Determinants and spatial patterns of adult overweight and hypertension in a high HIV prevalence rural South African population

Abstract

We conducted a large population-based survey among adults measuring weight, height, and blood pressure nested within an HIV survey in rural KwaZulu-Natal, South Africa, to identify and characterize clusters of overweight and hypertension in a typical rural African population and to explore whether geographic clusters can be accounted for by established individual-level risk factors. 58.4% of the participants were overweight and 22.6% were hypertensive. One cluster of high prevalence of overweight (RR=1.50, p<0.001) was identified using Kulldorff spatial scan statistic as the most likely cluster, whereas a low-risk cluster was identified in the nearby high-density settlement area (RR=0.62, p<0.05). No geographic clusters of hypertension were identified. After controlling for age, sex, educational attainment, household wealth, marital status, place of residence, and HIV status, no spatial clustering of overweight remained. The results provided clear evidence for the localized clustering of overweight. Identification of clustering of chronic disease could provide additional insights into the prevention and control for the rural South African population.

Fig. 1

Maps of the study area showing the approximate location of participants. Participants were coded according to overweight status (left) and hypertension status (right).

Fig. 2

Geographic clusters of high prevalence and low prevalence of overweight indentified by SaTScan. Left: cluster 1 of high prevalence of overweight was identified using the Bernoulli model as the most likely cluster. This cluster was found to be in the only urban township in the surveillance area next to the national road. Additionally, cluster 2 was identified as low-risk area, in the nearby high-density settlement area. Both clusters were unlikely to have arisen by chance alone. Right: cluster of low mean value of standardized Pearson residuals of overweight model after adjusting for sex, 5-year age groups, educational attainment (no schooling, primary school and secondary school or higher), family wealth quintiles, marital status (married/coupled vs. single), and rural vs. urban residence.