Cardiovascular risk factor mapping and distribution among adults in Mukono and Buikwe districts in Uganda: small area analysis

Abstract

Abstract Background Sub-Saharan Africa (SSA) is experiencing an increasing burden of Cardiovascular Diseases (CVDs). Modifiable risk factors including hypertension, diabetes, obesity, central obesity, sedentary behaviours, smoking, poor diet (characterised by inadequate vegetable and fruit consumption), and psychosocial stress are attributable to the growing burden of CVDs. Small geographical area mapping and analysis of these risk factors for CVD is lacking in most of sub-Saharan Africa and yet such data has the potential to inform monitoring and exploration of patterns of morbidity, health-care use, and mortality, as well as the epidemiology of risk factors. In the current study, we map and describe the distribution of the CVD risk factors in 20 parishes in two neighbouring districts in Uganda. Methods A baseline survey benchmarking a type-2 hybrid stepped wedge cluster randomised trial design was conducted in December 2018 and January 2019. A sample of 4372 adults aged 25–70 years was drawn from 3689 randomly selected households across 80 villages in 20 parishes in Mukono and Buikwe districts in Uganda. Descriptive statistics and generalized linear modelling controlled for clustering were conducted for this analysis in Stata 13.0, and a visual map showing risk factor distribution developed in QGIS. Results Mapping the prevalence of selected CVD risk factors indicated substantial gender and small area geographic heterogeneity which was masked on aggregate analysis. Patterns and clustering were observed for hypertension, physical inactivity, smoking, alcohol consumption and risk factor combination. Prevalence of unhealthy diet was very high across all parishes with no significant observable differences across areas. Conclusion Modifiable cardiovascular risk factors are common in this low-income context. Moreover, across small area geographic setting, it appears significant differences in distribution of risk factors exist. These differences suggest that underlying drivers such as sociocultural, environmental and economic determinants may be promoting or inhibiting the observed risk factor prevalences which should be further explored. In addition, the differences emphasize the value of small geographical area mapping and analysis to inform more targeted risk reduction interventions.