Mapping tuberculosis prevalence in Africa using a Bayesian geospatial analysis
Abstract Background Worldwide, 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...
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Nature Portfolio
2025-05-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-025-00831-9 |
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| author | Alemneh Mekuriaw Liyew Eyob Alemayehu Gebreyohannes Andre Python Archie C. A. Clements Beth Gilmour Peter W. Gething Punam Amratia Kefyalew Addis Alene |
| author_facet | Alemneh Mekuriaw Liyew Eyob Alemayehu Gebreyohannes Andre Python Archie C. A. Clements Beth Gilmour Peter W. Gething Punam Amratia Kefyalew Addis Alene |
| author_sort | Alemneh Mekuriaw Liyew |
| collection | DOAJ |
| description | Abstract Background Worldwide, 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. Methods We 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. Results Here 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. Conclusions We 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. |
| format | Article |
| id | doaj-art-fd87e2f2fc9d47019930c47744c928c2 |
| institution | DOAJ |
| issn | 2730-664X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
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| series | Communications Medicine |
| spelling | doaj-art-fd87e2f2fc9d47019930c47744c928c22025-08-20T03:22:13ZengNature PortfolioCommunications Medicine2730-664X2025-05-01511710.1038/s43856-025-00831-9Mapping tuberculosis prevalence in Africa using a Bayesian geospatial analysisAlemneh Mekuriaw Liyew0Eyob Alemayehu Gebreyohannes1Andre Python2Archie C. A. Clements3Beth Gilmour4Peter W. Gething5Punam Amratia6Kefyalew Addis Alene7Institute of Public Health, College of Medicine and Health Sciences, University of GondarGeospatial and Tuberculosis Research Team, The Kids Research Institute AustraliaCenter for Data Science, Zhejiang UniversitySchool of Biological Sciences, Queen’s University BelfastSchool of Population Health, Faculty of Health Sciences, Curtin UniversitySchool of Population Health, Faculty of Health Sciences, Curtin UniversityChild Health Analytics Team, The Kids Research Institute AustraliaSchool of Population Health, Faculty of Health Sciences, Curtin UniversityAbstract Background Worldwide, 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. Methods We 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. Results Here 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. Conclusions We 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.https://doi.org/10.1038/s43856-025-00831-9 |
| spellingShingle | Alemneh Mekuriaw Liyew Eyob Alemayehu Gebreyohannes Andre Python Archie C. A. Clements Beth Gilmour Peter W. Gething Punam Amratia Kefyalew Addis Alene Mapping tuberculosis prevalence in Africa using a Bayesian geospatial analysis Communications Medicine |
| title | Mapping tuberculosis prevalence in Africa using a Bayesian geospatial analysis |
| title_full | Mapping tuberculosis prevalence in Africa using a Bayesian geospatial analysis |
| title_fullStr | Mapping tuberculosis prevalence in Africa using a Bayesian geospatial analysis |
| title_full_unstemmed | Mapping tuberculosis prevalence in Africa using a Bayesian geospatial analysis |
| title_short | Mapping tuberculosis prevalence in Africa using a Bayesian geospatial analysis |
| title_sort | mapping tuberculosis prevalence in africa using a bayesian geospatial analysis |
| url | https://doi.org/10.1038/s43856-025-00831-9 |
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