Identifying high-risk population segments for underweight, overweight, and obesity among reproductive-age women in sub-Saharan Africa - Amare Abera Tareke; Amare Abera Tareke; Anissa Mohammed; Amare Muche; Yeshimebet Ali (Frontiers in Public Health)
BackgroundDespite significant progress in addressing underweight in developing countries, the recent rise in the number of overweight and obese individuals has confirmed that the double burden of malnutrition will remain a crucial problem in the foreseeable future. Some countries that previously succeeded in reducing underweight rates are now experiencing a resurgence. Initiatives in sub-Saharan African (SSA) countries aimed at reducing malnutrition often lack robust evidence. This study aimed to identify risk groups for malnutrition among women of reproductive age in SSA countries and prioritize intervention areas.MethodsThis analysis utilized data from 247,911 reproductive-age women across recent demographic and health surveys conducted in 33 SSA countries. Nutritional status was assessed using body mass index (BMI). We computed the pooled prevalence of different forms of malnutrition using the random effects inverse variance method. We evaluated the factors associated with different forms of malnutrition using multilevel multinomial regression. We reported the adjusted odds ratios and 95% confidence intervals (CIs).ResultsThe pooled prevalence of underweight, overweight, and obesity among SSA women was 11% (95% CI: 9–12%), 18% (95% CI: 16–20%), and 10% (95% CI: 8–12%), respectively. Significant factors influencing malnutrition included women’s age, highest educational level, wealth index, current breastfeeding status, contraceptive use, parity, media exposure, marital status, place of residence, and regional location within SSA. Factors such as education, wealth, age, contraceptive use, parity, and marital status were risk factors for overweight and obesity but were protective against underweight. Employment was protective against all three malnutrition forms.ConclusionIncreased age, wealth index, not-breastfeeding status during the survey, contraceptive use, higher parity, marital status, urban residency, and living in southern or central Africa are associated with higher odds of increased BMI and lower odds of underweight. In designing interventions for overweight and obesity, emphasis should be given to the wealthy, reproductive-age women in later age, urban residents, and multiparous. Whereas the poorest and youngest is priority intervention segments for underweight.