Determinants of body mass index by gender in the Dikgale Health and Demographic Surveillance System site, South Africa

Abstract

Background: The study was conducted in the Dikgale Health and Demographic Surveillance System (DHDSS) site where we have observed increasing obesity levels, particularly in women, despite evidence of high physical activity (PA) and a relatively low daily energy intake. Objective: This study aimed to assess the socio-demographic, behavioural and biological determinants of body mass index (BMI) in adult residents permanently residing in the DHDSS. Methods: A cross-sectional study was conducted in which socio-demographic, behavioural and biological characteristics from 1143 participants (aged 40–60 years) were collected using a paper questionnaire and standard anthropometric measures. Human immunodeficiency virus (HIV) testing was performed on all participants except those who indicated that they had tested positive. Chi-square and Mann-Whitney tests were used to analyze categorical and continuous variables, respectively, while hierarchical multivariate regression was used to analyze predictors of BMI. Results: The median age of women and men was 51 (46–56) and 50 (45–55) years, respectively. The prevalence of overweight-obesity was 76% in women and 21% in men. A significant negative association of BMI with HIV and smoking and a significant positive association with socio-economic status (SES) was observed in both sexes. In women, BMI was negatively associated with sleep duration (p = 0.015) and age (p = 0.012), but positively associated with sugar-sweetened beverages (SSBs) (p = 0.08). In men, BMI was negatively associated with alcohol use (p = 0.016) and positively associated with being married (p < 0.001). PA was not associated with BMI in either sexes. Full models explained 9.2% and 20% of the variance in BMI in women and men, respectively. Conclusion: BMI in DHDSS adults is not associated with physical inactivity but is associated wealth, marital status, sleep, smoking, alcohol use, and HIV status. Future studies should explore the contribution of nutrition, stunting, psycho-social and genetic factors to overweight and obesity in DHDSS.

Regional and sex-specific variation in BMI distribution in four sub-Saharan African countries: The H3Africa AWI-Gen study

“…lean, overweight and obesity between the six AWI-Gen Centres. In the accompanying set of papers, including one from each Centre, the risk factors and correlates with obesity were examined [7][8][9][10][11][12]. At five of the six study sites women had significantly higher BMI compared to men, whereas in Nanoro, men had higher BMI.…”

“…The strengths of the study include harmonized data collection, a large sample size, and the measurement of many potential confounders and modifiers of body fat mass. Phase 1 of the AWI-Gen study has generated base-line data [9] 0 Directionality of associations is positive unless stated otherwise. Marital status: Being married or co-habiting is associated with higher BMI.…”

“…Marital status: Being married or co-habiting is associated with higher BMI. Details for each study site are provided in the Special Issue [7][8][9][10][11][12].…”

“…In the Human Heredity and Health in African (H3Africa) Consortium AWI-Gen Study (Africa Wits-INDEPTH partnership for Genomic studies) we have collected data from 10,702 participants between the ages of 40 and 60 years, from six study sites in four African countries [5,6]. In this special issue of Global Health Action, each of the six AWI-Gen participant Centres has reported individually on the distribution of BMI and its determinants [7][8][9][10][11][12]. They have analysed BMI in the context of participant socio-demographic factors (sex, age, ethnicity, educational status, employment, household assets, partner/marital status), behaviour (smoking/ tobacco exposure, alcohol consumption and exercise) and other environmental factors (infections and pesticide exposure), and used an hierarchical modelling approach, and in one case multiple linear regression, to examine their association with BMI.…”

Patterns of adult body mass in sub-Saharan Africa

“…HIV, malaria and tuberculosisin combination with the effects of clinical treatments and approaches to these epidemics in different African countriescontribute to body composition and at a population level will influence the prevalence and morbidity associated with obesity. The site-specific papers presented in this Special Issue [8][9][10][11][12][13] make important contributions to understanding some of the correlates with obesity in six communities in four African countries, highlighting the diversity of contributing factors and revealing some interesting regional differences. From a public health perspective it is clear that interventions to address the sequelae of increases in obesity in several African countries will require careful assessment and targeted strategies, informed by good quality data.…”