Determinants of body mass index by gender in the Dikgale Health and Demographic Surveillance System site, South Africa. (16th November 2018)

• Record Type: Journal Article
• Authors: Mashinya, Felistas; Alberts, Marianne; Cook, Ian; Ntuli, Sam
• Is Part Of: Global health action. Volume 11(2018)Supplement 2
• Publication Date: 2018-11-16
• Subjects: BMI distribution across African communities; Body mass index -- hierarchical modelling -- rural -- older adults; World health -- Periodicals; Global Health; World health; Periodicals; 362.1
• DOI: 10.1080/16549716.2018.1537613
• Languages: English
• ISSNs: 1654-9716

Abstract

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.