Abstract P238: Sociodemographic Factors Associated With Under- and Overweight Among Women of Reproductive Age in Sub-Sahara Africa

Abstract

Introduction: In the last three decades, many countries in SSA have undergone rapid epidemiological and nutritional transitions, with an ever-increasing prevalence of cardio-metabolic conditions. Because of these changes, health care systems in the sub-region are faced with the simultaneous challenge of handling both underweight and overweight related sequelae. There is still limited epidemiological evidence on the drivers of this phenomenon within and across countries in SSA. Hypotheses: We assessed the following hypotheses: 1) SSA countries vary in terms of the type and extent of malnutrition; 2) Macro (country level) and micro (individual level) sociodemographic factors are associated with the double burden of malnutrition (i.e. under- and overweight) in SSA. Methods: Macro- and micro-level data for 34 SSA countries were acquired from the World Bank data base and Demographic and Health (DHS) surveys respectively. A total of 247,691 eligible participants (women between the age of 15 and 49) from the DHS surveys were analyzed. We first determined malnutrition categories in SSA from country level prevalence estimates of underweight and overweight using statistically defined logic expressions. Second, we used random forest analysis to investigate the association between the malnutrition groups and macro-level variables. Third, we used random forest analysis and multivariable multinomial logistic regression models to investigate the association between micro level variables and the three BMI categories i.e. underweight (BMI <18.5), normal weight (BMI 18.5 to 24.9) and overweight (BMI≥25). Results: Out of the 34 countries studied, the prevalence of underweight was greater than 10% in 3 countries, whereas 13 countries had both underweight and overweight prevalence exceeding 10%. Overweight (BMI 25 to 29.9) prevalence exceeded 10% in 7 countries, while the obese (BMI≥30) prevalence was at least 10% in 11 countries. Macro level random forest analysis showed that fertility rate and gross domestic product (GDP) were key correlates of the malnutrition groups found in SSA. However, age, wealth and parity were key correlates of women’s nutritional status at the micro level. Old age and wealth were consistently associated with overweight across all the countries. However, parity was a risk factor for underweight in underweight countries, and a risk factor for overweight in overweight countries. Conclusion: In conclusion, we observed consistent cross-sectional associations between measures of fertility, wealth and age and the dual forms of malnutrition (under- and overweight) among women in SSA.