.8 11 3.3 8 2.3 Partner’s employment status (N=651) (n=315) (n=336) Full-time employed and self-employed 442 67.9 222 70.5 220 65.5 0.3433 Part-time employed 133 20.4 61 10.2 72 21.4 Unemployed 76 11.7 32 19.4 44 13.1 Sources of income: Wages and salaries from formal employment (N=681) (n=330) (n=351) Yes 415 60.9 206 62.4 209 59.5 0.4414 No 266 39.1 124 37.6 142 40.5 Self-employment (N=681) (n=331) (n=350) Yes 157 23.1 87 26.3 70 20.0 0.0517 No 524 77.0 244 73.7 280 80.0 Casual employment (N=681) (n=331) (n=350) Yes 208 30.5 98 29.6 110 31.4 0.6060 No 473 69.5 233 70.4 240 68.6 Crop production and livestock sales (N=681) (n=331) (n=350) Yes 29 4.3 15 4.5 14 4.0 0.7312 No 652 95.7 316 95.5 336 96.0 Pension or state grants (N=681) (n=331) (n=350) Yes 420 61.7 214 64.7 206 58.9 0.1200 No 261 38.3 117 35.4 144 41.1 Domestic work (N=680) (n=331) (n=349) Yes 44 6.5 20 6.0 24 6.9 0.6584 No 636 93.5 311 94.0 325 93.1 111 Variable Total sample Responders Non-responders p-value# n % n % n % Monthly household income (N=654) (n=329) (n=351) R0 – R1000 204 31.2 98 30.5 106 31.8 R 1 001 – R 3 000 160 24.5 83 25.9 77 23.1 0.5197 R 3 001 – R 5 000 201 30.7 102 31.8 99 29.7 Over R 5 000 89 13.6 38 11.8 51 15.3 Monthly income in relation to income over the (N=665) (n=326) (n=339) past six months More 123 18.5 64 19.6 59 17.4 0.2014 Less 156 23.5 84 25.8 72 21.2 The same 386 58.1 178 54.6 208 61.4 #p-value for percentage difference between responders and non-responders using chi-square or Fisher’s exact tests, as appropriate; p <0.05 considered statistically significant indicated with * Table 4.3 provides an overview of the reported health and lifestyle of the participants. Overall, 30.0%, 40.3% and 12.1% of the participants who had ever smoked, used snuff or chewed tobacco, or used alcohol, respectively, were still doing so while pregnant. Almost half (46.6%) also lived with a household member who currently smoked. In terms of medication use, 60.7% indicated that they used medication at least once a week with antiretroviral (31.9%) and antihypertensive (23.7%) medications being the most commonly used medications. Regarding pregnancy history, 89.6% had been pregnant before, with 88.9% of firstborns being born alive. The firstborn child of most participants was born via normal vaginal delivery (71.1%) and was born full-term (85.0%). Almost a quarter of participants (23.5%) had been hospitalised during the current pregnancy, mostly for abdominal pain (26.6%) or hypertension (18.5%). Constipation (39.2%), nausea (56.6%), vomiting (54.8%), loss of appetite (60.8%) and swollen feet (49.6%) were some of the common symptoms experienced during pregnancy. Only 6.4% of the participants were expecting twins. A significantly smaller percentage of non-responders reported experiencing constipation (p=0.0001) or heartburn (p=0.0100) during the current pregnancy while also being diagnosed with or treated for vaginal infection (p=0.0146) than responders. A higher percentage of non- responders reported being diagnosed or treated for hypertension (p=0.0057) in the current pregnancy. 112 Table 4.3: Overview of reported health and lifestyle Variable Total sample Responders Non-responders p-value# TOBACCO USE n % n % n % Ever smoked (N=682) (n=331) (n=351) Yes 141 20.7 60 18.1 81 23.1 0.1106 No 541 79.3 271 81.9 270 76.9 If ever smoked, still currently smoking (N=140) (n=60) (n=80) Yes 42 30.0 23 38.3 19 23.8 0.0624 No 98 70.0 37 61.7 61 76.3 Frequency of smoking for current smokers (N=42) (n=23) (n=19) Daily 35 83.3 19 82.6 16 84.2 1.0000 Occasionally 7 16.7 4 17.4 3 15.8 Ever used snuff or chewed tobacco (N=678) (n=331) (n=347) Yes 151 22.3 69 20.9 82 23.6 0.3863 No 527 77.7 262 79.2 265 76.4 If ever used snuff or chewed tobacco, still (N=149) (n=68) (n=81) currently using 0.6429 Yes 60 40.3 26 38.4 34 42.0 No 89 59.7 42 61.8 47 58.0 Frequency of using snuff or chewing tobacco for (N=60) (n=26) (n=34) current users 0.6045 Daily 37 61.7 17 65.4 20 58.8 Occasionally 23 38.3 9 34.6 14 41.2 Members of household currently smoking (N=680) (n=331) (n=349) Yes 317 46.6 152 45.9 165 47.3 0.7230 No 363 53.4 179 54.1 184 52.7 Father in current pregnancy’s smoking status (N = 659) (n=317) (n=342) Smoker 317 48.1 150 47.3 167 48.8 0.6979 Non-smoker 342 51.9 167 52.7 175 51.2 ALCOHOL USE Ever used alcohol (N=682) (n=331) (n=351) Yes 506 74.2 252 76.1 254 72.4 0.2610 No 176 25.8 79 23.9 97 27.6 If ever used alcohol, currently using (N=505) (n=252) (n=253) Yes 61 12.1 29 11.5 32 12.7 0.6942 No 444 87.9 223 88.5 221 87.4 MEDICATION USE Medications used at least once a week (N=680) (n=329) (n=351) Yes 413 60.7 199 60.5 214 61.0 0.8976 No 267 39.3 130 39.5 137 39.0 Most common medications used (N=680) (n=329) (n=351) Antiretroviral medication Yes 217 31.9 110 33.4 107 30.5 0.4095 No 463 68.1 219 66.6 244 69.5 Hypertension medication Yes 161 23.7 69 21.0 92 26.2 0.1083 No 519 76.3 260 79.0 259 73.8 113 Variable Total sample Responders Non-responders p-value# MEDICATION USE n % n % n % Antibiotics 0.9689 Yes 25 3.7 12 3.7 13 96.3 No 655 96.3 317 96.4 338 3.7 Asthma medication 0.4374 Yes 25 3.7 14 4.3 11 3.1 No 655 96.3 315 95.7 340 96.9 Oral glucose-lowering medication Yes 25 3.7 14 4.3 11 3.1 0.4374 No 655 96.3 315 95.7 340 96.9 Tuberculosis medication Yes 21 3.1 10 3.0 11 3.1 0.9433 No 659 96.9 319 97.0 340 96.9 Aspirin 0.9433 Yes 21 3.1 10 3.0 11 3.1 No 659 96.9 319 97.0 340 96.9 Paracetamol Yes 19 2.8 11 3.3 8 2.3 0.4000 No 661 97.2 318 96.7 343 97.7 PREVIOUS PREGNANCIES Have been pregnant before (N=682) (n=331) (n=351) Yes 611 89.6 304 91.8 307 87.5 0.0613 No 71 10.4 27 8.2 44 12.5 FIRSTBORN CHILD n % n % n % p-value Born alive (N=604) (n=301) (n=303) Yes 537 88.9 272 90.4 265 87.5 0.2554 No 67 11.1 29 9.6 38 12.5 Delivery method (N=539) (n=275) (n=264) Vaginal 383 71.1 187 68.0 196 74.2 0.1101 Caesarean 156 28.9 88 32.0 68 25.8 Gestation period (N=535) (n=272) (n=263) Full-term 455 85.0 230 84.6 225 85.6 0.7476 Premature 80 15.0 42 15.4 38 14.5 Feeding method in the early months (N=533) (n=272) (n=261) Breastmilk 400 75.1 204 75.0 196 75.1 Formula milk 62 11.4 35 12.9 27 10.3 0.7225 Breastmilk and formula milk 69 12.6 32 11.8 37 14.2 Other 2 0.4 1 0.4 1 0.4 Number of other live children (N=539) (n=274) (n=265) 0 214 39.7 102 37.2 112 42.3 1 221 41.0 115 42.0 106 40.0 0.2224 2 74 13.7 41 15.0 33 12.5 3 26 4.8 12 4.4 14 5.3 4 4 0.7 4 1.5 0 0.0 Feeding method of other children in the early (N=322) (n=172) (n=150) months 0.6893 Breastmilk 221 68.6 116 67.4 98 65.3 Other (formula milk, breast plus formula, other) 53 16.5 56 32.6 52 34.7 114 Variable Total sample Responders Non-responders p-value# TOBACCO USE n % n % n % CURRENT PREGNANCY Expecting singletons or twins (N=672) (n=331) (n=341) Singletons 629 93.6 313 94.6 316 92.7 0.3160 Twins 43 6.4 18 5.4 25 7.3 Hospital admission during pregnancy (N=682) (n=331) (n=351) Yes 160 23.5 78 23.6 82 23.4 0.9501 No 522 76.5 253 76.4 269 76.6 Main reported reasons for hospital admission (N=160) (n=78) (n=82) Abdominal pain 46 28.8 28 35.9 18 22.0 0.0514 Hypertension 32 20.0 15 19.2 17 20.7 0.8125 Vaginal bleeding 9 5.6 4 5.1 5 6.1 1.0000 Vomiting 7 4.4 4 5.1 4 4.9 1.0000 Unknown 6 3.8 2 2.6 4 4.9 0.6821 Dizziness and headache 5 3.1 0 0.0 5 6.1 0.0590 Severe diarrhoea 7 4.4 1 1.3 6 7.3 0.1176 Main symptoms experienced during pregnancy Coughing for at least two weeks (N=682) (n=331) (n=351) Yes 117 17.2 53 16.0 64 18.2 0.4418 No 565 82.8 278 84.0 287 81.8 Diarrhoea for at least three days (N=682) (n=331) (n=351) Yes 94 13.8 41 12.4 53 15.1 0.3043 No 588 86.2 290 87.6 298 84.9 Constipation (N=682) (n=331) (n=351) Yes 267 39.2 154 46.5 113 32.2 0.0001* No 415 60.8 177 53.5 238 67.8 Nausea (N=682) (n=331) (n=351) Yes 386 56.6 198 59.8 188 53.6 0.0994 No 296 43.4 133 40.2 163 46.4 Vomiting (N=682) (n=331) (n=351) Yes 374 54.8 186 56.2 188 53.6 0.4900 No 307 45.2 145 43.8 163 46.4 Appetite loss (N=681) (n=331) (n=350) Yes 414 60.8 210 63.4 204 58.3 0.1682 No 267 39.2 121 36.6 146 41.7 Feet swelling (N=682) (n=331) (n=351) Yes 338 49.6 169 51.1 169 48.2 0.4476 No 344 50.4 162 48.9 182 51.9 Urinary tract infection (N=681) (n=331) (n=350) Yes 177 26.0 85 25.7 92 26.3 0.8570 No 504 74.0 246 74.3 258 73.7 Weight loss of more than three kilograms (N=680) (n=329) (n=351) Yes 117 17.2 49 14.9 68 19.4 0.1219 No 563 82.8 280 85.1 283 80.6 Heartburn (N=682) (n=331) (n=351) Yes 90 13.2 56 16.9 34 9.7 0.0100* No 592 86.8 275 83.1 317 90.3 115 Variable Total sample Responders Non-responders p-value# TOBACCO USE n % n % n % Have you been diagnosed or treated for the following? Hypertension (N=682) (n=331) (n=351) Yes 157 23.0 61 18.4 96 27.4 0.0057* No 525 77.0 270 81.6 255 72.7 Heart disease (N=681) (n=331) (n=350) Yes 4 0.6 1 0.3 3 0.9 0.6245 No 677 99.4 330 99.7 347 99.1 Diabetes mellitus (N=681) (n=331) (n=350) Yes 31 4.6 16 4.8 15 4.3 0.7316 No 650 95.4 315 95.2 335 95.7 Tuberculosis (N=681) (n=331) (n=350) Yes 7 1.0 2 0.6 5 1.4 0.4520 No 674 99.0 329 99.4 345 98.6 Asthma (N=681) (n=331) (n=350) Yes 21 3.1 12 3.6 9 2.6 0.4265 No 660 96.9 319 96.4 341 97.4 Any sexually transmitted disease (N=682) (n=331) (n=351) Yes 126 18.5 58 17.5 68 19.4 0.5337 No 556 81.5 273 82.5 283 80.6 Vaginal infection/discharge (N=682) (n=331) (n=351) Yes 106 15.4 63 19.0 43 12.3 0.0146* No 576 84.6 268 81.0 308 87.8 Lung diseases (N=681) (n=331) (n=350) Yes 1 0.2 0 0.0 1 0.3 1.0000 No 349 99.8 331 100.0 349 99.7 Elevated cholesterol (N=680) (n=331) (n=349) Yes 3 0.4 1 0.3 2 0.6 1.0000 No 677 99.6 330 99.7 347 99.4 Stroke (N=680) (n=330) (n=350) Yes 1 0.2 1 0.3 0 0.0 0.4853 No 679 99.8 329 99.7 350 100.0 HIV (N=682) (n=331) (n=351) Yes 28 4.1 18 5.4 10 2.9 0.0886 No 654 95.9 313 94.6 341 97.2 #p-value for percentage difference between responder and non-responder groups using chi-square or Fisher’s exact tests, as appropriate; p <0.05 considered statistically significant indicated with * Information relating to social support and stress is summarised in Table 4.4. More than one out of ten participants (12.6%) did not have anyone to turn to for help if they encountered a “really big problem” such as money, the children, accommodation, etc. Most participants (70.9%) could talk to their husband or partner about any problems that they might have, while almost three quarters (74.6%) belonged to a church group or organisation. 116 Just over a third of participants (36.0%) found themselves in so much debt that they did not know how they would repay the money, while 70.9% reported that they or anyone in their close family were unable to find a job for more than six months. During the past six months, 39.9% indicated that they or someone in their immediate family had been or was seriously ill and 29.3% had experienced the loss of a close family member. Almost a third (31.3%) indicated that they had someone in their close family who has a problem with drugs or alcohol, while almost one in ten participants (8.2%) had been hit or beaten by their partner in the past six months. Significantly more responders than non-responders reported that they experienced so much debt during the past six months that they did not know how they were going to repay the money (p=0.0158). Table 4.4: Information relating to social support and stress Variable Total sample Responders Non-responders SOCIAL SUPPORT n % n % n % p-value Are there people who could help you if you had a really big problem and needed help, such as with money, the children, accommodation and so on? (N=682) (n=331) (n=351) Nobody 86 12.6 41 12.4 45 12.8 0.9240 Maybe / unsure 35 5.1 16 4.8 19 5.4 A number of people 561 82.3 274 82.8 287 81.8 If you have a husband or partner, can you talk to your husband or partner about any problems you might have? (N=666) (n=324) (n=342) Never 44 6.6 20 6.2 24 7.0 0.9008 Sometimes 150 22.5 74 22.8 76 22.2 Always 472 70.9 230 71.0 242 70.8 SOCIAL SUPPORT Do you belong to a church group or any other organisation? (N=682) (N=331) (N=351) Yes 509 74.6 251 75.8 258 73.5 0.4852 No 173 25.4 80 24.2 93 26.5 STRESS During the last six months, have you or a member of your close family been in real danger of being killed by criminals? (N=682) (N=331) (N=351) Yes 43 6.3 21 6.3 22 6.3 0.9672 No 639 93.7 310 93.7 329 93.7 During the last six months, have you or a member of your close family been in real danger of being killed by police, army or other officials? (N=681) (N=330) (N=351) Yes 5 0.7 2 0.6 3 0.9 1.0000 No 676 99.3 328 99.4 348 99.1 117 Variable Total sample Responders Non-responders STRESS n % n % n % p-value During the last six months, have you or a member of your close family been in real danger of being killed during political activities? (N=681) (N=330) (N=351) Yes 5 0.7 4 1.2 1 0.3 0.2038 No 676 99.3 326 98.8 350 99.7 During the past six months, did you witness a violent crime (e.g. murder, robbery, assault, rape)? (N=680) (N=331) (N=349) Yes 79 11.6 38 11.5 41 11.8 0.9134 No 601 88.4 293 88.5 308 88.2 During the past six months, have you found that you are in so much debt that you don’t know how you will repay it? (N=680) (N=330) (N=350) Yes 245 36.0 134 40.6 111 31.7 0.0158* No 435 64.0 196 59.4 239 68.3 Have you or one of your close family members not been able to find a job for more than six months? (N=681) (N=331) (N=350) Yes 483 70.9 238 71.9 245 70.0 0.5846 No 198 29.1 93 28.1 105 30.0 During the last six months, have you or anyone in your close family been seriously ill? (N=682) (N=331) (N=351) Yes 272 39.9 136 41.1 136 38.8 0.5326 No 410 60.1 195 58.9 215 61.3 During the last six months, did any member of your close family die? (N=682) (N=331) (N=351) Yes 200 29.3 89 26.9 111 31.6 0.1746 No 482 70.7 242 73.1 240 68.4 Is there anyone in your close family who has a problem with drugs or alcohol? (N=680) (N=331) (N=349) Yes 213 31.3 108 32.6 105 30.1 0.4749 No 467 68.7 223 67.4 244 69.9 During the last six months, have you had a break-up with your husband or partner? (N=682) (N=331) (N=351) Yes 107 15.7 56 17.0 51 14.7 0.4179 No 570 83.6 274 83.0 296 85.3 During the last six months, has your husband or partner hit or beat you? (N=670) (N=325) (N=345) Yes 56 8.2 21 6.5 35 10.1 0.0851 No 614 90.2 304 93.5 310 89.9 #p-value for percentage difference between responder and non-responder groups using chi-square or Fisher’s exact tests, as appropriate; p <0.05 considered statistically significant indicated with * More than half of participants (56.5%) were classified as obese based on their GBMI (Table 4.5), with no significant difference observed between responders and non-responders. 118 Table 4.5: Gestational BMI Variable Total sample Responders Non-responders (N=680) (N=331) (N=349) GBMI n % n % n % p-value Underweight 43 6.3 21 6.3 22 6.3 0.7210 Normal weight 166 24.4 79 23.9 87 24.9 Overweight 87 12.8 38 11.5 49 14.0 Obese 384 56.5 193 58.3 191 54.7 #p-value for percentage difference between responder and non-responder groups using chi-square or Fisher’s exact tests, as appropriate; p <0.05 considered statistically significant indicated with * Almost three-quarters of participants experienced some form of food insecurity with no significant difference observed between responders and non-responders (Table 4.6). Table 4.6: Household food security status Total sample Responders Non-responders Variable (N=681) (N=331) (N=350) Household food security score n % n % n % p-value Food secure 181 26.6 93 28.1 88 25.1 0.8368 Mildly food insecure 75 11.0 36 10.9 39 11.1 Moderately food insecure 222 32.6 104 31.4 118 33.7 Severely food insecure 203 29.8 98 29.6 105 30.0 #p-value for percentage difference between responder and non-responder groups using chi-square or Fisher’s exact tests, as appropriate; p <0.05 considered statistically significant indicated with * Median intakes for energy, total protein, total fat and total carbohydrates were 8 616.9 kJ, 64.5 g, 63.6 g and 313.8 g, respectively (Table 4.7). Median intakes of 1 023.1 RE µg of vitamin A, 410.2 µg of folic acid, 3.6 µg of vitamin B12, 52.8 mg of vitamin C, 3.6 µg vitamin D, 471.3 mg of calcium, 16.5 mg of iron and 12.6 mg of zinc were reported from the diet. Significant differences between the two groups were observed for the intake of all macro- and micronutrients determined except for trans fatty acids (p=0.2482), total carbohydrates (p=0.1702), total fibre (p=0.0681), thiamine (p=0.2640), folic acid (p=0.1687), vitamin K (p=0.2366), iron (p=0.0870), magnesium (p=0.0802) and manganese (p=0.0517). Similar to the socio-demographic findings that showed that responders were generally better off than non-responders, responders generally had a higher median intake of all macro- and micronutrients compared to non-responders. 119 The respective distribution of median total protein, total carbohydrates and total fat were 12.7%, 61.9% and 28.0% of total median energy intake. The median for saturated fat was 7.8% of the total median energy intake, while monounsaturated fatty acids (MUFAS) were 8.6%, polyunsaturated fatty acids (PUFAS) were 8.5% and added sugar were 8.3% of the median total energy intake. 120 Table 4.7: Median nutrient intake Total sample (N=681) Responders (N=331) Non-responders (N=350) 95% CI for the p-value percentage Unit Median IQR Median IQR Median IQR difference Energy kJ 8616.9 6827.0 ̶10894.2 8977.3 7155.9 ̶11296.1 8459.8 6462.2 ̶ 10552.1 0.0046* [199.5%;1119.0%] Total protein g 64.5 50.2 ̶83.7 69.4 52.1 ̶85.5 61.5 47.7 ̶ 78.7 0.0009* [2.6%;10.1%] Total fat g 63.6 43.3 ̶88.8 69.7 47.7 ̶94.4 58.5 41.0 ̶ 81.7 0.0004* {4.0%;13.6%] Saturated fat g 18.0 11.4 ̶25.0 19.1 12.9 ̶26.7 16.7 10.5 ̶ 23.5 0.0008* [1.1%;4.0%] Monounsaturated fatty acids g 19.4 12.2 ̶28.0 21.0 13.7 ̶30.9 17.9 11.1 ̶ 25.7 0.0008* [1.2%;4.5%0 Polyunsaturated fatty acids g 19.3 11.7 ̶25.0 19.3 12.7 ̶26.5 16.2 10.5 ̶ 23.8 0.0005* [1.2%;4.2%] Trans fatty acids g 0.6 0.3 ̶1.2 0.7 0.3 ̶1.3 0.6 0.3 ̶ 1.1 0.2482 [-0.0%;0.1%] Cholesterol mg 201.8 120.1 ̶321.2 234.7 137.5 ̶364.1 177.5 103.7 ̶275.3 <0.0001* [25.8%;67.4%] Total carbohydrates g 313.8 251.1 ̶385.6 319.1 257.8 ̶390.8 306.1 241.2 ̶383.2 0.1702 [-4.6%;27.1%] Total sugar g 53.6 36.8 ̶74.8 56.5 39.5 ̶76.8 51.9 33.8 ̶ 70.7 0.0138* [1.1%;9.4%] Added sugar g 42.1 25.5 ̶63.4 44.6 27.6 ̶67.2 39.1 23.3 ̶ 62.0 0.0275* [0.5%;9.0%] Total fibre g 24.8 19.6 ̶31.3 25.5 20.2 ̶31.8 24.2 18.5 ̶ 30.9 0.0681 [-0.1%;2.6%] VITAMINS Vitamin A RE µg 1023.1 666.6 ̶1769.2 1104.4 748.6 ̶2030.9 932.8 608.5 ̶1594.5 0.0011* [67.7%;274.7%] Thiamine mg 1.9 1.4 ̶2.4 1.9 1.5 ̶2.4 1.9 1.4 ̶ 2.4 0.2640 [-0.0%;0.2%] Riboflavin mg 1.7 1.2 ̶2.3 1.7 1.2 ̶2.3 1.6 1.1 ̶ 2.2 0.0178* [0.0%;0.3%] Niacin mg 21.4 15.7 ̶28.5 22.8 16.4 ̶30.2 20.4 14.6 ̶ 27.9 0.0035* [0.7%;3.5%] Pyridoxine mg 3.4 2.4 ̶4.6 3.7 2.5 ̶4.9 3.2 2.3 ̶ 4.3 0.0049* [0.1%;0.6%] Folate µg 410.2 309.1 ̶536.6 420.5 319.0 ̶547.7 401.8 303.4 ̶536.5 0.1687 [-7.7%;44.2%] Vitamin B12 µg 3.6 2.0 ̶6.4 4.2 2.6 ̶7.6 3.2 1.9 ̶ 5.6 <0.0001* [0.5%;1.3%] Pantothenic acid mg 4.9 3.6 ̶6.6 5.2 3.9 ̶6.9 4.7 3.3 ̶ 6.2 0.0017* [0.2%;0.9%] Biotin µg 40.1 29.5 ̶53.6 42.7 32.8 ̶56.0 37.9 26.7 ̶ 51.3 0.0002* [2.4%;7.9%] Vitamin C mg 52.8 29.8 ̶93.6 56.3 32.8 ̶93.6 48.7 27.5 ̶ 94.7 0.0293* [0.7%;12.6%] Vitamin D µg 3.6 1.8 ̶5.5 4.2 2.0 ̶6.1 3.1 1.5 ̶ 4.9 <0.0001* [0.4%;1.2%] Vitamin E mg 10.6 7.4 ̶15.3 11.8 8.1 ̶16.3 9.7 6.7 ̶ 14.5 0.0004* [0.7%;2.4%] Vitamin K µg 58.9 28.7 ̶107.5 62.8 30.8 ̶110.0 57.4 26.7 ̶ 104.0 0.2366 [-2.9%;11.4%] 121 Total sample (N=681) Responders (N=331) Non-responders (N=350) 95% CI for the p-value percentage Unit Median IQR Median IQR Median IQR difference MINERALS Calcium mg 471.3 306.7 ̶643.6 484.3 342.7 ̶670.8 451.0 284.3 ̶631.9 0.0453* [0.8%;74.1%] Iron mg 16.5 12.6 ̶20.2 17.0 13.2 ̶20.4 16.0 12.3 ̶ 20.2 0.0870 [-0.1%;1.7%] Magnesium mg 293.6 237.0 ̶370.1 301.3 244.1 ̶371.9 286.7 225.0 ̶336.8 0.0802 [-1.6%;29.5%] Phosphorous mg 996.7 778.5 ̶1246.2 1039.0 824.7 ̶1295.1 968.9 738.1 ̶1224.9 0.0064* [21.3%;132.0%] Potassium mg 2204.1 1662.0 ̶2797.4 2287.6 1795.5 ̶2319.6 2120.5 1547.3 ̶ 2684.8 0.0023* [71.0%;320.5%] Sodium mg 1902.9 1263.9 ̶2748.1 2096.5 1400.9 ̶2882.8 1752.6 1152.3 ̶ 2567.2 0.0006* [123.2%;441.3%] Chloride mg 1107.8 673.7 ̶1756.6 1180.6 713.4 ̶1839.3 1021.7 631.2 ̶1647.8 0.0282* [12.8%;235.5%] Zinc mg 12.6 9.7 ̶15.9 13.3 10.0 ̶16.4 12.4 9.4 ̶ 15.4 0.0220* [0.1%;1.5%] Copper mg 1.3 1.0 ̶1.8 1.4 1.1 ̶1.8 1.3 0.9 ̶ 1.7 0.0027* [0.0%;0.2%] Chromium µg 34.0 19.9 ̶53.6 36.5 21.4 ̶54.8 31.1 18.0 ̶ 53.0 0.0447* [0.1%;7.0%] Selenium µg 33.6 20.4 ̶51.5 35.8 22.5 ̶54.4 31.8 18.3 ̶ 47.1 0.0007* [2.4%;8.9%] Manganese µg 2289.1 1844.7 ̶3030.7 2372.9 1944.2 ̶3015.5 2260.8 1677.3 ̶ 3063.4 0.0517 [-0.9%;270.1%] Iodine µg 37.2 24.2 ̶53.2 40.7 27.0 ̶6.3 33.0 21.6 ̶ 47.7 <0.0001* [3.9%;10.3%] #p-value for median difference between responder and non-responder groups using the Kruskall-Wallis test; p <0.05 considered statistically significant indicated with * 122 4.5 DISCUSSION The current study aimed to describe the nutritional status of pregnant women attending the antenatal clinic at Pelonomi Hospital, Bloemfontein, Free State and to determine differences between those women who responded to the request to provide their baby’s Road to Health Booklet and those who did not. Socio-demography Maternal childbearing age is increasing due to various social and cultural influences (Londero et al., 2019, Molina-Garcia et al., 2019). The median age of participants in the current study was 31.9 years, which is similar to the mean maternal age of 31.9 years at delivery of 22 933 single pregnancies at a tertiary referral centre in Udine, Italy between 2001 and 2014 (Londero et al., 2019). Similarities between the findings of these studies and the current study may be ascribed to the fact that both these were conducted in tertiary hospitals to which higher risk pregnancies, amongst others older pregnant women, are referred to. Molina- Garcia et al. (2019) conducted an analytic observational study amongst 373 pregnant women who gave birth during 2017 in different hospitals in Spain and found that maternal age over 35 years was significantly associated with gestational diabetes, spontaneous onset of labour and caesarean section. Older maternal age in the current study may also be ascribed to the fact that the antenatal clinic at Pelonomi Hospital is a high-risk clinic to which women older than 35 years of age are referred. Exposure to poor housing conditions, particularly overcrowding, during pregnancy may contribute to stress and is associated with poor outcomes for both the mother and her offspring (Nelson, 2010:112). While the median HDR for the total sample was 85.7, which is not an indication of overcrowding, almost half of the participants (47.4%) were living in overcrowded households. Of the overcrowded households, 79.6% (257/323) had children that were younger than 10 years of age. Household with more adults could mean more social support or a means of pooling resources as the stokvel principle is deeply embedded in the Sout African cultures. According to Statistics South Africa (2016), 26.1% of South African households living in government subsidised dwellings and 46.2% of households living in informal dwellings lived in overcrowded conditions in South Africa in 2014. Nkosi et al. (2019) analysed data collected as part of an 11-year study in two Johannesburg large-scale, low-cost 123 housing suburbs from 2006 to 2016 and found that 57.6% of households were overcrowded which is slightly higher than the findings of the current study. However, the Johannesburg study was conducted on a larger sample and included all households in the two suburbs, whereas the current study only focused on pregnant women attending the antenatal clinic at Pelonomi Hospital. Although half of the current participants had access to their own tap inside the house, some had to share a tap and/or outside toilet with other households. Some households still made use of a bucket system or a pit toilet, while almost one in ten participants still lived in households that did not have access to electricity. Inadequate sanitation may contribute to poor pregnancy outcome (Patel et al., 2019) since unsanitary practices may promote infection and induce stress during pregnancy (Pahdi et al., 2015). Various socio-economic factors such as educational level, economic conditions and residence location may play an important role in determining healthcare behaviour. Low socio- economic status may be a barrier to seeking health services due to factors such as cost of transport and medicine (Siddique et al., 2016). While most participants had an indoor cooking area and owned basic storage and cooking equipment, just over one in ten made use of fuel sources other than electricity for cooking. Unemployment was prevalent among the participants in the current study, while just over two-thirds (67.9%) of their husbands or partners were full-time employees or self-employed with wages and salaries as well as pension or state grants reported as the main sources of income. Almost a third (31.2%) of participants had a monthly income between R0.00 and R1000.00. Women living on a lower monthly household income are more likely to suffer from higher rates of malnutrition (Black et al., 2008:243), engage in risky behaviours such as smoking and alcohol use (Agaku et al., 2014:30), while also presenting with greater levels of psychological stress (Strutz et al., 2014:e125). Poverty during pregnancy may hold life-long consequences for child health and cognitive development (Hamad & Rehkopf, 2015:445). Maternal age, employment, education and income are well-known factors associated with child health and malnutrition (Yaya et al., 2020). 124 Reported health and lifestyle Smoking (Lange et al., 2018) and alcohol use during pregnancy pose major threats to the foetus (Popova et al., 2017). Tobacco use during pregnancy is associated with an increased risk of premature delivery, spontaneous abortion and various other long-term risks in both the mother and her offspring (WHO, 2016), while alcohol exposure may lead to increased risk of growth impairment, stillbirth and foetal alcohol spectrum disorders (De Jong et al., 2019). In the current study, 30.0% of the 140 participants (6.2% of the total sample) who had ever smoked reported that they were still smoking during pregnancy, while 40.3% of the 149 participants who indicated that they had ever used snuff or chewed tobacco were still doing so while pregnant. These findings are lower than the global estimated prevalence of 52.9% of women who ever smoked and continued to smoke while pregnant, but higher than the 0.8% in the African Region (Lange et al., 2018:e770). Phaswana-Mafuya et al. (2019) analysed data of ever-pregnant women from the South African National Health and Nutrition Examination Survey (SANHANES-1) of 2012 and found that 5.0% of the women reported using tobacco while pregnant. These differences may be attributed to cultural differences in populations included in the different studies. A study conducted at the midwife obstetric unit in the East Metropole district, Cape Town, found that a concerning 36.8% of the 323 women included in the study smoked while pregnant (Vythilingum et al., 2012). Cessation of tobacco use before and during pregnancy has been found to significantly affect pregnancy-related outcomes (WHO, 2019); however, the impact of tobacco use is not limited to the direct use by the pregnant mother (WHO, 2013). Second-hand smoke exposure may lead to reductions in birth weight, amongst others, and consequently may increase the risk of delivering an infant with a low birth weight (Leonardi-Bee, et al., 2011). Overall, 46.6% of participants in the current study indicated that other member(s) in the household were also smoking, while almost half (48.1%) of the fathers were smokers. The World Health Organization (WHO) Recommendations for the Prevention and Management of Tobacco use and Second-hand Smoke Exposure in Pregnancy state that (WHO, 2013): “health care providers should routinely offer advice to current tobacco users and recent tobacco quitters, as well as provide information to expectant mothers and, where possible, their partners or other household members about the harms of second-hand smoke.” 125 No current treatment or established diagnostic or therapeutic tools are available to prevent and/or improve some of the adverse effects associated with alcohol consumption during pregnancy (De Jong et al., 2019). Alcohol is a teratogen that can freely cross the placenta. Currently, no known safe level of alcohol consumption during pregnancy exists, therefore, complete abstinence during pregnancy is recommended (Graves et al., 2020). Of those participants who reported to ever using alcohol, 12.2% of the 505 participants (8.9% of the total sample) indicated that they were still using alcohol while pregnant. This is in line with the global prevalence of alcohol use during pregnancy of 9.8% as well as the estimated prevalence of 10.0% in the African region, and 13.2% in South Africa (Popova et al., 2017:e290). Peltzer and Pengpid (2019) also analysed data of 5089 ever-pregnant women from the SANHANES-1 to determine the prevalence of maternal alcohol use during pregnancy and found that 3.7% of South African women consumed alcohol while pregnant. The study by Vythilingum et al. (2012) among pregnant women at the midwife obstetric unit in the East Metropole district, Cape Town, found that 20.2% of pregnant women in their study consumed alcohol while pregnant. Concern regarding medication use during pregnancy and the effects thereof on maternal and foetal health is growing (Lynch et al., 2019:92). Limited evidence is available regarding the safety of many medications taken during pregnancy (Fisher et al., 2008). Certain medications, when taken during pregnancy, have been associated with increased risk of congenital disabilities and adverse pregnancy outcomes (Briggs et al. 2008). In the current study, 60.7% of participants reported using medications at least once a week, of which ART (31.9%) and anti-hypertensive (23.7%) medications were the most commonly used. A study conducted amongst pregnant women at an antenatal clinic in Friuli Venezia Giulia region in Italy, from 2007 to 2009, found that 39.8% of pregnant women reported medication use (Pisa et al., 2015), lower than findings of the current study. This difference may be due to the fact that the current study was conducted at a high-risk clinic. Medications provided to these pregnant women are, however, prescribed in order to prevent severe consequences of certain conditions, such as hypertension and HIV, for both the mother and her offspring. Pregnancy history The recurrence of poor birth outcomes from one pregnancy to the next is well known (Malacova et al., 2017). A four-fold increase in relative risk is reported in pregnancies where 126 a stillbirth was suffered (Lamont et al., 2015). Similarly, premature birth is associated with premature birth in a subsequent pregnancy (Laughon et al., 2014) with an even greater risk if the birth occurred before 34 weeks of gestation (Esplin et al., 2008). In the current study, 11.1% of participants reported that their first pregnancy ended in a loss, reasons for which were not indicated, while 15.0% of the participants reported that they experienced premature delivery during their first pregnancy. Three-quarters of the participants (75.1%) indicated that they breastfed their first-born, while 68.6% also breastfed their other children in the early months of life. The current study, however, did not ask about exclusive breastfeeding, only about initiation of breastfeeding. Breastfeeding may prolong lactation amenorrhoea, which can promote the recommended interpregnancy interval of 24–36 months to reduce poor child health outcomes. Appropriate interpregnancy intervals are associated with improved maternal, perinatal, infant and child outcomes (Yaya et al., 2019). Only a few participants in the current study were expecting twins (6.6%). Risk of perinatal morbidity and mortality increases with twin pregnancy (Santana et al., 2016). Almost a quarter of the participants (23.5%) in this study were hospitalised during pregnancy, mostly for abdominal pain (28.8%) and hypertension (20.2%). Admission to hospital during pregnancy exposes the pregnant mother and her family to a situation of vulnerability and concern. Gestational hypertension, a common complication during pregnancy, may result in episodes of emergency care and hospitalisation if not treated appropriately (Falavina et al., 2018). A relatively large percentage of participants reported experiencing nausea, vomiting, appetite loss and swelling of the feet, which are common symptoms experienced during pregnancy. Although nausea and vomiting are commonly experienced during pregnancy, few studies have investigated the impact thereof on the daily lives of pregnant women (Heitman et al., 2017). Heitman et al. (2017) found that nausea and vomiting during pregnancy may have a major impact on different aspects of the lives of pregnant women, including quality of life and willingness to become pregnant again. A meta-analysis with the aim of summarising global rates of nausea and vomiting in pregnancy found that reported rates of nausea and vomiting range between 35% and 91% (median 69%) (Einarson et al., 2013). Nausea, vomiting and loss 127 of appetite during pregnancy have been associated with low birth weight, small for gestational age and premature birth (Wallin et al., 2020). Social support includes the emotional and material resources obtained through interaction between persons and the environment. These interactions aid in protecting an individual from the negative consequences of stress (Iranzad et al., 2014). Social support may serve as a protective factor in coping with challenging situations, particularly during motherhood (Milgrom et al., 2019). In the current study, just over one in ten participants did not have anyone who could help if they had a big problem for which they needed help. Some participants could never talk to their husbands or partners about any of their problems, while a quarter did not belong to a church group or similar organisation. Pregnant women are susceptible to various stressors, including financial concerns and relationship problems (Taylor et al., 2020). Participants attending the antenatal clinic at Pelonomi Hospital experienced various sources of stress. These included having witnessed a violent crime, being in so much debt that they did not know how they would repay the money, struggling to find a job for themselves or a close member of the family, losing a close family member, or having had someone in the close family with a drug or alcohol problem. Stress during pregnancy is associated with foetal risks that may increase the risk of neonatal complications and reduce cognitive ability in children (Lima et al., 2018). According to the Centers for Disease Control and Prevention (CDC) Pregnancy Risk Assessment Monitoring System, in the United States in 2009–2010, 75% of postpartum women reported at least one stressful event in the year before the birth of their baby. Arguing with a partner more than usual, serious illness and hospitalisation of a family member, and inability to pay bills were identified as some of the most experienced stressors (Burns et al., 2015). Gestational body mass index Median GBMI for the total sample as well as the two different groups were in the obese category. Overweight and obesity during pregnancy may increase the risk of unfavourable clinical outcomes in both the mother and child (Stubert et al., 2018:276). Experiencing these conditions during pregnancy have been associated with gestational diabetes mellitus, hypertension and caesarean section in the mother. Overweight and obesity increase the risk of premature delivery, macrosomia, shoulder dystocia, congenital abnormalities and neonatal death, amongst others. Maternal overweight and obesity also affect the long-term health of 128 both the mother and her offspring (Vernini et al., 2016; Yang et al., 2019:368). Maternal overweight and obesity furthermore influence the growth patterns related to weight, height and body mass index in the offspring (Oostvogels et al., 2017). More than half (56.5%) of the participants in the current study had a GBMI in the obese category. Chen et al. (2018) estimated that globally 38.9 million pregnant women were overweight and obese in 2017, of which 14.6 million were obese. Davies et al. (2012) found that 33.5% of women in Khayelitsha, Western Cape had a GBMI in the obese category. Differences between the findings of the current study and that of Davies et al. (2012), might be due to the difference in the study populations as Davies et al. (2012) recruited participants from neighbourhoods in a peri-urban settlement. In contrast, the current study was conducted in a high-risk antenatal clinic that would be more likely to include overweight and obese participants. Household food security status A third (32.6%) of participants in the current study suffered from moderate food insecurity with a further 29.8% suffering from severe food insecurity. Zar et al. (2019) conducted a study among pregnant women attending two public health clinics in a poor peri-urban area of South Africa and found that food insecurity was also common (37%) in their study. Food insecurity was also associated with lower infant gestational age (Zar et al., 2019). A study conducted among 394 pregnant women from eight urban health and medical centres in Iran found that 43.9% of pregnant women were food insecure, which is lower than findings of the current study, and that household food insecurity may decrease total quality of life among pregnant women (Moafi et al., 2018). Food insecurity may increase the risk of pregnancy complications (Hoseini et al., 2018) such as low birth weight (Borders et al., 2007) and certain congenital disabilities (Carmachael et al., 2007). Nutrient intake Food and nutrient intake during pregnancy holds important short- and long-term consequences for both the mother and her offspring (Saunders et al., 2019). Energy and protein requirements increase during pregnancy to make provision for the pregnant mother’s own needs as well as the growing foetus (Mousa et al., 2019). The median energy intake in the total group (8616.9 kJ) was lower than the DRI for both the second and third trimesters (EER: 11 521 kJ and 11 991 kJ, respectively) as well as the WHO/FAO recommendations (EAR: 10 500 kJ), while the distribution of the medians for all the macronutrients were within the 129 acceptable macronutrient distribution ranges. The median intake of fibre was slightly below the DRI as well as the WHO/FAO recommendations. Napier et al. (2019) found that pregnant women at a Public Health Care Facility at Umkhumbane in Kwa Zulu Natal had a mean energy intake of 5772.72 kJ which is much lower than that of the participants in the current study. Similarly, mean protein (50.4 g) and carbohydrate (191.8 g) intake was lower than the median intakes of the current study. In the Kwa Zulu Natal study, mean intakes for vitamin A (643.5 mcg), folate (270.8 mcg), iron (10.7 mg), zinc (9.0 mg), magnesium (170.5 mg) and calcium (308.9 mg) were also lower than the median intakes for the micronutrients in the current study (Napier et al., 2019). A study conducted among pregnant women from a Nordic mother-child birth cohort, however, found a median energy intake of 10 082 kJ (Sauders et al., 2019) which is slightly higher than that observed in the current study. Saunders et al. (2019) also found that the total fat intake among the pregnant women in their study was within the recommendations, however, the contribution of saturated fatty acids was above the recommendations. The carbohydrate intake in the Nordic study was below the recommended intake (Saunders et al., 2019), while in the current study carbohydrate intake, although within the acceptable macronutrient distribution range, was much higher than the DRI of 135 grams per day. In the Nordic study, median intakes of vitamin A, vitamin C, vitamin D, vitamin B12, iodine, zinc, calcium, selenium and iron were above the recommended intake according to the Nordic Nutrition Recommendations while folate was slightly below the recommendations (Saunders et al., 2019). In contrast, the median intakes of folate, pantothenate, vitamin C, vitamin D, vitamin E, vitamin K, calcium, iron, potassium, selenium and iodine of participants in the current study were below the recommended intakes (EAR of the DRI and/or the EAR of the WHO/FAO recommendations). These difference may be ascribed to the differences in cultural eating habits between the two studies. A study conducted by Liu et al. (2015:1778) among pregnant women from eight cities in China found a median energy intake of 2098 kCal (8811.6 kJ) per day, which is similar to the findings of the current study. The macronutrient distribution in the Liu et al. (2015:1781) study, however, differed slightly as their distribution of fat, carbohydrates and protein were 36.4%, 51.5% and 15.0% respectively. The intakes of vitamin A, vitamin B6, calcium, magnesium and 130 selenium were below the RNI and EAR of the Chinese DRIs while the median folate intake was below the RNI (Liu et al., 2015:1780). Kiboi et al. (2016:378) conducted a study among 254 pregnant women attending the antenatal clinic at a teaching and referral hospital in Laikipia County, Kenya. Mean energy intake in the Kenya study was 1890.6 kCal (8507.5 kJ) which is similar to that observed in the current study. Although the distribution of fat (18.7%), carbohydrates (70.2%) and protein (11.2%) (Kiboi et al., 2016:381) differed from the current study, it was closer to the distribution of the current study than that reported by Saunders et al. (2019) or Liu et al. (2015). The means for vitamin C, calcium, folic acid, iron and zinc were below the reference values in the Kiboi et al. (2016) study. The differences in median energy and nutrient intakes across studies could possibly be ascribed to the obvious limitations of obtaining diet history (such as differences in tool used) and the fact that some samples were from developed countries where micronutrient intakes of pregnant women are likely to be higher than in samples from developing countries, such as South Africa, where a nutrition transition from healthier traditional to more unhealthy Westernised eating patterns is taking place (Tydeman-Edwards et al., 2018). Comparison of responders and non-responders Significantly more responders owned a refrigerator (p=0.0318), microwave (p=0.0173) and a kettle (p=0.0167) than non-responders. Greater access to these types of cold storage and cooking equipment could be an indication of better socio-economic circumstances, which, in turn, may reflect in the health-seeking behaviour of the women who provided their child’s Road to Health Booklet. Similarly, significantly more participants in the responder group had an education level between grade 10–12 or tertiary education (p=0.0457) compared to non- responders. Participants in the responder group may have been more likely to bring their child’s Road to Health booklet because of the association between the level of education and health seeking behaviour (Siddique et al., 2016). Responders were more likely to experience constipation (p=0.0001) and heartburn (p=0.0100) during pregnancy than non-responders. Significantly more participants in the non- responder group were also treated for hypertension (p=0.0057), while more participants in the responder group were treated for vaginal infection/discharge (p=0.00146) during the 131 current pregnancy. Munguambe et al. (2016) and Qureshi et al. (2016) report that pregnant women tend to visit health facilities when experiencing complications or discomfort during pregnancy. The participants who were already admitted to hospital during pregnancy might have been more familiar with the hospital setting or more concerned for their babies’ health and therefore more willing to bring their baby’s Road to Health Booklet to the dietitians. Compared to non-responders, significantly more participants in the responder group reported that they had so much debt in the past six months that they did not know how they would repay the money (p=0.0158). This is in contrast to the trend observed for basic indicators of socio-demographic status. Participants who were socio-economically better off may have been more likely to help others by taking in orphans or relatives with nowhere else to live which may also be linked to the higher percentage of women in the responders group living in overcrowded households. In the current study, significant differences between the two groups of participants were observed for the median intake of most macro- and micronutrients. Median intakes for all macro- and micronutrients were higher in the responders group compared to the non- responder group. It is common for individuals with higher socio-economic status to follow healthier diets than those with lower socio-economic status. Their dietary profiles are also more likely to be more consistent with nutritional recommendations or dietary guidelines (James et al., 1997; Smith & Brunner, 1997; Alkerwi et al., 2015; Pechey & Monsivais, 2016). Few studies comparing the characteristics of responders (participants for whom follow-up data in a research study are available) and non-responders (participants for whom no follow- up data are available) regarding nutrition are available, particularly among pregnant women. A study aimed at determining the characteristics, feeding intentions and feeding behaviours of responders and non-responders among couples whose babies were born in the Forth Valley Health Board in 1995 found that non-responders were more likely to smoke and were from a lower socio-economic class (Shepherd et al., 1998:275). A similar trend was observed in the current study where participants in the responder group were better off with regard to the basic indicators of socio-demographic status. It is, however, important to note that this study was conducted at a high-risk clinic. Thus, if intrauterine deaths, stillbirths or maternal death occurred, information of the birth outcome of those women may have been lost. 132 4.6 CONCLUSION AND RECOMMENDATIONS This study was conducted at a high-risk clinic which may have an effect on the representativeness of the results in settings that are different from the current setting. The inclusion of twins may also be seen as a major limitation which may also have an impact on the representativeness of the results. Although most participants in the current study had access to basic sanitation, electricity and household storage and cooking equipment, a noteworthy percentage of participants did not. More than half of the women were unemployed, while 67.9% of their husbands or partners had full-time employment. Although women were mostly reliant on their husbands or partners for income, a significantly higher percentage of responders had access to appliances associated with a higher socio-demographic status, than non-responders. The relatively high percentages of participants that smoked, used tobacco and snuff, as well as alcohol were concerning. In addition, the participants in the current were predominantly overweight and obese. Several women were treated for abdominal pain and hypertension during their pregnancy, while common symptoms such as nausea, vomiting, appetite loss and swelling of the feet were also reported. A substantial percentage of participants were exposed to high levels of stress and insecurity, including household food insecurity which in turn contributed to inadequate dietary and nutrient intakes. In terms of participants who responded to the request to provide their baby’s Road to Health Booklet (responders) and those who did not (non-responders), responders were generally characterised by access to appliances that may be linked to a better socio-demographic status. Furthermore, median intakes for all macro- and micronutrients were generally higher in the responder group than the non-responder group, which may be indirectly related to socio-demography. These differences need to be considered when considering how representative the responder group is of pregnant women who receive their antenatal care at the high-risk clinic at a public hospital in a South African setting. Differences between the responders and non-responders may also reflect the health-seeking behaviour of responders. 133 In order to increase the chances of a successful pregnancy, pregnant women should be educated on the importance of regular attendance of antenatal follow-up visits. Promoting a healthy lifestyle that includes eating a balanced diet needs to begin before pregnancy. 4.7 ACKNOWLEDGEMENTS The authors would like to acknowledge the staff at the antenatal clinic and the dietitians working at Pelonomi Hospital for their assistance as well as the pregnant mothers for their willingness to participate. The authors have no conflict of interest to declare. 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Study design: A cross-sectional observational study, embedded in a larger quantitative, cohort analytical study. Setting: A high-risk antenatal clinic in a public hospital setting. Subjects: 347 neonates born to mothers who attended the antenatal clinic. Methods: 700 conveniently sampled pregnant women attending the antenatal clinic at Pelonomi Hospital, included in the cohort study, were requested to take their baby’s Road to Health Booklet to the dietitians’ offices at Pelonomi Hospital after delivery. Data related to the birth outcomes, namely gestational age, method of delivery, human immunodeficiency virus (HIV) exposure, immunisations and congenital disabilities, as well as weight, length and head circumference at birth, were recorded from the booklet. Results: Birth information was received for 347 infants. Median gestational age was 39.0 weeks (interquartile range 37.0–39.0 years) and the majority (61.6%) were born by caesarean section. Almost one in ten (9.2%) of the neonates were part of a twin pregnancy, and 1.5% were born with a disability. Low birth weight was evident in 14.4%, while 4.4% were large- for-gestational age. Just over a third (33.6%) of neonates were exposed to HIV and most received their Bacillus Calmette–Guérin (BCG) immunisation (95.0%) and Oral Polio Vaccine (OPV0) (84.2%) at birth. According to the World Health Organization’s (WHO) Z-scores, 12.6% were classified as underweight, 18.9% as stunted and 14.5% as wasted at birth. The same percentage (2.7%) of neonates presented with micro- and macrocephaly at birth. Conclusions: The prevalence of caesarean section, twin pregnancies and congenital disabilities were high in the current study, while the prevalence of prematurity, HIV exposure, low birth weight and wasting was similar to findings of other studies. Prevalence of stunting 146 was lower than that reported by others. Screening of pregnant women c to identify those in need of specialised nutrition care could decrease the risk of adverse birth outcomes. Keywords: Birth outcomes, low birth weight, stunting, wasting, HIV exposed 5.2 INTRODUCTION Worldwide, tens of thousands of children are still affected by undernutrition (UNICEF, 2019). The goals set for South Africa as part of the National Plan of Action for Children in South Africa (2012–2017) aimed to reduce the neonatal mortality rate to 10 per 1000 live births by 2017 (Republic of South Africa, 2012). Recent statistics show that this goal has not been achieved. Neonatal deaths in South Africa were reported as 65 per 1000 live births in 1990, 57 per 1000 live births in 2000 and 40 per 1000 live births in 2018 (UNICEF, 2019). The Global Strategy for Women’s, Children’s and Adolescent Health (2016–2030) has set the target for reducing newborn mortality to at least as low as 12 per 1000 live births in every country (Every Woman Every Child, 2015). Although a decreasing trend has been observed for neonatal deaths from 1990 to 2018, the neonatal mortality rates in South Africa remain above the targets. One of the targets that form part of the 17 Sustainable Development Goals (SDGs) adopted by all United Nations (UN) Member States in 2015 (UN, 2015) reads as follows: “[b]y 2030, end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce neonatal mortality to at least as low as 12 per 1 000 live births and under-5 mortality to at least as low as 25 per 1 000 live births.” According to the 2020 UN report on the progress made towards achieving the SDGs, much progress has been made towards reducing child mortality over the past two decades, however, approximately 5.3 million children died before their fifth birthday in 2018, with almost half of these deaths occurring within the neonatal period (first 28 days of life). The Sub-Saharan African region continues to see the highest mortality rate in children under five years (UN, 2020:28). The first 1000 days of life is a period between conception and a child’s second birthday. This period is considered a crucial time for growth and development (Cusick & Georgief, 2017; Adu-Afarwuah et al., 2017:18) when the foundations of optimal health, growth, and neurodevelopment throughout the lifespan are laid. In developing countries, however, 147 poverty and malnutrition, often undermine this foundation, resulting in earlier mortality and significant morbidities (Cusick & Georgief, 2017). Adverse birth outcomes commonly occur in developing countries and may hold major health consequences for infants (Tsegaye & Kassa, 2018). Poor birth outcomes may also add to the economic burden faced by families, communities and health care systems in settings where resources are limited (Kassahun et al., 2019). Adverse birth outcomes not only hold consequences for both the mother and child in the short-term, but lifelong impairments in physical, neurological or educational areas are also evident (Tsegaye & Kassa, 2018). The major causes of impaired immunity among infants and children have been identified as low birth weight, undernutrition and human immunodeficiency virus (HIV) infection (Bourke et al., 2016). Currently, premature birth is the leading cause of neonatal mortality worldwide. Premature birth is also the leading cause of disability and significantly increases the risk of poor health in the short- as well as long-term. Approximately 15 million babies are born prematurely each year, with 60% occurring in South Asia and Sub-Saharan Africa (Kassahun et al., 2019). Between 50 000 and 100 000 premature deaths were recorded in South Africa for 2010 (Blencowe et al., 2013). Foetal growth restriction, evidenced by low birth weight, has also been associated with infant survival. While the causes of foetal growth restriction are multifactorial, foetal growth restriction may be an important contributor to both stunting and wasting in children (Black et al., 2013:427). Low birth weight is also a common risk factor for neonatal mortality and is often associated with subsequent child undernutrition (UNICEF, 2019). Low birth weight is also linked to short gaps between pregnancies (Bener et al., 2012), maternal hypertension (Li et al., 2016) and infection, especially Human Immunodeficiency Virus (HIV) (Gibango & Ntuli, 2018). The factors that impact foetal growth restriction are closely linked to lifestyle choices (Bourke et al., 2016). Undernutrition commonly presents as stunting and wasting amongst those children who do not receive adequate nutrition, particularly during the first 1000 days (UNICEF, 2019). Risk of adverse pregnancy outcomes also increases with maternal HIV infection during pregnancy, apart from also increasing the risk of transmission. Widespread implementation of the prevention of mother-to-child transmission (PMTCT) has led to a reduction in transmissions from mother to child, however, risks of adverse birth outcomes associated with 148 maternal HIV infection remain (Li et al., 2020). Maternal HIV infection has been associated with increased risk of stillbirth, neonatal death, low birth weight, premature birth and small- for-gestational-age (Ndirangu et al., 2012; Xiao et al., 2015; Arab et al., 2017; Gonzalez et al., 2017; Santosa et al., 2019). Maternal antiretroviral therapy (ART) may also increase the risk of adverse birth outcomes (Fowler et al., 2016:1272). Children below the age of five years are at risk of acute infectious diseases resulting in infant and child mortality. Control of these infectious diseases is important for the reduction of childhood morbidity and mortality (WHO, 2020). In this regard, vaccination can provide immunity against specific childhood infections (Janeway et al., 2001). To address the targets set as part of the Global Strategy for Women’s, Children’s and Adolescent Health (2016–2030) as well as the SDGs, careful assessment of the challenges within a given community is needed before strategies to address the problems can be planned and implemented. The current study was, therefore, to determine the birth outcomes of neonates born to mothers who received antenatal care at Pelonomi Hospital, Bloemfontein. 5.3 METHODS 5.3.1 Study setting, study design, population and sampling This study was undertaken at the antenatal clinic at Pelonomi Hospital in Bloemfontein, Free State. This clinic is a high-risk clinic providing care to a variety of obstetric and gynaecological conditions including pregnant women with known chronic medical conditions such as hypertension or diabetes; pregnant women who are diagnosed with diabetes or hypertension during pregnancy; mothers who had two previous caesarean sections; pregnant women who previously gave birth to a stillborn baby; pregnant women who experienced previous neonatal death of an unknown cause; pregnant women with advanced maternal age of > 35 years; women who are pregnant with two or more babies; pregnant women with previous premature delivery; pregnant women with a BMI between 40 kg/m2 and 48 kg/m2 as well as pregnant women with a total number of confirmed pregnancies (gravida) of six or more. This study forms part of a larger quantitative, cohort analytical study that aimed to determine the nutritional status of pregnant women attending the antenatal clinic at Pelonomi Hospital 149 from May 2018 to April 2019 and their offspring. A total of 682 pregnant women attending the antenatal clinic at Pelonomi Hospital were interviewed in the first phase of the main study. The current publication focuses on the follow-up birth information of the infants born to these mothers. Mothers who were expecting more than two babies were excluded. Information regarding the child’s birth obtained from the Road to Health Booklet included the method of delivery, gestational age, birth weight, birth length, birth head circumference, HIV exposure, immunisations received and presence of congenital disabilities. Premature birth was classified as any baby born at 28 weeks of gestation or later, but before 37 weeks, while extreme prematurity was classified as any baby born before 28 weeks of gestation. Babies born at 37 weeks or later were classified as term (WHO, 2016a). Birth weight, length and head circumference were interpreted using the WHO Z-scores (WHO, 2008) as indicated in Table 5.1. Table 5.1: Interpretation of World Health Organization Z-Scores (WHO, 2008) Z-score Growth indicators Length-for-age Weight-for-age Weight-for- Head- length circumference- for-age Above 3 SD Obese Macrocephaly Above 2 SD Overweight Above 1 SD Possible risk for overweight 0 (median) Below -1 SD Below -2 SD Stunted Underweight Wasted Below -3 SD Severely stunted Severely Severely wasted Microcephaly underweight Birth weight was also classified using the International Statistical Classification of Diseases and Related Health Problems of the WHO (2016a). “Low birth weight” thus refers to babies that weigh less than 2500 grams at birth, “very low birth weight” refers to babies weighing less than 1500 grams but greater or equal to 1000 grams at birth. “Extremely low birth weight” refers to babies weighing less than 1000 grams at birth. Heavy for gestational age refers to a baby with a birth weight of 4000 grams or more or greater than the 90th percentile 150 for gestational age. Appropriate for gestational age refers to a baby with a birth weight greater than 2500 grams and lighter than 4000 grams at birth (WHO, 2016a). 5.3.2 Study procedures This study was approved by the Health Sciences Research Ethics Committee of the Faculty of Health Sciences at the University of the Free State (UFS-HSD2018/0148/2905) as well as the Free State Department of Health. Pregnant women who were included in the cohort baseline study at the antenatal clinic at Pelonomi Hospital during the period May 2018 to April 2019 were asked to take their baby’s Road to Health Booklet to the dietitians’ offices at Pelonomi Hospital after the delivery where the dietitians took a photo of the specified pages of the booklet and sent it to the researcher. Women could also send photographs of the specific pages of the booklet to the researcher via Whatsapp or multimedia messaging service (MMS). The researcher monitored the due dates of all the mothers and sent out a short message service (SMS) at the end of each month to each mother who had an estimated due date within that month. After the due dates of all the mothers had passed, the researcher sent out an additional minimum of six rounds of SMS reminders to all those mothers of whom the Road to Health Booklets were outstanding at the time. The final SMS was sent on 29 May 2020. Those women who took the Road to Health Booklet to the dietitians at Pelonomi Hospital were given R100.00 for transport, while those who sent photos of the booklet received R20.00 airtime on the number used to send the photos. 5.3.3 Statistical analysis Two researchers (doctoral students) entered all the data onto an Excel spreadsheet after which data checking and statistical analysis were performed by the Department of Biostatistics, Faculty of Health Sciences, University of the Free State. Data were analysed using SAS/STAT software, Version 9.4 of the SAS system for Windows, Copyright © 2010 SAS Institute Inc. Descriptive statistics, namely frequencies and percentages were determined for categorical data and medians and percentiles for numeric data. 151 5.4 RESULTS Birth information was received for 347 neonates. The median gestational age of the neonates in this study was 39.0 weeks, while the median birth weight was 3000.0 grams (Table 5.2). Table 5.2: Summary of gestational age and birth anthropometry Variable n Median P25 P75 Minimum Maximum Gestational age (weeks) 341 39.0 37.0 39.0 24.0 42.0 Birth weight (g) 335 3000.0 2700.0 3400.0 900.0 4600.0 Birth length (cm) 333 49.0 47.0 51.0 34.0 57.0 Birth head circumference (cm) 319 34.0 33.0 35.0 23.0 42.0 According to weight-for-age, 12.6% of neonates were either underweight (≥ -3 SD and < -2 SD) or severely underweight (< -3 SD), while 22.7% could be considered at risk of being underweight (≥ -2 SD and < -1 SD) (Table 5.3). Almost one in five (18.9%) neonates were either stunted (≥ -3 SD and < -2 SD) or severely stunted (< -3 SD) at birth. According to the weight- for-length, 14.5% of neonates were either wasted (≥ -3 SD and < -2 SD) or severely wasted (< -3 SD). Although very few were classified as overweight or obese, 9.1% of neonates were at risk of developing overweight (weight-for-length > 1 SD but ≤ 2 SD). Nine neonates presented with microcephaly, and another nine presented with macrocephaly. 152 Table 5.3: Results for birth weight, birth length and birth head circumference Variable n % Birth weight (N=341): Extremely low birth weight 2 0.6 Very low birth weight 0 0.0 Low birth weight 47 13.8 Normal weight 277 81.2 Heavy for gestational age 15 4.4 Weight-for-age (N=340) < -3 SD 15 4.4 ≥ -3 SD and < -2 SD 28 8.2 ≥ -2 SD and < -1 SD 77 22.7 ≥ -1 SD and ≤ 1 SD 196 57.7 > 1 SD and ≤ 2 SD 22 6.5 > 2 SD 2 0.6 > 3 SD 0 0.0 Length-for-age (N=334) < -3 SD 30 9.0 ≥ -3 SD and < -2 SD 33 9.9 ≥ -2 SD and < -1 SD 35 10.5 ≥ -1 SD and ≤ 1 SD 185 55.4 > 1 SD and ≤ 2 SD 31 9.3 > 2 SD and ≤ 3 SD 17 5.1 > 3 SD 3 0.9 Weight-for-length (N=298) < -3 SD 16 5.4 ≥ -3 SD and < -2 SD 27 9.1 ≥ -2 SD and < -1 SD 68 22.8 ≥ -1 SD and ≤ 1 SD 152 51.0 > 1 SD and ≤ 2 SD 27 9.1 > 2 SD and ≤ 3 SD 5 1.7 > 3 SD 3 1.0 Head circumference-for-age (N=332) < -3 SD 9 2.7 ≥ -3 SD and < -2 SD 10 3.0 ≥ -2 SD and < -1 SD 59 17.8 ≥ -1 SD and ≤ 1 SD 184 55.4 > 1 SD and ≤ 2 SD 49 14.8 > 2 SD and ≤ 3 SD 12 3.6 > 3 SD 9 2.7 Table 5.4 provides an overview of the birth information investigated in the current study. In this sample, most neonates were born via caesarean section (61.6%) with 16.0% born before 37 weeks of gestation. One neonate died shortly after birth due to lung problems. Only the 153 death certificate was available and other birth information was not available for this neonate. Another mother informed the researcher that her baby was stillborn at 8 months. A third of the neonates were exposed to HIV. Most neonates received their Bacillus Calmette– Guérin (BCG) and Oral Polio Vaccine (OPV0). Five neonates were born with some form of disability, however, details regarding the disability were only available for two of these neonates and were indicated as club foot on the Road to Health Booklet. Table 5.4: Birth information obtained from Road to Health Booklets Birth outcome n % Mode of delivery (N=315): Vaginal delivery 121 38.4 Caesarean section 194 61.6 Gestational age (N=319): Premature 51 16.0 Term 268 84.0 Multiple births (N=347): Singletons 315 90.8 Twins 32 9.2 Born with disability (N=323): Yes 5 1.5 No 318 98.5 HIV exposed (N=333) Yes 112 33.6 No 221 66.4 BCG received (N=341) Yes 324 95.0 No 17 5.0 OPV0 received (N=341) Yes 287 84.2 No 54 15.8 5.5 DISCUSSION This study aimed to evaluate the birth information of newborn infants whose mothers attended the high-risk antenatal clinic at Pelonomi Hospital. Growth is an important indicator of nutritional status and health, even before birth (WHO, 2010). The medians for birth weight (3 000 grams), birth length (49.0 cm) and birth head circumference (34.0 cm) were all within normal ranges. Tshotetsi et al. (2019) conducted a 154 case-control study on 1 073 randomly selected mothers who delivered babies in four hospitals in the Tshwane district, South Africa. Slight differences between weight and head circumference of the neonates in the current study and those included in the study by Tshotetsi et al. (2019) were observed. In that study, a mean birth weight of 2675.5 grams, birth length of 48.9 cm and birth head circumference of 33.2 cm was noted (Tshotetsi et al., 2019). Although the mean birth weight in the Tshwane study was still considered normal (> 3000 grams), the difference in findings between the two studies may be ascribed to more specialised attention in the current study as the high-risk clinic was a referral centre for high- risk pregnancies. Low birth weight occurs as a result of intrauterine growth restriction, prematurity or both (WHO, 2011). Low birth weight is an important marker of maternal as well as foetal health and is a predictor of mortality, stunting and adult-onset chronic conditions. Low birth weight is also associated with short- and long-term complications, including prematurity and its associated morbidities (WHO, 2011; WHO, 2014; Watkins et al., 2016). Associations between low birth weight and increased risk for various chronic diseases including insulin resistance, ischaemic heart disease, metabolic syndrome, type two diabetes, cardiovascular disease, hypertension, dyslipidaemia, obesity, breast and testicular cancer, amongst others, are well documented (Negrato & Gomes, 2013; Edelstein, 2015; Baird et al., 2017). Global estimates show that low birth weight occurs in 15–20% of all births every year (WHO, 2014). In the current study, 14.4% of neonates had a birth weight below 2.5 kg which is similar to the findings of Harrison et al. (2017), who conducted a study in Guatemala, India, Kenya, Pakistan, Zambia and the Democratic Republic of Congo, as well as the UNICEF-WHO low birth weight estimates in 2015 which indicated that 14.2% of babies in South Africa were born with a low birth weight (UNICEF & WHO, 2019). When considering the WHO Z-score results, 8.2% of the neonates had a low birth weight which concurs with the findings of Zar et al. (2019) where 9.0% (99/1127) of newborn infants born to women at two public health clinics in a poor peri-urban area of South Africa were below the -2 SD. In Bangladesh, also a developing country, Khan et al. (2017) used data sets from the Bangladesh Demographic and Health Survey conducted between 2011 and 2014 to determine the effect of maternal undernutrition and excessive body weight on a range of maternal and child health outcomes. Of the 6548 women included in their study, 20.0% reported giving birth to a baby with a low 155 birth weight (Khan et al., 2017). Although a birth weight that is greater or equal to the -2 SD but below the -1 SD is still considered normal, neonates within this range should be carefully monitored as they may be at risk to develop underweight in future. In the current study, approximately one out of five neonates (22.7%) fell within this range. Poor intrauterine growth has several causal factors, of which smoking, pre-eclampsia and HIV are important factors. Stunting may occur in a cycle as women who were stunted during the childhood years are prone to have stunted offspring, which may create an intergenerational cycle of poverty and reduced human capital that is challenging to break (Prendergast & Humphrey, 2014). In the current study, 9.9% of neonates were stunted at birth, while 9.0% were severely stunted at birth. Stunting reflects the cumulative effects of undernutrition and infections before and since birth (WHO, 2010). Stunting remains prevalent in children from low- and middle-income countries and may result from maternal undernutrition and infectious diseases in pregnancy (Black & Heidkamp, 2018:105). Of the 6548 women included in the study by Khan et al. (2017), 36.5% reported giving birth to a child with stunting which is higher than the findings of the current study. This difference in the prevalence of stunting may be attributed to the differences in populations included, with the study by Khan et al. (2017) using population-based data and the current study only looking at women attending a single high-risk antenatal clinic. The South African Demographic and Health Survey (SADHS) of 2016 emphasises that stunting remains a national concern. According to the SADHS, of children aged six months and younger, 32.3% were stunted and 18.3% severely stunted (NDoH, 2019). Prevalence estimates of stunting and severe stunting amongst children under five years in Southern Africa for 2018 are 32.9% and 29.3% respectively, while the prevalence of wasting (3.5%) and severe wasting (0.9%) is lower (UNICEF et al., 2019). The SADHS of 2016 reported that 3.3% of children aged six months and younger were wasted and 0.6% severely wasted in 2016 (NDoH, 2019). Overall, 14.5% of the neonates in the current study suffered from wasting, of which 5.4% suffered from severe wasting at birth. Similarly, a prevalence of 14.4% was found among the women included in the Bangladesh study (Khan et al., 2017). Wasting is a reflection of acute undernutrition and usually develops as a result of inadequate food intake (Harding et al., 2018) or a high incidence of infectious disease, particularly 156 diarrhoea. Wasting may impair immune functioning, which may, in turn, increase the risk of and susceptibility to severe infectious diseases (WHO, 2010) as well as death (Harding et al., 2018). Approximately one in five neonates (22.8%) in the current study could also be considered at risk for wasting. Although only 1.7% (5/298) and 1.0% (3/298) of the neonates in the current study were respectively overweight and obese at birth, considering the weight-for-length according to the WHO Z-scores, 9.1% were at risk of becoming overweight. In 2016, 28.9% of South African children aged six months and younger had a weight-for-length above the +2 SD (NDoH, 2019). Slightly more neonates (15/319) were classified as heavy-for-gestational-age when using the cut-off point of 4000 grams. As with undernutrition, overweight and obesity are also associated with short- and long-term health consequences including an increased risk of developing cardiovascular diseases, diabetes, musculoskeletal disorders and cancers (WHO, 2010; Black et al., 2013; Chiavaroli et al., 2016). Head circumference may provide an indication of past nutritional status and development of the brain in children younger than five years (Tigga et al., 2016:17), while premature birth is a major contributor to neurodevelopmental morbidity (Miller & Georgieff, 2017). Serial head circumference measurements during early childhood may indicate brain volume, which may help to plot the trajectory of brain growth and cognitive functionality in later life (Sindhu et al., 2019). Microcephaly is often associated with intellectual disability and neurological abnormalities, however, the definition of microcephaly at birth is not uniform (Aagaard et al., 2020; Leibovitz et al., 2016). Macrocephaly is a nonspecific clinical finding without implications about the underlying cause (Orru et al., 2018:848). The same number of neonates presented with microcephaly (9/332, 2.7%) and macrocephaly (9/332, 2.7%). Herber et al. (2019:600) conducted a study in Rio Grande do Sul (an area where no Zika Virus was detected), Brazil, to identify the causes of congenital microcephaly and found that the prevalence of microcephaly was 3.8 for 10 000 live births, which is lower than the findings of the current study. A study examining data from electronic medical records of term singleton births between January 2010 and December 2012 in Jerusalem found that 5.7% of the babies in their study had a large head circumference at birth (Lipschuetz et al., 2015), which is slightly higher than the findings of the current study. Amare et al. (2015:1568) measured head circumference among babies aged zero to 24 months in health centres in Ethiopia and found 157 that of the 1973 babies included in their study, 2.1% had microcephaly while 7.1% had macrocephaly. Differences between the findings for the current study and the Ethiopian study may be because the Ethiopian study measured head circumference in children zero to 24 months of age. Table 5.5 provides a comparison of the birth outcomes of the current study to other similar studies. Table 5.5: Comparison of birth outcomes to other studies Birth outcome Current study Zar et al. (2019) Solanki et al. Harrison et al. (2019) (2017) Setting Pelonomi Two public South African Guatemala, Hospital, South health clinics in a private health India, Kenya, Africa poor peri-urban insurance Pakistan, Zambia area of South scheme and the Africa members Democratic Republic of Congo Mode of delivery: N=315 N=1133 N=6542 N=384 461 Vaginal delivery 38.4% 79.8% 26.4% 87.7% Caesarean section 61.6% 20.2% 73.6% 12.3% Gestational age: N=319 N=1137 Not determined N=372 698 Premature 16.0% 16.8% 12.9% Term 84.0% 83.2% 87.1% Multiple births: N=347 N=1132 Not determined Not determined Singletons 90.8% 99.6% Twins 9.2% 0.4% Born with disability: N=323 Not determined Not determined Not determined Yes 1.5% No 98.5% Birth weight: N=341 Not determined Not determined N=387 500 Extremely low birth weight 0.6% 0.5% Very low birth weight 0.0% 1.1% Low birth weight 13.8% 12.4% Normal weight 81.2% 86.0% Heavy for gestational age 4.4% Not determined The reported national rate of caesarean sections in South Africa during the 2015/2016 period was 26.2% (Massyn et al., 2016). Zar et al. (2019) found that the respective percentages of women giving birth via vaginal delivery and caesarean section were 79.8% and 20.2%. Similarly, a prospective population-based study between 2010 and 2015 among 384 461 158 pregnant women and their offspring in Guatemala, India, Kenya, Pakistan, Zambia and the Democratic Republic of Congo reported that 12.3% gave birth by caesarean section and 87.7% by vaginal delivery (Harrison et al., 2017:410). Caesarean section deliveries were much lower in both these studies compared to the current study. These differences may be attributed to the fact that the antenatal clinic at Pelonomi Hospital is considered a high-risk clinic to which patients from smaller clinics in both the urban and rural areas in and around Bloemfontein are referred. It is also important to highlight that the number of neonates reported on in the current study is much lower than that of the other two studies. While the current study reports on deliveries in a public hospital, findings of the current study are similar to that of a study by Solanki et al. (2019) among private medical scheme members in South Africa. Approximately one out of six children were born premature. This prevalence of prematurity was similar in the current study and studies by Zar et al. (2019) and Harrison et al. (2017:410). Complications associated with premature delivery remain the leading cause of death amongst neonates and part of the leading causes of under-five mortality. The epidemiological understanding of this syndrome is limited by a lack of accurate nationally representative estimates of premature delivery (Ramokolo et al., 2019). A slightly higher prevalence of twin pregnancies was also evident in the current study compared to the findings of Zar et al. (2019) which may be ascribed to the fact that twin pregnancies are considered high-risk pregnancies and would, therefore, be more common in a clinic of this nature. The risk of obstetric complications and perinatal mortality and morbidity increases in twin pregnancies (Singh & Trivedi, 2017:2272). The Road to Health Booklet of five neonates indicated that a disability was present at birth, two of which were specified as club foot. According to Ansar et al. (2018), clubfoot is a common congenital disability that affects approximately one in every 1000 live births, with most being born in low- to middle-income countries. The pooled estimate for club foot in Africa is estimated at 1.11 per 1000 live births (Smythe et al., 2017:269). Considering this, the prevalence of club foot seems to be higher in the current study. Since all high-risk pregnancies are referred to the antenatal clinic at Pelonomi Hospital, more cases of disabilities may be reported in this setting. HIV exposure during pregnancy has been associated with numerous complications and adverse birth outcomes (González et al., 2017; Li et al., 2020). A third (33.6%) of the neonates 159 in the current study were HIV exposed. Diale et al. (2016:98) determined the HIV status of 2368 pregnant women attending the antenatal clinic of 1 Military Hospital in Tshwane, South Africa. In their study, 20.5% of the women tested positive for HIV, which is slightly lower than in current study (Diale et al., 2016:98). Findings from a cross-sectional study undertaken in Vulindlela, a rural community in Kwa-Zulu Natal, amongst pregnant women at any one of the seven primary health care clinics in the area between 2001 and 2013 were, however, similar to the findings of the current study (Kharsany et al., 2015:289). Kharsany et al. (2015:289) found that HIV prevalence increased from 35.3% in the period 2001–2003, to 39.0% in the period 2004–2008, to 39.3% in the period 2009–2013 in Vulindlela. Considering these differences, it is important to note that the study populations and the number of participants for these studies differ from the population in the current study. Maternal HIV infection amongst those who do not have access to antiretroviral (ARV) medications has been associated with adverse birth outcomes, including premature delivery, low birth weight, small-for-gestational-age as well as stillbirth. Although ARVs are provided free of charge through public health services, access to these medications is often limited and delayed (González et al., 2017). Studies have also found that children born to HIV-infected mothers have an increased risk of mortality, regardless of their own HIV status (Newell et al., 2004; Kuhn et al., 2005; Naniche et al., 2009; González et al., 2017). Immunizations play an important role in preventing childhood diseases. It is recommended that HIV-exposed infants and children receive all vaccines under the Expanded Programme for Immunizations (EPI) which should be administered according to national immunization schedules (WHO, 2011). Modifications to EPI schedules may, however, be required for those infants and children who are infected with HIV, since these children have a greater risk of developing infections that are preventable by vaccines (Menson et al., 2012:333). The EPI in South Africa recommends the provision of OPV0 and BCG vaccines at birth (SA DoH, 2010). In the current study, most infants received both OPV0 and BCG shortly after birth. The current recommendations of the EPI-SA indicate that BCG vaccine should not be given to children who are sick with Acquired Immunodeficiency Syndrome (AIDS) or if a child is HIV exposed and there is reason to believe that the child may be infected with HIV (SA DoH, 2010). BCG is administered shortly after birth to protect against early childhood tuberculosis (TB) in most settings. However, this occurs before definitive HIV testing is done (Gasper et al., 2017). 160 The WHO revised their policy on providing BCG to infants infected with HIV and made HIV infection a full contraindication to BCG vaccination. The decision is based on data from retrospective studies from South Africa, amongst others, that showed that HIV infected children who are vaccinated with BCG, had a much higher risk of disseminated BCG disease (a rare life-threatening complication of BCG vaccination) (WHO, 2007). Newer research also suggests that BCG vaccination may induce immune changes in HIV-exposed infants (Gasper et al., 2017). OPV0 has been associated with lower infant mortality and morbidity (Lund et al., 2015:1505). Even though immunological abnormalities have been described in HIV-exposed uninfected infants, it seems that immune responses to OPV0 are similar amongst HIV-exposed uninfected infants compared to their HIV-unexposed counterparts (Church et al., 2017:2544). Although limited, available evidence also suggests that OPV0 is safe to use in HIV-infected individuals (WHO, 2016b). We acknowledge certain limitations, including that not all pregnant women included in the baseline of the cohort study provided the information from the Road to Health Booklet after their babies were born (48.5% response rate). The researcher tried to increase the number of Road to Health Booklets received by sending various rounds of reminders to participants via SMS. Weight and length measurements of the newborns were taken by nursing staff and not by the researchers themselves. The dietitians at Pelonomi Hospital provide regular training sessions of all the staff working in the maternity as well as paediatric wards, where the majority of the mothers attending the antenatal clinic are expected to deliver their babies. The inclusion of twin pregnancies may also be viewed as a limitation since it increases the outcome of low birth weight, prematurity and caesarean section deliveries. 5.6 CONCLUSION AND RECOMMENDATIONS The current study investigated the birth outcomes of neonates born to mothers attending the antenatal clinic at a regional hospital in an urban setting and therefore included a sample of high-risk pregnancies. Findings of the current study are similar to that of other studies in similar settings for prevalence of prematurity, HIV exposure, low birth weight and wasting, while the prevalence of caesarean section, twin pregnancies and congenital disabilities were higher in the current study compared to others. Prevalence of stunting was lower than that 161 reported by others. Any one of the factors investigated in this study may be associated with negative short- as well as long-term risks. These factors may, however, also be intricately linked, with the one influencing the other. Differences between the findings of the current study and that of other studies may be ascribed to the fact that the current study was conducted in a high-risk clinic. In order to reduce child mortality, in particular newborn mortality, it is recommended that PMTCT should be provided where required; newborns should be resuscitated when needed; standard protocols should be consulted when caring for newborns that are small or ill; Kangaroo Mother Care (KMC) should be promoted for stable infants with low birth weight; and post-natal visits should occur within six days and should include newborn care and support for breastfeeding mothers (Republic of South Africa, 2012). Programmes that aim to advocate for appropriate care and attendance of regular follow-ups during pregnancy should be implemented in health care facilities where pregnant mothers receive antenatal services. 5.7 ACKNOWLEDGEMENTS The authors wish to acknowledge the staff at the antenatal clinic and the dietitians working at Pelonomi Hospital for their assistance as well as the pregnant mothers for their willingness to participate. The authors have no conflict of interest to declare. This project was funded by the researchers themselves. 5.8 REFERENCES Aagaard, K., Matthiesen, N.B., Bach, C.C., Larsen, R.Y. & Henriksen, T.B. 2020. Head circumference at birth and intellectual disability: A nationwide cohort study. Pediatric Research, 87:595 ̶601, October. Adu-Afarwuah, S., Lartey, A. & Dewey, K.G. 2017. Meeting nutritional needs in the first 1000 days: a place for small-quantity lipid-based nutrient supplements. 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PLoS ONE, November 21. https://doi.org/10.1371/journal.pone.0222399 [17 August 2020]. 170 6 CHAPTER 6 - ASSOCIATIONS BETWEEN INDICATORS OF SOCIO-DEMOGRAPHY AND BIRTH OUTCOMES OF PREGNANT WOMEN ATTENDING THE ANTENATAL CLINIC AT PELONOMI HOSPITAL, BLOEMFONTEIN 6.1 ABSTRACT Background: Low socioeconomic status has been associated with numerous poor birth outcomes, including stunting, wasting and premature birth. In countries with limited resources, such as those in sub-Saharan Africa, poor birth outcomes significantly contribute to morbidity and mortality. This study aimed to determine the associations between socio- demographic variables among pregnant women attending the antenatal clinic at Pelonomi Hospital, Bloemfontein and birth outcomes in their offspring. Methods: This study formed part of a quantitative, cohort analytical study to develop a nutrition screening tool for pregnant women attending the antenatal clinic at Pelonomi Hospital, Bloemfontein, Free State, which included 682 women. Post-delivery, 331 of these mothers provided the researchers with the Road to Health Booklets for 347 babies, from which the birth data were recorded. Associations between socio-demographic variables and the following individual birth outcomes were investigated: method of delivery, prematurity, birth-length-for-age and birth weight-for-length. Logistic regression with backward selection (p<0.05) was used to select independent socio-demography factors that were significantly associated with overall birth outcome (defined as premature delivery or low length-for-age or weight-for-length based on Z-score at birth). Variables with a p-value of < 0.15 on univariate analysis were considered for inclusion in the model. Results: Maternal age was significantly associated with method of delivery (p=0.0003) as well as weight-for-length at birth (p=0.0052). Significantly more babies from mothers who lived in brick houses or a flat (p=0.0441), and from household that had their own flush toilet inside or outside the house (p=0.0202), were born by caesarean section than by normal delivery. Compared to babies born via normal delivery, significantly more babies who were born by means of a caesarean section formed part of a household that had their own flush toilet inside or outside the house (p=0.0202). Babies born prematurely, had significantly more mothers with lower than grade 11–12 education (p=0.0265), and mothers whose partners were employed only part-time (p=0.0226) compared to babies who were born term. A greater 171 percentage of babies who were born term formed part of a household where someone in the household obtained an income from wages and salaries from formal employment (p=0.0020). Babies who were born with a length-for-age greater than or equal to the -2 standard deviation (SD) but below the -1 SD (considered at risk of developing stunting) were more likely to have a mother who was unemployed (p=0.0442). Overall birth outcome could be determined for 307 women, with 37.1% experiencing overall poor birth outcome. Logistic regression analysis found that the odds of experiencing overall poor birth outcome were higher for women with only primary school (OR: 3.26) or grade 8–10 (OR: 5.67) or grade 10–12 (OR: 4.20) level education, compared to women with tertiary education. The odds ratio of women experiencing overall poor birth outcome was higher for women with part-time employment (OR: 2.55) compared to women with full-time or self-employment. The odds ratio of experiencing overall poor birth outcome was, however, lower for women who were unemployed (OR: 0.75) or housewives by choice (OR: 0.54), compared to women who were full-time or self-employed. Conclusion: Various sociodemographic variables investigated in the current study were significantly associated with delivery method, prematurity, length-for-age and weight-for- length at birth. These associations may be a sign that improved wealth may influence the health of the pregnant mother and consequently the health of her offspring and may result in improved birth outcomes. Early screening of women of childbearing age may prove valuable in identifying women who may be at increased risk for poor birth outcomes. Keywords: pregnant, delivery method, premature, stunting, wasting, poor birth outcomes. 6.2 INTRODUCTION Social determinants of health refer to conditions in which individuals are born, grow, work, live and age that affect health outcomes and include, amongst others, income, education, employment status, housing and basic amenities (WHO, 2020). In South Africa, the prevalence of stunting seems to decrease with an increase in maternal education and household wealth (NDoH et al., 2019), which may provide an indication of socioeconomic inequalities (UNICEF, 2020). Poverty is a known environmental cause of foetal and postnatal growth impairment on a population level. High levels of stunting and wasting may be attributed to lower socioeconomic status among women in poorer communities (Dhaded et al., 2020). 172 Low socioeconomic status has also been linked to a higher risk of premature births. The exact prevalence of premature births in South Africa is unknown (Ramokolo et al., 2019), but according to modelled estimates, eight out of every 1000 live births were premature in 2010 (Blencowe et al., 2012), and this figure increased to 12.4 in 2014 (Chawanpaiboon et al., 2019). Poor birth outcomes, along with infant undernutrition, significantly contribute to morbidity and mortality in sub-Saharan Africa. Impairments in growth and development often start in the womb and may persist during the years thereafter (Ngandu et al., 2020:317). The effects of growth impairment during the foetal period directs attention to the environment in which the pregnant mother functions (Dhaded et al., 2020). This study, therefore, aimed to determine the associations between socio-demographic variables among pregnant women attending the antenatal clinic at Pelonomi Hospital, Bloemfontein and birth outcomes in their offspring. 6.3 MATERIALS AND METHODS 6.3.1 Study design, setting and participants This study formed part of a quantitative, cohort analytical study to develop a nutrition screening tool for pregnant women, attending the antenatal clinic at Pelonomi Hospital, Bloemfontein, Free State. All pregnant women attending the antenatal clinic who were 18 years and older; at 12 weeks gestation and longer (which is the time that most pregnant women present at the clinic); who could speak English and/or Afrikaans and/or Sesotho and gave informed consent were included in the first phase of this study. Women who were pregnant with more than two babies were excluded. All mothers who participated in the first phase were invited to provide the information on their babies’ Road to Health Booklets, post-delivery; 331 mothers complied and were included with their babies (N=347) in the second phase. The mothers were asked to deliver the Road to Health Booklets to the dietitians’ offices at Pelonomi, or, alternatively, to send a photo of the designated pages via SMS to the researcher (an amendment to the protocol was requested from the Health Sciences Ethics Committee in March 2020 to allow the second option). The researcher sent reminders via SMS messages 173 close to and for six (monthly) rounds after the expected delivery date of each participant. Those who delivered the booklets received R100 for travel expenses and those who used the SMS option received R20 data on the cell phone number that they used to send the message. 6.3.2 Ethical considerations This study was approved by the Health Sciences Research Ethics Committee, Faculty of Health Sciences, University of the Free State (UFS-HSD2018/0148/2905) and the Free State Department of Health. During the recruitment phase, women were informed of the study after which informed consent was obtained. All information was kept strictly confidential. 6.3.3 Outcomes measures The primary outcome measures for this study were delivery method, prematurity, length-for- age and weight-for-length at birth, which were all obtained from the neonate’s Road to Health Booklets. Premature birth refers to a gestational age of 28 weeks of gestation or later, but before 37 weeks, while extreme prematurity refers to a gestational age before 28 weeks (WHO, 2016). Only one neonate was classified as “extremely premature” and was therefore grouped with the “premature” group when performing analysis. Anthropometric data at birth were interpreted using the WHO Z-scores (WHO, 2008) indicated in table 6.1. . 174 Table 6.1: Interpretation of World Health Organization Z-scores (WHO, 2008:14) Z-score Growth indicators Height-for-age Weight-for-height Above 3 SD Obese Above 2 SD Overweight Above 1 SD Possible risk for overweight 0 (median) Below -1 SD Below -2 SD Stunted Wasted Below -3 SD Severely stunted Severely wasted Women with either premature delivery (<37 weeks) or who had a baby with birth length-for- age below the -2 SD or birth weight-for-length below the -2 SD, were classified as having experienced overall poor birth outcome. Those women who delivered a baby full-term (37+ weeks) with a birth length-for-age and a birth weight-for-length above or equal to the -2 SD, were classified as having experienced overall good birth outcome. Since it was not possible to determine whether the method of delivery was spontaneous or planned, it was not included in the set of variables used to determine overall birth outcome. In the case where a mother delivered twins of which at least one had a poor outcome, the mother was considered to have a poor outcome. 6.3.4 Exposure measurements Questionnaires were used to obtain socio-demographic and household information during a structured interview in the first phase of the study and the data have been described in detail in Jordaan et al. (2020). 6.3.5 Statistical analysis The researcher was responsible for entering all the data onto an Excel spreadsheet after which data checking and statistical analysis were performed by the Department of Biostatistics, Faculty of Health Sciences, University of the Free State. Data were analysed using SAS/STAT software, Version 9.4 of the SAS system for Windows, Copyright © 2010 SAS Institute Inc. 175 Descriptive statistics, including frequencies and percentages (for categorical data) and medians and percentiles (for numerical data), were calculated. Differences between groups were assessed by chi-squared tests (for categorical variables) or Fisher’s exact test (for categorical variables with sparse data) and Kruskall-Wallis tests (for numerical variables). Analysis of associations for various individual birth outcomes was done using babies as units of analysis, whereas analysis of overall birth outcome considered mothers as the units of analysis. Logistic regression with backward selection (p<0.05) was used to select significant independent factors associated with overall birth outcome. Variables with a p-value of < 0.15 on univariate analysis were considered for inclusion in the model. If a variable with multiple missing values (such as information regarding partner) was found not to be significant in the model, the model was fitted again, excluding that variable. 6.4 RESULTS Participant characteristics and birth outcomes related to delivery A total of 682 pregnant women attending the antenatal clinic at Pelonomi Hospital, Bloemfontein, Free State were included in the first phase of this study. Of these, 331 women provided the required birth information of their baby for phase two of the study (48.5%). Overall, 331 women and 347 babies were included in the second phase of this study. Associations between socio-demographic variables and delivery method and prematurity are indicated in Table 6.2. A significant association was found between maternal age and method of delivery (p=0.0003). A significantly higher percentage of babies born via caesarean section had mothers in the 18–35 years age group compared to babies born via normal delivery. Most mothers in both the normal vaginal delivery (95.8%) and caesarean section delivery (95.3%) groups, as well as the premature (95.9%) and term (95.1%) groups, were married or in a relationship. A significantly higher percentage of babies who were born via caesarean section had mothers who lived in brick houses or a flat (p=0.0441). Significantly more babies who were born term were part of households that had their own tap inside the house (p=0.0169). With regard to toilet facilities, significantly more babies who were born by means of a caesarean section formed part of a household that had their own flush toilet inside or outside the house (p=0.0202) compared to babies born via normal 176 delivery. Almost two-thirds of participants in both the premature (64.7%) and term (66.4%) delivery groups had their own flush toilet inside or outside the house. Significantly more babies who were born prematurely came from households who did not have a stove (p=0.0066), with similar trends observed regarding households who did not own a microwave (p=0.0467) or kettle (p=0.0440). The mother's highest level of education was significantly associated with prematurity (p=0.0265), while their partners’ highest level of education was significantly associated with method of delivery (p=0.0239). Compared to preterm infants, significantly more term babies had mothers with grade 11–12 as their highest level of education. Babies born via normal delivery had a greater percentage of mothers with a partner who had grade 8-10 as highest level of education. No significant association was observed for participant employment status, while a significantly higher percentage of premature babies had a mother whose partner was employed part-time (p=0.0226). Obtaining an income from wages and salaries from formal employment was also significantly associated with prematurity (p=0.0020). Significantly more babies who experienced a term delivery had someone in the household who obtained an income from wages and salaries from formal employment than babies that were born prematurely. 177 Table 6.2: Associations between socio-demographic variables, delivery method and prematurity Variable Normal vaginal Caesarean section p-value# Premature Term p-value# delivery n % n % n % n % Age (n=121) (n=194) (n=52) (n=268) 18-35 years 72 59.5 152 78.4 43 82.4 188 70.2 36 years and older 49 40.5 42 21.7 0.0003* 9 17.6 80 29.8 0.0749 Pregnancy stage (n=121) (n=194) (n=51) (n=268) Second trimester 50 41.3 64 33.0 27 47.1 89 33.2 Third trimester 71 58.7 130 67.0 0.1344 24 52.9 179 66.8 0.0580 Marital status (n=120) (n=191) (n=49) (n=266) Married or in a relationship 115 95.8 182 95.3 47 95.9 253 95.1 Not in a relationship, divorced, separated or widowed 5 4.2 9 4.7 0.8214 2 4.1 13 4.9 0.2878 Household density ratio (n=121) (n=194) (n=51) (n=268) Overcrowded 60 49.6 104 53.6 29 56.9 136 50.8 Not overcrowded 61 50.4 90 46.4 0.4871 22 43.1 132 49.3 0.4230 Type of housing (n=121) (n=194) (n=51) (n=268) Brick house or flat 89 73.6 161 83.0 40 78.4 210 78.4 Shack or other 32 26.5 33 17.0 0.0441* 11 21.6 58 21.6 0.9907 Access to electricity (n=121) (n=194) (n=51) (n=268) Yes 109 90.1 183 94.3 46 90.2 248 92.5 No 12 9.9 11 5.7 0.1587 5 9.8 20 7.5 0.1766 Access to water (n=121) (n=194) (n=51) (n=268) Indoor tap 57 47.1 111 57.2 19 37.3 149 55.6 Own tap outside the house 40 33.1 51 26.3 0.2147 17 33.3 79 29.5 0.0169* Share a tap with other households 24 19.8 32 16.5 15 29.4 40 14.9 Toilet facilities (n=121) (n=194) (n=51) (n=268) Own flush toilet inside or outside the house 70 57.9 137 70.6 33 64.7 178 66.4 0.0202* 0.8128 Share outside toilet, use bucket system or own outside pit toilet 51 42.2 57 29.4 18 35.3 90 33.6 Stove in the house (n=121) (n=194) (n=51) (n=268) Yes 118 97.5 188 96.9 46 90.2 264 98.5 0.7506 0.0066* No 3 2.5 6 3.1 5 9.8 4 1.5 178 Variable Normal vaginal Caesarean section p-value# Premature Term p-value# delivery n % n % n % n % Refrigerator in the house (n=121) (n=193) (n=50) (n=268) Yes 102 84.3 174 90.2 43 86.0 235 87.7 0.1214 0.7413 No 19 15.7 19 9.8 7 14.0 33 12.3 Freezer in the house (n=121) (n=193) (n=50) (n=268) Yes 90 74.4 152 78.8 37 74.0 207 77.2 0.3693 0.6188 No 31 25.6 41 21.2 13 26.0 61 22.8 Microwave in the house (n=121) (n=193) (n=50) (n=268) Yes 93 76.9 147 76.2 32 64.0 207 77.2 0.8879 0.0467* No 28 23.1 46 23.8 18 36.0 61 22.8 Kettle in the house (n=121) (n=193) (n=50) (n=268) Yes 108 89.3 182 94.3 42 84.0 250 93.3 0.1016 0.0440* No 13 10.7 11 5.7 8 16.0 18 6.7 Radio in the house (n=121) (n=193) (n=50) (n=268) Yes 96 79.3 160 82.9 36 72.0 223 832 0.4285 0.0612 No 25 20.7 33 17.1 14 28.0 45 16.8 Television in the house (n=121) (n=193) (n=50) (n=268) Yes 105 86.8 178 92.2 41 82.1 242 90.3 0.1150 0.0852 No 16 13.2 15 7.8 9 18.0 26 9.7 Motor vehicle in the household (n=121) (n=193) (n=50) (n=268) Yes 36 29.8 70 36.3 18 36.0 91 34.0 0.2346 0.7797 No 85 70.3 123 63.7 32 64.0 177 66.0 Type of fuel used for cooking (n=121) (n=194) (n=51) (n=268) Electricity 107 88.4 180 92.8 46 90.2 242 90.3 0.1866 0.2015 Gas, paraffin, wood, coal, sun, open fire or other 14 11.6 14 7.2 5 9.8 26 9.7 Participant’s highest level of education (n=120) (n=194) (n=51) (n=268) Primary school 5 4.2 11 5.7 3 5.9 13 4.9 Grade 8 to 10 37 30.8 38 19.6 20 39.2 54 20.2 0.1306 0.0265* Grade 11 to 12 63 52.5 122 62.9 24 47.1 166 62.2 Tertiary education 15 12.5 23 11.9 4 7.8 34 12.7 179 Variable Normal vaginal Caesarean section p-value# Premature Term p-value# delivery n % n % n % n % Partner’s highest level of education (n=107) (n=179) (n=46) (n=242) Primary school 7 6.5 7 3.9 4 8.7 9 3.7 Grade 8 to 10 21 19.6 15 8.4 7 15.2 33 13.6 0.0239* 0.4925 Grade 11 to 12 63 58.9 121 67.6 27 58.7 156 64.5 Tertiary education 16 15.0 36 20.1 8 17.4 44 18.2 Participant’s employment status (n=108) (n=187) (n=51) (n=258) Part-time employment 17 14.4 16 8.6 5 9.8 28 10.9 Unemployed 53 44.9 111 59.4 33 64.7 132 51.2 0.2562 0.0653 Housewife by choice 9 7.6 8 4.3 1 2.0 18 7.0 Full-time employment or self-employment 39 33.1 52 27.8 12 23.5 80 31.0 Partner’s employment status (n=114) (n=186) (n=51) (n=254) Part-time employment 19 16.7 42 22.6 18 35.3 46 18.1 Unemployed 12 10.5 17 9.1 0.4577 4 7.8 27 10.6 0.0226* Full-time employment and self-employed 83 72.8 127 68.3 29 56.9 181 71.3 Does anyone in the household obtain income from the following? Wages and salaries from formal employment (n=120) (n=194) (n=51) (n=268) Yes 74 61.7 122 62.9 22 43.1 177 66.0 0.8283 0.0020* No 46 38.3 72 37.1 29 56.9 91 34.0 Self-employment (n=121) (n=194) (n=51) (n=268) Yes 35 28.9 47 24.2 16 31.4 68 25.4 0.3553 0.3726 No 86 71.1 147 75.8 35 68.6 200 74.6 Casual employment (n=121) (n=194) (n=51) (n=268) Yes 27 22.3 63 32.5 17 33.3 80 29.9 0.0522 0.6202 No 94 77.7 131 67.5 34 66.7 188 70.1 Crop production and livestock sales (n=121) (n=194) (n=51) (n=268) Yes 3 2.5 8 4.1 3 5.9 10 3.7 0.4393 0.2084 No 118 97.5 186 95.9 48 94.1 258 96.3 Pension or state grants (n=121) (n=194) (n=51) (n=268) Yes 79 65.3 127 65.5 33 64.7 176 65.7 0.9747 0.8942 No 42 34.7 67 34.5 18 35.3 92 34.3 180 Variable Normal vaginal Caesarean section p-value# Premature Term p-value# delivery n % n % n % n % Domestic work (n=121) (n=194) (n=51) (n=268) Yes 9 7.4 10 5.2 1 2.0 16 6.0 0.4077 0.1636 No 112 92.6 184 98.9 50 98.0 252 94.0 Household income per month (n=119) (n=186) (n=51) (n=259) < R1000 13 10.9 21 11.3 18 35.3 82 31.7 R1001 ̶ R3000 43 36.1 51 27.4 8 15.7 72 27.8 0.1828 0.1129 R3001 ̶ R5000 23 19.3 55 29.6 15 29.4 79 30.5 > R5000 40 33.6 59 31.7 10 19.6 26 10.0 #p-value for percentage difference between normal vaginal delivery versus caesarean section and premature delivery versus term delivery using chi-square or Fisher’s exact tests, as appropriate; p <0.05 considered statistically significant indicated with * 181 Birth outcomes related to length and weight Maternal age was significantly associated with weight-for-length at birth (p=0.0052) (Table 6.3). A significantly higher percentage of babies born with a weight-for-length greater than or equal to the -2 standard deviation (SD) but below the -1 SD (considered at risk of developing wasting) had a mother who was aged 18–35 years compared to mothers older than 35 years. Significantly more babies with a length-for-age above or equal to the -1 SD (considered normal) lived in a household with access to a stove (p=0.0271). Similar trends were observed for having a kettle (p=0.0122) and radio (p=0.0315) in the house. Significantly more babies with a normal weight-for-length lived in a house with a stove (p=0.0087), while significantly more babies with a weight-for-length below the -2 SD (wasted) come from households who had a microwave (p=0.0285). Significantly more babies born with a length-for-age below the -2 SD (considered stunted) had a mother whose partner had grade 11–12 as their highest level of education (p=0.0429). Employment status of both the participant (p=0.0442) and partner (p=0.0023) was also significantly associated with length-for-age at birth. A significantly higher percentage of babies who were born with a length-for-age greater than or equal to the -2 SD but below the -1 SD (considered at risk of developing stunting) had a mother who was unemployed, while a significantly higher percentage of babies who were born at risk of developing stunting had a mother whose partner was full-time or self-employed. 182 Table 6.3: Associations between socio-demography, length-for-age and weight-for-length at birth Variable Length-for-age Weight-for-length < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# n % n % n % n % n % n % Age (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) 18-35 years 45 71.4 27 77.1 166 70.3 28 65.1 59 86.8 125 66.8 36 years and older 18 28.6 8 22.9 70 29.7 0.7082 15 34.9 9 13.2 62 33.2 0.0052* Pregnancy stage (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Second trimester 29 46.0 14 40.0 80 33.9 17 39.5 20 29.4 66 35.3 Third trimester 34 54.0 21 60.0 156 66.1 0.1905 26 60.5 48 70.6 121 64.7 0.5189 Marital status (n=61) (n=35) (n=234) (n=43) (n=68) (n=185) Married or in a relationship 57 93.4 33 94.3 225 96.2 41 95.4 64 94.1 178 96.2 Not in a relationship, divorced, separated or 4 6.6 2 5.7 9 3.9 0.6242 2 4.7 4 5.9 7 3.8 0.6506 widowed Household density ratio (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Overcrowded 33 52.4 21 60.0 122 61.7 25 58.1 37 54.4 93 49.7 Not overcrowded 30 47.6 14 40.0 114 48.3 0.6549 18 41.9 31 45.6 94 50.3 0.5507 Type of housing (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Brick house or flat 48 76.2 29 82.9 184 78.0 34 79.1 49 72.1 149 79.7 Shack or other 15 23.8 6 17.1 52 22.0 0.7407 9 20.9 19 27.9 38 20.3 0.4226 Access to electricity (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Yes 57 90.5 31 88.6 220 93.2 38 88.4 60 88.2 177 94.7 No 6 9.5 4 11.4 16 6.8 0.4063 5 11.6 8 11.8 10 5.4 0.1380 Access to water (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Indoor tap 31 49.2 20 57.1 126 53.4 22 51.2 33 48.5 102 54.6 Own tap outside the house 14 22.2 10 28.6 75 31.8 0.1135 13 30.2 19 27.9 59 31.6 0.4882 Share a tap with other households 18 28.6 5 14.3 35 18.4 8 18.6 16 23.5 26 13.9 Toilet facilities (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Own flush toilet inside or outside the house 39 61.9 24 68.6 158 67.0 31 72.1 39 57.4 126 67.4 Share outside toilet, use bucket system or 24 38.1 11 31.4 78 33.0 0.7167 12 27.9 29 42.7 61 32.6 0.2103 own outside pit toilet 183 Variable Length-for-age Weight-for-length < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# n % n % n % n % n % n % Stove in the house (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Yes 59 93.7 33 94.3 233 98.7 40 93.0 65 95.6 186 99.5 No 4 6.4 2 5.7 3 1.3 0.0271* 3 7.0 3 4.4 1 0.5 0.0087* Refrigerator in the house (n=63) (n=35) (n=235) (n=42) (n=68) (n=187) Yes 55 87.3 30 85.7 205 87.2 35 83.3 54 79.4 169 90.4 No 8 12.7 5 14.3 30 12.8 0.9677 7 16.7 14 20.6 18 9.6 0.0553 Freezer in the house (n=63) (n=35) (n=235) (n=42) (n=68) (n=187) Yes 44 69.8 25 71.4 186 79.2 30 71.4 47 69.1 151 80.8 No 19 30.2 10 28.6 49 20.8 0.2257 12 28.6 21 30.9 36 19.3 0.1021 Microwave in the house (n=63) (n=35) (n=235) (n=42) (n=68) (n=187) Yes 44 69.8 23 65.7 183 77.9 36 85.7 44 64.7 145 77.5 No 19 30.2 12 34.3 52 22.1 0.1700 6 14.3 24 35.3 42 22.5 0.0285* Kettle in the house (n=63) (n=35) (n=235) (n=42) (n=68) (n=187) Yes 54 85.7 29 82.9 222 94.5 40 95.2 59 86.8 176 94.1 No 9 14.3 6 17.1 13 5.5 0.0122* 2 4.8 9 13.2 11 5.9 0.1092 Radio in the house (n=63) (n=35) (n=235) (n=42) (n=68) (n=187) Yes 45 71.4 28 80.0 201 85.5 35 83.3 52 76.5 161 86.1 No 18 28.6 7 20.0 34 14.5 0.0315* 7 16.7 16 23.5 26 13.9 0.1869 Television in the house (n=63) (n=35) (n=235) (n=42) (n=68) (n=187) Yes 55 87.3 29 82.9 213 90.6 37 88.1 57 83.8 172 92.0 No 8 12.7 6 17.1 22 9.4 0.3329 5 11.9 11 16.2 15 8.0 0.1604 Motor vehicle in the household (n=63) (n=35) (n=235) (n=42) (n=68) (n=187) Yes 18 28.6 15 42.9 76 32.3 14 33.3 22 32.4 61 32.6 No 45 71.4 20 57.1 159 67.7 0.3428 28 66.7 46 67.7 126 67.4 0.9942 Type of fuel used for cooking (n=63) (n=35) (n=236) (n=43) (n=68) (n=160 Electricity 54 85.7 31 88.6 217 92.0 37 86.1 59 86.8 147 93.1 Gas, paraffin, wood, coal, sun, open fire or 9 14.3 4 11.4 19 8.0 0.3034 6 14.0 9 13.2 13 7.0 0.1704 other 184 Variable Length-for-age Weight-for-length < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# n % n % n % n % n % n % Participant’s highest level of education (n=63) (n=34) (n=206) (n=43) (n=67) (n=187) Primary school 3 4.8 3 8.8 11 4.7 4 9.3 2 3.0 9 4.8 Grade 8 to 10 20 31.8 10 29.4 49 20.8 5 11.6 18 26.9 43 23.0 0.0708 0.4151 Grade 11 to 12 38 60.3 16 47.1 144 61.0 29 67.4 37 55.2 113 60.4 Tertiary education 2 3.2 5 14.7 32 13.6 5 11.6 10 14.9 22 11.8 Partner’s highest level of education (n=56) (n=31) (n=215) (n=42) (n=59) (n=167) Primary school 5 8.9 0 0.0 10 4.7 4 9.5 2 3.4 6 3.6 Grade 8 to 10 8 14.3 9 29.0 22 10.2 5 11.9 8 13.6 21 12.6 0.0429* 0.6341 Grade 11 to 12 37 66.1 16 51.6 140 65.1 23 54.8 39 66.1 110 65.9 Tertiary education 6 10.7 6 19.4 43 20.0 10 23.8 10 17.0 30 18.0 Participant’s employment status (n=61) (n=34) (n=229) (n=43) (n=63 (n=181) Part-time employment 10 16.4 0 0.0 25 11.0 9 20.9 6 9.5 15 8.3 Unemployed 28 45.9 21 61.8 125 54.8 22 51.2 41 65.1 95 52.5 0.0442* 0.0733 Housewife by choice 1 1.6 4 11.8 14 6.1 2 4.7 5 7.9 11 6.1 Full-time employment or self-employment 22 36.1 9 26.5 64 28.1 10 23.3 11 14.5 60 33.2 Partner’s employment status (n=60) (n=34) (n=225) (n=40) (n=65) (n=178) Part-time employment 21 35.0 4 11.8 39 17.3 8 20.0 9 13.9 33 18.5 0.0023* Unemployed 2 3.3 1 2.9 29 12.9 5 12.5 11 16.9 15 8.4 0.3834 Full-time employment and self-employed 37 61.7 29 85.3 157 69.8 27 67.5 45 69.2 130 73.0 Does anyone in the household obtain income from the following? Wages and salaries from formal (n=62) (n=35) (n=236) (n=43) (n=68) (n=186) employment Yes 37 59.7 28 80.0 142 60.2 29 67.4 41 60.3 117 62.9 No 25 40.3 7 20.0 94 39.8 0.0708 14 32.6 27 39.7 69 37.1 0.7491 Self-employment (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Yes 18 28.6 6 17.1 62 26.3 12 27.9 16 23.5 46 24.6 No 45 71.4 29 82.9 174 73.7 0.4378 31 72.1 52 76.5 141 75.4 0.8672 Casual employment (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Yes 23 36.5 6 17.1 69 29.2 14 32.6 16 23.5 53 28.3 No 40 63.5 29 82.9 167 70.8 0.1304 29 67.4 52 76.5 134 71.7 0.5688 185 Variable Length-for-age Weight-for-length < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# n % n % n % n % n % n % Crop production and livestock sales (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Yes 6 9.5 0 0.0 10 4.2 3 7.0 1 1.5 7 3.7 No 57 90.5 35 100.0 226 95.8 0.1033 40 93.0 67 98.5 180 96.3 0.3218 Pension or state grants (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Yes 40 63.5 23 65.7 155 65.7 30 69.8 50 73.5 118 63.1 No 23 36.5 12 34.3 81 34.3 0.9473 13 30.2 18 26.5 69 36.9 0.2617 Domestic work (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Yes 6 9.5 3 8.6 11 4.7 4 9.3 3 4.4 12 6.4 No 57 90.5 32 91.4 225 95.3 0.2207 39 90.7 65 95.6 175 93.6 0.6155 Household income per month (n=63) (n=34) (n=224) (n=40) (n=66) (n=182) < R1000 22 34.9 11 32.4 68 30.0 15 37.5 24 36.4 52 28.6 R1001 ̶R3000 15 23.8 11 32.4 58 25.6 7 17.5 16 24.2 54 29.7 0.7080 0.2259 R3001 ̶R5000 16 25.4 10 29.4 74 32.6 14 35.0 15 22.2 60 33.0 > R5000 10 15.9 2 5.9 24 11.9 4 10.0 11 16.7 16 8.8 #p-value for percentage difference between low, normal and high weight-for-length as well as low, normal or high weight-for-length using chi-square or Fisher’s exact tests, as appropriate; p <0.05 considered statistically significant indicated with * 186 Overall birth outcome could be determined for 307 women (307/331) of which 37.1% had overall poor birth outcome. Table 6.4 indicates those variables that were significantly associated with overall birth outcome. Significantly more participants in the overall good birth outcome group were in their third trimester of pregnancy (p=0.0209) at the time of data collection. A greater percentage of participants with overall good birth outcome owned a radio (p=0.0344). Significantly more participants with an overall good birth outcome had a tertiary education (p=0.0387), while significantly more participants with an overall poor birth outcome were part-time employed (p=0.0136). Table 6.4: Associations between socio-demographic variables and overall birth outcome Variable Poor outcome Good outcome p-value# n % n % Age (n=114) (n=193) 18–35 years 79 69.3 137 71.0 0.7546 ≥ 36 years 35 30.7 56 29.0 Pregnancy stage (n=114) (n=193) Second trimester 51 44.7 61 31.6 0.0209* Third trimester 63 55.3 132 68.4 Marital status (n=113) (n=191) Married or in a relationship 106 93.8 184 96.3 0.3092 Not married or in a relationship, divorced or widowed 7 6.2 7 6.7 HDR (n=114) (n=193) Overcrowded 60 52.6 99 51.3 0.8209 Not overcrowded 54 47.4 94 48.7 Type of housing (n=114) (n=193) Brick house 89 78.1 148 76.7 0.7797 Shack or other 25 21.9 45 23.3 Access to electricity (n=114) (n=193) Yes 102 89.5 179 92.8 0.3197 No 12 10.5 14 7.2 Access to water (n=114) (n=193) Indoor water 55 48.2 108 56.0 0.1313 Own tap outside the house 33 29.0 58 30.0 Share a tap with other households 26 22.8 27 14.0 Type of toilet system (n=114) (n=193) Flush toilet inside the house or own flush toilet outside the house 75 65.8 124 64.2 0.7847 Share an outside toilet with other households, bucket system or own 39 34.2 69 35.8 pit toilet Own a stove (n=114) (n=193) Yes 108 94.7 191 99.0 0.0555 No 6 5.3 2 1.0 Own a refrigerator (n=113) (n=193) Yes 97 85.8 167 86.5 0.8660 No 16 14.2 26 13.5 187 Variable Poor outcome Good outcome p-value# n % n % Own a freezer (n=113) (n=193) Yes 81 71.7 153 79.3 0.1307 No 32 28.3 40 20.7 Own a microwave (n=113) (n=193) Yes 87 77.0 144 74.6 0.6405 No 26 23.0 49 25.4 Own a kettle (n=113) (n=193) Yes 101 89.4 178 92.2 0.3967 No 12 10.6 15 7.8 Own a radio (n=113) (n=193) Yes 85 75.2 164 85.0 0.0344* No 28 24.8 29 15.0 Own a television (n=113) (n=193) Yes 98 86.7 174 90.2 0.3569 No 15 13.3 19 9.8 Own a motor vehicle (n=113) (n=193) Yes 33 29.2 66 34.2 0.3675 No 80 70.8 127 65.8 Fuel most commonly used for cooking (n=114) (n=193) Electricity 98 86.0 177 91.7 0.1115 Gas, paraffin, wood, sun, open fire or other 16 14.0 16 8.3 Participant’s highest level of education (n=114) (n=192) Primary school 6 5.3 11 5.7 0.0387* Grade 8 – 10 33 29.0 39 20.3 Grade 11 – 12 69 60.5 113 58.9 Tertiary education 6 5.3 29 15.1 Partner’s highest level of education (n=107) (n=173) Primary school 9 8.7 6 3.5 0.2035 Grade 8 – 10 15 14.6 21 12.1 Grade 11 – 12 64 62.1 112 64.7 Tertiary education 15 14.6 34 19.7 Participant’s employment status (n=112) (n=185) Full-time employed or self-employed 21 18.8 13 7.0 0.0136* Part-time employed 54 48.2 104 56.2 Unemployed 4 3.6 13 7.0 Housewife by choice 33 24.5 55 29.7 Partner’s employment status (n=108) (n=185) Full-time employed or self-employed 29 26.9 30 16.2 0.0893 Part-time employed 10 9.3 21 11.4 Unemployed 69 63.9 134 72.4 Does anyone in the household earn an income from: Wages and salaries from formal employment (n=113) (n=193) Yes 68 60.2 124 64.3 0.4771 No 45 39.8 69 35.8 Self-employment (n=114) (n=193) Yes 31 27.2 48 24.9 0.6529 No 83 72.8 145 75.1 Casual employment (n=114) (n=193) Yes 40 35.1 53 27.5 0.1600 No 74 64.9 140 72.5 188 Variable Poor outcome Good outcome p-value# n % n % Crop production and livestock sales (n=114) (n=193) Yes 8 7.0 7 3.6 0.1830 No 106 93.0 186 96.4 Pension or state grants (n=114) (n=193) Yes 72 63.2 127 65.8 0.6391 No 42 36.8 66 34.2 Domestic work (n=114) (n=193) Yes 11 9.7 8 4.1 0.0531 No 103 90.3 185 95.9 Monthly household income (n=111) (n=187) R0 – R1000 17 15.3 19 10.2 0.3935 R1001-R3000 36 32.4 58 31.0 R3001-R5000 24 21.6 54 28.9 Over R5000 34 30.6 56 30.0 #p-value for percentage difference between overall poor birth outcome and overall good birth outcome using chi-square or Fisher’s exact tests, as appropriate; p <0.05 considered statistically significant indicated with * Ownership of a radio (p=0.0344), participant’s highest level of education (p=0.0387) and participant’s employment status (p=0.0136) were all significantly associated with overall birth outcome, while a p-value < 0.15 on univariate analysis was identified for access to water (p=0.1313), ownership of a stove (p=0.0555), ownership of a freezer (p=0.1307), fuel most commonly used for cooking (p=0.1115) and partner’s employment status (p=0.0893). All these variables were considered for selection in the final model. While pregnancy stage and earning an income from domestic work were also significantly associated with overall birth outcome, it was decided not to include these in the model since pregnancy stage is not influenced by nutrition and it was decided to focus more on forms of employment instead of types of income. Results from logistic regression analysis Ownership of a stove, participant’s highest level of education and participant’s employment status was found to be independent predictors of overall birth outcome. Results from the logistic regression showed that the odds ratio for women experiencing overall poor birth outcome was lower for women who owned a stove compared to those who did not (Table 6.5). The odds of experiencing overall poor birth outcome were higher for women with primary school compared to those who had a tertiary education, with a similar trend observed for women with grade 8–10 and grade 10–12 compared to those with a tertiary education. In terms of participant’s employment status, the odds of women experiencing overall poor birth 189 outcome were higher for women with part-time employment compared to full-time or self- employment, but lower for women who were unemployed or housewives by choice compared to women with full-time or self-employment. Table 6.5: Odds ratios of socio-demographic factors associated with overall poor birth outcome Variable Description Odds ratio (95% CI) Own a stove yes vs no 0.11 (0.02;0.69) Participant’s highest level of primary school vs tertiary education 3.26 (0.75;14.09) education Participant’s highest level of grade 8–10 vs tertiary education 5.67 (1.83;17.58) education Participant’s highest level of grade 10–12 vs tertiary education 4.20 (1.45;12.22) education part-time employed vs full-time Participant’s employment status 2.55 (1.10;5.93) and/or self-employed unemployed vs full-time and/or self- Participant’s employment status 0.75 (0.42;1.33) employed housewife by choice vs full-time Participant’s employment status 0.54 (0.16;1.82) and/or self-employed 6.5 DISCUSSION Socio-economic inequalities amongst pregnant women may have a considerable impact on child health (Cantarutti et al., 2017). Young maternal age (Workicho et al., 2020), as well as advanced maternal age, is associated with poor birth outcomes (Fall et al., 2015; Ogawa et al., 2017). While the current study focused on pregnant women aged 18 years and older, both young maternal age (≤ 19 years) and older maternal age (≥ 35 years) are associated with an increased risk of premature delivery and intrauterine growth restriction (Fall et al., 2015). While the causes of poor birth outcomes are multifactorial, both younger and older maternal age is often linked to low socio-economic status and less schooling in certain settings, which may further influence the risk of poor birth outcomes (Fall et al., 2015). In the current study, age was significantly associated with both method of delivery (p=0.0003) and weight-for- length at birth (p=0.0052). While the majority of the participants in both the normal vaginal delivery and caesarean section groups were in the younger age category of 18–35 years, a 190 significantly higher percentage of babies born via caesarean section had mothers in the 18– 35 year age group compared to those who were born via normal delivery. The rate of caesarean section delivery is increasing globally (Abbaspoor & Noori, 2016:1765; Khan et al., 2017) due to various reasons including the mother’s preference, maternal age over 35 years and previous negative birth outcomes, amongst others (Abbaspoor & Noori, 2016:1766). Abbaspoor and Noori (2016:1766) conducted a cross-sectional study among 510 women in two educational hospitals in Ahvaz-Iran and found that the mean age of women who delivered via caesarean section was higher than those who had a normal delivery. Khan et al. (2017), investigated socio-demographic predictors of caesarean section among nationally representative data obtained between 2004 and 2014 as part of the Bangladesh Demographic and Health Survey (BDHS). While Khan et al. (2017) also found a significant association between age and method of delivery, women ≥35 years were more likely to have a caesarean compared to those 20–34 years. Delivery by caesarean section may increase the risk of mortality and morbidity of both the mother and neonate if performed unnecessarily (Abbaspoor & Noori, 2016:1765; Khan et al., 2017). Differences between the studies and that of the current study may be ascribed to the fact that the current study was conducted in a high-risk antenatal clinic, which may require more caesarean sections to be performed, since caesarean section is indicated with foetal distress, maternal pre-eclampsia, etc. which will occur more at a high-risk clinic. Fall et al. (2015) conducted a prospective study including data of five live birth cohorts enrolled between 1969 and 1989 in Brazil, Guatemala, India, the Philippines and South Africa (low- and middle-income countries) to examine associations between maternal age and childbirth, child and adult outcomes in the offspring. The authors found that young maternal age (≤ 19 years) was associated with an increased risk of wasting in infancy and childhood, amongst others (Fall et al., 2015). The current study did not include pregnant women younger than 18 years but did find a significant association between maternal age and wasting with more babies at risk of developing wasting having mothers in the 18–35 years category. Being married has been associated with improved pregnancy outcomes as the father is able to provide support, despite societal normalisation of childbearing outside of marriage (Barr & Marugg, 2019:225). Married women have been found to have a lower risk of premature delivery and delivering a small for gestational age baby. Married women among the 138 118 191 live singleton births in the Centers for Diseases Control and Presentation Pregnancy Risk Assessment Monitoring System from 2012 to 2014, were more likely to have a vaginal delivery than a caesarean section (Barr & Marugg, 2019:225). No significant associations were, however, observed between being married or in a relationship, and any of the birth outcomes investigated in the current study. Poor housing, as well as overcrowding, may exacerbate exposure to various potentially harmful risk factors, such as hazardous alcohol use or tobacco smoke exposure, in both pregnant women and their offspring (Zar et al., 2019). In the current study, a higher percentage of babies who were born via caesarean section had mothers who lived in brick houses or a flat (p=0.0441). No other studies investigating the relationship between the type of housing and method of delivery could be found, however, poor quality housing has been found to contribute to unfavourable child outcomes (Moore et al., 2017). A higher percentage of babies who were born term formed part of a household that had their own tap inside the house (p=0.0169). Exposure to unsafe water during pregnancy has been associated with increased risk of infection, which may result in low birth weight and premature delivery (Padhi et al., 2015). Patel et al. (2019) investigated the effects of sanitation practices on adverse pregnancy outcomes in India using data from the Demographic Health Survey – India conducted between 2015 and 2016. Pregnant women who did not have access to water near their toilet were more likely to experience poor pregnancy outcomes (Patel et at., 2019). The health status of pregnant women may be affected by distance to the source of water, quality of water and having and using a clean toilet, amongst others (Songa et al., 2015:98). According to Chauhan et al. (2020), the relationship between sanitation and poor birth outcomes remains under-researched. Type of toilet facilities available was significantly associated with method of delivery (p=0.0202) in the current study, with significantly more babies born through a caesarean section forming part of a household that had their own flush toilet inside or outside the house. While a small number of studies have considered the relationship between toilet facilities and poor birth outcomes, particularly in India (Padhi et al., 2015; Patel et al., 2019), none have considered the relationship between toilet facilities and method of delivery. 192 Significantly more babies who were born prematurely came from households who did not have a stove in the house (p=0.0066) compared to babies born term. Significantly more babies with a normal length-for-age lived in a household with access to a stove (p=0.0271) with a similar trend observed for weight-for-length at birth (p=0.0087). Results from the logistic regression analysis showed that the odds of experiencing overall poor birth outcome were lower for those women who owned a stove compared to those who did not. Owning assets such as a stove and a fridge are associated with modern wealth (Kabudula et al., 2017). These associations, along with the majority of the participants residing in brick houses, could therefore provide an indication of better living conditions which may be associated with improved birth outcomes. Maternal socioeconomic disadvantage, including lower levels of maternal education, have been associated with increased risk of premature delivery (Bushnik et al., 2017). A significant association was found between the highest level of education among the participants attending the antenatal clinic at Pelonomi Hospital and prematurity (p=0.0265). Significantly more babies in the term delivery group were born to mothers who had grade 11–12 as their highest level of education. In the current study, the odds of experiencing overall poor birth outcome were greater for women with only primary school education, compared to those who had a tertiary education, with a similar trend observed for women with grade 8–10 and grade 10–12 compared to those with a tertiary education. A population based-based study performed on 383 103 singleton live births between 2005 and 2010 in Lombardy, Italy, found that mothers with higher levels of education have a lower risk of premature delivery and delivering babies of low birth weight (Cantarutti et al., 2017). Vikram and Vanneman (2020) used data from the India Human Development Survey (2004 – 2005) to determine the associations between maternal education and child health outcomes. Although the authors acknowledge that educated mothers are more likely to live in more affluent households, have fewer children and live in areas where antenatal check-ups are more common, Indian mothers who were educated were much more likely to have four or more antenatal care check-ups (Vikram & Vanneman, 2020). Shapiro et al. (2017:67) determined the associations between paternal education and poor birth outcomes amongst 131 285 singleton deliveries investigated as part of the 2006 Canadian Birth-Census Cohort. Low paternal education was found to increase the risk of 193 adverse birth outcomes (Shapiro et al., 2017:67). In the current study, significantly fewer babies born via caesarean section had mothers with a partner who had grade 8–10 as their highest level of education (p=0.0239), while a significantly higher percentage of babies with a birth length-for-age below the -2 SD had mothers with a partner who had grade 10–12 as their highest level of education (p=0.0429). According to Vollmer et al. (2017), paternal education is just as important in reducing childhood undernutrition, including stunting, and should therefore be emphasised along with maternal education. Social resources, including parenting skills and education level (Hertzman, 2010), as well as economic factors, such as wealth and occupational status, may significantly affect the development of children (Braveman & Gottlieb, 2014; Hertzman, 2010). In the current study, both participant (p=0.0442) and partner’s (p=0.0023) employment status were significantly associated with length-for-age at birth, while partner’s employment status was also significantly associated with prematurity (p=0.0226). Significantly more premature babies formed part of households where someone did not obtain a salary from wages and salaries from formal employment (p=0.0020). Results from the logistic regression analysis also showed that the odds of women experiencing overall poor birth outcome were greater for women with part-time employment compared to women with full-time or self-employment, while the odds of experiencing overall poor birth outcome of women who were unemployed or housewives by choice compared to those who were full-time or self-employed were lower. Although the odds of experiencing overall poor birth outcome for women who were unemployed or housewives by choice, were unexpected, this finding probably points to the mother’s choice not to work rather than an inability to find work. Participants who were housewives by choice might have also misunderstood the question and/or indicated that they were unemployed because they were not working. Whatever the reason, the findings of the current study support the concept that social determinants of health play a role in the development of poor birth outcomes. Social determinants of poor health have been associated with poor birth outcomes (Kozhihannil et al., 2017:308). This is supported by the significant associations observed between overall birth outcome and owning a radio (p=0.0344), highest level of education of participant (p=0.0387), employment status of participant (p=0.0136) as well as obtaining an income from domestic work (p=0.0531) in the current study. Because pregnancy stage is not 194 influenced by food security and nutritional status, it was decided to exclude it from the model. It was decided that the focus should be more on the forms of employment and not necessarily the type of income, therefore obtaining and income from domestic work was excluded from the model. We acknowledge that weight and length of the newborns were measured by nursing staff and not by the researchers themselves. Regular training sessions are provided to all the staff working in the maternity as well as paediatric wards by the dietitians working a Pelonomi Hospital where the majority of the mothers attending the antenatal clinic are expected to deliver their babies. It is also important to note that, as published in Jordaan et al. (2020a), significant differences were observed for certain socio-demographic variables between women who provided the birth information of their babies and those who did not. 6.6 CONCLUSION This study identified several social determinants of health that were significantly associated with at least one birth outcome. The odds of experiencing overall poor birth outcome were lower for women who owned a stove, were unemployed or housewives by choice while the odds of experiencing overall poor birth outcome were higher for women who had grade 8– 10 or grade 10–12 as their highest level of education compared to tertiary education level as well as being employed part-time compared to being self-employed or full-time employed. All significant associations between better socio-demographic status and good birth outcomes may point to the fact that a better socio-economic situation seems to influence the health of the pregnant mother and, consequently, the health of her offspring, which may result in improved birth outcomes. Early screening of women of childbearing age may prove to be valuable in identifying women who may be at increased risk for poor birth outcomes. It is therefore recommended that pregnant women be routinely screened to identify socio-demographic factors that may place them at increased risk for poor birth outcomes and be referred for appropriate support such as social and/or psychological support. 195 6.7 ACKNOWLEDGEMENTS The authors would like to acknowledge the participants for their willingness to contribute to our research as well as the staff at Pelonomi Hospital for accommodating the research team in their clinic. 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Maternal nutritional status mediates the association between maternal 199 age and birth outcomes. Maternal & Child Nutrition, April 30. https://doi.org/10.1111/mcn.13015 [16 October 2020]. Zar, H.J., Pellowski, J.A., Cohen, S., Barnett, W., Vanker, A., Koen, N., & Stein, D.J. 2019. Maternal health and birth outcomes in a South African birth cohort study. PLoS ONE, November 21. https://dx.doi.org/10.1371%2Fjournal.pone.0222399 [4 August 2020]. 200 7 CHAPTER 7 - ASSOCIATIONS BETWEEN REPORTED HEALTH AND LIFESTYLE AND BIRTH OUTCOMES OF PREGNANT WOMEN ATTENDING THE ANTENATAL CLINIC AT PELONOMI HOSPITAL, BLOEMFONTEIN 7.1 ABSTRACT Background: Healthy lifestyle and a pre-pregnancy weight in the normal range contribute to a successful pregnancy. This study, therefore, aimed to determine the associations between reported health and lifestyle and birth outcomes of pregnant women attending the antenatal clinic at Pelonomi Hospital, Bloemfontein. Methods: A quantitative, cohort analytical study was conducted with the view of developing a nutrition screening tool for pregnant women. A total of 682 pregnant women attending the high-risk antenatal clinic at Pelonomi Hospital, Bloemfontein, South Africa were included in phase one of the current study. Thereafter, mothers and their babies for whom the information on their Road to Health Booklet was provided by mothers, were included in the second phase of the study, resulting in a final sample of 331 mothers and 347 infants. Information on tobacco and alcohol use patterns; social support; levels of stress and behaviours related to the control of stress and medical history and medications were obtained by means of a questionnaire. Method of delivery, gestational age, and birth weight and length were obtained from the Road to Health Booklet of the neonate. Associations between reported health and lifestyle variables and the following individual birth outcomes were investigated: method of delivery, prematurity, birth-length-for-age and birth weight- for-length. Logistic regression with backward selection (p<0.05) was used to select significant independent reported health and lifestyle factors associated with overall poor birth outcome (babies born prematurely or with birth length-for-age below -2 SD or birth weight-for-length below -2 SD). Variables with a p-value of < 0.15 on univariate analysis were considered for inclusion in the model. Results: Significantly more newborns who were born with a weight-for-length above or equal to the -1 SD had a mother who did not smoke during pregnancy (p=0.0016). Most women had some form of support with significantly more babies born full-term having a mother who could always talk to her husband or partner (p=0.0364) compared to babies born prematurely. Significantly more premature babies had a mother or a close family member 201 who were or had been seriously ill in the past six months (p=0.0249) as well as a close family member with an alcohol or drug problem (p=0.0172) than babies born full-term. Furthermore, a significantly higher percentage of premature babies had mothers who were hospitalised (p=0.0169), had experienced diarrhoea for at least three days at any given time (p<0.0001) or suffered from loss of appetite (p=0.0213) during the current pregnancy. More babies who were born with a length-for-age above or equal to the -1 SD (considered normal) had a mother who was diagnosed with or treated for diabetes during the current pregnancy (p=0.0255). All four birth outcomes investigated in the current study were significantly associated with the number of babies expected during the current pregnancy (more mothers that were expecting twins had poor birth outcomes), while gestational body mass index (GBMI) of the mother was significantly associated with gestational age (p=0.0052) (more mothers with a higher GBMI had a longer gestational age). Overall birth outcome could be determined for 307 of the women, with 114/307 (37.1%) experiencing overall poor birth outcome. The odds of experiencing overall poor birth outcome were higher for women who were themselves, or had a close family member, in real danger of being killed by criminals compared with those who were not, as well as for women who were diagnosed with or treated for high blood pressure during the current pregnancy (OR: 1.94). The odds of experiencing poor pregnancy outcome were lower for women who were normal weight (OR: 0.31), overweight (OR: 0.34) or obese (OR: 0.23) compared to those who were underweight. Conclusion: The current study identified significant associations between various reported health and lifestyle factors and birth outcomes, most notably, premature delivery. Awareness programmes should inform pregnant women of the risks associated with poor lifestyle choices during pregnancy and encourage them to make use of available support networks to help with stress management during pregnancy. Furthermore, screening mothers early in the pregnancy could assist in identifying those that could benefit from more intensive intervention with the goal of improving birth outcomes. Keywords: Heath and lifestyle, method of delivery, premature delivery, stunting, wasting. 7.2 INTRODUCTION A healthy lifestyle and pre-pregnancy weight within a normal range increase the chances of a successful pregnancy and may influence both short- and long-term birth outcomes (Soltani et 202 al., 2017). Physical, as well as mental health, are important to consider during pregnancy as they have a significant impact on birth outcomes (Zar et al., 2019). The vision of the Global Strategy for Women’s, Children’s and Adolescents’ Health (2016–2030) reads (Every Woman Every Child, 2015): “By 2030, a world in which every woman, child and adolescent in every setting realises their rights to physical and mental health and well-being, has social and economic opportunities, and is able to participate fully in shaping prosperous and sustainable societies.” Pregnant women are more prone to experiencing anxiety and depressive disorders than those who are not pregnant and often encounter a variety of stressors during pregnancy (Taylor et al., 2020). Anxiety during pregnancy may lead to initiating or maintaining previous alcohol and substance use during pregnancy (Vythilingum et al., 2012:852) and anxiety has been found to predict women who are prone to continue to use alcohol and/or substances during pregnancy (Meschke et al., 2008). Both active and second-hand smoking, and alcohol use during pregnancy are significant clinical and public health problems (WHO, 2013). Pregnancy-related symptoms such as nausea, vomiting and heartburn are common throughout the course of pregnancy and may, depending on the symptom, contribute to worry and stress, while these symptoms may also be a result of worry and stress (Lutterodt et al., 2019). Chronic disease (the disease itself and/or the medical treatment thereof), during pregnancy can also negatively affect pregnancy outcomes and hold both short- and long-term consequences for the mother and her offspring (Jolving et al., 2016:1296). Various maternal diseases such as those affecting the cardiovascular and pulmonary systems, diabetes, addictive disorders and chronic intrauterine infections, including human immunodeficiency virus (HIV), are indicative of a high-risk pregnancy and require specialised antenatal care (Kersten et al., 2014). Developing countries face challenges of both undernutrition on the one hand and obesity on the other. Healthcare systems are overwhelmed by the effects of this double burden of malnutrition on the pregnant mother and her offspring (Soltani et al., 2017). Maternal body mass index has been associated with short- and long-term health outcomes in both the mother and her child (Liu et al., 2019). Overweight and obesity during pregnancy increase the risk of complications such as childhood obesity, diabetes, cardiovascular diseases, various 203 types of cancer and metabolic syndrome during the life of the offspring (Poston et al., 2016) while underweight increases the risk of intrauterine growth restriction and delivering a baby with low birth weight (Liu et al., 2019). The recording of pregnancy history is an important factor to consider since women who experienced pre-eclampsia or spontaneous premature birth have an increased risk of recurrence in subsequent pregnancies (Varner, 2017). A previous premature delivery is consistently reported to be a risk factor for premature delivery as well as multiple gestations i.e. carrying more than one foetus in a subsequent pregnancy (Cobo et al., 2020). In view of the reported links between health and lifestyle of pregnant women with the development of poor birth outcomes, this study aimed to determine associations between reported health and lifestyle and birth outcomes of pregnant women attending the antenatal clinic at Pelonomi Hospital, Bloemfontein. 7.3 MATERIALS AND METHODS 7.3.1 Study design and participants This study formed part of a quantitative, cohort analytical study to develop a nutrition screening tool for pregnant women, attending the antenatal clinic at Pelonomi Hospital, Bloemfontein, Free State. The detailed study design and participants have been described elsewhere (Jordaan et al., 2020a). 7.3.2 Outcomes measures The primary outcome measures for this study were delivery method, gestational age, length- for-age and weight-for-length at birth. Details of the outcome measures have been previously described in Jordaan et al. (2020b), but are summarised as follows: Method of delivery distinguished between normal vaginal delivery and caesarean section. Premature birth referred to a gestational age of 28 weeks of gestation or later, but before 37 weeks (WHO, 2016a). Only one baby was born before 28 weeks and was included in the premature group (extreme prematurity). 204 Birth weight and length were obtained from the Road to Health Booklet of each baby. Length- for-age and weight-for-length at birth were interpreted using the World Health Organization (WHO) Z-scores (WHO, 2008:14). Women with either premature delivery (< 37 weeks of gestation) or low birth length-for-age (< -2 SD) or low birth weight-for-length (< -2 SD) were classified as having experienced overall poor birth outcome. Those women who delivered a full-term baby with a birth length-for-age and a birth weight-for-length above or equal to the -2 SD were classified as having experienced overall good birth outcome. Since it was not possible to determine whether method of delivery was spontaneous or planned, it was not included in the set of variables used to determine overall birth outcome. Where mothers were expecting twins, mothers were considered to have an overall poor birth outcome if at least one twin had a poor outcome. 7.3.3 Exposure measurements and techniques Reported health and lifestyle. Information on tobacco and alcohol use patterns, social support, levels of stress and behaviours related to the control of stress, and medical history and medications were obtained during a structured interview of reported health and lifestyle. Questions included in the reported health and lifestyle questionnaire were based on the questions included in the Birth to Twenty study (University of Witwatersrand, 2017), a longitudinal study focussing on child and adolescent health and development in Africa. Pregnancy history considered information about previous pregnancies, including the number of children born alive. For the current pregnancy, information on illness as well as other symptoms experienced during the current pregnancy were asked. Questions relating to the current pregnancy were based on questions included in the questionnaires of the Birth to Twenty study (University of Witwatersrand, 2017). Anthropometric measurements. Current weight and height of the participants were measured according to the standards suggested by the International Society for the Advancement of Kinanthropometry (Stewart et al., 2011). Weight and height were used to determine gestational body mass index (GBMI) as suggested by Davies et al. (2013:117) and interpreted as proposed by Cruz et al. (2007). 205 7.3.4 Statistical analysis The researcher was responsible for entering all the data onto an Excel spreadsheet after which data checking and statistical analysis were performed by the Department of Biostatistics, Faculty of Health Sciences, University of the Free State. Data were analysed using SAS/STAT software, Version 9.4 of the SAS system for Windows, Copyright © 2010 SAS Institute Inc. Descriptive statistics, including frequencies and percentages (for categorical data) and medians and percentiles (for numerical data), were calculated. Differences between groups were assessed by chi-squared tests (for categorical variables) or Fisher’s exact test (for categorical variables with sparse data) and Kruskall-Wallis tests (for numerical variables). Analysis of associations for various individual birth outcomes was done using babies as units of analysis, whereas analysis of overall birth outcome considered mothers as unit of analysis. Logistic regression with backward selection (p<0.05) was used to select significant independent factors associated with overall birth outcome. Variables with a p-value of < 0.15 on univariate analysis were considered for inclusion in the model. If a variable with multiple missing values (such as information regarding previous pregnancies) was found not to be significant in the model, the model was fitted again excluding that variable. 7.3.5 Ethical considerations This study was approved by the Health Sciences Research Ethics Committee, Faculty of Health Sciences, University of the Free State (UFS-HSD2018/0148/2905) and the Free State Department of Health. 206 7.4 RESULTS The sample consisted of 331 mothers and 347 babies. Birth outcomes related to delivery A relatively large percentage of participants continued to smoke, use snuff or chew tobacco and consume alcohol while pregnant (Table 7.1). In this sample, none of these practices were, however, significantly associated with method of delivery or prematurity. Most of the women attending the antenatal clinic at Pelonomi Hospital had some form of support. Significantly more premature babies had mothers who reported that they could only “sometimes or never” talk to their husband or partner about their problems (p=0.0364) compared to babies who were born term. Significant associations were also observed between prematurity and having a close family member who was or had been ill (p=0.0249) in the past six months, as well as having a close family member who has a problem with drugs or alcohol (p=0.0172). Significantly fewer babies in the term delivery group had a mother who indicated that they had a family member who was or had been seriously ill in the past six months (p=0.0249). Significantly more babies in the premature delivery group had a mother who indicated that a member in their close family had a problem with drugs or alcohol than babies that were born term (p=0.0172). Significantly more term babies had mothers who reported breastfeeding their other previous children after birth (p<0.0001) than premature babies. A significantly higher percentage of premature babies had mothers hospitalised during the current pregnancy (p=0.0169) compared to term babies. Prematurity was also significantly associated with experiencing diarrhoea for at least three days during the current pregnancy (p<0.0001) and experiencing loss of appetite during the current pregnancy (p=0.0213). A similar trend was observed for these associations where significantly more babies in the premature group had mothers who experienced either diarrhoea or loss of appetite compared to babies in the term delivery group. 207 As expected, significantly more babies were born via caesarean section when their mothers were expecting twins (p<0.0001). Similarly, significantly more premature babies formed part of a twin pregnancy (p<0.0001). 208 Table 7.1: Associations between health and lifestyle questions and delivery method and prematurity Variable Normal vaginal Caesarean p-value# Premature Term p-value# delivery section n % n % n % n % Do you currently smoke? (n=121) (n=194) (n=51) (n=268) Yes 9 7.4 15 7.7 0.9238 7 13.7 17 6.3 0.0811 No 112 92.6 179 92.3 44 86.3 251 93.7 Do you currently use snuff or chew tobacco? (n=120) (n=194) (n=51) (n=267) Yes 13 10.8 12 6.2 0.1393 6 11.8 18 6.7 0.2437 No 107 89.2 182 93.8 45 88.2 249 93.3 Are there members in your household who currently smoke? (n=121) (n=194) (n=51) (n=268) Yes 57 47.1 83 42.8 0.4525 23 54.1 122 45.5 0.9555 No 64 52.9 111 57.2 28 54.9 146 54.5 Do you currently use alcohol? (n=121) (n=194) (n=51) (n=268) Yes 10 8.3 16 8.3 0.9957 5 9.8 20 7.5 0.1766 No 111 91.7 178 91.8 46 90.2 248 92.5 Do you currently smoke and use alcohol? (n=121) (n=194) (n=51) (n=268) Both 4 3.3 3 1.6 0.3654 2 3.9 5 1.9 0.3096 Either 11 9.1 25 12.9 8 15.7 27 10.1 Neither 106 87.6 166 85.6 41 80.4 236 88.1 Are there people who could help if you had a really big problem and needed help, such as with money, the children, accommodation, etc.? (n=121) (n=194) (n=51) (n=268) A number of people 98 81.0 163 84.0 0.4878 45 88.2 219 81.7 0.2586 Nobody, maybe or unsure 23 19.0 31 16.0 6 11.8 49 18.3 If you have a husband or partner, can you talk to your husband or partner about any problems you might have? (n=118) (n=190) (n=51) (n=261) Always 85 72.0 128 63.4 0.3888 29 56.9 187 71.7 0.0364* Never or sometimes 33 28.0 62 32.6 22 43.1 74 28.4 Do you belong to a church group or any other organisation? (n=121) (n=194) (n=51) (n=268) Yes 87 71.9 148 76.3 0.3860 33 64.7 206 76.9 0.0663 No 34 28.1 46 23.7 18 35.3 62 23.1 209 Variable Normal vaginal Caesarean p-value# Premature Term p-value# delivery section n % n % n % n % During the last 6 months, have you or a member of your close family (n=121) (n=194) (n=51) (n=268) been in real danger of being killed by criminals? Yes 6 5.0 14 7.2 0.4241 7 13.7 14 5.2 0.0566 No 115 95.0 180 92.8 44 86.3 254 94.8 During the last 6 months, did you witness a violent crime (e.g. murder, (n=121) (n=194) (n=51) (n=268) robbery, assault, rape)? Yes 10 8.3 23 11.9 0.3114 5 9.8 33 12.3 0.6121 No 111 91.7 171 88.1 46 90.2 235 87.7 During the last 6 months, have you found that you are in so much debt (n=120) (n=194) (n=51) (n=267) that you don't know how you will repay the money? Yes 48 40.0 78 40.2 0.9711 22 43.1 105 39.3 0.6106 No 72 60.0 116 59.8 29 56.9 162 60.7 Have you or one of your close family (husband/partner, mother, father, husband/partner's mother, husband/partner's father) (n=121) (n=194) (n=51) (n=268) members not been able to find a job for more than 6 months? Yes 88 72.7 142 73.2 0.9274 39 76.5 189 70.5 0.3885 No 33 27.3 52 26.8 12 23.5 79 29.5 During the last 6 months, have you or anyone in your close family husband/ partner, mother, father, husband/partner's mother, (n=121) (n=194) (n=51) (n=268) husband/partner's father) been seriously ill? Yes 54 44.6 76 39.2 0.3390 28 54.9 102 38.1 0.0249* No 67 55.4 118 60.8 23 45.1 166 61.9 During the last 6 months, did any member of your close family (husband/ partner, mother, father, husband/partner's mother, (n=121) (n=194) (n=51) (n=268) husband/partner's father) die? Yes 39 32.2 47 24.2 0.1209 13 25.5 72 26.9 0.8386 No 82 67.8 147 75.8 38 74.5 196 73.1 210 Variable Normal vaginal Caesarean p-value# Premature Term p-value# delivery section n % n % n % n % Is there anyone in your close family (husband/partner, mother, father, husband/partner's mother, husband/partner's father) who has a (n=121) (n=194) (n=51) (n=268) problem with drugs or alcohol? Yes 41 33.9 60 30.9 0.5845 25 49.0 85 31.7 0.0172* No 80 66.1 134 69.1 26 51.0 183 68.3 During the last 6 months, have you had a break-up with your husband (n=120) (n=194) (n=51) (n=267) or partner? Yes 22 18.3 32 16.5 0.6748 9 17.7 42 15.7 0.7325 No 98 81.7 162 83.5 42 82.4 225 84.3 During the last 6 months, has your husband/partner hit/beaten you? (n=118) (n=191) (n=51) (n=262) Yes 8 6.8 13 6.8 0.9928 4 7.8 17 6.5 0.2125 No 110 93.2 178 93.2 47 92.2 245 93.5 Have you been pregnant before? (n=121) (n=194) (n=51) (n=268) Yes 113 93.4 180 92.8 0.8377 50 98.0 243 90.7 0.0946 No 8 6.6 14 7.2 1 2.0 25 9.3 Was your first-born baby born alive? (n=112) (n=178) (n=50) (n=241) Yes 99 88.4 164 92.1 0.2856 46 92.0 217 90.0 0.7972 No 13 11.6 14 7.9 4 8.0 24 10.0 Was your first-born baby full-term? (n=99) (n=164) (n=46) (n=217) Yes 87 87.9 137 83.5 0.3371 39 84.8 185 85.3 0.9349 No 12 12.1 27 16.5 7 15.2 32 14.8 How is your first-born’s health now? (n=89) (n=163) (n=46) (n=216) Healthy 87 87.9 140 85.9 0.6463 39 84.8 187 86.6 0.7486 Deceased, unwell or don’t know 12 12.2 23 14.1 7 15.2 29 13.4 How did you feed your other previous children after birth? (n=72) (n=88) (n=25) (n=134) Breastfeed only 51 70.8 57 64.8 0.4155 8 32.0 97 72.4 <0.0001* Formula, mixed feeding, cow’s milk or other 21 29.2 31 35.2 17 68.0 37 27.6 Have you been admitted to hospital during this pregnancy? (n=121) (n=194) (n=51) (n=268) Yes 25 20.7 53 27.3 0.1830 19 37.3 58 21.6 0.0169* No 96 79.3 141 72.7 32 62.7 210 78.4 211 Variable Normal vaginal Caesarean p-value# Premature Term p-value# delivery section n % n % n % n % Have you experienced diarrhoea for at least three days during this (n=121) (n=194) (n=51) (n=268) pregnancy? Yes 15 12.4 27 13.9 0.6993 16 31.4 27 10.1 <0.0001* No 106 87.6 167 86.1 35 68.6 241 90.0 Have you experienced constipation during this pregnancy? (n=121) (n=194) (n=51) (n=268) Yes 52 43.0 94 45.5 0.3429 27 52.9 122 45.5 0.3304 No 69 57.0 100 51.5 24 47.1 146 54.5 Have you experienced nausea during this pregnancy? (n=121) (n=194) (n=51) (n=268) Yes 70 57.9 121 62.4 0.4245 30 58.8 166 61.9 0.6751 No 51 42.2 73 37.6 21 41.2 102 38.1 Have you experienced vomiting during this pregnancy? (n=121) (n=194) (n=51) (n=268) Yes 62 51.2 111 57.2 0.2998 29 56.9 153 57.1 0.9761 No 59 48.8 83 42.8 22 43.1 115 42.9 Have you experienced loss of appetite during this pregnancy? (n=121) (n=194) (n=51) (n=268) Yes 77 63.6 125 64.4 0.8860 40 78.4 165 61.6 0.0213* No 44 36.4 69 35.6 11 21.6 103 38.4 Have you experienced a urinary tract infection during this pregnancy? (n=121) (n=194) (n=51) (n=268) Yes 32 26.5 48 24.7 0.7354 18 35.3 68 25.4 0.1433 No 89 73.6 146 75.3 33 64.7 200 74.6 Have you experienced weight loss of more than 3 kg during this (n=120) (n=193) (n=49) (n=268) pregnancy? Yes 21 17.5 30 15.5 0.6487 12 24.5 39 14.6 0.0817 No 99 82.5 163 84.5 37 75.5 229 85.4 Have you experienced heartburn during this pregnancy? (n=121) (n=194) (n=51) (n=268) Yes 22 18.2 29 15.0 0.4486 9 17.7 43 16.0 0.7765 No 99 81.8 165 85.1 42 82.4 225 84.0 Have you been diagnosed or treated for high blood pressure? (n=121) (n=194) (n=51) (n=268) Yes, now 21 17.4 35 18.0 0.8769 8 15.7 46 17.2 0.7964 Yes, in the past or never 100 82.6 159 82.0 43 84.3 222 82.8 212 Variable Normal vaginal Caesarean p-value# Premature Term p-value# delivery section n % n % n % n % Have you been diagnosed or treated for heart disease? (n=121) (n=194) (n=51) (n=268) Yes, now 0 0.0 1 0.5 0.4289 0 0.0 1 0.4 1.0000 Yes, in the past or never 121 100.0 193 99.5 51 100.0 267 99.6 Have you been diagnosed or treated for diabetes? (n=121) (n=194) (n=51) (n=268) Yes, now 5 4.1 11 5.7 0.5454 3 5.9 13 4.9 0.7277 Yes, in the past or never 116 95.9 183 94.3 48 94.1 255 95.2 Have you been diagnosed or treated for tuberculosis? (n=121) (n=194) (n=51) (n=268) Yes, now 1 0.8 1 0.5 0.4747 1 2.0 1 0.4 0.2946 Yes, in the past or never 120 99.2 193 99.5 50 98.0 267 99.6 Have you been diagnosed or treated for asthma? (n=121) (n=194) (n=51) (n=268) Yes, now 5 4.1 6 3.1 0.6250 4 7.8 6 2.2 0.0582 Yes, in the past or never 116 95.9 188 96.9 47 92.2 262 97.8 Have you been diagnosed or treated for any sexually transmitted (n=121) (n=194) (n=51) (n=268) disease? Yes, now 23 19.0 32 16.5 0.5676 11 21.6 40 14.9 0.2354 Yes, in the past or never 98 81.0 162 83.5 40 78.4 228 85.1 Have you been diagnosed or treated for vaginal discharge? (n=121) (n=194) (n=51) (n=268) Yes, now 18 14.9 42 21.7 0.1365 17 33.3 45 17.8 0.7486 Yes, in the past or never 103 85.1 152 78.4 34 66.7 223 83.2 Have you been diagnosed or treated for Human Immunodeficiency (n=121) (n=194) (n=51) (n=268) Virus? Yes, now 6 5.0 10 5.2 0.9386 2 3.9 17 6.3 0.2281 Yes, in the past or never 115 95.0 184 94.9 49 96.1 251 93.7 Currently use Antiretroviral medications (n=119) (n=194) (n=51) (n=266) Yes 43 36.1 60 30.9 0.3413 21 41.2 80 30.1 0.1191 No 76 63.9 134 69.1 30 58.8 186 70.0 Currently use medication for tuberculosis (n=119) (n=194) (n=51) (n=266) Yes 3 2.5 9 4.6 0.3434 4 7.8 8 3.0 0.0802 No 116 97.5 185 95.4 47 92.2 258 97.0 213 Variable Normal vaginal Caesarean p-value# Premature Term p-value# delivery section n % n % n % n % Currently use medication for diabetes (n=119) (n=194) (n=51) (n=266) Yes 3 2.5 11 5.7 0.1907 3 5.9 11 4.1 0.2250 No 116 97.5 183 94.3 48 94.1 255 95.9 How many babies are you expecting? (n=121) (n=194) (n=51) (n=268) One 121 100.0 165 85.1 <0.0001* 37 72.6 254 94.8 <0.0001* Two 0 0.0 29 15.0 14 27.4 14 5.2 #p-value for percentage difference between normal vaginal delivery versus caesarean section and premature delivery versus term delivery using chi-square or Fisher’s exact tests, as appropriate; p <0.05 considered statistically significant indicated with * 214 Birth outcomes related to weight and length at birth Smoking was significantly associated with weight-for-length at birth (p=0.0016) (Table 7.2). Significantly more babies who were born with a weight-for-length above or equal to the -1 standard deviation (SD) (normal) had a mother who did not smoke during pregnancy. In terms of support and stress, significantly more babies with a weight-for-length below the -2 SD (wasted) had mothers who indicated that they themselves or a close family member were in real danger of being killed by criminals (p=0.0305). More babies who were stunted at birth during the current pregnancy had mothers who indicated that their firstborn was not born full-term (p=0.0449). A significantly higher percentage of babies who were born with a length-for-age above or equal to the -1 SD (normal) had a mother who was diagnosed or treated with diabetes during the current pregnancy (p=0.0255). Number of babies expected for the current study was also significantly associated with both length-for-age (p<0.0001) and weight-for-length (p=0.0018) at birth. Significantly more babies who were born with a birth length-for-age and/or weight-for-length below the -2 SD were part of a set of twins. 215 Table 7.2: Associations between health and lifestyle questions and length-for-age and weight-for-length Variable Length-for-age Weight-for-length < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# n % n % n % n % n % n % Do you currently smoke? (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Yes 8 12.7 3 8.6 14 5.9 0.1716 6 14.0 8 11.8 5 2.7 0.0016* No 55 87.3 32 91.4 222 94.1 37 86.0 60 88.2 182 97.3 Do you currently use snuff or chew tobacco? (n=63) (n=35) (n=235) (n=43) (n=67) (n=187) Yes 5 7.9 4 11.4 18 7.7 0.7468 6 14.0 3 4.5 15 8.0 0.2051 No 58 92.1 31 88.6 217 92.3 37 86.0 64 95.5 172 92.0 Are there members in your household who currently smoke? (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Yes 33 52.4 15 42.9 104 44.1 0.4732 23 53.5 35 51.5 73 39.0 0.0829 No 30 47.6 20 57.1 132 55.9 20 46.5 33 48.5 114 61.0 Do you currently use alcohol? (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Yes 5 7.9 3 8.6 21 8.9 0.9711 4 9.3 5 7.4 18 9.6 0.8538 No 58 92.1 32 91.4 215 91.1 39 90.7 63 92.7 169 90.4 Do you currently smoke and use alcohol? (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Both 2 3.2 2 5.7 4 1.7 0.3199 2 4.7 4 5.9 2 1.1 0.1207 Either 9 14.3 2 5.7 27 11.4 6 14.0 5 7.4 19 10.2 Neither 52 82.5 31 88.6 205 86.9 35 81.4 59 86.8 166 88.8 Are there people who could help if you had a really big problem and needed help, such as (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) with money, the children, accommodation, etc.? A number of people 56 88.9 31 88.6 191 80.9 0.2172 36 83.7 56 82.4 156 83.4 0.9754 Nobody, maybe or unsure 7 11.1 4 11.4 45 19.1 7 16.3 12 17.7 31 16.6 If you have a husband or partner, can you talk to your husband or partner about any problems (n=61) (n=35) (n=231) (n=42) (n=66) (n=183) you might have? Always 38 62.3 21 60.0 168 72.7 0.1280 27 64.3 50 75.8 128 70.0 0.4313 Never or sometimes 23 37.7 14 40.0 63 27.3 15 35.7 16 24.2 55 30.0 216 Variable Length-for-age Weight-for-length < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# n % n % n % n % n % n % Do you belong to a church group or any other organisation? (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Yes 49 77.8 24 68.6 179 75.9 0.5774 34 79.1 53 77.9 140 74.9 0.7820 No 14 22.2 11 31.4 57 24.1 9 20.9 15 22.1 47 25.1 During the last 6 months, have you or a member of your close family been in real (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) danger of being killed by criminals? Yes 5 7.9 2 5.7 15 6.4 0.8811 6 14.0 1 1.5 12 6.4 0.0305* No 58 92.1 33 94.3 221 93.6 37 86.0 67 98.5 175 93.6 During the last 6 months, did you witness a violent crime (e.g. murder, robbery, assault, (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) rape)? Yes 6 9.5 5 14.3 26 11.0 0.7707 4 9.3 4 5.9 26 13.9 0.1831 No 57 90.5 30 85.7 210 89.0 39 90.7 64 94.1 161 86.1 During the last 6 months, have you found that you are in so much debt that you don't know (n=63) (n=35) (n=235) (n=42) (n=68) (n=187) how you will repay the money? Yes 22 34.9 16 45.7 97 41.3 0.5308 16 38.1 26 38.2 81 43.3 0.6864 No 41 65.1 19 54.3 138 58.7 26 61.9 42 61.8 106 56.7 Have you or one of your close family (husband/partner, mother, father, husband/partner's mother, husband/partner's (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) father) members not been able to find a job for more than 6 months? Yes 43 68.3 27 77.1 170 72.0 0.6403 31 72.1 49 72.1 131 70.1 0.9336 No 20 31.7 8 22.9 66 28.0 12 27.9 19 27.9 56 30.0 217 Variable Length-for-age Weight-for-length < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# n % n % n % n % n % n % During the last 6 months, have you or anyone in your close family husband/ partner, mother, (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) father, husband/partner's mother, husband/partner's father) been seriously ill? Yes 25 39.7 16 45.7 98 41.5 0.8438 24 55.8 23 33.8 77 41.2 0.0713 No 38 60.3 19 54.3 138 58.5 19 44.2 45 66.2 110 58.8 During the last 6 months, did any member of your close family (husband/ partner, mother, (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) father, husband/partner's mother, husband/partner's father) die? Yes 17 27.0 9 25.7 65 27.5 0.9733 12 27.9 21 30.9 47 25.1 0.6479 No 46 73.0 26 74.3 171 72.5 31 72.1 47 69.1 140 74.9 Is there anyone in your close family (husband/partner, mother, father, husband/partner's mother, husband/partner's (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) father) who has a problem with drugs or alcohol? Yes 17 27.0 14 40.0 80 33.9 0.3910 17 39.5 25 36.8 60 32.1 0.5730 No 46 73.0 21 60.0 156 66.1 26 60.5 43 63.2 127 67.9 During the last 6 months, have you had a break- up with your husband or partner? (n=63) (n=35) (n=235) (n=42) (n=68) (n=188) Yes 10 15.9 8 22.9 39 16.6 0.6292 9 21.4 12 17.7 31 16.6 0.7558 No 53 84.1 27 77.1 196 83.4 33 78.6 56 82.3 156 83.4 During the last 6 months, has your husband or partner hit or beaten you? (n=61) (n=35) (n=242) (n=42) (n=67) (n=183) Yes 4 6.6 2 5.7 16 6.9 1.000 1 2.4 8 11.9 10 5.5 0.1271 No 57 93.4 33 94.3 216 93.1 41 97.6 59 88.1 173 94.5 218 Variable Length-for-age Weight-for-length < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# n % n % n % n % n % n % Have you been pregnant before? (n=63) (n=35) (n=236) (n=43) (n=68) (n=183) Yes 60 95.2 34 97.1 213 90.3 0.2123 40 93.0 59 86.8 173 92.5 0.3227 No 3 4.8 1 2.9 23 9.8 3 7.0 9 13.2 14 7.5 Was your first-born baby born alive? (n=60) (n=34) (n=210) (n=40) (n=59) (n=170) Yes 55 91.7 31 91.2 191 91.0 0.9853 37 92.5 53 89.8 156 91.8 0.8712 No 5 8.3 3 8.8 19 9.0 3 7.5 6 10.2 14 8.2 Was your first-born baby full-term? (n=55) (n=31) (n=191) (n=37) (n=53) (n=156) Yes 42 76.4 25 80.7 170 89.0 0.0449* 31 83.8 42 79.3 136 87.2 0.6386 No 13 23.6 6 19.4 21 11.0 6 16.2 11 20.7 20 12.8 How is your first-born’s health now? (n=55) (n=31) (n=190) (n=37) (n=53) (n=155) Healthy 48 87.3 25 80.7 165 86.8 0.6298 32 86.5 49 92.5 130 83.9 0.2954 Deceased, unwell or don’t know 7 12.7 6 19.4 25 13.2 5 13.5 4 7.5 25 13.1 How did you feed your other previous children after birth? (n=30) (n=18) (n=122) (n=23) (n=29) (n=101) Breastfeed only 18 60.0 14 77.8 82 67.2 0.4461 17 73.9 22 75.9 65 64.4 0.4048 Formula, mixed feeding, cow’s milk or other 12 40.0 4 22.2 40 32.8 6 26.1 7 24.1 36 35.6 Have you been admitted to hospital during this pregnancy? (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Yes 16 25.4 10 28.6 51 21.6 0.5848 11 25.6 22 32.4 37 19.8 0.1052 No 47 74.6 25 71.4 185 78.4 32 74.4 46 67.6 150 80.2 Have you experienced diarrhoea for at least three days during this pregnancy? (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Yes 10 15.9 7 20.0 26 11.0 0.2447 8 18.6 7 10.3 23 12.3 0.4216 No 53 84.1 28 80.0 210 89.0 35 81.4 61 89.7 164 87.7 Have you experienced constipation during this pregnancy? (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Yes 27 42.9 18 51.4 113 47.9 0.6804 24 55.8 27 39.7 88 47.1 0.2489 No 36 57.1 17 48.6 123 52.1 19 44.2 41 60.3 99 52.9 219 Variable Length-for-age Weight-for-length < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# n % n % n % n % n % n % Have you experienced nausea during this pregnancy? (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Yes 37 58.7 25 71.4 140 59.3 0.3737 24 55.8 41 60.3 114 61.0 0.8236 No 26 41.3 10 28.6 96 40.7 19 44.2 27 39.7 73 39.0 Have you experienced vomiting during this pregnancy? (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Yes 34 54.0 24 68.6 127 53.8 0.2528 20 46.5 37 54.4 107 57.2 0.4419 No 29 46.0 11 31.4 109 46.2 23 53.5 31 45.6 80 42.8 Have you experienced loss of appetite during this pregnancy? (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Yes 45 71.4 24 68.6 149 63.1 0.4281 31 72.1 44 64.7 118 63.1 0.5383 No 18 28.6 11 31.4 87 36.9 12 27.9 24 35.3 69 36.9 Have you experienced a urinary tract infection during this pregnancy? (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Yes 12 19.1 6 17.1 68 28.8 0.1357 17 39.5 16 23.5 42 22.5 0.0627 No 51 81.0 29 82.9 168 71.2 26 60.5 52 76.5 145 77.5 Have you experienced weight loss of more than 3 kg during this pregnancy? (n=63) (n=33) (n=236) (n=43) (n=67) (n=186) Yes 15 23.8 6 18.2 31 13.1 0.1073 9 20.9 13 19.4 22 11.8 0.1576 No 48 76.2 27 81.8 205 86.9 34 79.1 54 80.6 164 88.2 Have you experienced heart burn during this pregnancy? (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Yes 12 19.1 4 11.4 35 14.8 0.5686 7 16.3 6 8.8 30 16.0 0.3256 No 51 81.0 31 88.6 201 85.2 36 83.7 62 91.2 157 84.0 220 Variable Length-for-age Weight-for-length < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# n % n % n % n % n % n % Have you been diagnosed or treated for high blood pressure? (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Yes, now 13 20.6 8 22.9 35 14.8 0.3263 9 20.9 10 14.7 31 16.6 0.6889 Yes, in the past or never 50 79.4 27 77.1 201 85.2 34 79.1 58 85.3 156 83.4 Have you been diagnosed or treated for heart disease? (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Yes, now 0 0.0 0 0.0 1 0.4 1.000 0 0.0 1 1.5 0 0.0 0.3725 Yes, in the past or never 63 100.0 35 100.0 235 99.6 43 100.0 67 98.5 187 100.0 Have you been diagnosed or treated for diabetes? (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Yes, now 0 0.0 0 0.0 16 6.8 0.0255* 1 2.3 1 1.5 14 7.5 0.1248 Yes, in the past or never 63 100.0 35 100.0 220 93.2 42 97.7 67 98.5 173 92.5 Have you been diagnosed or treated for tuberculosis? (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Yes, now 1 1.6 0 0.0 1 0.4 0.5014 1 2.3 0 0.0 0 0.0 0.1443 Yes, in the past or never 62 98.4 35 100.0 235 99.6 42 97.7 68 100.0 187 100.0 Have you been diagnosed or treated for asthma? (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Yes, now 3 4.8 1 2.9 8 3.4 0.8887 1 2.3 3 4.4 5 2.7 0.7897 Yes, in the past or never 60 95.2 34 97.1 228 96.6 42 97.7 65 95.6 182 97.3 Have you been diagnosed or treated for any sexually transmitted disease? (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Yes, now 11 17.5 9 25.7 35 14.8 0.2618 4 9.3 10 14.7 34 18.2 0.3382 Yes, in the past or never 52 82.5 26 74.3 201 85.2 39 90.7 58 85.3 153 81.8 221 Variable Length-for-age Weight-for-length < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# n % n % n % n % n % n % Have you been diagnosed or treated for vaginal discharge? (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Yes, now 12 19.1 6 17.1 48 20.3 0.8953 11 25.6 12 17.7 35 18.7 0.5389 Yes, in the past or never 51 81.0 29 82.9 188 79.7 32 74.4 56 82.4 152 81.3 Have you been diagnosed or treated for Human Immunodeficiency Virus? (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) Yes, now 6 9.5 0 0.0 13 5.5 0.1537 0 0.0 5 7.4 12 6.4 0.2074 Yes, in the past or never 57 90.5 35 100.0 223 94.5 43 100.0 63 92.6 175 93.6 Currently use Antiretroviral medications (n=63) (n=35) (n=234) (n=43) (n=68) (n=185) Yes 22 34.9 14 40.0 72 30.8 0.5005 11 25.6 27 39.7 58 31.4 0.2643 No 41 65.1 21 60.0 162 69.2 32 74.4 41 60.3 127 68.7 Currently use medication for tuberculosis (n=63) (n=35) (n=234) (n=43) 9n=68) (n=185) Yes 4 6.4 2 5.7 6 2.6 0.1800 3 7.0 0 0.0 6 3.2 0.0932 No 59 93.6 33 94.3 228 97.4 40 93.0 68 100.0 179 96.8 Currently use medication for diabetes (n=63) (n=35) (n=234) (n=43) (n=68) (n=185) Yes 0 0.0 1 2.9 13 5.6 0.1298 1 2.3 3 4.4 10 5.4 0.9219 No 63 100.0 34 97.1 221 94.4 42 97.7 65 95.6 175 94.6 How many babies are you expecting? (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) One 50 79.4 28 80.0 225 95.3 <0.0001* 34 79.1 66 97.1 177 94.7 0.0018* Two 13 20.6 7 20.0 11 4.7 9 20.9 2 2.9 10 5.3 #p-value for percentage difference between low, normal of high length-for-age as well as low, normal or high weight-for-length using chi-square or Fisher’s exact tests, as appropriate; p <0.05 considered statistically significant indicated with * 222 Maternal anthropometry and birth outcomes GBMI was significantly associated with prematurity (p=0.0052) with significantly more premature babies being born to mothers with a GBMI in the underweight category compared to those who were born term (Table 7.3). Table 7.3: Associations between gestational body mass index and birth outcomes Birth outcome Underweight Normal Overweight Obese p-value# weight n % n % n % n % Method of delivery (N=315) 0.0807 Vaginal 11 9.1 28 23.1 19 15.7 63 52.1 Caesarean section 9 4.6 43 22.2 18 9.3 124 63.9 Gestational age (N=318) 0.0052* Premature 9 17.7 13 25.5 4 7.8 25 49.0 Term 12 4.5 64 23.9 31 11.6 161 60.1 Length-for-age (N=334) 0.3616 < -2 SD 7 11.1 18 28.6 8 12.7 30 47.6 ≥-2 SD < -1 SD 1 2.9 6 17.1 5 14.3 23 65.7 ≥ -1 SD 14 5.9 55 23.3 24 10.2 143 60.6 Weight-for-length (N=298) 0.0779 < -2 SD 3 7.0 11 25.6 5 11.6 24 55.8 ≥-2 SD < -1 SD 7 10.3 21 30.9 7 10.3 33 48.5 ≥ -1 SD 7 3.7 34 18.2 21 11.2 125 66.8 #p-value for percentage difference between underweight, normal weight, overweight or obese using chi-square or Fisher’s exact tests, as appropriate; p <0.05 considered statistically significant indicated with * Overall birth outcome could be determined for 307 of the 331 women included in the current study. Of the 307 women, 37.1% (114/307) experienced overall poor birth outcome. Table 7.4 provides an overview of associations between reported health and lifestyle variables and overall birth outcome. Women who experienced overall good birth outcome were less likely to smoke while pregnant (p=0.0143), while participants who experienced overall poor birth outcome were more likely to have been in real danger of being killed by criminals (they themselves or a close family member) (p=0.0076). Significantly more participants who experienced overall poor birth outcome suffered from loss of appetite (p=0.0433) or weight loss of more than 3 kg (p=0.0376) during the current pregnancy. Participants who experienced overall poor birth outcome were more likely to be pregnant with twins (p<0.0001). Interestingly, significantly more participants who experienced overall good birth outcome in this sample had a GBMI in the obese category, 223 while more participants who experienced overall poor birth outcome were underweight (p=0.0459). Table 7.4: Associations between reported health and lifestyle and overall birth outcome Variable Poor outcome Good outcome p-value# n % n % Currently smoking (n=114) (n=193) Yes 14 12.3 9 4.7 0.0143* No 10 87.7 184 95.3 Currently using alcohol (n=114) (n=193) Yes 12 10.5 15 7.8 0.4103 No 102 89.5 178 92.2 Currently smoking and using alcohol (n=114) (n=193) Both 6 5.3 2 1.0 0.0650 Either 14 12.3 20 10.4 Neither 94 82.5 171 88.6 Currently using antiretroviral medication (n=114) (n=191) Yes 40 35.1 58 30.4 0.3930 No 74 64.9 133 69.6 Currently using medication for the management of tuberculosis (n=114) (n=191) Yes 5 4.4 5 2.6 0.4015 No 109 95.6 186 97.4 Currently using oral glucose lowering medication (n=114) (n=191) Yes 4 3.5 10 5.2 0.4857 No 110 96.5 181 94.8 Are there people who could help you if you had a really big (n=114) (n=193) problem and needed help, such as with money, the children, accommodation and so on? A number of people 96 84.2 157 81.4 0.5243 Nobody, maybe or unsure 18 15.8 36 18.6 If you have a husband or partner, can you talk to your husband or (n=111) (n=189) partner about any problems you might have? Always 71 64.0 140 74.1 0.0642 Never or sometimes 40 36.0 49 25.9 Do you belong to a church group or any other organisation? (n=114) (n=193) Yes 86 75.4 144 74.6 0.8717 No 28 24.6 49 25.4 During the last 6 months, have you or a member of your close (n=114) (n=193) family been in real danger of being killed by criminals? Yes 13 11.4 7 3.6 0.0076* No 101 88.6 186 96.4 During the last 6 months, did you witness a violent crime (e.g. (n=114) (n=193) murder, robbery, assault, rape)? Yes 13 11.4 24 12.4 0.7885 No 101 88.6 169 87.6 During the last 6 months, have you found that you are in so much (n=113) (n=193) debt that you don't know how you will repay the money? Yes 46 40.7 75 38.9 0.7497 No 67 59.3 118 61.1 224 Variable Poor outcome Good outcome p-value# n % n % Have you or one of your close family (husband/partner, mother, (n=114) (n=193) father, husband/partner's mother, husband/partner's father) members not been able to find a job for more than 6 months? Yes 82 71.9 138 71.5 0.9360 No 32 28.1 55 28.5 During the last 6 months, have you or anyone in your close family (n=114) (n=193) husband/ partner, mother, father, husband/partner's mother, husband/partner's father) been seriously ill? Yes 49 43.0 76 39.4 0.5346 No 65 57.0 117 60.6 During the last 6 months, did any member of your close family (n=114) (n=193) (husband/ partner, mother, father, husband/partner's mother, husband/partner's father) die? Yes 30 26.3 55 28.5 0.6798 No 84 73.7 138 71.5 Is there anyone in your close family (husband/partner, mother, (n=114) (n=193) father, husband/partner's mother, husband/partner's father) who has a problem with drugs or alcohol? Yes 42 36.8 64 33.2 0.5121 No 72 63.2 129 66.8 During the last 6 months, have you had a break-up with your (n=113) (n=193) husband or partner? Yes 22 19.5 30 15.5 0.3776 No 91 80.5 163 84.5 During the last 6 months, has your husband or partner hit or (n=111) (n=190) beaten you? Yes 5 4.5 15 7.9 0.2545 No 106 95.5 175 92.1 Have you been pregnant before? (n=114) (n=193) Yes 108 94.7 173 89.6 0.1210 No 6 5.3 20 10.4 If pregnant before, was your firstborn born alive? (n=108) (n=171) Yes 99 91.7 154 90.1 0.6526 No 9 8.3 17 9.9 If pregnant before, was the baby full-term? (n=99) (n=154) Yes 79 79.8 136 88.3 0.0643 No 20 20.2 18 11.7 I pregnant before, who is the child’s health now? (n=99) (n=153) Healthy 86 86.9 131 85.6 0.7797 Deceased, sick or unwell, don’t know 13 13.1 22 14.4 How did you feed your other previous children after birth? (n=63) (n=97) Breastmilk only 39 61.9 70 72.2 0.1736 Formula milk only, breastmilk with formula milk, cow’s milk, other 24 38.1 27 27.8 Have you been admitted to hospital during this pregnancy? (n=114) (n=193) Yes 28 24.6 45 23.3 0.8044 No 86 75.4 148 76.7 Have you experienced loose stools / diarrhoea for at least three (n=114) (n=193) days during this pregnancy? Yes 18 15.8 21 10.9 0.2121 No 96 84.2 172 89.1 225 Variable Poor outcome Good outcome p-value# n % n % Have you experienced constipation during this pregnancy? (n=114) (n=193) Yes 55 48.3 90 46.6 0.7844 No 59 51.7 103 53.4 Have you experienced nausea during this pregnancy? (n=114) (n=193) Yes 65 57.0 120 62.2 0.3722 No 49 43.0 73 37.8 Have you experienced vomiting during this pregnancy? (n=114) (n=193) Yes 59 51.7 112 58.0 0.2848 No 55 48.3 81 42.0 Have you experienced loss of appetite during this pregnancy? (n=114) (n=193) Yes 81 71.0 115 59.6 0.0433* No 33 29.0 78 40.4 Have you experienced urinary tract infection during this (n=114) (n=193) pregnancy? Yes 28 24.6 51 26.4 0.7182 No 86 75.4 142 73.6 Have you experienced weight loss of more than 3kg during this (n=112) (n=193) pregnancy? Yes 24 21.4 24 12.4 0.0376* No 88 78.6 169 87.6 Have you experienced heartburn during this pregnancy? (n=114) (n=193) Yes 21 18.4 27 14.0 0.3016 No 93 81.6 166 86.0 Have you ever been diagnosed or treated for the following? High blood pressure (n=114) (n=193) Yes, currently 25 21.9 28 14.5 0.0964 In the past or never 89 78.1 165 85.5 Heart disease (n=114) (n=193) Yes, currently 0 0.0 1 0.5 1.0000 In the past or never 114 100.0 192 99.5 Diabetes (n=114) (n=193) Yes, currently 4 3.5 12 6.2 0.3022 In the past or never 110 96.5 181 93.8 Tuberculosis (n=114) (n=193) Yes, currently 2 1.8 0 0.0 0.0649 In the past or never 112 98.2 193 100.0 Asthma (n=114) (n=193) Yes, currently 5 4.4 5 2.6 0.5082 In the past or never 109 95.6 188 97.4 Any sexually transmitted disease (n=114) (n=193) Yes, currently 20 17.5 29 15.0 0.5606 In the past or never 94 82.5 164 85.0 Vaginal infections / discharge (n=114) (n=193) Yes, currently 27 23.7 32 16.6 0.1269 In the past or never 87 76.3 161 83.4 HIV (n=114) (n=193) Yes, currently 5 4.4 12 6.2 0.4978 In the past or never 109 95.6 181 93.8 226 Variable Poor outcome Good outcome p-value# n % n % Number of babies expecting (n=114) (n=193) One 100 87.7 191 99.0 <0.0001* Two 14 12.3 2 1.0 GBMI (n=114) (n=193) Underweight 13 11.4 7 3.6 0.0459* Normal weight 27 23.7 46 23.8 Overweight 14 12.3 20 10.4 Obese 60 52.6 120 62.2 #p-value for percentage difference between overall poor birth outcome and overall good birth outcome using chi-square or Fisher’s exact tests, as appropriate; p <0.05 considered statistically significant indicated with * Currently smoking (p=0.0143), being in real danger of being killed by criminals in the past six months (participant herself or a close family member) (p=0.0076), experiencing loss of appetite during the current pregnancy (p=0.0433), experiencing weight loss of more than 3 kg during the current pregnancy (p=0.0376), number of babies expecting (p<0.0001) and GBMI (p=0.0459) were all significantly associated with overall birth outcome. More participants experiencing overall poor birth outcome smoked, were in danger of being killed by criminals, experienced loss of appetite or weight loss, expected twins and were underweight compared to those who experienced overall good birth outcome. Furthermore, a p-value < 0.15 on univariate analysis was identified for smoking and using alcohol during the current pregnancy (p=0.0650), being able to talk to one’s husband or partner about any problems (p=0.0642) being pregnant before (p=0.1210), having a first-born who was born full- term (p=0.0643) and being diagnosed with or treated for high blood pressure (p=0.0964), tuberculosis (TB) (p=0.0649) or vaginal infections or discharge (p=0.1269). All these variables were considered for selection in the final model. The results of the logistic regression analysis showed that being in real danger of being killed by criminals in the past six months (self or a close family member), being diagnosed with or treated for high blood pressure, expecting more than one baby and GBMI were independent predictors of overall birth outcome. The odds of experiencing overall poor birth outcome were much higher (OR: 3.81) for participants who were (themselves or a close family member) in real danger of being killed by criminals compared with those who were not (Table 7.5). The odds of experiencing overall poor birth outcome were higher (OR: 1.94) for participants diagnosed or treated for high blood pressure during the current pregnancy compared to those who had been diagnosed or treated for high blood pressure in the past or never. 227 The odds of experiencing overall poor birth outcome were much lower (OR: 0.06) for participants who were expecting one baby compared to those who were expecting two. In terms of GBMI, the odds of experiencing overall poor birth outcome were lower (OR: 0.31) for participants who were normal weight compared to those who were underweight. Similarly, the odds of experiencing overall poor birth outcome were lower for participants who were overweight (OR: 0.34) or obese (OR: 0.23) compared to those who were underweight. Table 7.5: Odds ratios for reported health and lifestyle factors associated with overall birth outcome Variable Description Odds ratio (95% CI) Being in real danger of being killed by criminals (self or close family yes vs no 3.81 (1.34;10.88) member) Being diagnosed with or treated currently vs in the past or never 1.94 (1.01;3.71) for high blood pressure Number of babies expecting one vs two 0.06 (0.01;0.28) Gestational body mass index normal vs underweight 0.31 (0.11;0.92) Gestational body mass index overweight vs underweight 0.34 (0.10;1.15) Gestational body mass index obese vs underweight 0.23 (0.08;0.65) 7.5 DISCUSSION This study aimed to determine the associations between the reported health and lifestyle and birth outcomes (method of delivery, prematurity, length-for-age at birth and weight-for- length at birth) of pregnant women attending the antenatal clinic at Pelonomi Hospital, Bloemfontein. Smoking, along with tobacco and alcohol use during pregnancy, is universally discouraged. Smoking during pregnancy has been associated with poor health consequences for the 228 mother and her offspring (Lange et al., 2018) while the consumption of alcohol is the direct cause of foetal alcohol syndrome (Popova et al., 2017). Use of tobacco during pregnancy increases the risk of premature delivery and other poor outcomes. Exposure to second-hand smoking should also receive attention since exposure during pregnancy increases the risk of stillbirth, congenital malformations and low birth weight (WHO, 2019). In the current study, significantly more babies who were born with a normal weight-for-length had a mother who did not smoke during pregnancy (p=0.0016). Most studies consider the relationship between low birth weight and smoking during pregnancy. Zheng et al. (2016), however, noted that smoking during pregnancy reduces birth weight while shifting both birth weight and length distribution to the left, i.e. towards the lower end of the growth chart. This could therefore affect weight-for-length at birth as well. In addition to an increased risk of foetal alcohol syndrome, alcohol consumption during pregnancy has been associated with growth impairment and stillbirth (De Jong et al., 2019). The frequency and pattern of heavy alcohol consumption during pregnancy as well as the timing of exposure is associated with the severity of outcomes. Foetal alcohol syndrome is the most severe form of the foetal alcohol spectrum disorders and is characterised by growth impairment, facial abnormalities and central nervous system damage (WHO, 2016b). Although no significant associations were found between consuming alcohol and any of the birth outcomes investigated in the current study, the consumption of alcohol during pregnancy has been associated with miscarriage, premature delivery, intrauterine growth restriction and stillbirth (De Jong et al., 2019). Access to social support may reduce stress and consequently protect an individual from the harmful effects of stressful situations (Iranzad et al., 2014). Stress during pregnancy may come in various forms, e.g. perceived stress, depressive symptoms, stressful life events, racial discrimination, as well as pregnancy-specific anxiety. Maternal stress has been associated with premature delivery, low birth weight and adverse health and behavioural outcomes, amongst others (Bale et al., 2010; Grote et al., 2010; Iranzad et al., 2014). Few studies have assessed the impact that fathers or partners may have on perinatal conditions and poor birth outcomes (Cheng et al., 2016:672). In the current study, significantly fewer premature babies had a mother who could always talk to her husband or partner, compared to those who were born term (p=0.0364). In contrast, Cheng et al. (2016:672) did not find an association between 229 partner support and gestational age among 1764 women from two areas in Boston. The study conducted in Boston included both women who were privately insured and those attending three community health centres (Cheng et al., 2016:673) which may serve as a reason for this difference as the current study only included women from clinic catering for high-risk pregnancies in an urban area in South Africa. Also, the current study represented a resource- poor setting with high poverty and low food security that may have a direct effect on birth outcomes as published by Jordaan et al., (2020a). A review by East et al. (2019) to determine the effect of programmes offering additional social support compared with routine care among pregnant women thought to be at risk of delivering low birth weight or premature babies, found that additional support slightly reduced the number of babies born before 37 weeks of gestation. Women in the current study were exposed to various sources of stress. The logistic regression results showed that pregnant women who were in real danger of being killed by criminals (they themselves or a close family member) had greater odds of experiencing overall poor birth outcome (OR: 3.81). Stress during pregnancy may lead to disruptions in maternal- placental-foetal endocrine and immune system responses and so influence homeostasis in the mother. This disruption may increase the risk of premature birth as well as low birth weight, amongst others (Taylor et al., 2020). Malacova et al. (2017) conducted a systematic review and meta-analysis to evaluate the risk of stillbirth, premature delivery and small for gestational age as a proxy for foetal growth restriction following exposure to any of these factors in a previous birth. The risk of these conditions was moderately elevated in women who had previous single exposure to any of the three conditions with the risks even grated with two prior poor outcomes were combined (Malacova et al., 2017). In the current study, more babies who were born stunted (weight- for-length below the -2 SD) had mothers who had experienced a premature delivery with their first-born (p=0.0449). More babies who were born term had mothers who breastfed their previous children after birth (p<0.0001). No other studies that investigated this relationship could be found. Diabetes, pre-eclampsia, foetal growth retardation, pregnant with more than one foetus and maternal infections, amongst many other factors, may increase the risk for premature delivery (Mahande et al., 2013; Alijahan et al., 2014). Maternal hypertension increases the 230 risk of poor birth outcomes (Wu et al., 2020). In the current study, participants who were diagnosed with or treated for high blood pressure during the current pregnancy had higher odds (OR: 1.94) of experiencing overall poor birth outcome compared to those who were diagnosed or treated in the past or never. While maternal diabetes is associated with increased risk of delivering a baby who is large for gestational age and premature delivery (Kong et al., 2019), more babies who were not stunted at birth (born with a birth length-for- age above or equal to the -1 SD) had a mother who was diagnosed with or treated for diabetes in the current pregnancy (p=0.0255). No studies investigating a link between maternal diabetes and length-for-age at birth could be found. In the current study, a higher percentage of babies who were born prematurely had mothers who were hospitalised during the current pregnancy (p=0.0169), experienced diarrhoea for at least three days (p<0.0001) or loss of appetite (p=0.0213) during the current pregnancy. Wallin et al. (2020) found that symptoms of nausea, vomiting or poor appetite during pregnancy were associated with low birth weight, small for gestational age as well as premature delivery among pregnant women in the Sarlahi District in rural Nepal between 2011 and 2013. No significant differences were noted between the medications reported in the current study and method of delivery, time of delivery, length-for-age at birth and weight-for-length at birth. The effects of medication use during pregnancy on the health of both mother and foetus are, however, unknown for many medications (Fisher et al., 20018). The benefits of antiretroviral therapy (ART) in the prevention of mother-to-child transmission (PMTCT) are obvious, however, conflicting findings regarding the association between antiretroviral (ARV) exposure and poor birth outcomes exist (Li et al., 2016:1057). The authors of a systematic review on ART related adverse birth outcomes among Human Immunodeficiency Virus (HIV) infected women concluded that there is a growing body of evidence that indicates that ART may cause poor birth outcomes in the offspring of pregnant women in developing countries (Alemu et al., 2015:31), however, maternal HIV infection without appropriate treatment may contribute to vertical transmission in the foetus while also increasing the risk for poor birth outcomes (Li et al., 2020). Few women in the current study reported using medication for the management of TB and diabetes and no significant associations were observed with using these medications and any 231 of the birth outcomes investigated. TB during pregnancy may hold severe consequences for the mother and her offspring. Currently, no significant association seems to exist between exposure to first-line TB medication during pregnancy and child abnormalities (Nguyen et al., 2014). Although the teratogenic potential of second-line drugs used in the treatment of multidrug-resistant TB (MDR-TB) is unclear, risks are greater if left untreated (Rohilla et al., 2016). Twin pregnancy holds various risks for both the mother and her offspring (Rissanen et al., 2019). Twin pregnancies are associated with an increased risk of delivery via caesarean section (Rissanen et al., 2019), premature delivery, foetal growth restriction, perinatal morbidity and mortality (Antonakopoulos et al., 2020). The findings of the current study confirmed the risks associated with a twin pregnancy, as significant associations were observed between number of babies expected and all four individual birth outcomes investigated (method of delivery, prematurity, birth length-for-age and birth weight-for-age). It is, however, important to note that twin pregnancies have different growth trajectories than singleton pregnancies irrespective of other exposures (Blucker & Green, 2004). The logistic regression analysis also confirmed this, as participants who were expecting one baby had lower odds (OR: 0.06) of experiencing overall poor birth outcome compared to those who were expecting two babies. The risks associated with maternal overweight and obesity are well documented (Vernini et al., 2016; Stubert et al., 2018; Yang et al., 2019) and include poor health in both the mother and her offspring (Denker et al., 2016). In addition, maternal underweight continues to remain prevalent and also contributes to poor birth outcomes, including an increased likelihood of a premature delivery (Han et al., 2011:65; Liu et al., 2019). While the majority of the women in the current study were overweight and obese, significantly more babies who were born prematurely had a mother who was in the underweight category for GBMI (p=0.0052). Li et al. (2019) found that high maternal body mass index is associated with macrosomia (birth weight ≥ 4000 grams), large for gestational age as well as premature delivery while underweight is associated with an increased risk of low birth weight and small for gestational age. Differences between findings of the study by Liu et al. (2019) and the current study may be because the current study only applied length-for-age and weight-for- 232 length at birth below the -2 SD along with premature delivery in determining overall birth outcome. Participants in the current study who were normal weight, overweight or obese had lower odds of experiencing overall poor birth outcome compared to women who were underweight. This is in contrast to findings from other studies that found that underweight as well as overweight and obesity are associated with an increased risk of experiencing poor birth outcomes (Agrawal & Singh, 2016; Ratnasiri et al., 2019). These differences may be explained by the concept of metabolically healthy obesity which refers to individuals with obesity who have normal blood glucose levels, lipid metabolism as well as the absence of hypertension (Blüher, 2020). Considering that all pregnant women with a BMI in the obese range are referred to the high-risk antenatal clinic at Pelonomi Hospital, some women in the current study may only have obesity without any other metabolic abnormalities which may explain the difference in findings. It is, however, important to note that although the risk of type 2 diabetes and cardiovascular diseases is lower among individuals with metabolically healthy obesity compared to their metabolically unhealthy counterparts, the risks are still higher compared to healthy lean individuals (Blüher, 2020). In addition, the risks to the offspring that are associated with the mother being overweight and obese during pregnancy, include an increased risk of being overweight as a child and increased risk for NCDs later in life (Poston et al., 2016). It is thus probable that the offspring of obese mothers may only be affected as they grow older, and not at birth. Certain limitations are acknowledged. Weight and length were measured by nursing staff and not by the researchers themselves. In order to improve the reliability of the measurements, the dietitians at Pelonomi Hospital provide regular training sessions of all the staff working in the maternity as well as paediatric wards where the majority of the mothers attending the antenatal clinic are expected to deliver their babies. Most of the information reported on in this article is self-reported, although it is possible that participants may not have answered truthfully, they were informed that the purpose of the study was not to criticise them, but to obtain information that may benefit pregnant women in the long term. It is also important to consider that, as published in Jordaan et al., (2020a), certain reported health and lifestyle variables differed significantly between women who provided the birth information of their 233 babies from those who did not. The inclusion of twin pregnancies increases the outcome of low birth weight and prematurity. The current study attempted to give a broad overview of the reported health and lifestyle and associated factors of pregnant women attending the antenatal clinic at Pelonomi Hospital. Future studies may include additional aspects such as exposure to environmental toxins to determine how environmental factors other than smoking and alcohol could impact pregnancy outcomes. Finally, reasons for findings that were obtained by the quantitative methods that were applied in the current study could be investigated in more depth by applying qualitative methods such as focus group discussions that have the potential to provide insights about the challenges and barriers that pregnant women face. 7.6 CONCLUSION AND RECOMMENDATIONS The current study identified significant associations between various reported health and lifestyle factors and birth outcomes, most notably, premature delivery. The odds of experiencing overall poor birth outcome were higher for women who were (themselves or a close family member) in real danger of being killed by criminals compared to those who were not as well as for women who were diagnosed with or treated for high blood pressure during the current pregnancy. The odds of experiencing overall poor birth outcome were lower for women who were expecting one baby as well as for women who were normal weight, overweight or obese compared to those who were underweight. Education programmes should target mothers attending antenatal clinics to inform them of the risks associated with poor lifestyle choices during pregnancy and the benefits of relying on the available support networks to help with stress management during pregnancy. Health psychology and behaviour change techniques may be beneficial in informing and motivating mothers to improve their lifestyle during pregnancy to achieve healthier birth outcomes (Soltani et al., 2017). Establishing and referring pregnant women to pregnancy support groups may prove valuable in providing additional support to pregnant women, particularly those in the high-risk category. Future publications should rather compare the findings for twins and singletons separately. 234 7.7 ACKNOWLEDGEMENTS The authors would like to acknowledge the participants for their willingness to contribute to our research as well as the staff at Pelonomi Hospital for accommodating the research team in their clinic. The fieldworkers are thanked for the time and effort put into collecting the data. The authors have no conflict of interest to declare. This project was funded by the researchers themselves. 7.8 REFERENCES Agrawal, S. & Singh, A. 2016. Obesity or underweight – What is the worse in pregnancy? Journal of Obstetrics and Gynaecology India, 66(6):48–452, December. Alemu, F.M., Yalew, A.W., Fantahun, M. & Ashu, E.E. 2015. 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PLoS One, January 21. https://dx.doi.org/10.1371%2Fjournal.pone.0146241 [4 August 2020]. 241 8 CHAPTER 8 - HOUSEHOLD FOOD SECURITY AND NUTRIENT INTAKE AND THEIR ASSOCIATION WITH BIRTH OUTCOMES OF PREGNANT WOMEN ATTENDING THE ANTENATAL CLINIC AT PELONOMI HOSPITAL, BLOEMFONTEIN 8.1 ABSTRACT Background and objectives: Nutritional needs of pregnant women should receive attention to promote healthy behaviours that may lead to improved birth outcomes. The aim of this study was, thus, to describe the nutrient intake and dietary diversity of pregnant women attending the antenatal clinic at Pelonomi Hospital, Bloemfontein and to determine the associations of household food security and dietary diversity with birth outcomes. Methods: This study formed part of a quantitative, cohort analytical study to develop a nutrition screening tool for pregnant women, attending the high-risk antenatal clinic at Pelonomi Hospital, Bloemfontein, Free State which included 682 pregnant women. Post- delivery, 331 of these mothers provided the researchers with the Road to Health Booklets for 347 babies, from which the birth data were recorded. Questionnaires related to food security and dietary intake (24-hour recall to determine dietary diversity and quantitative food frequency questionnaire to determine nutrient intake) were completed during a structured interview. Information regarding birth outcomes was obtained from the Road to Health Booklets of the babies. Associations between dietary diversity variables and individual birth outcomes (method of delivery, prematurity, birth-length-for-age and birth weight-for-length) and overall birth outcome (babies born prematurely, birth length-for-age < -2 SD or birth weight-for-length < -2 SD) were investigated. Results: No significant associations were observed between household food security status and any of the birth outcomes included in the current study. Significantly more babies who were born prematurely had a mother who had vitamin C (p=0.0207) and iron from food and supplement (p=0.0472) intakes below the relevant DRIs. Significantly more babies with a birth length-for-age between the -2 SD and the -1 SD (at risk of developing stunting) had a mother with a protein intake below the % of the acceptable macronutrient distribution range (ADMR) of the dietary reference intake (DRI) (p=0.0067) and estimated average requirement (EAR) of of the World Health Organization and Food and Agriculture Organization of the United Nations (WHO/FAO) (p=0.342). Significantly more babies with a birth length-for-age at risk of 242 developing stunting had mothers with an intake below the % of the AMDR of the WHO/FAO recommendations for total fat (p=0.0008), while significantly more babies with a birth length- for-age at risk of developing stunting had a mother with an intake from added sugar above the % of the AMDR of the DRI (p=0.0058). A significantly higher percentage of babies with a birth weight-for-length greater or equal to the -2 SD but less than the -1 SD (at risk of developing wasting) had mothers with an energy intake below the WHO/FAO recommendations (p=0.0157), a similar trend was also observed between weight-for-length at birth and % of the AMDR of the DRI for protein (p=0.0408), vitamin A (p=0.0468) and vitamin C (p=0.0425). Significantly more babies with a normal weight-for-length at birth had mothers with a vitamin D intake equal to or above the DRI (p=0.0337). Interestingly, significantly fewer babies with a weight-for-length at birth below the -2 SD (wasted) had mothers with a calcium intake below the DRI (p=0.0415). Significantly more babies with a birth weight-for-length at risk of developing wasting had mothers with a zinc intake below the DRI (p=0.0220) and WHO/FAO recommendations (p=0.0013). With the addition of intake from supplements, significant associations were observed for calcium, folic acid and iron for both the DRI and WHO/FAO recommendations and length-for-age at birth. A similar trend was observed for the micronutrient intake with supplements added for calcium, folic acid as well as iron where significantly more babies with a birth length-for-age at risk of developing stunting had mothers with an intake below the DRI and WHO/FAO recommendations for calcium, folic acid and iron. In terms of dietary diversity, none of the food groups was significantly associated with overall birth outcome, with only the legumes, nuts and seeds group having a p-value <0.15. Conclusion: Significant associations between birth outcomes and nutrient intakes indicate that improved birth outcomes are generally associated with improved nutrient intake. Education programmes should inform women of childbearing age about the importance of nutrition in ensuring a successful pregnancy. Women should also be informed about the importance of taking the supplements that are recommended during pregnancy as prescribed. Keywords: food insecurity, nutrient, macronutrient, micronutrient, birth outcome 243 8.2 INTRODUCTION Three overarching objectives drive the Global Strategy for Women’s, Children’s and Adolescents’ Health (2016–2030) namely “survive” (to end preventable deaths), “thrive” (to ensure health and well-being) and “transform” (to expand enabling environments). As part of ensuring health and well-being, all forms of malnutrition should be eradicated and the nutritional needs of children, adolescent girls, and pregnant and lactating women should be addressed (Every Woman Every Child, 2015). The basic antenatal care approach currently implemented in South Africa emphasises the provision of routine antenatal care that includes the promotion of healthy behaviours, with specific mention of adequate nutrition and moderate exercise (SADoH, 2015). Food insecurity refers to the lack of nutritionally adequate and safe food or a limited ability to obtain necessary food in socially acceptable ways (Bickel et al., 2000). Food insecurity affects the quality and sufficiency of nutrition and holds various risks for individual health and may influence the quality of life of pregnant women (Moafi et al., 2018). Food insecurity during pregnancy has been associated with poor pregnancy outcomes and may increase the risk of complications (Zar et al., 2019). A moderate increase is observed in the energy requirements of normal weight, moderately active pregnant women, depending on the stage of pregnancy. This increase in requirements can be met with a slight increase in energy intake with a balanced intake of macronutrients (Maragoni et al., 2016). Excessive intakes of both energy and macronutrients during pregnancy may be just as harmful as deficient intakes, particularly among pregnant women who are overweight and obese (Catalano et al., 2015). Micronutrient deficiencies refer to insufficient amounts of essential vitamins and minerals that need to be obtained from the diet to meet recommended daily allowances to allow for good health, growth and development (Black et al., 2013; Darnton-Hill & Mkparu, 2015). Micronutrients are, therefore, vital to sustain life and to promote optimal function. Due to the increased requirements, pregnant women are at high risk of developing micronutrient deficiencies (Bailey et al., 2015:22). Deficiencies may worsen during pregnancy as a result of the increased energy and nutrient demands and may contribute to poor outcomes in both the mother and her offspring (Oh et al., 2020). The most widespread micronutrient 244 deficiencies among high-risk populations such as pregnant women include iron, iodine, folate, vitamin A and zinc deficiencies. These may contribute to poor growth, perinatal complications and increased morbidity and mortality, amongst others (Bailey et al., 2015:22). The risks of micronutrient deficiency can, however, be decreased by implementing interventions such as micronutrient supplementation (Oh et al., 2020). The World Health Organization (WHO) updated their recommendations on multiple micronutrient supplements during pregnancy and recommend the use of multiple micronutrient supplements that include both iron and folic acid and that multiple micronutrient supplements providing 30 mg of iron may be more acceptable than iron and folic acid supplements that contain higher doses of iron. Providing 60 mg of elemental iron is recommended for populations where anaemia is identified as a severe public health problem (anaemia prevalence greater or equal to 40%) (WHO, 2020). Evidence on the current prevalence of anaemia among women of childbearing age in South Africa is, however, lacking (Dorsamy et al., 2020). The South African National Health and Nutrition Examination Survey (SANHANES-1) of 2013 reported that 23.1% of women of childbearing age (16–35 years of age) suffered from anaemia, with 9.7% suffering from iron deficiency anaemia (Shisana et al., 2013:162) while the South African Demographic and Health Survey (SADHS) of 2016 found that 33% of women aged 15–49 years suffered from anaemia. The current standard clinical practice in public sector hospitals in South Africa is, however, to prophylactically supplement pregnant women with iron, folate and calcium throughout pregnancy. As per the “Guidelines for maternity care in South Africa”, all pregnant women should receive supplements of ferrous sulphate (200 mg daily, the equivalent of approximately 65 mg elemental iron), calcium (1000 mg daily) and folic acid (5 mg daily) (SADoH, 2015). Currently, the recommended dietary allowance (RDA) for folic acid in non-pregnant women is 600 µg per day while the tolerable upper intake level (UL) during pregnancy is 1 mg per day (IOM, 2006) which is much lower than the guideline of 5 mg per day during pregnancy. A diverse diet may provide an indication of nutrient adequacy since it is impossible for one food group alone to meet all of the nutritional requirements of an individual (Labadarios et al., 2011; Kiboi et al., 2017). An intake of a variety of food groups may promote health in terms of both mental and physical development. If pregnant women fail to consume a healthy diet with a variety of foods necessary to provide all the essential nutrients, it can lead to the 245 development of malnutrition during pregnancy. This, in turn, may lead to multiple poor outcomes such as maternal anaemia, postpartum complications and increased neonatal morbidity and mortality (Desta, 2019). The aim of this study was, thus, to describe the nutrient intake and dietary diversity of pregnant women attending the antenatal clinic at Pelonomi Hospital, Bloemfontein and to determine the associations between household food security, dietary diversity and birth outcomes. 8.3 MATERIALS AND METHODS 8.3.1 Study design and participants This study formed part of a quantitative, cohort analytical study to develop a nutrition screening tool for pregnant women, attending the antenatal clinic at Pelonomi Hospital, Bloemfontein, Free State. The study design and participants have been described elsewhere (Jordaan et al., 2020a). 8.3.2 Outcomes measures The primary outcome measures for this study were delivery method, prematurity, length-for- age and weight-for-length at birth. The outcome measures have been previously described in Jordaan et al. (2020b). Women with either premature delivery (< 37 weeks of gestation) or low birth length-for-age (< -2 SD) or low birth weight-for-length (< -2 SD) were classified as having experienced overall poor birth outcome. Those women who delivered a full-term baby with a birth length-for-age and a birth weight-for-length above or equal to the -2 SD were classified as having experienced overall good birth outcome. Since it was not possible to determine whether method of delivery was spontaneous or planned, it was not included in the set of variables used to determine overall birth outcome. Where mothers were expecting twins, mothers were considered to have an overall poor birth outcome if at least one twin had a poor outcome. 246 8.3.3 Exposure measurements Household food security status Household food security was determined using the Household Food Insecurity Access Scale (HFIAS) which categorises food security as “food secure”, “mildly food insecure”, “moderately food insecure” and “severely food insecure” (Coates, Swindale & Bilinsky, 2007) which has been described in more detail in Jordaan et al. (2020a). Dietary intake information. Individual dietary intake was determined using a quantitative food frequency questionnaire (QFFQ) which were analysed for energy, macronutrients and micronutrients. Since no dietary reference intakes exist specifically for the South African population, energy intake was compared to the estimated average requirement (EAR) of the US Dietary Reference Intakes (DRI) (IOM, 2006), as well the EAR of the World Health Organization / Food and Agriculture Organization of the United Nations (WHO/FAO) (2004) recommendation for the pregnant population. Notably, the WHO/FAO recommendations are specifically aimed at populations in developing countries. Adequacy of protein, fat and carbohydrate intakes were also determined through the acceptable macronutrient distribution ranges (AMDR). Since the intake of groups should be evaluated using the EAR, requirements for micronutrients were also compared to the EAR of the US DRIs, as well the EAR of the the WHO/FAO (2004) recommendations. The probability method is used to assess the adequacy of iron intake at different levels of bioavailability, usually at 5% and 10% (Gibson & Ferguson, 2008). Since the women mostly consumed a mixed diet containing animal protein, zinc requirements were determined based on a diet of moderate bioavailability using the EAR-cut point method (Allen et al., 2006:60). As part of the dietary intake, pregnant women were also asked about supplement use. Women were asked to report on the type of supplement(s) used, the number of capsules/pills used as a time, how often they consumed the supplements (how many times per week) and when they first started using the supplements. Women were asked to report on supplements obtained at the clinic as well as those bought elsewhere. 247 Dietary diversity. The 24-hour recall was used to determine dietary diversity. The 16 groups of the Food and Nutrition Technical Assistance used to determine dietary diversity, including cereals; white roots and tubers; vitamin A-rich vegetables and tubers; dark green leafy vegetables; other vegetables; vitamin A-rich fruits; other fruits; organ meat; flesh-meat; eggs; fish and seafood; legumes, nuts, and seeds; milk and milk products; oils and fat; and sweets and spices, condiments and beverages. These 16 food groups were combined to a total of nine groups that were used (Table 8.1) to determine the women’s dietary diversity score (FAO, 2010). Table 8.1: Food groups included in the WDDS (FAO, 2011) Food group number Food group 1, 2 Starchy staples1 4 Dark, leafy green vegetables 3, 6 (and red palm oil if applicable) Other vitamin A-rich fruit and vegetables2 5, 7 Other fruits and vegetables3 8 Organ meat 9, 11 Meat and fish4 10 Eggs 12 Legumes, nuts and seeds 13 Milk and milk products 1The starchy staples food group is a combination of cereals and white roots and tubers 2 The other vitamin A-rich fruit and vegetable group is a combination of vitamin A-rich vegetables and tubers and vitamin A-rich fruit 3 The other fruit and vegetable group is a combination of all other fruit and vegetables 4 The meat group is a combination of meat and fish Dietary diversity scores were interpreted as low if less or equal to three food groups were consumed, as medium if between four and five food groups were consumed, and as high if six or more food groups were consumed (FAO, 2011). Each food group determined as part of the dietary diversity score was also considered individually. 8.3.4 Statistical analysis The researcher was responsible for entering all the data onto an Excel spreadsheet after which data checking and statistical analysis were performed by the Department of Biostatistics, Faculty of Health Sciences, University of the Free State. 248 Data were analysed using SAS/STAT software, Version 9.4 of the SAS system for Windows, Copyright © 2010 SAS Institute Inc. Descriptive statistics, including frequencies and percentages (for categorical data) and medians and percentiles (for numerical data), were calculated. Differences between groups were assessed by chi-squared tests (for categorical variables) or Fisher’s exact test (for categorical variables with sparse data) and Kruskall-Wallis tests (for numerical variables). Analysis of associations for various individual birth outcomes was done using babies as units of analysis, whereas analysis of overall birth outcome considered mothers as unit of analysis. 8.3.5 Ethical considerations This study was approved by the Health Sciences Research Ethics Committee, Faculty of Health Sciences, University of the Free State (UFS-HSD2018/0148/2905) and the Free State Department of Health. 8.4 RESULTS A response rate of 48.5% (331/682) of the pregnant women included in phase one of the study was observed in the second phase. Birth information was available for 347 babies. Household food security Table 8.2summerises the associations between household food security and the birth outcomes investigated in the current study. No significant associations were found between any of the four birth outcomes and household food security status. 249 Table 8.2: Association between household food security status and birth outcomes Birth Food secure Mildly food Moderately Severely food p-value outcome insecure food insecure insecure n % n % n % n % Delivery method (N=315) 0.8997 Vaginal delivery 32 26.5 14 11.6 37 30.6 38 31.5 Caesarean section 56 28.9 20 10.3 63 32.5 55 28.4 Gestational age (N=319) 0.1764 Premature 9 17.7 8 15.7 14 27.5 20 39.2 Term 79 29.5 29 10.8 84 31.3 76 28.4 Length-for-age Z-scores (N=334) 0.8793 < -2 SD 19 30.2 8 12.7 17 27.0 19 30.2 ≥ -2 SD < -1 SD 10 28.6 3 8.6 14 40.0 8 22.9 ≥ -1 SD 65 27.5 26 11.0 72 30.5 73 30.9 Weight-for-length Z-score (N=298) 0.8052 < -2 SD 10 23.3 7 16.3 12 27.9 14 32.6 ≥ -2 SD < -1 SD 17 25.0 9 13.2 20 29.4 22 32.4 ≥ -1 SD 55 29.4 18 9.6 61 32.6 53 28.3 #p-value for percentage difference between levels of food security using chi-square or Fisher’s exact tests, as appropriate; p <0.05 considered statistically significant indicated with * Nutrient intake Most participants had an energy intake below both the EAR of the DRI (80.1%) and the WHO/FAO recommendations (69.2%) (Table 8.3). In terms of protein, more than half of the participants consumed less than the DRI (57.4%) while almost a third consumed less than the RNI (63.8%) for protein. Majority of the participants were within the % of the AMDR of the DRI for protein (93.7%), while also within or above the % of the AMDR of the WHO/FAO recommendations for protein (93.4%). Regarding total fat intake, 17.8% of participants consumed more than the percentage of the AMDR of the DRI, while 47.1% consumed more than the % of the AMDR of the WHO/FAO recommendations for total fat intake. Most participants were below the % of the AMDR of the WHO/FAO recommendations for saturated fat as well as trans fats. Majority of the participants (86.1%) in the current study had intakes of monounsaturated fatty acids (MUFAs) below the % of the AMDR of the WHO/FAO recommendations while half (55.6%) had polyunsaturated fatty acid (PUFA) intakes that were within the % of the AMDR of the WHO/FAO recommendations. Significantly more babies who were born via normal delivery had mothers with a cholesterol intake below the WHO/FAO recommendations for cholesterol 250 (p=0.0194) compared to those who were born normally. Intakes of docosahexaenoic acid (DHA), DHA plus eicosapentaenoic acid (EPA) and α-linolenic acid were mostly below the recommendations while 68.0% had intakes of linoleic acid above or equal to the DRI. Almost all participants (98.8%) had a carbohydrate intake above or equal to the DRI. Consequently, few participants consumed carbohydrates below the % of the AMDR of both the DRI and WHO/FAO recommendations for carbohydrates. Interestingly, most participants had an intake below the % of the AMDR of the DRI for added sugar. Most participants (66.5%) had an intake from dietary fibre below the DRI while this changed to just less than half when considering the WHO/FAO recommendations for fibre. A relatively small number of participants reported consuming alcohol during pregnancy. Vitamin A intake was within the normal range for most participants. Vitamin C intake of most participants fell below the DRI, but an opposite trend was observed with regard to the WHO/FAO recommendations. Vitamin C intake was significantly associated with prematurity (p=0.0207) with significantly more premature babies with mothers who had a vitamin C intake below the DRI. Most participants also had low intakes of vitamin D compared to the DRI and WHO/FAO recommendations, a trend that was similar for folate, calcium and iron. Interestingly, significantly fewer babies who were born term had mothers with a folate intake above or equal to the DRI (p=0.0393) compared to premature infants. Intake of vitamin B12 and zinc was considered normal for most when compared to both the DRI and WHO/FAO recommendations. With the addition of the intake of calcium from supplements, the intake of most participants changed from below the DRI and WHO/FAO recommendations to within normal limits. Significantly more babies who were born prematurely had mothers with intakes of iron from food and supplements combined below the DRI (p=0.0472) compared to those who were born term. 251 Table 8.3: Nutrient intake and associations with delivery method and prematurity Variable Total sample Normal delivery Caesarean section # Premature Term p-value p-value# n % n % n % n % n % ENERGY AND MACRONUTRIENTS (N=331) (n=121) (n=194) (n=51) (n=268) DRI for energy 0.3400 0.3226 Below 265 80.1 99 81.8 150 77.3 38 74.5 216 80.6 Above or equal to 66 19.9 22 18.2 44 22.7 13 25.5 52 19.4 WHO/FAO recommendations for energy 0.7974 0.8250 Below 229 69.2 84 69.4 132 68.0 36 70.6 185 69.0 Above or equal to 102 30.8 37 30.6 62 32.0 15 29.4 83 31.0 DRI for protein 0.4948 0.0532 Below 190 57.4 67 55.4 115 59.3 23 45.1 160 59.7 Above or equal to 141 42.6 54 44.6 79 40.7 28 54.9 108 40.3 WHO/FAO recommendations for protein 0.9301 0.2089 Below 211 63.8 78 64.5 126 65.0 29 56.9 177 66.0 Above or equal to 120 36.2 43 35.5 68 35.1 22 43.1 91 34.0 % of AMDR of DRI for protein 0.5314 1.0000 Below range 21 6.3 9 7.4 11 5.7 3 5.9 18 6.7 In range 310 93.7 112 92.6 183 94.3 48 94.1 250 93.3 % of AMDR of WHO/FAO for protein 0.8194 0.7977 Below range 21 6.3 9 7.4 11 5.7 3 5.9 18 6.7 In range 254 76.7 92 76.0 151 77.8 41 80.4 204 76.1 Above range 56 16.9 20 16.5 32 16.5 7 13.7 46 17.2 % of AMDR of DRI for total fat 0.4415 0.4863 Below range 41 12.4 17 14.1 22 11.3 7 13.7 34 12.7 In range 231 69.8 86 71.1 133 68.6 39 76.5 190 70.9 Above range 59 17.8 18 14.9 39 20.1 5 9.8 44 16.4 % of AMDR of WHO/FAO for total fat 0.1468 0.0997 Below range 10 3.0 4 3.3 6 3.1 4 7.8 6 2.2 In range 165 49.9 67 55.4 86 44.3 26 51.0 136 50.8 Above range 156 47.1 50 41.3 102 52.6 21 41.2 126 41.0 % AMDR of WHO/FAO for saturated fat 0.2362 0.2384 Below 259 78.3 98 81.0 146 75.3 44 86.3 212 79.1 Above or equal to 72 21.7 23 19.0 48 24.7 7 13.7 56 20.9 252 Variable Total sample Normal delivery Caesarean section Premature Term p-value# p-value# n % n % n % n % n % ENERGY AND MACRONUTRIENTS (N=331) (n=121) (n=194) (n=51) (n=268) % AMDR of WHO/FAO for 0.7511 0.1256 monounsaturated fatty acids Below range 285 86.1 106 87.6 164 84.5 49 96.1 230 85.8 In range 43 13.0 14 11.6 28 14.4 2 3.9 36 13.4 Above range 3 0.9 1 0.8 2 1.0 0 0.0 2 0.8 % AMDR of RNI for polyunsaturated fatty 0.3298 0.5040 acids Below range 72 21.8 30 24.8 36 18.6 14 27.5 54 20.2 In range 184 55.6 66 55.6 108 55.7 26 51.0 152 56.7 Above range 75 22.7 25 20.7 50 25.8 11 21.6 62 23.1 % AMDR of WHO/FAO for total trans fats 0.7370 1.0000 Below 320 96.7 117 96.7 189 97.4 50 98.0 261 97.4 Above or equal to 11 3.3 4 3.3 5 2.6 1 2.0 7 2.6 WHO/FAO recommendation for cholesterol 0.0194* 0.3785 Below 215 65.0 88 72.7 116 59.8 36 70.6 172 64.2 Above or equal to 116 35.0 33 27.3 78 40.2 15 29.4 96 35.8 WHO/FAO recommendations for DHA 0.4122 0.5092 Below range 238 71.9 90 74.4 136 70.1 35 68.6 196 73.1 In range 93 28.1 31 25.6 58 29.9 16 31.4 72 26.9 WHO/FAO recommendations for DHA + EPA 0.8835 0.5463 Below range 235 71.0 87 71.9 138 71.1 35 68.6 195 72.8 In range 96 29.0 34 28.1 56 28.9 16 31.4 73 24.2 DRI for linoleic acid 0.4918 0.9198 Below 106 32.0 40 33.1 57 29.4 16 31.4 86 32.1 Above or equal to 225 68.0 81 66.9 137 70.6 35 68.6 182 67.9 DRI for α-linolenic acid 0.3841 1.0000 Below 330 99.7 120 99.2 194 100.0 51 100.0 267 99.6 Above or equal to 1 0.3 1 0.8 0 0.0 0 0.0 1 0.4 % AMDR of DRI for omega 6 fatty acids 0.2708 0.2001 Below range 63 19.0 27 22.3 32 16.5 14 27.5 46 17.2 In range 218 65.9 79 65.3 128 66.0 29 56.9 182 67.9 Above range 50 15.1 15 12.4 34 17.5 8 15.7 40 14.9 253 Variable Total sample Normal delivery Caesarean section Premature Term p-value# p-value# n % n % n % n % n % ENERGY AND MACRONUTRIENTS (N=331) (n=121) (n=194) (n=51) (n=268) % AMDR of WHO/FAO for omega 6 0.2504 0.2242 Below range 63 19.0 27 22.3 32 16.5 14 27.5 46 17.2 In range 134 40.5 50 41.3 75 38.7 19 37.3 111 41.4 Above range 134 40.5 44 36.4 87 44.9 18 35.3 111 41.4 % AMDR of DRI for omega 3 fatty acids 1.000 0.5946 Below range 325 98.2 119 98.4 191 98.5 51 100.0 262 97.8 In range 6 1.8 2 1.7 3 1.6 0 0.0 6 2.2 DRI for carbohydrates 0.2880 1.0000 Below 327 98.8 0 0.0 3 1.6 0 0.0 3 1.1 Above or equal to 4 1.2 121 100.0 191 98.5 51 100.0 265 98.9 % AMDR of DRI for carbohydrates 0.8067 0.4387 Below range 6 1.8 2 1.7 4 2.1 1 2.0 5 1.9 In range 231 69.8 82 67.8 137 70.6 31 60.8 187 69.8 Above range 94 28.4 37 30.6 53 27.3 19 37.3 76 28.4 % AMDR of WHO/FAO for carbohydrates 0.4384 0.2248 Below range 86 26.0 27 22.3 56 28.9 8 15.7 66 24.6 In range 217 65.6 83 68.6 122 62.9 36 70.6 180 67.2 Above range 28 8.4 11 9.1 16 8.3 7 13.7 22 8.2 % AMDR of DRI for added sugar 0.5610 0.4478 Below 328 99.1 119 98.4 193 99.5 51 100.0 265 98.9 Above or equal to 3 0.9 2 1.7 1 0.5 0 0.0 3 1.1 % AMDR of WHO/FAO for added sugar 0.9376 0.9162 Below 206 62.2 76 62.8 121 62.4 31 60.8 165 61.6 Above or equal to 125 37.8 45 37.2 73 37.6 20 32.9 103 38.4 DRI for dietary fibre 0.3145 0.2497 Below 220 66.5 75 62.0 131 67.5 21 41.2 88 32.8 Above or equal to 111 33.5 46 38.0 63 32.5 30 58.8 180 67.2 WHO/FAO recommendation for fibre 0.8591 0.5863 Below 161 48.6 58 47.9 91 46.9 28 54.9 136 50.8 Above or equal to 170 51.4 63 52.1 103 53.1 23 45.1 132 49.2 DRI for alcohol 0.9414 0.5395 Zero 305 92.1 112 92.6 180 92.8 47 92.2 252 94.0 Above or equal to 26 7.9 9 7.4 14 7.2 4 7.8 16 6.0 254 Total sample Normal delivery Caesarean section # Premature Term p-value p-value# MICRONUTRIENTS n % n % n % n % n % (N=331) (n=121) (n=194) (n=51) (n=268) DRI for Vitamin A 0.6147 0.5870 Below 51 15.4 20 16.5 28 14.4 7 13.7 45 16.8 Above or equal to 280 84.6 101 83.5 166 85.6 44 86.3 223 83.2 WHO/FAO recommendations for Vitamin A 0.7066 0.5454 Below 51 15.4 20 16.5 29 15.0 7 13.7 46 17.2 Above or equal to 280 84.6 101 83.5 165 85.1 44 86.3 222 82.8 DRI for Vitamin C 0.6091 0.0207* Below 199 60.1 74 61.2 113 58.3 39 76.5 159 59.3 Above or equal to 132 39.9 47 38.8 81 41.8 12 23.5 109 40.7 WHO/FAO recommendations for Vitamin C 0.1089 0.9068 Below 129 39.0 54 44.6 69 35.6 21 41.2 108 40.3 Above or equal to 202 61.0 67 55.4 125 64.4 30 58.8 160 59.7 DRI for Vitamin D 0.0953 0.3740 Below 319 96.4 120 99.2 185 95.4 51 100.0 258 96.3 Above or equal to 12 3.6 1 0.8 9 4.6 0 0.0 10 3.7 WHO/FAO recommendations for Vitamin D 0.1017 0.6102 Below 211 63.8 84 69.4 117 60.3 35 68.6 174 64.9 Above or equal to 120 36.2 37 30.6 77 39.7 16 31.4 94 35.1 DRI for Folate 0.6616 0.0393* Below 237 71.6 87 71.9 15 69.6 30 58.8 196 73.1 Above or equal to 94 28.4 34 28.1 59 30.4 21 41.2 72 26.9 WHO/FAO recommendations for Folate 0.7172 0.0667 Below 207 62.5 73 60.3 121 62.4 26 51.0 173 64.6 Above or equal to 124 37.5 48 39.7 73 37.6 25 49.0 95 35.5 DRI for Vitamin B12 0.5564 0.8073 Below 72 21.8 29 24.0 41 21.1 11 21.6 62 23.1 Above or equal to 259 78.2 92 76.0 153 78.9 40 78.4 206 76.9 WHO/FAO recommendation for Vitamin B12 0.5564 0.8073 Below 72 21.8 29 24.0 41 21.1 11 21.6 62 23.1 Above or equal to 259 78.2 92 76.0 153 78.9 40 78.4 206 76.9 DRI for Calcium 0.7136 0.6147 Below 287 86.7 103 85.1 168 86.6 43 84.3 233 86.9 Above or equal to 4 13.3 18 14.9 26 13.4 8 15.7 35 13.1 255 Total sample Normal delivery Caesarean section p-value# Premature Term p-value# MICRONUTRIENTS n % n % n % n % n % (N=331) (n=121) (n=194) (n=51) (n=268) WHO/FAO recommendations for Calcium 0.4384 0.4590 Below 290 87.6 103 85.1 171 88.1 43 84.3 236 88.1 Above or equal to 41 12.4 18 14.9 23 11.9 8 15.7 32 11.9 DRI for Iron 0.8295 0.7733 Below 271 81.9 98 81.0 159 82.0 41 80.4 220 82.1 Above or equal to 60 18.1 23 19.0 35 18.0 10 19.6 48 17.9 WHO/FAO recommendations for Iron - - Below 331 100.0 121 100.0 194 100.0 51 100.0 268 100.0 Above or equal to 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 DRI for Zinc 0.6835 0.0619 Below 71 21.5 26 21.5 38 19.6 6 11.8 63 23.5 Above or equal to 260 78.5 95 78.5 156 80.4 45 88.2 205 76.5 WHO/FAO recommendations for Zinc 0.7506 0.6906 Below 12 3.6 3 2.5 6 3.1 2 3.9 9 3.4 Above or equal to 319 96.4 118 97.5 188 96.9 49 96.1 259 96.6 Total sample Normal delivery Caesarean section Premature Term MICRONUTRIENTS WITH SUPPLEMENTS p-value# p-value# n % n % n % n % n % ADDED (N=331) (n=121)) (n=193) (n=51) (n=267) DRI for Calcium 0.2471 0.5996 Below 32 9.7 10 8.3 24 12.4 6 11.8 24 9.0 Above or equal to 298 90.3 111 91.7 169 87.6 45 88.2 243 91.0 WHO/FAO recommendations for Calcium 0.2471 0.5996 Below 32 9.7 10 8.3 24 12.4 6 11.8 24 9.0 Above or equal to 298 90.3 111 91.7 169 87.6 45 88.5 243 91.0 DRI for Folic acid 0.7777 0.0736 Below 15 4.6 6 5.0 11 5.7 5 9.8 10 3.8 Above or equal to 315 95.4 115 95.0 182 94.3 46 90.2 257 96.3 WHO/FAO recommendations for Folic acid 0.7777 0.0736 Below 15 4.6 6 5.0 11 5.7 5 9.8 10 3.8 Above or equal to 315 95.4 115 95.0 182 94.3 46 90.2 257 96.3 DRI for Iron 0.7411 0.0472* Below 28 8.5 12 9. 17 8.8 8 15.7 18 6.7 Above or equal to 302 91.5 109 90.1 176 91.2 43 84.3 249 93.3 256 Total sample Normal delivery Caesarean section MICRONUTRIENTS WITH SUPPLEMENTS p-value# Premature Term p-value# n % n % n % n % n % ADDED (N=331) (n=121)) (n=193) (n=51) (n=267) WHO/FAO recommendations for Iron 0.4388 0.1452 Below 35 10.6 16 13.2 20 10.4 8 15.7 24 9.0 Above or equal to 295 89.4 105 86.8 173 89.6 43 84.3 243 91.0 #p-value for percentage difference between normal delivery versus caesarean delivery and premature delivery versus term delivery using chi-square or Fisher’s exact tests, as appropriate; p <0.05 considered statistically significant indicated with * 257 Nutrient intake and birth outcomes related to birth length and weight Table 8.4 provides an overview of the associations between nutrient intake and length-for- age at birth as well as weight-for-length. Significantly more babies with a birth weight-for- length greater or equal to the -2 standard deviation (SD) but below the -1 SD (considered at risk of developing wasting) had mothers with an energy intake below the WHO/FAO recommendations (p=0.0157). Just over half of participants had a protein intake below the DRI and WHO/FAO recommendations for protein, except for participants who delivered babies with a birth length below the -2 SD for length-for-age (considered stunted). Significant associations were observed for both length-for-age (p=0.0067) and weight-for-length (p=0.0408) at birth and % of the AMDR of the DRI for protein. Similarly, a significant association was also observed between % the AMDR of the WHO/FAO recommendations for protein and length-for-age at birth (p=0.0342). Significantly more babies with a length-for- age at birth between the -2 SD and the -1 SD (considered at risk of developing stunting) had mothers with a protein intake below the % of the ADMR. Significantly more babies at risk of developing stunting had mothers with an intake below the % of the AMDR of the WHO/FAO recommendations for total fat (p=0.0008). Most participants also had intakes below the % of the AMDR of the WHO/FAO recommendations for saturated fats, MUFAS and trans fats, but not for PUFAS. Intakes of cholesterol, DHA, DHA plus EPA, linoleic acid as well as α-linolenic acid were below the WHO/FAO recommendations for most participants. Few participants had an omega 6 intake below the % of the ADMR of both the DRI and WHO/FAO recommendations, while most participants had an omega 3 intake below the % of the AMDR of both the DRI and WHO/FAO recommendations. Majority of the participants had a carbohydrate intake above or equal to the DRI. A significant association was observed between the % of the AMDR of the DRI for added sugar and length- for-age at birth (p=0.0058). Significantly more babies at risk of developing stunting had mothers with an intake from added sugar above or equal to the % of the AMDR of the DRI. While most participants had a fibre intake below the DRI for dietary fibre, an opposite trend was observed for the WHO/FAO recommendations for dietary fibre. A small number of participants reported consuming alcohol during pregnancy. 258 Most participants had a vitamin A intake above or equal to the DRI and WHO/FAO recommendations, with a significant association noted between the EAR of the WHO/FAO for vitamin A and weight-for-length at birth (p=0.0468). Significantly more babies born at risk of developing wasting had mothers with a vitamin A intake below the WHO/FAO recommendations for vitamin A. Similarly, significantly more babies born at risk of developing wasting had mothers with a vitamin C intake below the DRI (p=0.0425). Significantly more babies who were born with a weight-for-length above or equal to the -1 SD (considered normal) had a vitamin D intake above or equal to the DRI (p=0.0337). Folate intakes were below the DRI and WHO/FAO recommendations for most participants. Participants mostly had an intake of vitamin B12 above or equal to the DRI and WHO/FAO recommendations. Majority of the participants had a calcium intake below the DRI and WHO/FAO recommendations, with a similar trend observed for iron intakes. Interestingly, significantly fewer babies who were born with a weight-for-length at birth below the -2 SD (considered wasted) had mothers with a calcium intake below the DRI (p=0.0415). Zinc intake was significantly associated with weight-for-length at birth for both the DRI (p=0.0220) and EAR of the WHO/FAO (p=0.0013). Significantly more babies at risk of developing wasting had mothers with a zinc intake below the DRI and WHO/FAO recommendations. With the addition of intake from supplements, significant associations were observed for calcium, folic acid and iron for both the DRI and WHO/FAO recommendations and length-for- age at birth. A similar trend was observed for the micronutrient intake with supplements added for calcium, folic acid as well as iron where significantly more babies with a birth length- for-age greater or equal to the -2 SD but below the -1 SD had mothers with an intake below the DRI and WHO/FAO recommendations for calcium, folic acid and iron. 259 Table 8.4: Associations between length-for-age and weight-for-length at birth and energy, macro- and micronutrient intake of participants Variable Length-for-age Weight-for-length < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p- < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# n % n % n % value# n % n % n % ENERGY AND MACRONUTRIENTS (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) DRI for energy 0.4271 0.1533 Below 50 79.4 25 71.4 191 80.9 36 83.7 59 86.8 143 76.5 Above or equal to 13 20.6 10 28.6 45 19.1 7 16.3 9 13.2 44 23.5 WHO/FAO recommendations for energy 0.7218 0.0157* Below 42 66.7 23 65.7 167 70.8 25 58.1 56 82.3 126 67.4 Above or equal to 21 33.3 12 34.3 69 29.2 18 41.9 12 17.7 61 32.6 DRI for protein 0.0879 0.4130 Below 29 46.0 20 57.1 145 61.4 22 51.2 43 63.2 114 61.0 Above or equal to 34 54.0 15 42.9 91 38.6 21 48.8 25 36.8 73 39.0 WHO/FAO recommendations for protein 0.0928 0.9080 Below 33 52.4 21 60.0 158 66.9 28 65.1 46 67.7 121 64.7 Above or equal to 30 47.6 14 40.0 78 33.1 15 34.9 22 32.4 66 35.3 % of AMDR of DRI for protein 0.0067* 0.0408* Below range 1 1.6 6 17.1 13 5.5 3 7.0 9 13.2 8 4.3 In range 62 98.4 29 82.9 223 94.5 40 93.0 59 86.8 179 95.7 % of AMDR of WHO/FAO for protein 0.0342* 0.1646 Below range 1 1.6 6 17.1 13 5.5 3 7.0 9 13.2 8 4.3 In range 50 79.4 25 71.4 184 78.0 33 76.7 48 70.6 149 76.7 Above range 12 19.0 4 11.4 39 16.5 7 16.3 11 16.2 30 16.0 % of AMDR of DRI for total fat 0.4209 0.9047 Below range 4 6.4 6 17.1 32 13.6 4 9.3 10 14.7 25 13.4 In range 49 77.8 24 68.6 160 67.8 30 69.8 47 69.1 126 67.4 Above range 10 15.9 5 14.3 44 18.6 9 20.9 11 16.2 36 19.3 % of AMDR of WHO/FAO for total fat 0.0008* 0.6871 Below range 0 0.0 5 14.3 6 2.5 2 4.7 3 4.4 6 3.2 In range 32 50.8 11 31.4 125 53.0 22 51.2 38 55.9 88 47.1 Above range 31 46.2 19 54.3 105 44.5 19 44.2 27 39.7 93 49.7 % AMDR of WHO/FAO for saturated fat 0.2653 0.6138 Below 55 87.3 27 77.1 185 78.4 36 83.7 54 79.4 144 77.0 Above or equal to 8 12.7 8 22.9 51 21.6 7 16.3 14 20.6 43 23.0 260 Variable Length-for-age Weight-for-length < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p- < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# n % n % n % value# n % n % n % ENERGY AND MACRONUTRIENTS (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) % AMDR of WHO/FAO for monounsaturated 0.9112 0.6075 fatty acids Below range 55 87.3 31 88.6 202 85.6 39 90.7 60 88.2 156 83.4 In range 7 11.1 4 11.4 32 13.6 4 9.3 8 11.8 29 15.5 Above range 1 1.6 0 0.0 2 0.9 0 0.0 0 0.0 2 1.1 % AMDR of WHO/FAO for polyunsaturated 0.8389 0.5806 fatty acids Below range 11 17.5 9 25.7 51 21.6 8 18.6 19 27.9 35 18.7 In range 35 55.6 18 51.4 133 53.4 24 55.8 35 51.5 109 58.3 Above range 17 27.0 8 22.9 52 22.0 11 25.6 14 20.6 43 23.0 % AMDR of WHO/FAO for total trans fats 0.9848 0.2308 Below 61 96.8 34 97.1 228 96.6 40 93.0 67 98.5 182 97.3 Above or equal to 2 3.2 1 2.9 8 3.4 3 7.0 1 1.5 5 2.7 WHO/FAO recommendation for cholesterol 0.5662 0.1646 Below 43 68.3 25 71.4 150 63.6 29 67.4 50 73.5 114 61.0 Above or equal to 20 31.8 10 28.6 86 36.4 14 32.6 18 26.5 73 39.0 WHO/FAO recommendations for DHA 0.2579 0.1449 Below range 44 69.8 21 60.0 173 73.3 29 67.4 55 80.9 129 69.0 In range 19 30.2 14 40.0 63 26.7 14 32.6 13 19.1 58 31.0 WHO/FAO recommendations for DHA + EPA 0.4558 0.1954 Below range 43 68.3 22 62.9 171 72.5 28 65.1 54 79.4 130 69.5 In range 20 31.8 16 37.1 65 27.5 15 34.9 14 20.6 57 30.5 DRI for linoleic acid 0.2646 0.1828 Below 15 23.8 9 25.7 79 33.5 11 25.6 27 39.7 54 28.9 Above or equal to 48 76.2 26 74.3 157 66.5 32 74.4 41 60.3 133 71.1 DRI for α-linolenic acid 0.8120 0.7425 Below 63 100.0 35 100.0 235 99.6 43 100.0 68 100.0 186 99.5 Above or equal to 0 0.0 0 0.0 1 0.4 0 0.0 0 0.0 1 0.5 261 V ariable Length-for-age Weight-for-length < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p- < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# n % n % n % value# n % n % n % ENERGY AND MACRONUTRIENTS (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) % AMDR of DRI for omega 6 fatty acids 0.5678 0.6460 Below range In range 10 15.9 8 22.9 45 19.1 6 14.0 16 23.5 33 17.7 Above range 40 63.5 24 68.6 156 66.1 30 69.8 41 60.3 129 69.0 13 20.6 3 8.6 35 14.8 7 16.3 11 16.2 25 13.4 % AMDR of WHO/FAO for omega 6 fatty 0.8287 0.6893 acids Below range 10 15.9 8 22.9 45 19.1 6 14.0 16 23.5 33 17.7 In range 24 38.1 15 42.9 94 39.8 17 39.5 25 36.8 79 42.3 Above range 29 46.0 12 34.3 97 41.1 20 46.5 27 39.7 75 40.1 % AMDR of DRI for omega 3 fatty acids 0.1256 0.9334 Below range 60 95.2 35 100.0 233 98.7 42 97.7 67 98.5 183 97.9 In range 3 4.8 0 0.0 3 1.3 1 2.3 1 1.5 4 2.1 DRI for carbohydrates 0.7726 0.1819 Below 1 1.6 0 0.0 3 1.3 0 0.0 2 2.9 1 0.5 Above or equal to 62 98.4 35 100.0 233 98.7 43 100.0 66 97.1 186 99.5 % AMDR of DRI for carbohydrates 0.4847 0.4497 Below range 2 3.2 0 0.0 4 1.7 0 0.0 1 1.5 5 2.7 In range 47 74.6 26 74.3 158 67.0 32 74.4 42 61.8 130 69.5 Above range 14 22.2 9 25.7 74 31.3 11 25.6 25 36.8 52 27.8 % AMDR of WHO/FAO for carbohydrates 0.5025 0.4576 Below range 14 22.2 11 31.4 61 25.9 9 20.9 15 22.1 58 31.0 In range 45 71.4 19 54.3 154 65.3 31 72.1 46 67.7 112 59.9 Above range 4 6.4 5 14.3 21 8.9 3 7.0 7 10.3 17 9.1 % AMDR of DRI for added sugar 0.0058* 0.1819 Below 63 100.0 33 94.3 235 99.6 43 100.0 66 97.1 186 99.5 Above or equal to 0 0.0 2 5.7 1 0.4 0 0.0 2 2.9 1 0.5 % AMDR of WHO/FAO for added sugar 0.1910 0.2262 Below 42 66.7 17 48.6 148 62.7 28 65.1 46 67.7 106 56.7 Above or equal to 21 33.3 18 51.4 88 37.3 15 34.9 22 32.4 81 43.3 262 V ariable Length-for-age Weight-for-length < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p- < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# n % n % n % value# n % n % n % ENERGY AND MACRONUTRIENTS (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) DRI for dietary fibre 0.5839 0.6900 Below 40 63.5 21 60.0 160 67.8 27 62.8 48 70.6 125 33.2 Above or equal to 23 36.5 14 40.0 76 32.2 16 37.2 20 29.4 62 66.8 WHO/FAO recommendation for fibre 0.5732 0.3253 Below 27 42.9 16 45.7 118 50.0 19 44.2 38 55.9 86 46.0 Above or equal to 36 57.1 19 54.3 118 50.0 24 55.8 30 44.1 101 54.0 DRI for alcohol 0.6582 0.4029 Zero 59 93.7 31 88.6 218 92.4 39 90.7 65 95.6 169 90.4 Above or equal to 4 6.3 4 11.4 18 7.6 4 9.3 3 4.4 18 9.6 < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p- < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# MICRONUTRIENTS n % n % n % value# n % n % n % (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) (n=63) DRI for Vitamin A 0.1564 0.0864 Below 7 11.1 9 25.7 36 15.3 5 11.6 17 25.0 27 14.4 Above or equal to 56 88.9 26 74.3 200 84.8 38 88.4 51 75.0 160 85.6 WHO/FAO recommendations for Vitamin A 0.1640 0.0468* Below 7 11.1 9 25.7 37 15.7 5 11.6 18 26.5 27 14.4 Above or equal to 56 88.9 26 74.3 199 84.3 38 88.4 50 73.5 160 85.6 DRI for Vitamin C 0.9353 0.0425* Below 39 61.9 22 62.9 142 60.2 26 60.5 50 73.5 105 56.2 Above or equal to 24 38.1 13 37.1 94 39.8 17 39.5 18 26.5 82 43.9 WHO/FAO recommendations for Vitamin C 0.2641 0.6780 Below 22 34.9 18 51.4 92 39.0 16 37.2 30 44.1 72 38.5 Above or equal to 41 65.1 17 48.6 144 61.0 27 62.8 38 55.9 115 61.5 DRI for Vitamin D 0.7684 0.0337* Below 60 95.2 34 97.1 229 97.0 43 100.0 68 100.0 176 94.1 Above or equal to 3 4.8 1 2.9 7 3.0 0 0.0 0 0.0 11 5.9 WHO/FAO recommendations for Vitamin D 0.7176 0.1268 Below 38 60.3 24 68.6 150 63.6 26 60.5 50 73.5 112 59.9 Above or equal to 25 39.7 11 31.4 86 36.4 17 39.5 18 26.5 75 40.1 263 < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p- < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# MICRONUTRIENTS n % n % n % value# n % n % n % (n=63) (n=35) (n=236) (n=43) (n=68) (n=187) (n=63) DRI for Folate 0.9864 0.3582 Below 44 69.8 25 71.4 166 70.3 30 69.8 44 64.7 138 73.8 Above or equal to 19 30.2 10 28.6 70 29.7 13 30.2 24 35.3 49 26.2 WHO/FAO recommendations for Folate 0.7881 0.9420 Below 37 58.7 23 65.7 146 61.9 26 60.5 42 61.8 118 63.1 Above or equal to 26 41.3 12 34.3 90 38.1 17 39.5 26 38.2 69 36.9 DRI for Vitamin B12 0.8046 0.4490 Below 12 19.1 8 22.9 54 22.9 7 16.3 18 26.6 41 21.9 Above or equal to 51 81.0 27 77.1 182 77.1 36 83.7 50 73.5 146 78.1 WHO/FAO recommendations for Vitamin B12 0.8046 0.4490 Below 12 19.1 8 22.9 54 22.9 7 16.3 18 26.6 41 21.9 Above or equal to 51 81.0 27 77.1 182 77.1 36 83.7 50 73.5 146 78.1 DRI for Calcium 0.6718 0.0415* Below 55 87.3 32 91.4 203 86.0 37 68.1 65 95.6 156 83.4 Above or equal to 8 12.7 3 8.6 33 14.0 6 14.0 3 4.4 31 16.6 WHO/FAO recommendations for Calcium 0.7092 0.0586 Below 56 88.9 32 91.4 205 86.9 38 88.4 65 95.6 158 84.5 Above or equal to 7 11.1 3 8.6 31 13.1 5 11.6 3 4.4 29 15.5 DRI for Iron 0.6649 0.3862 Below 49 77.8 29 82.9 195 82.6 33 76.7 59 86.8 152 81.3 Above or equal to 14 22.2 6 17.1 41 17.4 10 23.3 9 13.2 35 18.7 WHO/FAO recommendations for Iron - - Below 63 100.0 35 100.0 236 100.0 43 100.0 68 100.0 187 100.0 Above or equal to 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 DRI for Zinc 0.9202 0.0220* Below 14 22.2 7 20.0 47 19.9 10 23.3 21 30.9 29 15.5 Above or equal to 49 77.8 28 80.0 189 80.1 33 76.7 47 69.1 158 84.5 WHO/FAO recommendations for Zinc 0.7731 0.0013* Below 2 3.2 2 5.7 8 3.4 0 0.0 7 10.3 3 1.6 Above or equal to 61 96.8 33 94.3 228 96.6 43 100.0 61 89.7 184 98.4 264 < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p- < -2 SD ≥ -2 SD < -1 SD ≥ -1 SD p-value# MICRONUTRIENTS WITH SUPPLEMENTS n % n % n % value# n % n % n % ADDED (n=63) (n=35) (n=235) (n=43) (n=68) (n=186) DRI for Calcium 0.0038* 0.8214 Below 6 9.5 9 25.7 18 7.7 4 9.3 6 8.8 21 11.3 Above or equal to 57 90.5 26 74.3 217 92.3 39 90.7 62 91.2 165 88.7 WHO/FAO recommendations for Calcium 0.0038* 0.8214 Below 6 9.5 9 25.7 18 7.7 4 9.3 6 8.8 21 11.3 Above or equal to 57 90.5 26 74.3 217 92.3 39 90.7 62 91.2 165 88.7 DRI for Folic acid 0.0007* 0.1194 Below 4 6.4 6 17.1 6 2.6 3 7.0 0 0.0 10 5.4 Above or equal to 59 93.6 29 82.9 229 97.5 40 93.0 68 100.0 176 94.6 WHO/FAO recommendations for Folic acid 0.0007* 0.1194 Below 4 6.4 6 17.1 6 2.6 3 7.0 0 0.0 10 5.4 Above or equal to 59 93.6 29 82.9 229 97.5 40 93.0 68 100.0 176 94.6 DRI for Iron 0.0046* 0.9412 Below 5 7.9 8 22.9 15 6.4 3 7.0 6 8.8 15 8.1 Above or equal to 58 92.1 27 77.1 220 93.6 40 93.0 62 91.2 171 91.9 WHO/FAO recommendations for Iron 0.0240* 0.8719 Below 8 12.7 8 22.9 19 8.1 4 9.3 8 11.8 18 9.7 Above or equal to 55 87.3 27 77.1 216 91.9 39 90.7 60 88.2 168 90.3 #p-value for percentage difference between low, normal or high length-for-age as well as low, normal or high weight-for-length using chi-square or Fisher’s exact tests, as appropriate; p <0.05 considered statistically significant indicated with * 265 Overall birth outcome Dietary diversity was determined and included in the analysis for determining predictors of overall birth outcome in order to add a dietary intake component that would be easy to assess by someone who does not have a nutrition background. Of the total sample of participants, more than half (52.6 %, 271/681) had a medium diversity score, 9.8% had a low dietary diversity score (35/681), while only 7.6% (52/681) had high dietary diversity. Of the smaller group of participants (307) for whom overall birth outcome was determined, 16.3% (111/307) had a low dietary diversity, 55.0% (168/307) had a medium dietary diversity, while 8.8% (27/307) had a high dietary diversity. Table 8.5 provides an overview of the associations between dietary diversity and overall birth outcome. Of the 307 for which overall birth outcome could be determined, 114 had overall poor birth outcome. No significant associations were noted between any of the food groups or the dietary diversity score and overall birth outcome with only the legumes, nuts and seeds food group having a p-value of <0.15 which would make it eligible for inclusion as a possible factor in further logistic regression analyses. Table 8.5: Associations between dietary diversity and overall birth outcome Variable Poor outcome Good outcome p-value (n=114) (n=193) n % n % Starchy staples - Yes 114 100.0 193 100.0 No 0 0.0 0 0.0 Vitamin A rich fruit and vegetables 0.8278 Yes 20 17.5 32 16.6 No 94 82.5 161 83.4 Dark green leafy vegetables 0.7646 Yes 14 12.3 26 13.5 No 100 87.7 167 86.5 Other fruit and vegetables 0.3926 Yes 96 84.2 155 80.3 No 18 15.8 38 19.7 Organ meat 0.1772 Yes 4 3.5 14 7.3 No 110 96.5 179 92.8 Meat and fish 0.5530 Yes 98 86.0 161 83.4 No 16 14.0 32 16.6 266 Variable Poor outcome Good outcome p-value (n=114) (n=193) n % n % Eggs 0.8575 Yes 18 15.8 29 15.0 No 96 84.2 164 85.0 Legumes, nuts and seeds 0.1157 Yes 17 14.9 43 22.3 No 97 85.1 150 77.7 Milk and milk products 0.3630 Yes 71 62.3 110 57.0 No 43 37.7 83 43.0 Dietary diversity score 0.7924 Low 43 37.7 68 35.2 Medium 60 52.6 109 56.5 High 11 9.7 16 8.3 #p-value for percentage difference between overall poor and overall good birth outcome using chi-square or Fisher’s exact tests, as appropriate; p <0.05 considered statistically significant indicated with * Dietary diversity scores were also dichotomised using four food groups as cut-off point to determine associations with overall good and overall poor birth outcome, however, no significant differences were noted (p=0.6613). 8.5 DISCUSSION This study aimed to describe the nutrient intake and dietary diversity of pregnant women attending the antenatal clinic at Pelonomi Hospital, Bloemfontein and to determine the associations between household food security, dietary diversity and birth outcomes. Findings of the current study point to a relatively low intake of protein, a moderate intake of fat, with a high intake of carbohydrates. Micronutrient intakes from the diet were below the recommendations for most of the micronutrients investigated in the current study which may be indicative of micronutrient deficiency, also referred to as “hidden hunger”. A marked improvement was, however, observed when the intake from the diet and that from supplements were combined. Napier et al. (2019) found that pregnant women at a public health care facility in Kwa Zulu Natal also consumed a diet that was largely carbohydrate- based with the intake of all micronutrients below 100% of the recommendations. No significant association was observed between food security status and any of the birth outcomes investigated in the current study. Household food insecurity influences the quality 267 and adequacy of nutrition which may hold considerable effects for health (Moafi et al., 2018). Zar et al. (2019) conducted a multidisciplinary population-based birth cohort study among 1225 pregnant women attending two public health clinics in a peri-urban area outside Cape Town, South Africa and found women who were food insecure were significantly more likely to give birth to infants with a lower gestational age (Zar et al., 2019). Reasons for the difference in findings may be due to the differences in tools used to determine food security as well as the type of women included in the two studies. Nutrition remains important before and during pregnancy in ensuring a healthy pregnancy outcome. Maternal malnutrition may contribute to poor foetal growth, low birth weight as well as short-and long-term morbidity and mortality in the infant (Imdad & Bhutta, 2012:178; King, 2016:1437S). Energy requirements during pregnancy make provision for the increased demands of resting energy metabolism, physical activity and tissue growth (Butte & King, 2005:1011; Most et al., 2019). Majority of participants in the current study had an energy intake below the DRI while most also had an energy intake below the WHO/FAO recommendations. Similar to the findings of the current study, the Kwa Zulu Natal study found that 95% of pregnant women consumed less than 100% of the DRI for energy (Napier et al., 2019). A study by Khoushabi and Saraswathi (2010:1126) among 500 pregnant women selected from 20 hospitals in Mysore city, India found that 69% of the women had an adequate energy intake when using the recommended dietary allowance for India as a reference, which is different from the findings of the current study. Kiboi et al. (2016:381) conducted a study among 254 pregnant women attending the antenatal clinic at a teaching and referral hospital in Laikipia County, Kenya and found the energy intake of 28% of pregnant women met the recommended dietary allowance which is closer to the findings of the current study. Macronutrient needs are also increased during pregnancy in order to support adequate foetal growth (Cox & Carney, 2017:262). Napier et al. (2019) found that only 16.0% of the pregnant women in their study did not have a carbohydrate intake above 100% of the DRI whereas 84.0% had protein intakes below 100% of the DRI. A lower percentage of participants in the current study had carbohydrate and protein intakes below the DRI compared to the women in Kwa Zulu Natal. 268 A study among 1674 pregnant women in Norway found that 43.9%, 0.2% and 2.9% of women had a carbohydrate, protein and fat intake below the recommended intake ranges of the Nordic Nutrition Recommendations respectively (Saunders et al., 2019). More participants in the current study had a protein and total fat intake below the % of the AMDR for both the DRI and WHO/FAO recommendations compared to the findings of the study conducted in Norway, while fewer participants in the current study consumed a carbohydrate intake below the % of the AMDR for carbohydrates for both the DRI and WHO/FAO recommendations (Saunders et al., 2019). In the India study, 68% of pregnant women had an adequate protein intake (Khoushabi & Saraswathi, 2010:1126) which is also much higher than the findings of the current study where more than half had a protein intake below both the DRI and WHO/FAO recommendations for protein. Of the pregnant women in Kenya, 75.6%, 46.5% and 8.7% had adequate intakes for carbohydrates, protein and fat respectively (Kiboi et al., 2016:381). Findings from the current study are similar for carbohydrate and protein intake, but more participants in the current study had adequate intakes from fat as well. Saunders et al. (2019) also found that 14.8%, 9.4%, 23.7% and 22.4% had intakes below the recommended intake ranges for saturated fat, monounsaturated fat, polyunsaturated fat and fibre respectively. More participants in the current study had a saturated fat, monounsaturated fat and fibre intake below the recommendations than women in the Norway study while findings were similar to the Norway study when considering polyunsaturated fatty acids. A study conducted among 1838 pregnant women at St John’s Medical College Hospital in Bangalore, India found that 73.6% of women had a total fat intake between 20–30 % of total energy while 78.4% had an intake from saturated fatty acids below 10% (Mani et al., 2016:526). These findings are similar to the current study for the % of the AMDR of the DRI for total fat, although the cut off values differ slightly, as well as saturated fatty acids. Micronutrient requirements increase for most vitamins and minerals during pregnancy. All vitamins and minerals are needed for optimal pregnancy outcome (Cox & Carney, 2017:256). In terms of vitamin intake, Napier et al. (2019) found that the respective percentages of women who had intakes of vitamin A and folate below 100% of the DRI were 79.0% and 74.0%. In the current study, fewer participants had vitamin A intakes below the 269 recommendations, while the percentage of participants with folate intakes below the recommendations were similar to the findings of Napier et al. (2019). Saunders et al. (2019) reported that the respective percentage of women with intakes below the recommended range for vitamin A, vitamin C, vitamin D, folate and vitamin B12 were 9.6%, 4.4%, 28.7%, 54.4%, and 0.3%. In the current study, more participants had intakes of vitamin A, vitamin C, vitamin D, folate and vitamin B12 below the recommendations than in the Norway study. Kiboi et al. (2016:381) found that 46.5%, 70.5% and 8.3% of the pregnant women in their study had adequate intakes for vitamin A, vitamin C and folic acid, respectively. Although more participants in the current study had vitamin A and folate intakes that could be considered adequate compared to the findings of the Kenya study, it remains important to note that a large percentage of participants still consumed less than the recommendations for these nutrients. Fewer participants in the current study had adequate intakes of vitamin C compared to the study of Kiboi et al. (2016:381). Of the pregnant women in the Kwa Zulu Natal study, 98.0% had calcium and iron intakes below 100% of the DRI while 32.0% had zinc levels below 100% of the recommendation. Slightly fewer women in the current study had calcium and zinc intakes below the recommendations with similar findings to the Napier et al. (2019) for iron intakes. The intake of calcium, iron and zinc below the recommended intakes among the women in the study conducted in Norway were 49.6%, 36.2% and 10.2% respectively (Saunders et al., 2019). In the India study, 65%, 47% and 71% of women had adequate intakes for calcium, iron and zinc, respectively (Khoushabi & Saraswathi, 2010:1126). A greater percentage of participants attending the antenatal clinic at Pelonomi Hospital, however, had intakes below the recommendations for calcium and iron, while the percentage of participants with adequate zinc intakes differed when the DRI or WHO/FAO recommendations were used as a reference value. Majority of the participants, however, still had a zinc intake below the DRI and WHO/FAO recommendations. Adequate intakes were observed among 18.1%, 16.9% and 5.1% of pregnant women in the Kenya study for calcium, iron and zinc, respectively (Kiboi et al., 2016:381). These findings were similar to the current study for calcium and iron but differed again when the WHO/FAO recommendations for zinc were used. 270 Differences in findings between the nutrient intakes of the current study and that of other studies may be due to the differences in cultural eating practices as well as social determinants of health and eating, although not determined in the current study. Studies on associations between nutrient status and premature delivery are variable and consensus on any specific nutrient is limited (Carmichael et al., 2013:548). Significantly more babies who were born via normal delivery had mothers with a cholesterol intake below the EAR of the WHO/FAO for cholesterol (p=0.0194). No other studies could be found that investigated the link between method of delivery and nutrient intake, particularly cholesterol intake. In the current study, significantly more babies who were born prematurely had mothers with a vitamin C intake below the DRI. Siega-Riz et al. (2003:519) found that the risk for premature delivery was higher among women with low vitamin C intake among pregnant women who received care at prenatal clinics in central North Carolina between 1995 and 1998. Sengpiel et al. (2014) did not find a significant association between folate intake or folic acid intake from supplements and spontaneous premature delivery among 66 014 pregnant women enrolled in the Norwegian Mother and Child Cohort Study. The current study, however, found that significantly more babies who were born prematurely had mothers with a folate intake above or equal to the DRI (p=0.0393). A meta-analysis aimed at evaluating the available evidence on the associations between blood folate levels, dietary folate intake and folic acid supplementation and the risk of premature delivery found a significant negative association between dietary folate intake and the risk of premature delivery, while no significant relation was observed between folate intake from the diet and the risk of spontaneous premature birth (Li et al., 2019). The current study did not differentiate between the causes of premature delivery (spontaneous versus induced) and consequently reported on premature delivery as a whole. A study on 1961 mother-child pairs enrolled in a prospective cohort study in eastern Massachusetts found that higher protein intake was associated with shorter birth length in the offspring (Switkowski et al., 2016:1128). Significantly more babies in the current study 271 who were born at risk of developing stunting had mothers with a protein intake below the % of the ADMR for both the DRI and WHO/FAO recommendations for protein. In the current study, significantly more babies at risk of developing stunting had mothers with an intake below the % of the AMDR of the WHO/FAO recommendations for total fat, while significantly more babies born at risk of developing stunting also had mothers with an intake from added sugar above the % of the AMDR of the DRI. No studies investigating the relationship between total fat intake as well as added sugar intake and birth length could be found. Significant associations were found between length-for-age at birth and vitamin A, however, no studies investigating the relationship with vitamin A could be found. Significantly more babies at risk of developing stunting had mothers with an intake below the DRI and WHO/FAO recommendations for calcium, folic acid and iron from dietary intake plus intake from supplements. Studies on maternal calcium intake and growth mainly focus on bone length and not birth length, while no studies investigating the relationship between folic acid as well as iron intake and birth length could also not be found. In an attempt to add a dietary intake component that would be easy to assess by someone who does not have a nutrition background, dietary diversity was determined and included in the analysis for determining predictors of overall birth outcome. A significant association was not observed between dietary diversity score and overall birth outcome. A study by Zerfu et al. (2016) conducted among 432 pregnant women in Ethiopia found that dietary diversity was associated with low birth weight and premature delivery. Zerfu et al. (2016:1482), however, further classified dietary diversity as adequate (dietary diversity score ≤ 4) and adequate (dietary diversity score > 4), which may explain the difference in findings. Certain limitations were encountered during the completion of this study. The completion of the food frequency questionnaire took a long time to complete, and since it relies on the memory of the participant, under and/or overreporting may have occurred. The compilation of the dietary kit with food photographs served as a means to try and help will the recall process. Weight and length measurements of the newborns were taken by nursing staff and not by the researchers themselves. The dietitians at Pelonomi Hospital provide regular 272 training sessions of all the staff working in the maternity as well as paediatric wards where the majority of the mothers attending the antenatal clinic are expected to deliver their babies. As published in Jordaan et al. (2020a), women who provided the birth information of their babies differed significantly from those who did not for certain reported health and lifestyle variables. 8.6 CONCLUSION AND RECOMMENDATIONS The diet of the women attending the antenatal clinic at Pelonomi Hospital was mainly carbohydrate-based with a low micronutrient intake if intake from supplements is not considered. The distribution of macronutrient intake differs from studies conducted elsewhere and may be ascribed to the differences in cultural eating practices and social determinants of health and eating. Significant associations between birth outcomes and nutrient intakes seem to indicate that improved birth outcomes are associated with improved nutrient intake. Education programmes should be implemented at clinics where pregnant women receive antenatal care but should also target those women who are of childbearing age, but not yet pregnant. Women of childbearing age should be informed about the importance of nutrition in ensuring a successful pregnancy in a practical manner. Women should also be informed about the importance of taking the pregnancy supplements provided to them as prescribed. No studies investigating the relationship between weight-for-length at birth and nutrient intake could be found. Studies mainly focus on birth weight as a birth outcome. Future studies should therefore consider comparing nutrient intake to birth length as well as weight-for- length as a birth outcome. 8.7 ACKNOWLEDGEMENTS The authors would like to acknowledge the participants for their willingness to contribute to our research as well as the staff at Pelonomi Hospital for accommodating the research team in their clinic. The fieldworkers are thanked for the time and effort put into collecting the data. 273 The authors have no conflict of interest to declare. 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Dietary diversity during pregnancy is associated with reduced risk of maternal anemia, preterm delivery, and low birth weight in a prospective cohort study in rural Ethiopia. American Journal of Clinical Nutrition, 103(6):1482–1488, June. 278 9 CHAPTER 9 – DEVELOPMENT OF A NUTRITION SCREENING TOOL FOR THE PREDICTION OF BIRTH OUTCOMES OF WOMEN ATTENDING THE ANTENATAL CLINIC AT PELONOMI HOSPITAL 9.1 ABSTRACT Background and objectives: Nutrition screening during pregnancy may facilitate the identification of women at risk of overall poor birth outcome. The aim of this study was to develop a nutrition screening tool for the prediction of birth outcomes among pregnant women attending the antenatal clinic at Pelonomi Hospital, Bloemfontein. Methods: This study formed part of an analytical cohort study. During the first phase of the study, data were collected from 682 pregnant women about socio-demographic and household information, reported health and lifestyle, pregnancy history, household food security and individual dietary intake. All mothers who provided the birth information of their offspring were included in the second phase of this study, resulting in a final sample of 331 mothers and 347 newborns. Information obtained from the Road to Health Booklets of these newborns were used to determine overall birth outcome based on gestational age, birth length-for-age and birth weight-for-length. Overall poor birth outcome was defined as gestational age less than 37 weeks or birth length-for-age < -2 SD (stunted) or birth weight- for-length < -2 SD (wasted). Logistic regression with backward selection (p<0.05) was used to select significant independent factors associated with overall birth outcome. All the variables which were found to be significant in the preceding theme-specific logistic regressions (reported elsewhere), were considered for inclusion in the model. Results: Overall birth outcome could be determined for 307 women, of which 37.1% had overall poor birth outcome. Variables that were significant in the final logistic regression model included ownership of a stove, participant’s highest level of education, participant’s employment status, being in real danger of being killed by criminals (self or close family member), being diagnosed with or treated for high blood pressure during the current pregnancy, number of babies expected and gestational body mass index (GBMI). In order to simplify the tool, GBMI was replaced by current body mass index (BMI) in the final proposed screening tool. The 10th percentile of current BMI for each trimester group was used as cut- off (23.7 kg/m2 for the second trimester and 25.3 kg/m2 for the third trimester). Although 279 weight loss of more than 3 kg during the current pregnancy did not remain after the logistic regression analysis, it was included in the final model since unplanned weight loss is a variable that is often assessed in other screening tools for malnutrition. The proposed new screening tool contains eight items. If a score of 2 or more is taken as indicative of overall poor birth outcome, the proposed tool has a sensitivity of 68.8% and specificity of 70.5%, while the positive predictive value is 58.1% and negative predictive value is 79.1%. Conclusions: The screening tool that was developed may be used to detect pregnant women who are at increased risk for developing overall poor birth outcome, based on gestational age, birth length-for-age and birth weight-for-length. The sensitivity and specificity of the proposed tool are similar to that of other tools. The identification of those women who are at risk for overall poor birth outcome may facilitate early referral to a dietitian for appropriate nutritional management to decrease the risk of poor birth outcomes. Keywords: birth outcome, screening tool, premature, birth length, birth weight 9.2 INTRODUCTION Assessment of the nutritional risk of pregnant women forms an essential component of basic antenatal care (Langstroth et al., 2011). Poor nutritional status during pregnancy may influence early development processes and pregnancy outcomes (Ramakrishnan et al., 2012:285; Symington et al., 2018) which may hold major consequences for both the mother and her offspring (Black et al., 2013; Adu-Afarwuah et al., 2017:18). Despite the importance of delivering nutrition services, South Africa has a ratio of 5.4 nutrition professionals per 100 000 people (WHO, 2018). For this reason, dietitians depend on other health care professionals, such as nursing staff, to identify those in need of referral for specialised nutrition care. Malnutrition, acute and chronic diseases, environmental factors as well as genetic predisposition are known to influence maternal health before and during pregnancy (Hampton, 2004; Jacob et al., 2017). Both short- and long-term health of the offspring are affected by maternal health and diet before, during and even after pregnancy (Brenseke et al., 2013). Pregnant women who are at risk of poor birth outcomes, therefore, require specialised care and counselling, particularly those who are at risk of malnutrition (Duquette et al., 2008:30). Women from lower socio-economic backgrounds are often exposed to 280 negative environmental conditions and may not always receive the healthcare that is required (Lapillonne & Griffin, 2013:393). Poor birth outcomes are more prevalent among babies born to women from disadvantaged communities in developing countries (Duquette et al., 2008:30). The prevalence of premature birth and other poor birth outcomes may be decreased if risk can be identified during the early stages of pregnancy by means of a scoring system (Salunkhe et al., 2019). Nutrition screening during pregnancy may therefore be a valuable method for predicting the likelihood of a better or worse outcome (Ferguson et al., 1999:458; Wenhold, 2017:5) and may motivate specialised nutritional management that may influence birth outcomes while the trajectory can still be influenced (Ferguson et al., 1999:458; Wenhold, 2017:5). Currently, few tools for detecting malnutrition and/or poor birth outcomes amongst pregnant women and their offspring exist, with no such screening tools available for the South African population. Available screening or assessment tools determine outcomes such as gestational weight gain (Hillesund et al., 2014; Hrolfsdottir et al., 2019a), risk of gestational diabetes (Nombo et al., 2018; Hrolfsdottir et al., 2019b), birth weight (Kennedy, 1986; de Caunes et al., 1990; Michielutte et al., 1992; Gueorguieva et al., 2003; Hrolfsdottir et al., 2019a; Kennedy & Turner, 2019; Saeed et al., 2019), small for gestational age (Hillesund et al., 2014; Kennedy & Turner, 2019), birth head circumference (Kennedy & Turner, 2019), premature delivery (Mueller-Heubach & Guzick, 1989; Michielutte et al., 1992; Mercer et al., 1996; Berglund & Lindmark, 1999; Salunkhe et al., 2019) and pregnancy complications (Kennedy, 1986; Berglund & Lindmark, 1999). The aim of this study was therefore to develop a nutrition screening tool for the prediction of a combination of birth outcomes among pregnant women attending the antenatal clinic at Pelonomi Hospital, Bloemfontein. 9.3 METHODS 9.3.1 Sample description and data collection This study was approved by the Health Sciences Research Ethics Committee, Faculty of Health Sciences, University of the Free State (UFS-HSD2018/0148/2905) and the Free State 281 Department of Health. During the recruitment phase, women were informed of the study after which informed consent was obtained. This study consisted of two phases. During the first phase, all pregnant women attending the high-risk antenatal clinic at Pelonomi Hospital who were 18 years and older; at 12 weeks gestation and later (which is the time that most pregnant women present at the clinic); who could speak English and/or Afrikaans and/or Sesotho and gave informed consent were included. Women who were pregnant with more than two babies were excluded. Structured interviews were conducted with 682 pregnant women after which each participant was weighed and measured according to standard techniques. During the interview, information related to socio-demographic and household information, reported health and lifestyle, pregnancy history, household food security and individual dietary intake were obtained as previously described by Jordaan et al. (2020a). For phase two of the study, participants were asked to bring the neonate’s Road to Health Booklet to the dietitians’ offices at Pelonomi Hospital as soon as possible after delivery to obtain information related to the birth outcomes of the participants’ offspring. Participants were contacted via short message service (SMS) after their expected due date, to remind them to return for phase two. Those women who returned for phase two received R100 cash for transport. The researcher sent out an SMS at the end of each month to each mother who had an estimated due date within that month. After the due dates of all the mothers had passed, the researcher sent out an additional six rounds of monthly SMS reminders to all those mothers with outstanding Road to Health Booklets. In an effort to obtain outstanding information, the researcher submitted an amendment of the protocol to request that mothers send photos of the relevant pages of the Road to Health Booklet to the researcher directly via multimedia messaging service (MMS) or Whatsapp messenger. Airtime to the value of R20.00 was loaded onto the number from which the photos were sent. Women with either premature delivery (<37 weeks) or who had a baby with birth length-for- age below the -2 SD or birth weight-for-length below the -2 SD were classified as having experienced overall poor birth outcome. Those women who delivered a baby who was full- term (37+ weeks) with a birth length-for-age and a birth weight-for-length above or equal to the -2 SD were classified as having experienced overall good birth outcome. Since it was not possible to determine whether method of delivery was spontaneous or planned, it was not 282 included in the set of variables used to determine overall birth outcome. In the case where a mother delivered twins of which at least one had a poor outcome, the mother was considered to have an overall poor outcome. 9.3.2 Development of the nutrition screening tool The steps taken in the development of the screening tool were based on those proposed by Jones (2004) and include pre-analysis, which includes the identification of risk variables, checking for content validity, conducting a pilot study and designing the risk assessment checklist. The pre-analysis step is followed by the analysis which involves univariate analysis and multivariate analysis. An evaluation committee panel consisting of experts in the field of dietetics and nursing assessed the initial research proposal and measuring instruments with the aim of improving content validity. Questionnaires were compiled based on an in-depth literature review. A number of these have previously been validated in other studies. These included the quantitative food frequency questionnaire (QFFQ) (MacIntyre et al., 2002:239; Hattingh et al., 2007:28; Wentzel-Viljoen et al., 2011:143; Symington et al., 2018:5) and questions related to stress and social support (University of Witwatersrand, 2017). For the current study, variables and questions were grouped into themes based on the objectives set for the study namely, information related to socio-demographic and household information, reported health and lifestyle, pregnancy history, individual dietary intake and household food security as well as anthropometric measurements. A pilot study was conducted on 128 women attending the antenatal clinic at Pelonomi Hospital during the first two weeks in May 2018. Since only changes in the numbering of the questionnaires were needed, these women were included in the final sample. In order to test the second phase of this study (gathering of information from the neonates’ Road to Health Booklets), all mothers included in the pilot study of the first phase who were in their third trimester were asked to bring their child’s Road to Health Booklet to the dietitians’ offices after their child had been born. 283 Before statistical analysis was performed, dietary intake data obtained from the QFFQ were summarised on an Excel file for each participant. Data were then analysed for daily nutrient intake by the Biostatistics Unit at the South African Medical Research Council (SA MRC), using the South African Food Composition Database. Two researchers (doctoral students) entered all the data collected from the other questionnaires onto an Excel spreadsheet after which statistical analysis was performed by the Department of Biostatistics, Faculty of Health Sciences, University of the Free State. Data were analysed using SAS/STAT software, Version 9.4 of the SAS system for Windows, Copyright © 2010 SAS Institute Inc. Descriptive statistics, including frequencies and percentages (for categorical data) and medians and percentiles (for numerical data), were calculated. Differences between groups were assessed by chi-squared tests (for categorical variables) or Fisher’s exact test (for categorical variables with sparse data) and Kruskall-Wallis tests (for numerical variables). Logistic regression with backward selection (p<0.05) was used to select significant independent factors associated with overall birth outcome from those identified in theme- specific logistic regressions (socio-demography, reported health and lifestyle and nutrition). Variables with a p-value of < 0.15 on univariate analysis were considered for inclusion in the theme-specific models. Since one of the significant independent factors identified was gestational BMI, which is not always available because pre-pregnancy weight may not be known, the current study included current BMI in the place of gestational BMI (GBMI) in order to use a simpler parameter that is easy to calculate and interpret. No cut-off values for BMI in pregnancy exist (Tripathi et al., 2016). Tripathi et al. (2016) used the 5th percentile of their own sample as cut- off for underweight during pregnancy in their study. The current study included BMI below the 5th percentile of the current sample (per trimester) and BMI below the 10th percentile (per trimester) in two separate scenarios. 9.4 RESULTS Results from the univariate analysis and theme-specific logistic regressions have been described elsewhere (Jordaan et al., 2020b; Jordaan et al., 2020c; Jordaan et al., 2020d). Overall birth outcome could be determined for 307 women (307/331) of which 37.1% had overall poor birth outcome. Table 9.1 provides an overview of variables that were considered 284 for inclusion in the final logistic regression analysis for predictors of overall birth outcome, based on their significance in theme-specific logistic regressions. Table 9.1: Variables considered for inclusion in the final model Variable Socio-demographic variables Own a stove Yes No Participant’s highest level of education Primary school Grade 8 – 10 Grade 11 – 12 Tertiary education Participant’s employment status Full-time employed or self-employed Part-time employed Unemployed Housewife by choice Variable Reported health and lifestyle variables During the last 6 months, have you or a member of your close family been in real danger of being killed by criminals? Yes No Have you been diagnosed with or treated for high blood pressure? Yes, currently In the past or never Have you been diagnosed with or treated for Tuberculosis? Yes, currently In the past or never Number of babies expecting One Two GBMI Underweight Normal weight Overweight Obese Consumption of legumes, nuts and seeds during the previous 24 hours Yes No 285 Results from the logistic regression analysis showed that ownership of a stove, participant’s highest level of education, participant’s employment status, being in real danger of being killed by criminals in the past six months (self or a close family member), being diagnosed with or treated for high blood pressure, number of babies expecting and GBMI were found to be independent predictors of overall birth outcome (Table 9.2). Pregnant women in the current study had higher odds of experiencing overall poor birth outcome if they had primary school, grade 8–10 or grade 10–12 compared to tertiary education as their highest level of education. A similar trend was observed for part-time employment compared to full-time employment, being in real danger of being killed by criminals in the past six months (themselves or a close family member) and being diagnosed with or treated for high blood pressure during the current pregnancy. The odds of experiencing overall poor birth outcome were lower for those women who owned a stove compared to those who did not; being unemployed compared to being full- time employed; being a housewife by choice compared to being full-time employed and expecting one baby compared to expecting two. In terms of GBMI, women who were normal weight, overweight or obese had lower odds of experiencing overall poor birth outcome compared to those who were underweight. Table 9.2: Odds ratios of factors associated with overall poor birth outcome Variable Description Odds ratio (95% CI) Own a stove yes vs no 0.11 (0.02;0.79) Participant’s highest level of education primary school vs tertiary education 4.15 (0.91;19.0) Participant’s highest level of education grade 8–10 vs tertiary education 5.58 (1.76;17.69) Participant’s highest level of education grade 10–12 vs tertiary education 4.68 (1.57;13.91) part-time employed vs full-time and/or Participant’s employment status 2.90 (1.21;6.93) self-employed unemployed vs full-time and/or self- Participant’s employment status 0.67 (0.36;1.24) employed housewife by choice vs full-time and/or Participant’s employment status 0.41 (0.10;1.71) self-employed Being in real danger of being killed by yes vs no 3.60 (1.19;10.90) criminals (self or close family member) 286 Variable Description Odds ratio (95% CI) Being diagnosed with or treated for currently vs in the past or never 2.21 (1.11;4.40) high blood pressure Number of babies expecting one vs two 0.05 (0.01;0.24) Gestational body mass index normal vs underweight 0.24 (0.07;0.80) Gestational body mass index overweight vs underweight 0.21 (0.05;0.81) Gestational body mass index obese vs underweight 0.18 (0.06;0.58) Table 9.3 indicates the predicted probabilities of having an overall poor birth outcome according to the final model with their respective sensitivity, specificity, positive predictive value and negative predictive value. Only those predicted probabilities with adequate sensitivities and specificities (both above 60%) are listed. Table 9.3: Predicted probabilities of experiencing overall poor birth outcome Predicted Positive Negative Sensitivity Specificity probability predictive value predictive values 0.29702 72.3% 66.8% 57.0% 79.0% 0.31929 68.8% 69.0% 57.5% 78.4% 0.32559 67.9% 71.7% 59.4% 78.6% 0.34328 66.1% 73.4% 60.2% 78.0% 0.34542 66.1% 73.9% 60.7% 78.2% 0.34757 65.2% 74.5% 60.8% 77.8% 0.35886 62.5% 76.6% 61.9% 77.0% 0.37283 62.5% 77.2% 62.5% 77.2% To devise a simpler scoring method using these identified variables but not predicted probabilities as cut-off values, a tick sheet as indicated in Table 9.4 was developed. Considering 2 or more as indicative of overall poor birth outcome, this screening tool has a sensitivity of 60.7%, a specificity of 76.6% with a positive predictive value of 61.3% and a negative predictive value of 76.2%. Replacing GBMI due to its complexity of calculating with current BMI below trimester-specific 5th percentile, these values were a sensitivity of 59.5%, a specificity of 76.0%, a positive predictive value of 60.0% and a negative predictive value of 75.5%. Using current BMI below trimester-specific 10th percentile, the values were 62.2% for sensitivity, 73.8% for specificity, 59.0% for its positive predictive value was and 76.3% for its negative predictive value. It was therefore decided to use current BMI below 10th percentile as cut-off point in the screening tool. 287 Table 9.4: Initial tick sheet of variables included in the screening tool Contribution Factors Level Score to total score Yes 0 Do you own a stove? No 1 None 1 Primary school 1 What is your highest level of education? Grade 8 – 10 1 Grade 10 – 12 1 Tertiary education 0 Full-time or self-employed 0 Part-time employed 1 What is your employment status? Unemployed 1 Housewife by choice 1 Have you or a member of your close family been in real danger Yes 1 of being killed by criminals in the past six months? No 0 Have you been diagnosed with or treated for high blood Yes 1 pressure during the current pregnancy? No 0 1 0 How many babies are you pregnant with? 2 1 Underweight 1 GBMI Normal weight 0 Overweight 0 Obese 0 Total score: Although weight loss of more than 3kg during the current pregnancy did not remain after the logistic regression analysis, unplanned weight loss is a variable that is often assessed in other screening tools for malnutrition and was therefore included in the final proposed screening tool (Rubenstein et al., 2001; MAG, 2003). All variables in the final logistic regression model were thus included in the proposed nutrition screening tool, with gestational BMI being replaced by current BMI and experiencing weight loss of more than 3 kg during the current pregnancy added as indicated in Table 9.5. If a score of two or more is taken as indicative of a poor birth outcome, the final proposed nutrition screening tool has a sensitivity of 68.8% and specificity of 70.5%, while the positive predictive value is 58.1% and negative predictive value is 79.1%. If current BMI is removed as component the screening tool had a sensitivity of 63.6% and a specificity of 72.3%, with a positive predictive value of 57.9% and a negative predictive value of 76.9%. 288 Table 9.5: Proposed nutrition screening tool Contribution Factors Level Score to total score Yes 0 Do you own a stove? No 1 None 1 Primary school 1 What is your highest level of education? Grade 8 – 10 1 Grade 10 – 12 1 Tertiary education 0 Full-time or self-employed 0 Part-time employed 1 What is your employment status? Unemployed 1 Housewife by choice 1 Have you or a member of your close family been in real danger Yes 1 of being killed by criminals in the past six months? No 0 Have you experienced weight loss of more than 3 kg during the Yes 1 current pregnancy? No 0 Have you been diagnosed with or treated for high blood Yes 1 pressure during the current pregnancy? No 0 1 0 How many babies are you pregnant with? 2 1 Current BMI for: 2 2nd < 23.7 kg/m 1 trimester ≥ 23.7 kg/m2 0 OR OR OR rd < 25.3 kg/m 2 1 3 trimester ≥ 25.3 kg/m2 0 Total score: 9.5 DISCUSSION Research often focuses on an outcome variable, for example malnutrition or a specific aspect of poor birth outcome (e.g. small for gestational age), and the identification of factors that may influence the occurrence of that outcome (Jones, 2004). The process of nutrition screening facilitates the identification of those individuals who already present with a specific outcome as well as those who are at risk of developing it (Ferguson et al., 1999:458; Wenhold, 2017:5). Women who are identified as at risk by means of screening, could then be referred for appropriate services (Gueorguieva et al., 2003). Nutrition screening should ideally form part of a systematic approach where all patients are screened by nursing staff on admission (Reber et al., 2019). Most nutrition screening tools that are currently available aim to determine nutritional status, i.e. risk of malnutrition (van Bokhorst-de van der Schueren et al., 2014), without considering the social determinants of health. Few tools have looked at a 289 combination of birth outcomes, while none have combined premature birth, stunting and wasting as indicators of overall poor birth outcome. Since these outcomes are relatively common in the South African public health setting (Sartorius et al., 2020), assessment of risk is important to motivate relevant interventions. Currently, no tools exist specifically for the unique South African population. Screening tools should be applicable for use in a heterogeneous patient population; make use of readily available data; be easy and quick to complete by non-professional staff; be non- invasive and inexpensive, while also being valid and reproducible (Ferguson et al., 1999:458; Susetyowati et al., 2014:158). Tools that contain only a few simple questions have been shown to be able to accurately determine nutritional risk in a cost-effective and time-effective way (Ferguson et al., 1999:458; Wenhold, 2017:5). If no relevant or useful tool for a specific setting is available, the development of a new tool is justified (Jones, 2004:299). In the current study, the researchers reviewed those factors that were significantly associated with overall birth outcome to select the most appropriate variables for inclusion in the final model. Since basing screening on predicted probabilities requires the use of a calculator (Gueorguieva et al., 2003), it was decided to use a simpler scoring method using the variables included in the final model. Because the calculation of GBMI requires a complicated algorithm (Davies et al., 2013:117), current BMI which is easier to calculate and interpret was included instead as it does not require any specialised equipment (Tripathi et al., 2016). No defined cut-off values for BMI during pregnancy exist, thus, BMI less than the 10th percentile for each trimester (second and third) was used to classify the participant as underweight. We need to acknowledge that the cut-off values derived for current BMI are specific to the current study population attending a high-risk clinic and will need to be calculated for other populations. Other studies have identified risk factors for developing various poor birth outcomes and consequently used these factors in their screening tools. These include maternal diet, including variety and/or adequacy (Langstroth et al., 2013; Hrolfsdottir et al., 2019a; Hrolfsdottir et al., 2019b; Kennedy & Turner, 2019); maternal weight, height (Saeed et al., 2019) and mid upper arm circumference (Nombo et al., 2018; Saeed et al., 2019) and demographic and socio-economic factors (de Caunes et al., 1990; Mercer et al., 1996) including maternal age and education (Gueorguieva et al., 2003; Salunkhe et al., 2019). The current study identified socio-demographic factors (not owning a stove, having a level of 290 education below a tertiary education, being full-time or self-employed); being in danger of being killed by criminals in the past six months (one’s self or a close family member); medical factors (being diagnosed with or treated for high blood pressure in the current pregnancy, expecting twins) and an anthropometric factor (having a current BMI below the population specific cut-off point) as predictors of overall poor birth outcome. The final proposed nutrition screening tool had a sensitivity of 68.8% and specificity of 70.5%, while the positive predictive value was 58.1% and negative predictive value was 79.1%. Langstroth et al. (2011) have developed and validated a screening tool for predicting nutritional risk among pregnant women at East Lancashire Hospital, England that contains four questions that concern nutrient intake. Compared to this tool that had a sensitivity of 100% and a specificity of 66.0%, the sensitivity of the proposed screening tool in the current study is lower. Duquette et al. (2008) have also developed and validated a screening tool for the identification of nutritionally at-risk pregnancy for low-income women in primary care facilities in Montreal Canada. These authors found that their screening tool had a sensitivity and specificity of 85% and 50% respectively, while interestingly the positive predictive value was 87% and the negative predictive value was 55% (Duquette et al., 2008). The validated screening tool developed by Gueorguieva et al. (2003) included nine indicators for their model-based approach in their validation sample. The authors identified the highest sensitivity of 66.0% and specificity of 58.8% when three indicators were above the specified cut-off points. In view of the above, the sensitivity and specificity of the proposed tool are similar to the findings of other studies. It is important to note that the tools used in the other studies were validated whereas the proposed screening tool has not yet been validated. It is advisable to test the validity of the newly developed nutrition screening tool in a different sample in the setting where it will be applied before the adoption of the tool as a method for assessing nutritional risk (Jones, 2004). When validating a screening tool two approaches can be considered. Firstly, the tool can be validated in a different sample in the same setting and secondly the sample that was included in the development phase can be divided into two and the validity of the tool of the two groups can be compared, as done by Gueorguieva et al. (2003). Because of the relatively small sample size, this was not a feasible approach in the current study. 291 We acknowledge that this study was conducted at a high-risk antenatal clinic, which may therefore affect the representativeness of the screening tool for other populations. 9.6 CONCLUSION AND RECOMMENDATIONS Currently, no screening tool to detect women at risk of poor birth outcome exists specifically for the South African population. The current study developed a screening tool that may be used to detect pregnant women who are at increased risk for developing overall poor birth outcome, based on gestational age, birth length-for-age and birth weight-for-length. This tool is simple enough for it to be administered routinely by nursing staff to women who present at the antenatal clinic. The proposed nutrition screening tool had a sensitivity of 68.8% and specificity of 70.5%, while the positive predictive value was 58.1% and negative predictive value was 79.1%. These values are in line with the sensitivity and specificity of tools that have been developed to identify risk in pregnant women in other studies. Identification of those women who are at risk for overall poor birth outcome may facilitate the referral to a dietitian for nutrition intervention in order to address risk factors to improve birth outcomes. Before widespread use, validation of the tool is recommended. Validation in a relevant population for whom it was developed is recommended as a follow-up study. 9.7 ACKNOWLEDGEMENTS The staff at the antenatal clinic and the dietitians working at Pelonomi Hospital are acknowledged for their assistance as well as the pregnant mothers for their willingness to participate. The authors have no conflict of interest to declare. This project was funded by the researchers themselves. 9.8 REFERENCES Adu-Afarwuah, S., Lartey, A. & Dewey, K.G. 2017. Meeting nutritional needs in the first 1000 days: a place for small-quantity lipid-based nutrient supplements. Annals of the New York Academy of Sciences, 1392:18–29, March. 292 Berglund, A. & Lindmark, G. 1999. The usefulness of initial risk assessment as a predictor of pregnancy complications and premature delivery. Acta Obstetricia et Gynecologica Scandinavica, 78(10):871 ̶ 876, November. 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Nutrition during pregnancy and early development (NuPED) in urban South Africa: a study protocol for a prospective cohort. BMC Pregnancy and Childbirth, July 24. https://doi.org/10.1186/s12884-018-1943-6 [19 October 2020]. Tripathi, R., Chanchal, Tyagi, S., Mala, Y.M. & Singh, N. 2016. Second trimester body mass index (BMI) as a predictor of adverse maternal and perinatal outcome. Obstetrics and Gynecology International Journal, June 30. https://doi.org/10.15406/ogij.2016.04.00131 [16 November 2020]. University of Witwatersrand. 2017. Birth to Twenty. https://www.wits.ac.za/health/research- entities/birth-to-20/birth-to-twenty/ [24 January 2018]. van Bokhorst-de van der Schueren, M.A.E., Guaitoli, P.R., Jansma, E.P. & de Vet, H.C.W. 2014. Nutrition screening tools: Does one size fit all? A systematic review of screening tools for the hospital setting. Clinical Nutrition, 33:39 ̶58, February. Wenhold, F.A.M. 2017. Nutrition screening: science behind simplicity. South African Journal of Clinical Nutrition, 30(3):5 ̶ 6, October. Wentzel-Viljoen, E., Laubscher, R. & Kruger, A. Using different approaches to assess the reproducibility of a culturally sensitive quantified food frequency questionnaire. South African Journal of Clinical Nutrition, 24:143–148, July. World Health Organization (WHO). 2018. Nutrition Landscape Information System. Global nutrition policy review 2016-2017 - country progress in creating enabling poilcy environments, Geneva: World Health Organization. 297 10 CHAPTER 10 - CONCLUSIONS AND RECOMMENDATIONS 10.1 INTRODUCTION The main aim of this cohort analytical study was to develop a nutrition screening tool for the prediction of birth outcomes of pregnant women, attending the antenatal clinic at Pelonomi Regional Hospital, Bloemfontein, Free State. The conclusions that stemmed from the current study are summarised according to the characteristics of pregnant women in the sample, birth outcomes of neonates in the sample; the associations between indicators of socio- demography; reported health and lifestyle; as well as household food security and nutrient intake with birth outcomes and the development of a nutrition screening tool for pregnant women in the sample. 10.2 CONCLUSIONS 10.2.1 CHARACTERISTICS OF WOMEN ATTENDING THE ANTENATAL CLINIC AT PELONOMI REGIONAL HOSPITAL, BLOEMFONTEIN: A COMPARISON OF WOMEN WITH KNOWN BIRTH OUTCOMES AND WOMEN WITH UNKNOWN BIRTH OUTCOMES Majority of the 682 pregnant women in the current study had access to basic sanitation, electricity and household storage and cooking equipment. While more than half of the women were unemployed (52.5%), two-thirds of their husbands or partners had full-time employment (67.9%). Although women were mostly reliant on their husband or partner for income, they did have access to basic amenities. Smoking, tobacco and snuff, as well as alcohol use was reported by a relatively high percentage of participants. Medication use in the current study was high which may be due to the fact that the antenatal care clinic at Pelonomi Hospital is a high-risk clinic. More than half of participants were obese (56.5%). Women were treated for abdominal pain (28.8%) and hypertension (20.0%) during their pregnancy, while common symptoms such as nausea, vomiting, appetite loss and swelling of the feet were common. Although most (82.3%) pregnant women in the current study had a number of people who they could turn to if they needed help with important problems, various sources of stress were apparent. Household food insecurity was evident in the current study (11.0% of 298 participants were mildly food insecure, 32.6% were moderately food insecure and 298% were severely food insecure), increasing the risk of inadequate dietary and nutrient intake and stress. No significant differences were noted between the women who responded to the request to provide their baby’s Road to Health Booklet (331 responders) and those who did not (351 non-responders) with regard to age, pregnancy stage, food insecurity and GBMI. Differences were, however, noted regarding certain socio-demographic and reported health variables. Overall, the responder group were better off in terms of socio-demographic status. Furthermore, median intakes of most macro- and micronutrients were higher in the responder group than in the non-responder group. Differences between the responders and non-responders may have impacted on health-seeking behaviour. 10.2.2 BIRTH OUTCOMES OF NEONATES BORN TO MOTHERS WHO RECEIVED ANTENATAL CARE AT PELONOMI REGIONAL HOSPITAL, BLOEMFONTEIN Birth information was available for 347 babies. In the current study, the prevalence of prematurity (16.0%), HIV exposure (33.6%), low birth weight (14.4%) and wasting (14.5%) was similar to the findings of other studies, while the prevalence of caesarean section (61.6%), twin pregnancies (9.2%) and congenital disabilities (1.5%) were higher in the current study compared to others. Prevalence of stunting at birth (18.9%) was lower than that reported by others. Differences between the findings of the current study and that of other studies may be ascribed to the fact that the current study was conducted in a high-risk clinic. 10.2.3 ASSOCIATIONS BETWEEN INDICATORS OF SOCIO-DEMOGRAPHY AND BIRTH OUTCOMES OF PREGNANT WOMEN ATTENDING THE ANTENATAL CLINIC AT PELONOMI REGIONAL HOSPITAL, BLOEMFONTEIN Several social determinants of health were significantly associated with at least one birth outcome (method of delivery, prematurity, length-for-age at birth and weight-for-length at birth) in the current study. The odds of experiencing overall poor birth outcome (prematurity, or birth length-for-age below the -2 SD or birth weight-for-length below the -2 SD) were lower for women who owned a stove or who were unemployed or housewives by choice while the odds of experiencing poor birth outcome were higher for women who had grade 8–10 or 299 grade 10–12 as their highest level of education compared to those with tertiary education as well as being employed part-time versus being employed full-time. Significant associations between socio-demographic variables and birth outcomes point to the substantial impact of poverty on health and highlight the fact that social determinants of health need to be considered in screening and intervention programmes. 10.2.4 ASSOCIATIONS BETWEEN REPORTED HEALTH AND LIFESTYLE AND BIRTH OUTCOMES OF PREGNANT WOMEN ATTENDING THE ANTENATAL CLINIC AT PELONOMI REGIONAL HOSPITAL, BLOEMFONTEIN Significant associations between a number of reported health and lifestyle factors and birth outcomes were observed in the current study, most notably, premature delivery. Premature delivery was significantly associated with not having a husband or partner to talk to about problems, the pregnant women themselves or a close family member being seriously ill during the past six months, having a close family member who has a problem with drugs or alcohol, being admitted to hospital during the current pregnancy, experiencing diarrhoea for a least three days during the current pregnancy, experiencing appetite loss during the current pregnancy and being pregnant with twins. The odds of experiencing overall poor birth outcome were higher for women who were (themselves or a close family member) in real danger of being killed by criminals compared with those who were not as well as for women who were diagnosed with or treated for high blood pressure during the current pregnancy. The odds of experiencing overall poor pregnancy outcome were lower for women who were expecting one baby as well as for women who were normal weight, overweight or obese compared to those who were underweight. 10.2.5 HOUSEHOLD FOOD SECURITY AND NUTRIENT INTAKE AND THEIR ASSOCIATION WITH BIRTH OUTCOMES OF PREGNANT WOMEN ATTENDING THE ANTENATAL CLINIC AT PELONOMI HOSPITAL, BLOEMFONTEIN The diet of the women attending the antenatal clinic at Pelonomi Hospital was mainly carbohydrate-based. The distribution of macronutrient intake differs from studies conducted elsewhere and may be ascribed to differences in cultural eating practices as well as a nutrition 300 transition from healthier traditional diets to more unhealthy Westernised diets. Significant associations between birth outcomes and nutrient intakes seem to indicate that improved nutrient intake is associated with improved birth outcomes. No significant associations were observed between consumption of the different dietary diversity food groups and dietary diversity score and overall birth outcome. 10.2.6 DEVELOPMENT OF A NUTRITION SCREENING TOOL FOR THE PREDICTION OF BIRTH OUTCOMES OF PREGNANT WOMEN ATTENDING THE ANTENATAL CLINIC AT PELONOMI HOSPITAL A screening tool that may be used to detect pregnant women who are at increased risk for developing overall poor birth outcome, based on gestational age, birth length-for-age and birth weight-for-length was developed as part of the current study. The items that were included in the tool included ownership of a stove, participant’s highest level of education, participant’s employment status, being in real danger of being killed by criminals in the past six months (themselves or a close family member), experiencing weight loss of more than 3kg during the current pregnancy, being diagnosed with or treated for high blood pressure during the current pregnancy, number of babies expected and GBMI being replaced by current body mass index. The newly developed tool had a sensitivity of 68.8% and specificity of 70.5%, while the positive predictive value was 58.1% and negative predictive value was 79.1% which are in line with the sensitivity and specificity of other available tools. This tool is simple enough to be used routinely by nursing staff among women who present at an antenatal clinic. Identification of those women who are at risk for overall poor birth outcome may facilitate referral to a dietitian who can intervene while birth outcomes can still be influenced. 10.3 STUDY LIMITATIONS This study was conducted at a high-risk clinic in Bloemfontein, South Africa and included twin pregnancies which may affect the representativeness of the results and the newly developed nutrition screening tool in settings that are different to the current one. Although the fieldworkers were trained beforehand and a dietary kit containing a file with photographs and common household items and utensils were shown to participants, the completion of the quantitative food frequency proved to be challenging. Some participants 301 struggled to recall food consumed over a period of a month. Where uncertainties on the quantitative food frequency questionnaire were present for certain factors, such as for portion sizes, information was cross-checked using the 24-hour recall. As part of the reported health and lifestyle questionnaire, participants were asked how they fed their firstborn as well as previous other children. Participants were, however, not asked about exclusive breastfeeding. The main limitation of this study was the number of mothers and infants for which information from the Road to Health Booklet was available (about 50% of the initial sample). The researcher tried to increase the number of responders by sending various rounds of reminders to participants via short message service (SMS). It is important to note that significant differences were observed for certain socio-demographic, reported health and lifestyle as well as individual dietary intake variables between those women who provided the birth information of their babies and those who did not, a factor that may also have impacted on the representativeness of the responder group. Weight and length measurements of the newborns were taken by nursing staff and not by the researchers themselves. The dietitians at Pelonomi Regional Hospital provide regular training sessions for all the staff working in the maternity as well as paediatric wards where the majority of the mothers attending the antenatal clinic are expected to deliver their babies. It is hoped that this training would have ensured the reliability of the measurements taken by nursing staff. 10.4 RECOMMENDATIONS 10.4.1 Recommendations concerning nutrition education and intervention Women face interconnected health challenges influenced by poverty, inequality and marginalisation (Every Woman Every Child, 2015). Poverty and stress combined with lack of information, inadequate and poor-quality services as well as cultural beliefs may affect the health of the mother and consequently that of her offspring as well. These barriers should be addressed at both health system level and societal level to improve maternal health, morbidity and mortality (WHO, 2019). 302 Current priority health interventions for reducing maternal and neonatal mortality in South Africa include the following (Republic of South Africa, 2012):  Provide basic antenatal care;  Perform testing for human immunodeficiency virus (HIV) during pregnancy with the initiation of antiretroviral therapy (ART) and provisioning of other prevention of mother-to-child transmission (PMTCT) services as required;  Improve access to care during labour;  Improve intrapartum care and post-natal care within six days of delivery;  Resuscitate newborns when required;  Consult existing protocols when caring for newborns that are small or ill;  Promote Kangaroo Mother Care (KMC) for stable infants who are low birth weight; and  Include newborn care and support for breastfeeding mothers during post-natal visits within six days. Ngyuen et al. (2017) found that improved knowledge and self-efficacy, increased support from a husband (or partner), early entry to prenatal health care, and provision of supplements to pregnant women can improve maternal nutrition practices. As part of basic antenatal care, pregnant women should therefore be educated on the importance of regular attendance of antenatal follow-up visits in health care facilities where pregnant mothers receive antenatal services. Regular attendance of antenatal visits may increase the chances of pregnant women being screened and referred for appropriate nutrition counselling. In light of the findings of the current study, nutrition education should form part of the education offered to pregnant women during antenatal care and should be applicable to each woman’s individual circumstances e.g. access to basic sanitation and storage and cooking facilities. Education programmes should inform pregnant women, as well as women of childbearing age who are at risk of becoming pregnant, of the risks associated with poor lifestyle choices during pregnancy and the benefits of following a healthy diet and lifestyle. Women should also be informed about the importance of taking the pregnancy supplements provided to them as prescribed. 303 Pregnant women should be encouraged to rely on the available support networks to help with stress management during pregnancy. Establishing pregnancy support groups to which pregnant women can be referred may prove valuable in providing specialised support to pregnant women, particularly those in the high-risk category. Furthermore, it is recommended that pregnant women be screened as soon as possible to identify those factors that may place them at increased risk of poor birth outcomes. Based on the outcome of the screening, pregnant women can be referred to a dietitian for individualised and relevant nutrition intervention. It has become clear that social determinants of health have a major impact on birth outcome. In this regard, intervention programmes should focus on improving employment opportunities for pregnant women, alleviating poverty and managing stress in addition to promoting physical activity and dietary adequacy. A holistic approach can improve the chances of experiencing a successful pregnancy and so contribution to improved birth outcomes. 10.4.2 Recommendations for further research In light of the limitations of the current study, it is recommended that the newly developed nutrition screening tool be validated within the relevant population for whom it was developed as a follow-up study. Also, that publications separate the results for twin pregnancies and singletons and consider comparing the findings from these two groups. Considering that the current study was conducted in a high-risk antenatal clinic, it is recommended that similar studies are conducted in other settings to allow for comparison of the findings. 10.5 REFERENCES Every Woman Every Child. 2015. The global strategy for women’s, children’s and adolescents’ health (2016-2030), New York: Every Woman Every Child. Nguyen, P.H., Sanghvi, T., Kim, S.S., Tran, L.M., Afsana, K., Mahmud, Z., Aktar, B. & Menon, P. 2017. Factors influencing maternal nutrition practices in a large scale maternal, newborn and 304 child health program in Bangladesh. PLoS One, July 10. https://dx.doi.org/10.1371%2Fjournal.pone.0179873 [16 November 2020]. Republic of South Africa (RSA). 2012. National Plan of Action for Children in South Africa, Pretoria: Department of Women, Children and People with Disabilities. World Health Organization (WHO). 2019. WHO report on the global tobacco epidemic, 2019, Geneva: World Health Organization. 305 List of appendices Appendix A: Information document Development of a nutrition screening tool for the prediction of birth outcomes of women attending the antenatal clinic at Pelonomi Hospital INFORMATION DOCUMENT Study title: Development of a nutrition screening tool for the prediction of birth outcomes of women attending the antenatal clinic at Pelonomi Hospital Thank you for being willing to help us in this very important project. We are sure that the project will contribute to improving the health of pregnant women and their children making use of the services in this hospital. We, researchers at the University of the Free State, Faculty of Health Sciences, are doing research on determining the factors (things) that influence risk of low birth weight and length in babies in Pelonomi Hospital. Research is just the process to learn the answer to a question. In this study we want to learn what factors (things) need to be addressed in health programmes in the Free State. The study involves research and is not part of usual medical care. Invitation to participate: We are asking/inviting you to participate in this research study, and we are asking for your permission to provide us with information from your baby’s Road to Health Booklet once he / she has been born. What is involved in the study: The aim of the project is to get enough information regarding the factors (things) that influence birth outcomes in babies to develop a screening form that can help identify issues that should be addressed during pregnancy. For this study we need pregnant women attending the antenatal clinic at Pelonomi Hospital. We are also asking you to bring your neonate baby’s Road to Health Booklet to the dietitians’ offices, after your baby has been born, at Pelonomi Hospital so that we can obtain important information regarding your baby’s birth from it. All the questionnaires will be filled out at the antenatal clinic at Pelonomi Hospital by the researchers from the University of the Free State. Participants will be asked to answer questions on the following in an interview:  Socio-demographic information (information about conditions in the house and how you live);  Dietary intake (what and how much is eaten);  Reported health and lifestyle (e.g. whether you smoke or drink alcohol); and  Household food security (whether and how often you go without enough food to eat). We will also take some measurements such as weight and height. All of this will take approximately 1 hour and 30 minutes of your time. Once your baby has been born, we would also like for you to bring his/ her Road to Health Booklet to the dietitians’ offices at Pelonomi Hospital in order to photocopy some important information from the booklet. Researchers will try to provide information about the outcome of the research. If research generates information about you which may be of relevance to the health of other family members, your consent 306 will be asked before offering to disclose such information to the family members concerned. Your information will not be released for other uses without consent, unless required by law. Risks of being involved in the study: There are no risks involved with participating in this study. Benefits of being in the study: By participating in the study you will help us to develop health and nutrition strategies that will benefit the pregnant women attending the antenatal clinic and their babies. You will be given a R100 for transport fees after you have informed us about your baby’s birth and we have obtained the information from your baby’s Road to Health Booklet. Participation is voluntary, and refusal to participate will involve no penalty or loss of benefits to which you are entitled; you may discontinue participation at any time without penalty or loss of benefits to which you are otherwise entitled. Confidentiality: Efforts will be made to keep personal information confidential. Absolute confidentiality cannot be guaranteed. Personal information may be disclosed if required by law. If results are published, this may lead to individual/cohort identification. Kind regards Prof Corinna Walsh Contact details: 051 401 3818 307 Development of a nutrition screening tool for the prediction of birth outcomes of women attending the antenatal clinic at Pelonomi Hospital INLIGTINGSDOKUMENT Studietitel: Development of a nutrition screening tool for the prediction of birth outcomes of women attending the antenatal clinic at Pelonomi Hospital Dankie dat u bereid is om ons te help met hierdie baie belangrike projek. Ons is seker dat die projek sal bydra om die gesondheid van swangervroue en hul kinders wat van die dienste by hierdie hospitaal gebruikmaak sal verbeter. Ons, navorsers aan die Universiteit van die Vrystaat, Fakulteit Gesondheidswetenskappe, doen navorsing om faktore (dinge) wat die risiko vir lae geboorte massa en lengte in babas by Pelonomi Hospitaal beïnvloed, te bepaal. Navorsing is net die proses om die antwoord op ʼn vraag te leer. In hierdie studie wil ons leer watter faktore (dinge) in gesondheidsprogramme in die Vrystaat aangespreek behoort te word. Hierdie studie behels slegs navorsing en is nie deel van gewone mediese sorg nie. Uitnodiging om deel te neem: Ons versoek/nooi u uit om aan hierdie navorsingstudie deel te neem of/en vra u toestemming om ons met inligting van u baba se “Road to Health Booklet” te verskaf sodra hy / sy gebore is. Wat behels die studie – Die doelwit van hierdie projek is om genoeg inligting in te samel oor die faktore (dinge) wat geboorte uitkomste in babas beïnvloed om sodoende ʼn siftingsvorm te onwikkel wat kan help om kwessies wat tydens swangerskap aangespreek moet word, te identifiseer. Vir die studie benodig ons swangervroue wat die voorgeboortesorg-kliniek by Pelonomi Hospitaal bywoon. Ons vra ook dat u u pasgebore baba se ”Road to Health” Boekie na die dieetkundiges se kantore by Pelonomi Hospitaal sal bring sodat ons belangrike inligting oor u baba se geboorte daaruit kan kry. Al die vraelyste sal by die voorgeboortesorg-kliniek by Pelonomi Hospitaal deur die navorsers van die Universiteit van die Vrystaat voltooi word. Respondente sal gevra word om vrae oor die volgende tydens ʼn onderhoud te beantwoord:  Sosio-demografiese inligting (inligting oor omstandighede in die huis en hoe u leef);  Dieetinname (wat en hoeveel geëet word);  Gesondheid en leefstyl (bv. of u rook of alkohol drink); en  Huishoudelike voedselsekuriteit (of en hoe gereeld u nie genoeg kos het om te eet nie). Ons sal ook sekere metings soos massa en lengte neem. Al die bogenoemde sal ongeveer 1 uur en 30 minute van u tyd neem. Sodra u baba gebore is, wil ons u versoek om sy / haar “Road to Health” Boekie na die dieetkundiges se kantore by Pelonomi Hospitaal te neem sodat ons belangrike inligting in die boekie kan fotostateer. Indien dit te ver is om weer Pelonomi toe te kom om die boekie te bring, vra ons u om ʼn foto van die inligting in die boekie na die navorser te stuur. Navorsers sal probeer om inligting oor die uitkoms van die navorsing te verskaf. As navorsing inligting aan die lig bring wat van belang mag wees vir die gesondheid van u familielede, sal u toestemming 308 verkry word voordat sodanige inligting aan die betrokke familielede bekend gemaak word. U inligting sal nie sonder toestemming vir ander gebruike beskikbaar gestel word nie tensy vereis deur die wet. Risikos van deelname aan die studie: Deur aan hierdie studie deel te neem help u ons om gesondheid- en voedingstrategieë te ontwikkel wat swangervroue wat die voorgeboortesorg-kliniek bywoon en hul babas bevoordeel. U sal R100 vir vervoerkostes ontvang nadat u ons van u baba se geboorte in kennisgestel het en om die inligting van u baba die “Road to Health” Boekie ontvang het. Voordele van deelname aan die studie: Deur aan die studie deel te neem sal u ons help om gesondheids- en voedingstrategieë te ontwikkel wat die swangervroue wat die voorgeboortesorg kliniek bywoon en hul babas sal baat. U sal R100 vir vervoerkostes ontvang nadat u die “Road to Health Booklet” na die dieetkundiges se kantoor gebring het. Deelname is vrywillig, en weiering om deel te neem sal geen boete of verlies van voordele waarop die deelnemer andersins geregtig is behels nie; die proefpersoon kan te eniger tyd aan deelname onttrek sonder boete of verlies van voordele waarop die proefpersoon andersins geregtig is. Vertroulikheid: Daar sal gepoog word om persoonlike inligting vertroulik te hou. Volkome vertroulikheid kan nie gewaarborg word nie. Persoonlike inligting kan bekend gemaak word as die wet dit vereis. As resultate gepubliseer word kan dit lei tot individuele/groepsidentifikasie. Vriendelike groete Prof Corinna Walsh Kontakbesonderhede: 051 401 3818 309 Development of a nutrition screening tool for the prediction of birth outcomes of women attending the antenatal clinic at Pelonomi Hospital TOKOMANE YA TLHAHISO LESEDING Study title: Development of a nutrition screening tool for the prediction of birth outcomes of women attending the antenatal clinic at Pelonomi Hospital Re leboha ha o ikemiseditse ho re thusa projekeng ena e bohlokwa. Re na le tumelo yah ore projeke ena e tla thusa ka ho ntlafatsa maphelo a bomme ba baimana ba sebedisang ditshebeletso tsa sepetlele sena hammoho le bana ba bona. Rona jwalo ka babatlisisi ho tswa Yunivesithing ya Foreisitata, re etsa dipatlisiso moo teng re ratang ho lekola dintho tse amanang le boima bo tlase haholo ba masea sepetleleng sa Pelonomi. Dipatlisiso ke tsela feela yah o ithuta karaba ya potso e itseng. Dipatlisisong tsena re rata ho ithuta ka dintho tse hlokang hore ho shejwane le tsona meralong ya tsa bophelo Foreisitata. Ho re ho etsang ke dipatlisiso feela ha se karolo ya phekolo. Sememo sa ho nka karolo: Re ya o kopa/mema hore o nke karolo dipatlisisong tsena, ebile re kopa tumello yah ore o re fe dintlha mabapi le bukana wa lesea la hao ya tliliniki ha le se le tswetswe. Dipatlisiso tsena di kenyelleditse eng: projeke ena e ikemiseditse ho fumana tlhahiso leseding e lekaneng dintho tse amanang le boemo ba masea ha a qeta ho tswalwa hore re tle re tsebe ho rala foromo e tla thusang ka ho lekola dintho tsena nakong eo mme a ntseng a imile. Dipatlisisong tsena re hloka bo mme ba baimana ba tsamayang tliliniki ya baimana e sepetleleng sa Pelonomi. Re kopa le hore o tlise bukana ya tliliniki ya lesea la hao ha le qeta ho belehwa diofising tsa bo mmaphepo sepetleleng sa Pelonomi hore re tle re fumane tlhahiso leseding ya bohlokwa mabapi le tswalo ya lesea la hao. Diforomo tsa dipotso kaofela di tla tlatswa tliliniking ya baimana sepetleleng sa Pelonomi ke babatlisisi ho tswa Yunivesithing ya Foreisitata. Ba nka karolo ba tla botswa dipotso tsena tse latelang:  Dintlha mabapi le maemo a moo o phelang teng;  Dijo tseo o di jang;  Maemo a hao a bophelo; le  Phihlello ya lelala ya dijo. Hape re tla metha boima le botelele ba hao. Tsena tsohle di tla nka hora le metsotso e 30 ya nako ya hao. Hang ha lesea la hao le belehwa, re kopa o tlise bukana yah ae ya tliliniki ho bo mmaphepo hore bat le ba tsebe ho hatisa dintlhe tse bohlokwa. Babatlisisi bat la leka ho fan aka tlahiso leseding mabapi le sephetho sa dipatlisiso. Ha ebe dipatlisiso tsena di fan aka tlhahiso leseding e bohlokwa bakeng sa lelapa la hao, re tla kopa tumello ya hao pele re ka tswella pele ka ho tsebisa lelapa la hao. Tlhahiso leseding ya hao e keke ya lokollwa ntle le ha ho hlokeha hore ho etsahale jwalo ka molao. Dikotsi tse amangwang le ho nka karolo: ha hona kotsi e teng ha o nka karolo dipatlisisong tsena. 310 Melemo yah o nka karolo: Ho nka karolo dipatlisisong tsena ho bolela hore o thus aka ho rala meralo ya tsa bophelo le tsa phepo eo e tla bang molemo haholo ho bomme ba baimana ba tsayang ditliliniki tsa baimana. O tla fumantshwa R100 bakeng sa ditjeho tsa sepalangwang ha o se o re bolelletse ka peleho ya lesea la hao e bile re fumane tlhahiso leseding bukaneng ya lesea la hao. Ho nka karolo ke ka ho ithaopa ebile ho hana ho nka karolo ha ho kenye kotlo kapa tahlehelo ya melemo e o tshwanetseng; o ka tlohella ho nka karolo nako e nngwe le enngwe ntle le kotlo kapa ntle le ho lahlehelwa ke melemo e o tshwanetseng. Lekunutu: Hot la lekwa ka hohle-hohle hore dintlha tsa hao di bolokwe e le lekunutu. Ha ho kgonahale ho tshepisa lekunutu le phethahetseng. Dintlha tsa bohlokwa di ka phatlallatswa ha e be ho hlokeha hore ho etswe jwalo ka molao. Ho ngola dibukaneng tsa saense ho ka etsa hore sehlopha se le kaofela se tsebahale. Ka diteboho Prof Corinna Walsh Contact details: 051 401 3818 311 Appendix B: Consent form Development of a nutrition screening tool for the prediction of birth outcomes of women attending the antenatal clinic at Pelonomi Hospital CONSENT TO PERFORM RESEARCH You have been asked to participate in a research study. You have been informed about the study by ………………………………………. . You may contact Prof Corinna Walsh at 051 401 3818 (w) at any time if you have questions about the research or if you are injured as a result of the research. You may contact the Secretariat of the Health Science Research Ethics Committee, UFS at telephone number (051) 4017794/5 if you have questions about your rights as a research subject. Your participation in this research is voluntary, and you will not be penalized or lose benefits if you refuse to participate or decide to terminate participation. If you agree to participate, you will be given a signed copy of this document as well as the participant information sheet, which is a written summary of the research. The research study, including the above information has been verbally described to me. I understand what my involvement in the study means and I voluntarily agree to participate. _____________________ __________________ Signature of Participant Date 312 Development of a nutrition screening tool for the prediction of birth outcomes of women attending the antenatal clinic at Pelonomi Hospital TOESTEMMING TOT DEELNAME AAN NAVORSING U is versoek om aan ʼn navorsingstudie deel te neem. U is oor die studie ingelig deur ………………………………………. . U kan Prof Corinna Walsh enige tyd kontak by 051 401 3818 (w) indien u vrae oor die navorsing het of as gevolg van die navorsing beseer is. U kan die Sekretariaat van die Gesondheidswetenskappe Navorsingsetiekkomitee, UV by telefoonnommer (051) 4017794/5 kontak indien u enige vrae het oor u regte as ʼn proefpersoon. U deelname aan hierdie navorsing is vrywillig, en u sal nie gepenaliseer word of voordele verbeur as u weier om deel te neem of besluit om deelname te staak nie. As u instem om deel te neem, sal ʼn ondertekende kopie van hierdie dokument sowel as die deelnemerinligtingsblad, wat ʼn geskrewe opsomming van die navorsing is, aan u gegee word . Die navorsingstudie, insluitend die bogenoemde inligting is verbaal aan my beskryf. Ek begryp wat my betrokkenheid by die studie beteken en ek stem vrywillig in om deel te neem. ________________________ __________________ Handtekening van deelnemer Datum 313 Development of a nutrition screening tool for the prediction of birth outcomes of women attending the antenatal clinic at Pelonomi Hospital TUMELLO YA HO ETSA DIPATLISISO O Kopuwe ho nka karolo dipatlisisong. O tsebesitswe ka dipatlisiso ke ………………………………………. O ka ikopanya le Prof Corinna Walsh nomorong ya 051 401 3818 (w) nako enngwe le enngwe ha e be o na le dipotso ka dipatlisiso kapa ha o ka hlokofala ka lebaka la hore o nkile karolo dipatlisisong. O ka letsetsa bangodi ba komiti ya Etiki ya Bophelo bo bottle, Yunivesithing ya Foreisitata , mohaleng ona wa (051) 4017794/5 ha ebe o na le dipotso mabapi le ditokelo tsa hao jwalo ka motho ya nkanng karolo. O nka karolo dipatlisisong tsa hao ka ho ithaopa e bile o keke wa fumantshwa kotlo kapa hona ho lahlelwa ke ditshebeletso ha o hana ho nka karolo kapa ha o nka qeto ya ho tlohella ho nka karolo. Ha o dumela ho nka karolo, o tla fumantshwa tokomane e saenuweng hammoho le pampitshana e fanang ka tlhaloso ya dipatlisiso, e leng e kgutsufaditsweng. Dipatlisiso tse ka hodimo ke di hlaloseditswe ka puo. Ke ya utlwisisa hore na ho nka karolo dipatlisisong ho bolela eng e bile ke dumela ho ithaopa ho nka karolo. _____________________ __________________ Saena mona Letsatsi 314 Appendix C: Sociodemographic and household questionnaire Nutritional status of pregnant women SOCIODEMOGRAPHY AND HOUSEHOLD QUESTIONNAIRE 1. Date (dd/mm/yyyy): ________________ 2. Respondent number: _______________________________ 3. Fieldworker: _______________________________ 4. Area in the Free State (Please circle) 1. Mangaung (Pelonomi Hospital) 2. Southern Free state (Trompsburg/ Springfontein) 4.1 Town: ____________________________________ PERSONAL INFORMATION OF RESPONDENT Please encircle the relevant option for those questions where more than one option is given 5. What is your date of birth? (dd/mm/yyyy) _______________________________________ 6. Pregnancy stage? (in weeks): _________ weeks 7. How many years have you been living in this area? __________________ years 8. What is your marital status? 1. Married (including traditional marriage) 2. Not married, but in relationship 3. Not married, and not in relationship 4. Divorced/separated 5. Widowed 6. Other? Please specify: ____________________________________________________ If you are married or in a relationship with the future father of your baby, what is your 8.1 current living arrangement? 1. Living with partner 2. Not living with partner 3. Other? Please specify: ________________________________________ 315 HOUSEHOLD DEMOGRAPHICS OF RESPONDENT 9. What type of house do you live in? 1. Brick house 2. Shack 3. Flat 4. Other? Please specify: _______________________________________________ 10. Total number of rooms in the house: __________________________ 11. Number of bedrooms in the house: ____________________________ 12. Do you have a bathroom in the house? 1. Yes 2. No 13. Do you have a bathroom outside the house? 1. Yes 2. No 14. Do you have a kitchen or cooking area inside the house? 1. Yes 2. No 15. How many people live in the house with you? You + 16. How many of the people that live in the house with you are younger than 10 years? ___________________ 17. Does your house have electricity? 1. Yes 2. No 18. How do you usually get to work/shop/clinic etc.? 1. Car 2. Walk 3. Taxi 4. Bus 5. Other? Please specify: ________________________________________ 316 COOKING FACILITIES, WATER AND SANITATION 19. Do you have indoor water? 1. Yes 2. No 20. Which of the following do you have? (Select only the most applicable option) 1. Indoor water 2. Own tap outside the house 3. Share a tap with other households 21. Which of the following do you have? (Select only the most applicable option) 1. Flush toilet inside the house 2. Own flush toilet outside the house 3. Share an outside toilet with other households 4. Use a bucket system 5. Have own pit toilet outside the house 22. Does your household own the following? 1. Stove Yes = 1 No = 2 2. Refrigerator Yes = 1 No = 2 3. Freezer Yes = 1 No = 2 4. Microwave Yes = 1 No = 2 5. Kettle Yes = 1 No = 2 6. Radio Yes = 1 No = 2 7. TV Yes = 1 No = 2 8. Motor vehicle Yes = 1 No = 2 23. What fuel is used for cooking most of the time in the household? 1. Electric 2. Gas 3. Paraffin 4. Wood, coal 5. Sun 6. Open fire 7. Other, please specify: _______________________________________ 317 EDUCATION, EMPLOYMENT AND INCOME 24. What is your highest level of education? 1. None 2. Primary school 3. Std 6-8 (Grade 8 – 10) 4. Std 9-10 (Grade 11 – 12) 5. Tertiary education 6. Don’t know 25. What is your partner's highest level of education? 1. None 2. Primary school 3. Std 6-8 (Grade 8 – 10) 4. Std 9-10 (Grade 11 – 12) 5. Tertiary education 6. Don’t know 26. What is your employment status? 1. Full-time employed 2. Part time employed 3. Unemployed 4. Self-employed 5. Housewife by choice 6. Other? Please specify: ________________________________________________________ 27. What is your partner's employment status? 1. Full-time employed 2. Part time employed 3. Unemployed 4. Self-employed 5. Retired 6. Other? Please specify: ________________________________________________________ 318 28. Does anyone in the household obtain an income from the following? 1. Wages and salaries from formal employment Yes = 1 No = 2 2. Self-employment Yes = 1 No = 2 3. Casual employment (part-time employment) Yes = 1 No = 2 4. Crop production and livestock sales Yes = 1 No = 2 5. Pension or state grants Yes = 1 No = 2 6. Domestic work Yes = 1 No = 2 7. Other? Please specify: _____________________ 29. What is your household income per month (including wages, rent, sales of vegetables, State grants, etc.)? 1. None 2. R100-R500 3. R501-1000 4. R1001-R3000 5. R3001-R5000 6. Over R5000 7. Don't know 30.1 Is this income more or less the income that you had over the past six months? 1. More 2. Less 3. The same 319 Appendix D: Reported health, lifestyle pregnancy history and anthropometry questionnaire HEALTH AND LIFESTYLE, PREGNANCY HISTORY AND ANTHROPOMETRY QUESTIONNAIRE 1. Date: (ddmmyy) ____________________ 2. Respondent number: _______________ SMOKING AND TOBACCO USE Please encircle the relevant option for those questions where more than one option is given 3. Have you ever smoked? Yes = 1 No = 2 If NO, go to question 4 3.1 If YES, do you smoke currently? Yes = 1 No = 2 If NO, go to question 3.4 3.1.1 If YES, how often do you smoke? 1. Daily 2. Occasionally 3.2 If you smoke daily, how many cigarettes do you smoke per day? _____ 3.3 If you smoke occasionally, how many cigarettes do you smoke per week? _____ 3.4 If you do not smoke currently, but have in the past, how long ago did you stop? (year and month) Month: Year: ____________ ____________ 4. Have you ever used snuff or chewed tobacco? Yes = 1 No = 2 If NO, go to question 5 4.1 If YES, do you use it currently? Yes = 1 No = 2 If NO, go to question 4.4 4.1.1 If YES, how often do you use it? 1. Daily 2. Occasionally 4.2 If you use it daily, how many times per day? _____________ 4.3 If you use it occasionally, how many times per week? _____________ 4.4 If you do not use it currently, but have in the past, how long ago did you stop? (year and month) Month: Year: ____________ ____________ 5. Are there members in your household who currently smoke? Yes = 1 No = 2 5.1 If YES, how many people in your household smoke? 5.2 Does your baby's father smoke? (this pregnancy) Yes = 1 No = 2 320 ALCOHOL USE 6. Have you ever used alcohol? Yes = 1 No = 2 If NO, go to question 7 6.1 If YES, do you currently use alcohol? Yes = 1 No = 2 If NO, go to question 6.2 If YES, ask both questions below on typical alcohol consumption: 6.1.1 How many of the following do you usually drink on a typical week day during this pregnancy? 1. Small beer (330ml) __________ 2. Glasses of wine __________ 3. Tots of spirits (e.g. vodka/brandy) __________ 4. Spirit coolers (e.g. Smirnoff Spin) __________ 5. Glasses of homemade beer __________ 6.1.2 How many of the following do you usually drink on a typical day during the weekend during this pregnancy? 1. Small beer (330ml) __________ 2. Glasses of wine __________ 3. Tots of spirits (e.g. vodka/brandy) __________ 4. Spirit coolers (e.g. Smirnoff Spin) __________ 5. Glasses of homemade beer __________ 6.2 If you do not use alcohol currently, but have in the past, how long ago did you stop? (year and month) Year: Month: ____________ ____________ MEDICATION USE Are you using any medication regularly? (at least once a week) 7. (excluding supplements for pregnancy) Yes = 1 No = 2 7.1 If YES, list the medication that you are currently using (including traditional herbs and remedies): 321 SOCIAL SUPPORT 8. Are there people who could help you if you had a really big problem and needed help, such as with money, the children, accommodation and so on? 1. Nobody 2. Maybe / unsure 3. A number of people 9. If you have a husband or partner, can you talk to your husband or partner about any problems you might have? 1. Never 2. Sometimes 3. Always 10. Do you belong to a church group or any other organisation? 1. Yes 2. No 10.1 If YES, how often do you go to church? 1. Once a week 2. Once a month 3. Other ____________________ STRESS 11. During the last 6 months, have you or a member of your close family been in real danger of being killed in one of the following ways? 1. By criminals Yes = 1 No = 2 2. By police, army or other officials Yes = 1 No = 2 3. During political activities Yes = 1 No = 2 12. During the last 6 months, did you witness a violent crime (e.g. murder, robbery, assault, rape)? 1. Yes 2. No 13. During the last 6 months, have you found that you are in so much debt that you don't know how you will repay the money? 1. Yes 2. No 14. Have you or one of your close family (husband/partner, mother, father, husband/partner's mother, husband/partner's father) members not been able to find a job for more than 6 months? 1. Yes 2. No 322 15. During the last 6 months, have you or anyone in your close family husband/ partner, mother, father, husband/partner's mother, husband/partner's father) been seriously ill? 1. Yes 2. No 16. During the last 6 months, did any member of your close family (husband/ partner, mother, father, husband/partner's mother, husband/partner's father) die? 1. Yes 2. No 17. Is there anyone in your close family (husband/partner, mother, father, husband/partner's mother, husband/partner's father) who has a problem with drugs or alcohol? 1. Yes 2. No 18. During the last 6 months, have you had a break-up with your husband or partner? 1. Yes 2. No 3. Not applicable 19. During the last 6 months, has your husband or partner hit or beaten you? 1. Yes 2. No 3. Not applicable PREVIOUS PREGNANCIES 20. Have you been pregnant before? 1. Yes 2. No Please provide the following information for your first born child 21. Was the baby born alive? 1. Yes 2. No, please explain _________________________________________________ If NO, go to question 24. 21.1 Date of birth (ddmmyyyy): __________________________________________ 21.2 Delivery method 1. Vaginal 2. Caesarean 3. Don’t know 323 21.3 Gender: 1. Male 2. Female 21.4 Was the baby full-term? 1. Yes 2. No 21.5 How did you feed the baby? 1. Breastmilk 2. Formula milk 3. Breastmilk and formula milk 4. Cow's milk 5. Other? Please specify: _______________ 21.6 How is the child's health now? 1. Healthy 2. Deceased 3. Don’t know 4. Unwell/sick? Explain: _______________ 22. How many other live children do you have? _______________ 23. How did you feed your other previous children after birth? (Can select more than one) 1. Breastmilk 2. Formula milk 3. Breastmilk and formula milk 4. Cow's milk 5. Other: ______________________________ CURRENT PREGNANCY 24. Have you been admitted to hospital during this pregnancy? 1. Yes 2. No 24.1 If YES, what was the reason? 25. Have you experienced any of the following during this pregnancy? 25.1 Cough for at least 2 weeks 1. Yes 2. No 25.2 Loose stools/ diarrhoea for at least 3 days 1. Yes 2. No 324 25.3 Constipation 1. Yes 2. No 25.4 Nausea 1. Yes 2. No 25.5 Vomiting 1. Yes 2. No 25.6 Loss of appetite 1. Yes 2. No 25.7 Swelling of feet 1. Yes 2. No 25.8 Urinary infection 1. Yes 2. No 25.9 Weight loss of > 3 kg 1. Yes 2. No 25.10 Other? Please specify: 26. Have you ever been diagnosed or treated for the following? Yes (now) = 1; Yes (in past) = 2; No (never) = 3 1. High blood pressure _______________ 2. Heart disease _______________ 3. Diabetes _______________ 4. Tuberculosis _______________ 5. Asthma _______________ 6. Any sexually transmitted disease _______________ 7. Vaginal infection/discharge _______________ 8. Cancer _______________ 9. Lung diseases _______________ 10. High cholesterol _______________ 11. Stroke _______________ 12. Other? Please specify: _______________________________ 27. How many babies are you expecting? _______________ 325 ANTHROPOMETRY 1. Pre-pregnancy weight (kg) . 1.1 Date measured (dd/mm/yyyy): 2. Current weight (kg) . . . 3. Height (cm) . . . 3.1 If height cannot be measured: Knee height (cm) . Demi span (cm) . 4. Indicate pregnancy weight gain if noted on antenatal card: 326 Appendix E: Household food security questionnaire HOUSEHOLD FOOD SECURITY (HFIAS MEASUREMENT TOOL) QUESTIONNAIRE 1. Respondent number: Please encircle the relevant option for those questions where more than one option is given 2. In the past four weeks, did you worry that your household would not have enough food? 0. No 1. Yes 2.1 If YES, how often did this happen? 1. Rarely (once or twice in the past four weeks) 2. Sometimes (three to ten times in the past four weeks) 3. Often (more than ten times in the past four week) 3. In the past four weeks, were you or any household member not able to eat the kinds of foods you preferred because of a lack of resources? 0. No 1. Yes 3.1 If YES, how often did this happen? 1. Rarely (once or twice in the past four weeks) 2. Sometimes (three to ten times in the past four weeks) 3. Often (more than ten times in the past four week) 4. In the past four weeks, did you or any household member have to eat a limited variety of foods due to a lack of resources? 0. No 1. Yes 4.1 If YES, how often did this happen? 1. Rarely (once or twice in the past four weeks) 2. Sometimes (three to ten times in the past four weeks) 3. Often (more than ten times in the past four week) 5. In the past four weeks, did you or any household member have to eat some foods that you really did not want to eat because of a lack of resources to obtain other types of food? 0. No 1. Yes 5.1 If YES, how often did this happen? 1. Rarely (once or twice in the past four weeks) 2. Sometimes (three to ten times in the past four weeks) 3. Often (more than ten times in the past four week) 327 6. In the past four weeks, did you or any household member have to eat a smaller meal than you felt you needed because there was not enough food? 0. No 1. Yes 6.1 If YES, how often did this happen? 1. Rarely (once or twice in the past four weeks) 2. Sometimes (three to ten times in the past four weeks) 3. Often (more than ten times in the past four week) 7. In the past four weeks, did you or any other household member have to eat fewer meals in a day because there was not enough food? 0. No 1. Yes 7.1 If YES, how often did this happen? 1. Rarely (once or twice in the past four weeks) 2. Sometimes (three to ten times in the past four weeks) 3. Often (more than ten times in the past four week) 8. In the past four weeks, was there ever no food to eat of any kind in your household because of lack of resources to get food? 0. No 1. Yes 8.1 If YES, how often did this happen? 1. Rarely (once or twice in the past four weeks) 2. Sometimes (three to ten times in the past four weeks) 3. Often (more than ten times in the past four week) 9. In the past four weeks, did you or any household member go to sleep at night hungry because there was not enough food? 0. No 1. Yes 9.1 If YES, how often did this happen? 1. Rarely (once or twice in the past four weeks) 2. Sometimes (three to ten times in the past four weeks) 3. Often (more than ten times in the past four week) 10. In the past four weeks, did you or any household member go a whole day and night without eating anything because there was not enough food? 0. No 1. Yes 328 10.1 If YES, how often did this happen? 1. Rarely (once or twice in the past four weeks) 2. Sometimes (three to ten times in the past four weeks) 3. Often (more than ten times in the past four week) 329 Appendix F: Dietary intake questionnaire Nutritional status of pregnant women 24-Hour recall dietary intake form 11 Participant number: 12 Area code (01= Mangaung; 02= Trompsburg/Springfontein) 13 14 2 0 1 Today’s date: year month day 1. What day was yesterday? (tick correct one) 1. Monday 2. Tuesday 3. Wednesday 4. Thursday 5. Friday 6. Saturday 7. Sunday 2. Would you describe the food that you ate yesterday as typical of your usual food intake? Yes 1 No 2 Greetings! Thank you for giving up your time to participate in this study. I hope you are enjoying it so far. Here we want to find out what people living in this area eat and drink. This information is important to know as it will tell us if people are eating enough and if they are healthy. There are no right or wrong answers. Everything you tell me is confidential. Only your subject number appears on the form. Is there anything you want to ask now? Are you willing to go on with the questions? I want to first ask you a few general questions about your food intake, the preparation of food and the type of food that you use in your home. Answer the following few questions in terms of your habits in the past month. Instruction Tick the box with the participant’s answer 330 3. What type of pot do you usually use to prepare food in? (may answer more than one) 1. Iron pot 2. Stainless steel pot 3. Aluminium pot 4. Glassware 5. Other (specify) 4. Do you eat maize meal porridge? Yes No If YES, what type do you have at home now? a. Brand name: __________________________________________________________________ b. Don’t know Yes No c. Grind self: Yes No d. If the brand name is given, do you usually use this brand? Yes No Don’t know 4.1 Where do you get your maize meal from? (may answer more than one 1. Shop 2. Employer 3. Harvest and grind self 4. Other (specify) 5. Don’t know 5. Do you use oil in preparartion of food? 5.1 Do you deep fry any food? Yes No What type of oil do you buy for deep frying? a. Brand name: ____________________________________________________________________ b. Don’t know c. Do you use the same oil more than once? Yes No d. If yes, how many times will you use the same oil? ____________________________________________ 331 6. What type of salt do you use? a. Give brand names ___________________________________________________________________________ b. Don’t know c. Do you use Iodised salt? Yes No Don’t know Always Sometimes Never Don’t know d. Do you add salt to food while it is being cooked? Always e. Do you add salt to your food after it has been cooked? Sometimes Never f. Do you like salty foods e.g. salted peanuts, crips, chips, Fritos, bilting, dried sausage, etc? Very much Like it Not at all 7. Supplement use: 7.1 Did you receive any vitamins or vitamins and minerals at the antenatal clinic? Yes No Do you use any of of the following? Name of the product Quantity of How many When did you start (Brand)* capsules/pills times/week? taking these (before Type? at a time? first clinic visit OR after first clinic visit)? 1.1 Vitamins and/or minerals from the shop 1.2 Vitamins and/or minerals from the clinic Other: specify ______________ *If the answer is “I don’t know” request the woman to show you the supplement if possible 332 8.6 If you are using the the supplements from the shop or the clinic, do you take it with food or drink? Yes No 8.6.1 If yes, do you usualy take the supplement before, during or after a meal? Before During After 8.6.2 What is the typical meal you have when you take the supplement? ______________________________________________________________________________ ______________________________________________________________________________ 8.7 If you are not using the supplements from the clinic, please tell me why not? ______________________________________________________________________________ ______________________________________________________________________________ Now I want to find out about everything you ate or drank yesterday, including water or food you pick from the veld. Please tell me everything you ate from the time you woke up yesterday until the time you went to sleep. I will also ask you where you ate the food and how much you ate. To help you to describe the amount of a food you eat, I will show you pictures and examples of the different amounts of the food. Please say which picture or example is closest to the amount you eat, or if it is smaller, between the sizes or bigger than the pictures. After you woke up yesterday, when was the first time you ate yesterday? 333 OFFICE USE Time of day Type of food item and drink Preparation method (cooked/ Quantity of food Food code Gram consumed fried/ grilled/ steamed/ baked) item or drink What was added? consumed Waking up to about 9 o’clock (breakfast time) Mid-morning (09h00-12h00) Lunch time (12h00-14h00) Afternoon (14h00-17h00) Supper time (17h00- sunset) After sunset till waking up next morning 8.8 Did you taste any foods during preparation? Yes No 334 Nutritional status of pregnant women Quantitative Food Frequency Questionnaire Participant number: 15 Area in the Free state: ______________________________________ 16 Area code (01=Mangaung; 02= Southern Free state (Trompsburg/Springfontein) 17 18 2 0 1 Today’s date: Day of the week: __________________________________ year month day Please think carefully about the food and drink you have consumed during the PAST MONTH (four weeks). We have divided the foods into different groups for example all the porridges and cereals together. I will go through a list of food groups and drinks with you and I would like you to tell me:  Which foods you eat in each of the different food groups  How the food is prepared  How much of the food you eat at a time  How many times a day you eat it and if you do not eat it everyday, how many times a week or a month you eat it. To help you to describe the amount of a food you eat, I will show you pictures of different amounts of the food as well as other food models, containers, etc. There are no right or wrong answers. Everything you tell me is confidential. Only your participant number appears on the form. Is there anything you want to ask now? Are you willing to go on with the questions? 335 Before we start I would like to find out what type of margarine, oil and milk you USUALLY use in your home. 1. What type of MARGARINE do you USUALLY use in your home? Give brand name if possible 1. Tub/Soft margarine (brand name) _________________________________________________ 2. Brick/Hard margarine (brand name) ________________________________________________ 3. I don’t know 4. Do not use margarine in home 5. Butter (brand name) ____________________________________________________________ 2. What type of OIL do you USUALLY use in the preparation of food in your home? 1. Sunflower oil (give brand name) ___________________________________________________ 2. Canola oil (give brand name) _____________________________________________________ 3. Olive oil (give brand name) _______________________________________________________ 4. Other (give brand name) ________________________________________________________ 5. Oil previously used _____________________________________________________________ 6. I don’t know 7. Do not use OIL ever in the home 3. What type of MILK do you USUALLY use in your home? Mark only ONE 1. Full cream milk / Fresh cow’s milk/ Box milk full cream 2. Low fat milk / 2% milk / Box low fat or 2% milk 3. Fat free milk / Skim milk / Box fat free or skim milk 4. Powder milk (eg Elite; give brand name) ____________________________________________ 5. I don’t know 6. Do not use milk 336 4. What type of CREAMER do you USUALLY use in your home? 1. Cremora, Ellis Brown, Coffee Mate, Tea Mate etc 2. Cremora Lite 3. I don’t know 4. Do not use creamer 337 QUANTIFIED FOOD FREQUENCY QUESTIONNAIRE 19 INSTRUCTIONS: Circle the participant’s answer. Fill in the amount and times eaten in the appropriate columns. I shall now ask you about the type and the amount of food you have been eating in the LAST MONTH. Please tell if you eat the food, how much you eat and how often you eat it. We shall start with maize meal porridge. In the last four weeks, did you eat…..? 20 MAIZE MEAL, COOKED PORRIDGES AND BREAKFAST CEREALS FOOD DESCRIPTION AMOUNT TIMES EATEN CODE AMOUNT / WEEK 2 columns to be completed Per Per Per Seldom day week month /Never Maize-meal Stiff (pap) 4401 porridge Maize-meal Soft (slappap) 4400 porridge 1. Maize- Crumbly (phutu) 4402 meal porridge Sour porridge Maize meal 4429 (Ting) Mabella 3241 Other Mabele Stiff 3437 Soft Morvite Soft 4525 Oats 3239 Tastee wheat Soft 3240 Other cooked Type porridge Breakfast All bran flakes 3242 cereals Corn flakes plain 3243 Weetbix 3244 Rice crispies plain 3252 Other 338 5. Do you pour milk on your cooked porridge or cereal? 1 Y e s 2 No 5.1 If yes, what type of milk (whole fresh, sour, 1%, fat free, milk blend, etc) _________________________________________________ If no, go directly to the “sugar” section FOOD DESCRIPTION AMOUNT TIMES EATEN CODE AMOUNT / WEEK 2 columns to be completed Per Per Per Seldom day week month /Never If yes, how much Whole milk/full cream 2718 milk milk/ fresh cow’s milk Maas/sour milk 2787 Low fat / 2% milk 2772 Fat free / skim milk 2775 Other 6. Do you put sugar on your porridge or cereal? Y e s 1 N o 2 If no, go directly to the next question “do you put anything else in your porridge?” FOOD DESCRIPTION AMOUNT TIMES EATEN CODE AMOUNT / WEEK 2 columns to be completed Per Per Per Seldom day week month /Never If yes, how much Cooked porridge 3989 sugar Cereal 3989 WHITE or Other porridge / cereal 3989 BROWN Other 339 21 OTHER STARCH FOOD DESCRIPTION AMOUNT TIMES EATEN CODE AMOUNT / WEEK 2 columns to be completed Per Per Per Seldom day week month /Never Samp Bought 3250 Self ground Samp and beans Give ratio of samp:beans 3402 (1:1) Samp and Give ratio of 3250 (samp) peanuts samp:peanuts 3458 Rice White 3247 Brown 3315 Maize Rice 3250 Any fat added? Pasta Macaroni, plain 3262 Spaghetti, plain 3262 Spaghetti, canned in 3258 tomato sauce Macaroni & cheese Milk: Fat: Other specify Pizza Home made: Specify 3353 topping (base+ch) Bought: Specify topping 3353 (base+ch) 340 You are being very helpful. Can I now ask you about meat? 22 CHICKEN, MEAT, FISH 23 7. How many times do you eat meat (beef, mutton, pork, chicken, fish) per week? ____________________________________ FOOD DESCRIPTION AMOUNT TIMES EATEN CODE AMOUNT / WEEK 2 columns to be completed Per Per Per Seldom day week month /Never Chicken Meat & skin, boiled 2926 Meat & skin, 2925 roasted/grilled/fried in chicken fat Meat ONLY, boiled (no 2963 skin) Meat ONLY, roasted (no 2950 skin) White meat only, cooked 2964 (NO Skin) Kentucky / Chicken 3018 Licken (Fried in batter/crumbs) Nando’s 2925 Other 8. Do you eat chicken skin? A l w a y s 1 S o m e t im e s 2 N e v e r 3 Chicken stew With potato and onion 9813 WITH skin With tomato and onion 2985 WITH skin With vegetables 3005 WITH skin With tomato and onion 4379 NO skin With vegetables 4378 NO skin Chicken BONE With potato and onion 9814 stew and tomato Other Chicken feet Nothing added 2997 Stew with potato, onion 9815 and tomato Chicken head 2999 Chicken offal Stew with tomato and 9816 onion and sunflower oil Liver, cooked 2970 Other 341 FOOD DESCRIPTION AMOUNT TIMES EATEN CODE AMOUNT / WEEK 2 columns to be completed Per Per Per Seldom day week month /Never RED MEAT 9. How do you like your meat? With fat OR Fat trimmed Red meat BRISKET, boiled/fried 4363 without added fat BEEF CHUCK, boiled/fried 2945 without added fat BRISKET, fried in added 4363 fat Type of fat: CHUCK, fried in added 2945 fat Type of fat: Beef, stewed with 3006 cabbage Beef, stewed with potato 9817 and onion and tomato Beef, stewed with 3020 vegetables Mince , nothing added 2921 Mince, tomato & onion 2987 added Beef BONE stew with 9819 potato and onion and oil Other MUTTON Meat, with fat, cooked 2947 Mutton, no fat, cooked 3036 Mutton, chop, grilled 2927 Mutton, stewed with 2916 vegetables Other BRISKET: more fat and cheaper CHUCK: less fat than the brisket, but more expensive 342 FOOD DESCRIPTION AMOUNT TIMES EATEN CODE AMOUNT / WEEK 2 columns to be completed Per Per Per Seldom day week month /Never Beef/mutton Offal, cooked 3003 Offal Stewed with vegetables Liver, beef, fried/cooked 2920 Liver, sheep, fried/cooked 2955 Kidney, beef, cooked 2923 Kidney, sheep, cooked 2956 Brain, sheep, cooked 2952 Lung, beef, cooked 3019 Lung, sheep, cooked 4337 “Gemaldes” (lung & fat) 9809 Heart, beef, cooked 2968 Heart, sheep, cooked 2969 Other Goat meat Grilled/roasted/cooked 4281 Stewed with vegetables Other Venison/ 2913 Wild buck Horse/Donkey 4407 Rabbit 4327 Other type of Specify meat 1. What type of vegetables is usually put into meat stews? 343 FOOD DESCRIPTION AMOUNT TIMES EATEN CODE AMOUNT / WEEK 2 columns to be completed Per Per Per Seldom day week month / Never Wors / Sausage 2931 Bacon 2906 Patties Beef, fried 2984 Chicken, fried 3011 Cold meats Polony 2919 AND Ham 2967 Processed meats Vienna 2936 Frankfurter, beef & pork 2937 Frankfurter/Sausage, 3012 chicken Russian/Salami 2948 Other Canned meat Bully beef, plain 2940 Bully beef with potato & 2994 onion & oil Other Meat pie Beef 2939 Steak and kidney 2957 BOUGHT Sausage roll 2939 Or Cornish 2953 HOMEMADE Chicken 2954 Other Hamburger Bought 9818 Other Biltong Beef 3021 (with fat OR without fat) Dried wors Beef 2949 Dried sausage 344 FOOD DESCRIPTION AMOUNT TIMES EATEN CODE AMOUNT / WEEK 2 columns to be completed Per Per Per Seldom day week month / Never Dried beans Baked beans in tomato 3176 sauce Bean salad / Sousbone 3174 Soup with dried beans, 3145 beef and vegetables Sugar beans, cooked 3205 Other Lentils Whole, cooked 3203 Lentil soup with beef and 3153 vegetables Soya products Cooked 3196 eg. Imana, Knorr, Jileleke, Toppers Soup/Gravy 9831 Stewed with extra potato, 9830 onion and tomato Other 345 FOOD DESCRIPTION AMOUNT TIMES EATEN CODE AMOUNT / WEEK 2 columns to be completed Per day Per Per Seldom week month / Never Pilchards in Whole 3102 tomato sauce or chilli or brine Mashed with fried onion 3102 (70%) 3730 (30%) With tomato and onion 9820 Other Fish Hake, fried with 3072 batter/crumbs Hake, fried in oil 3060 Hake, steamed 4373 Moddervis / Yellow fish 3084 fried in oil Moddervis / Yellow fish 3089 baked with onion (NO oil added) Other Other canned Tuna in oil 3056 fish Other Fish cakes Bought: Fried 3080 Home made with potato 3098 Fish fingers Bought 3081 Eggs Boiled/poached 2867 Scrambled (full cream 2890 milk & oil) Scrambled (NO milk, 2869 ONLY oil added) Scrambled (NO oil, ONLY 2872 full cream milk) Fried in oil 2869 Fried in brick margarine 2877 Other 346 24 VEGETABLES FOOD DESCRIPTION AMOUNT TIMES EATEN CODE AMOUNT / WEEK 2 columns to be completed Per Per Per Seldom day week month /Never Cabbage How do you cook cabbage? Boiled, nothing added 3756 Boiled with potato and 3815 onion and oil Boiled with potato and 3813 onion and brick margarine Fried in oil 3812 Fried in brick margarine 3810 Boiled with potato, onion 9821 and tomato and oil Raw with nothing added 3704 Other Spinach or How do you cook spinach? morogo or beetroot leaves Boiled, nothing added 3913 or other green leafy Boiled with oil added Boiled with brick 3898 margarine added Boiled with tub 3899 margarine added Boiled with potato, onion 9822 and tomato and oil Other Tomato and With oil 9823 onion gravy Without fat 3925 Canned 4192 Thickened with packet 9832 soup powder Other 347 FOOD DESCRIPTION AMOUNT TIMES EATEN CODE AMOUNT / WEEK 2 columns to be completed Per Per Per Seldom day week month /Never Pumpkin Boiled, nothing added 4164 (yellow) Boiled with sugar only 3728 Butternut (NO fat) Hubbard squash Boiled with brick 3893 Table Queen margarine & sugar Etc Boiled with tub 9833 margarine and sugar Boiled with oil and sugar 9828 Other Carrots Boiled, nothing added 3757 Boiled with oil added Boiled with brick 3816 margarine added Boiled with tub 3817 margarine added Boiled with sugar only 3818 Boiled with oil and sugar Boiled with brick 3819 margarine and sugar Boiled with tub 3820 margarine and sugar Boiled with potato, onion 3824 and oil Boiled with potato, onion 3822 and brick margarine Boiled with potato, onion and tub margarine Chakalaka 9812 Raw, nothing added 3709 Other Mealies/ On cob – fat added 3725 Sweet corn Fat: On cob – no fat added 3725 Creamed sweet corn / 3726 canned Whole kernel/canned 3942 Whole kernel, frozen, 4132 boiled Other 348 FOOD DESCRIPTION AMOUNT TIMES EATEN CODE AMOUNT / WEEK 2 columns to be completed Per Per Per Seldom day week month /Never Beetroot Salad 3699 Boiled, nothing added 3698 How do you cook potatoes? Potatoes Boiled/baked with skin 4155 Boiled/baked without skin 3737 Boiled with oil added 3873 Boiled with brick 3867 margarine added Boiled with tub 3868 margarine added Mashed with whole milk 3876 and brick margarine Mashed with whole milk and oil Roasted in beef fat 3878 Roasted in oil 3979 French fries (chips) / 3740 Fried potatoes Other Sweet potatoes How do you cook sweet potatoes? Boiled/baked with skin 3748 Boiled/baked without skin 3903 Boiled with sugar and oil 9834 added Boiled with sugar and 3749 brick margarine added Other 349 FOOD DESCRIPTION AMOUNT TIMES EATEN CODE AMOUNT / WEEK 2 columns to be completed Per Per Per Seldom day week month /Never Broccoli Boiled 3701 Raw 3702 Cauliflower Boiled 3716 Green beans Boiled, nothing added 3696 Cooked with potato, 3794 onion and oil Cooked with potato, 3792 onion and brick margarine Other Mixed Canned 4264 vegetables Frozen, boiled 3727 Other Salad Mixed salad: tomato, 3921 vegetables lettuce and cucumber Raw tomato 3750 Cucumber, raw 4119 Coleslaw (cabbage) 3705 (mayonnaise) Coleslaw (cabbage) 3707 (commercial) Potato salad 3928 (mayonnaise) Baked bean salad 9824 Other salad vegetables Mayonnaise / Mayonnaise 3488 salad dressing Salad dressing made 3487 with vinegar and oil Other: Specify Other vegetables, specify + preparation 350 Now we come to fruit 25 FRUIT 11. Do you like fruit? Y e s 1 N o 2 FOOD DESCRIPTION AMOUNT TIMES EATEN CODE AMOUNT / WEEK 2 columns to be completed Per Per Per Seldom day week month /Never Apples 3532 Banana 3540 Pears 3582 Oranges 3560 Naartjie 3558 Grapes 3550 Peaches Fresh 3565 Canned 3567 Apricots Fresh 3534 Canned 3535 Mangoes 3556 Guavas Fresh 3551 Canned 3553 Watermelon Fresh 3576 Fruit salad Fresh 3588 Canned 3580 Custard with fruit salad 2716 Fig (Vye) 3544 Avocado 3656 Wild fruit/berries Specify type 351 FOOD DESCRIPTION AMOUNT TIMES EATEN CODE AMOUNT / WEEK 2 columns to be completed Per Per Per Seldom day week month /Never Dried fruit Apple, dried, raw 3600 Peach, dried, raw 3568 Mixed fruit, dried, raw 3593 Mixed fruit, dried and 3590 cooked with sugar Fruit roll, dried (all types) 3655 Other Other fruit ____________________ ____________________ Let me ask you about Custard. FOOD DESCRIPTION AMOUNT TIMES EATEN CODE AMOUNT / WEEK 2 columns to be completed Per Per Per Seldom day week month /Never Custard on its Homemade, full cream 2716 own milk or fresh cow’s milk Homemade, lowfat milk 2779 Homemade, skim milk 2717 Commercial eg Ultramel 2716 Other 352 26 BREAD AND BREAD SPREADS FOOD DESCRIPTION AMOUNT TIMES EATEN CODE AMOUNT / WEEK 2 columns to be completed Per Per Per Seldom day week month /Never Bread / Bread White 3210 rolls Brown 3211 Whole wheat 3212 12. Do you spread anything on the bread? A l w a y s 1 S o m e t i m e s 2 N e v e r 3 Margarine What brand do you have at home now? Tub, regular 3496 Tub, medium fat 9806 Tub, light/low fat 3524 Brick, regular 3484 Brick, medium fat 9805 Brick, lite/low fat 3528 Other Peanut butter 3485 Jam/syrup/ 3985 honey Marmite / Fray 4058 bentos / Oxo/ Bovril Fish/meat paste 3109 Cheese Cheddar 2722 Gouda 2723 Other Sandwich 3522 spread Achaar 3117 Other spreads Specify 353 FOOD DESCRIPTION AMOUNT TIMES EATEN CODE AMOUNT / WEEK 2 columns to be completed Per Per Per Seldom day week month /Never Dumpling White flour 3210 Whole wheat flour 3212 Vetkoek White flour 3257 Whole wheat flour 3324 Provita, Provita 3235 crackers, etc Cream crackers 3230 Other savoury biscuits 3331 like Bacon kips, wheat crackers, etc 354 27 DRINKS FOOD DESCRIPTION AMOUNT TIMES EATEN CODE AMOUNT / WEEK 2 columns to be completed Per Per Per Seldom day week month / Never Tea English /Ceylon 4038 Rooibos 4054 Coffee 4037 White sugar Tea 3989 Coffee 3989 Brown sugar Tea 4005 Coffee 4005 Milk per cup of 13. Do you use milk in your TEA? Y e s N o If YES, What type of milk do you use in TEA? TEA Fresh / long life 2718 whole/full cream Fresh/long life: 2%/low 2772 fat Fresh/long life: fat free / 2775 skim milk Creamer/whitener like 2751 Ellis Brown / Cremora Cremora Lite Condensed milk 2714 Evaporated milk 2715 Other None Milk per cup of 14. Do you use milk in your COFFEE? Y e s N o If YES, What type of milk do you use in COFFEE? COFFEE Fresh/long life: 2718 whole/full Fresh/long life: 2%/low 2772 fat Fresh/long life: fat free 2775 Creamer/whitener like 2751 Ellis Brown Cremora Lite Condensed milk 2714 Evaporated milk 2715 Other None 355 FOOD DESCRIPTION AMOUNT TIMES EATEN CODE AMOUNT / WEEK 2 columns to be completed Per Per Per Seldom day week month / Never Milk as such 15. Do you drink milk on its own? Yes No What type of milk do you drink as such? Fresh/long life: whole / 2718 full cream milk Fresh/long life: 2% milk 2772 / low fat milk Fresh/long life: fat free / 2775 skim milk Condensed milk 2714 Sour/maas 2787 Other Milk drinks Flavoured milk 2774 Milo made with full 2735 cream milk Milo made with skim 2747 milk Drinking chocolate 4287 made with water Other Yoghurt Drinking yoghurt low fat 2756 Plain low fat 2734 Low fat sweetened with 2732 fruit Squash Sweet O 4027 Six O Oros/Lecol – with sugar 3982 or other - artificially sweetener 3990 KoolAid 4027 Other Fizzy drinks Sweetened 3981 Coke, fanta, etc Diet 356 FOOD DESCRIPTION AMOUNT TIMES EATEN CODE AMOUNT / WEEK 2 columns to be completed Per Per Per Seldom day week month / Never Fruit juice Fresh/Liquifruit/Ceres 2866 Tropica (Dairy –fruit 2791 juice mix) Other Mageu/Motogo 4056 Home brew 4039 Tlokwe 4039 Beer 4031 Cider Sweet 4057 Spirits 4035 Eg Brandy, gin, vodka, whisky, cane, etc Wine red 4033 Wine White 4033 Other specify WATER Tap, borehole, dam, 4042 river, etc Bottled 4042 357 28 SNACKS AND SWEETS FOOD DESCRIPTION AMOUNT TIMES EATEN CODE AMOUNT / WEEK 2 columns to be completed Per Per Per Seldom day week month / Never Potato crisps 3417 Peanuts Raw 4285 Roasted 3458 Cheese curls, 3267 Niknaks, etc Raisins 3552 Peanuts and raisins Chocolates Milk chocolate, plain 3987 Kit Kat etc 4024 Chocolate coated bars 3997 like Bar One, TV bar, etc Other Candies/Sweets Sugus, gums, hard 4000 sweets, etc Toffees / Fudge / 3991 caramels Biscuits/cookies Homemade, plain 3233 Commercial, plain 3216 Commercial, with filling 3217 Other Cakes Butter cake, 3288 homemade with whole milk and brick margarine NO icing Chocolate cake, 3289 homemade with whole milk and brick margarine NO icing Icing for cake made 4014 with brick margarine Other 358 FOOD DESCRIPTION AMOUNT TIMES EATEN CODE AMOUNT / WEEK 2 columns to be completed Per Per Per Seldom day week month / Never Tarts Apple tart with a batter 3327 made with whole milk and brick margarine Other Scones Plain made with whole 3237 milk and brick margarine Other Muffin Bran 3407 Plain 3408 Other Rusks Buttermilk, commercial 3329 Homemade, white 3222 Other Savouries Sausage rolls 2939 Samoosas: Meat filling 3355 Samoosas: Vegetable 3414 filling Biscuits eg bacon kips 3331 Other 359 FOOD DESCRIPTION AMOUNT TIMES EATEN CODE AMOUNT / WEEK 2 columns to be completed Per Per Per Seldom day week month / Never Jelly On its own 3983 Custard added to jelly 2716 made with whole milk Other Baked pudding Baked in a syrup 3312 Baked without a syrup 3429 Custard added to 2716 pudding made with whole milk Other Instant pudding Made with whole milk 3266 Made with low fat milk 3395 Other Ice cream Regular 3483 Soft serve 3518 Other Sorbet 3491 Other specify 360 29 SAUCES, GRAVIES AND CONDIMENTS FOOD DESCRIPTION AMOUNT TIMES EATEN CODE AMOUNT / WEEK 2 columns to be completed Per Per Per Seldom day week month / Never Tomato sauce 3139 Worcester sauce 4309 Chutney 3168 Pickles 3866 White sauce Made with whole milk 3142 and brick margarine Packet soups Dry powder (all types) 3158 Made with water (all 3165 types) Gravy Made from meat and 3120 thickened Other WILD FRUITS, WILD BIRDS, ANIMALS OR INSECTS (hunted in rural areas or on farms) FOOD DESCRIPTION AMOUNT TIMES EATEN CODE AMOUNT / WEEK 2 columns to be completed Per Per Per Seldom day week month / Never MISCELLANEOUS: Please mention ANY OTHER FOODS used more than once/two times a week which we have NOT talked about 30 INDIGENOUS/TRADITIONAL FOODS/PLANTS/ANIMALS 31 Please tell me if you use any indigenous plants OR other indigenous foods like mopani worms, locusts ect to eat PLEASE GIVE DETAILS 361 Appendix G: Participant checklist Participant checklist Participant number: _____________________________ Name of Participant: _____________________________________________________ Contact details Cellphone number: __________________________________ Alternative cellphone number: __________________________ Expected due date: _____/______/__________ Assessments Please tick once completed Socio demographic questionnaire 24 HR Recall QFFQ Food security questionnaire Reported health questionnaire Anthropometry Urine collection Blood collection Additional comments: _____________________________________________________________________________________ _____________________________________________________________ 362 Appendix H: Researcher contact card Please remember Keep this card safe. Take your baby’s Road to Health Booklet to the dietitians’ offices at Pelonomi (floor 4, block K) once he / she has been born to copy. You will then be given R100 for transport. or Send us a please call me as soon as possible after your baby has been born. Marizeth: 0761544988 or Corinna: 0832976030 363 Appendix I: SMS requesting mothers to provide the Road to Health Booklet of their babies Original reminder sent to participants: This is about the research at Pelonomi antenatal clinic. Please remember to take your baby's clinic book to the dietitians (block K, floor 4) at Pelonomi Hospital. The dietitian will give you R100 for transport after she has made a copy of the book. You can send someone with the book if you cannot go yourself Message sent after the amendment to allow participants to send photos of the Road to Health Booklet directly to the researcher: This message is about the research at Pelonomi antenatal clinic. You cannot take your baby's book to the dietitians anymore, but you have a last chance to send a photo of some of the pages to 0761544988. If your baby has a pink or green book, send a photo of pages 4,5,6,7. If your baby has a blue book with 2 feet on it, send a photo of pages ii,27,38. You will receive R20 airtime loaded onto the number that you send the photos from after you have sent the photo. 364 Appendix J: Ethics clearance letter 365