Multilevel Analysis of Urban–Rural Variations of Body Weights and Individual-Level Factors among Women of Childbearing Age in Nigeria and South Africa: A Cross-Sectional Survey

Abstract

Background: An unhealthy body weight is an adverse effect of malnutrition associated with morbidity among women of childbearing age. While there is increasing attention being paid to the body weights of children and adolescents in Nigeria and South Africa, a major surge of unhealthy body weight in women has received less attention in both countries despite its predominance. The purpose of this study was to explore the prevalence of body weights (underweight, normal, overweight, and obese) and individual-level factors among women of childbearing age by urban–rural variations in Nigeria and South Africa. Methods: This study used the 2018 Nigeria Demographic Health Survey data (n = 41,821) and 2016 South Africa Demographic Health Survey (n = 8514). Bivariate, multilevel, and intracluster correlation coefficient analyses were used to determine individual-level factors associated with body weights across urban–rural variations. Results: The prevalence of being overweight or obese among women was 28.2% and 44.9%, respectively, in South Africa and 20.2% and 11.4% in Nigeria. A majority, 6.8%, of underweight women were rural residents in Nigeria compared to 0.8% in South Africa. The odds of being underweight were higher among women in Nigeria who were unemployed, with regional differences and according to breastfeeding status, while higher odds of being underweight were found among women from poorer households, with differences between provinces and according to cigarette smoking status in South Africa. On the other hand, significant odds of being overweight or obese among women in both Nigeria and South Africa were associated with increasing age, higher education, higher wealth index, weight above average, and traditional/modern contraceptive use. Unhealthy body weights were higher among women in clustering areas in Nigeria who were underweight (intracluster correlation coefficient (ICC = 0.0127), overweight (ICC = 0.0289), and obese (ICC = 0.1040). Similarly, women of childbearing age in clustering areas in South Africa had a lower risk of experiencing underweight (ICC = 0.0102), overweight (ICC = 0.0127), and obesity (ICC = 0.0819). Conclusions: These findings offer a deeper understanding of the close connection between body weights variations and individual factors. Addressing unhealthy body weights among women of childbearing age in Nigeria and South Africa is important in preventing disease burdens associated with body weights in promoting Sustainable Development Goal 3. Strategies for developing preventive sensitization interventions are imperative to extend the perspectives of the clustering effect of body weights on a country level when establishing social and behavioral modifications for body weight concerns in both countries.

A Comparative Cross-Sectional Study of the Prevalence and Determinants of Health Insurance Coverage in Nigeria and South Africa: A Multi-Country Analysis of Demographic Health Surveys

“…For instance, there were higher odds of being insured among individuals with increasing education status in both countries. This, therefore, is a justification of closing the gap of health inequalities and creating frameworks in promoting health equity, and fostering stringent advocacy across all stakeholders in playing a major role in the social contributing factors of health [95][96][97][98][99][100][101]. Education empowers women to look after their own health by seeking appropriate healthcare when they have an ailment.…”

“…Education empowers women to look after their own health by seeking appropriate healthcare when they have an ailment. Therefore, being protected by health insurance supports them to prevent catastrophic health expenses that they would have to make out-of-pocket when they fall sick and do not have health insurance coverage [97,98], and being educated makes it likely for them to insure themselves against the unforeseen out-of-pocket costs [99][100][101].…”

Prevalence and factors associated with underweight among 15–49-year-old women in Sierra Leone: a secondary data analysis of Sierra Leone demographic health survey of 2019

“…Also, our study found that it was unlikely of being underweight among residents of northern Sierra Leone compared to the east although, there was no significant associations between underweight and northwestern, western, and southern regions of Sierra Leone compared to the east (Table 5 ). Previous studies showed that regions of residence were associated with underweight in similar low-income African settings [ 39 , 64 , 65 ] and Afghanistan [ 66 ]. Similar DHS studies in Uganda found a high prevalence of underweight among women (15–49 years) residents of northeastern region of Uganda who are the poorest and most food insecure [ 67 , 68 ].…”

Determinants of anaemia prevalence in women of reproductive age in Nigeria: A cross-sectional study using secondary data from Nigeria Demographic and Health Survey 2018

“…47 Second, Nigerian women residing in urban areas are more likely to be overweight/obese, while rural women are more likely to be either underweight or overweight. 48 Third, rural women are less likely to use modern contraceptives compared to urban women. 49 In our study, being underweight and non-use of contraceptives increased the odds of being anaemic among all WRA and NPW.…”