Mapping tuberculosis prevalence in Africa using a Bayesian geospatial analysis

BackgroundWorldwide, tuberculosis (TB) remains the leading cause of death from infectious diseases. Africa is the second most-affected region, accounting for a quarter of the global TB burden, but there is limited evidence whether there is subnational variation of TB prevalence across the continent. Therefore, this study aimed to estimate sub-national and local TB prevalence across Africa.MethodsWe compiled geolocated data from 50 population-based surveys across 14 African countries. A total of 212 data points were identified and linked to covariates assembled from publicly available sources. Bayesian geostatistical modelling was used to predict TB prevalence across Africa, and results were aggregated to estimate number of TB cases at national and subnational levels.ResultsHere we estimate 1.28 million TB cases (95% uncertainty interval [UI] 0.14-4.87) across 14 countries, with marked spatial variations. The highest cases are estimated in Nigeria (460,247 95% UI 7954-1,783,106), and Mozambique (120,622 95%UI 20,027-321,177) while the lowest in Guinea-Bissau (1952 95%UI 154-7365) and Rwanda (2207 95% UI 1050-9225). National TB prevalence range from 0.25 to 7.32 per 1000 with significant variation at higher spatial resolution. Temperature (°C) (OR = 1.27; 95% CrI: 1.20-1.35), precipitation (mm) (OR = 1.34; 95% CrI: 1.26-1.40), and access to city (minute) (OR = 1.21; 95% CrI: 1.14-1.25) are positively associated with TB prevalence, while altitude (m) (OR = 0.83; 95% CrI: 0.78-0.87) is negatively associated.ConclusionsWe find substantial variations in TB prevalence at national, sub-national, and local levels in Africa. These considerable spatial variations suggest the need for geographically targeted interventions to control TB in Africa.