Mapping TB incidence across districts in Uganda to inform health program activities
Nathaniel J Henrya b, Stella Zawedde‐Muyanjac, Robert Kaos Majwalad, Stavia Turyahabwed, Ruanne V. Barnabase f, Robert C. Reinerg h, Catrin E. Moorei, Jennifer M. Rossj
- aBig Data Institute, Li Ka Shing Centre for Information Discovery, University of Oxford, Oxford, UK
- bHenry Spatial Analysis, Seattle, WA, USA
- cInfectious Diseases Institute, Makerere University, Kampala
- dUganda Ministry of Health, National Tuberculosis and Leprosy Program, Kampala, Uganda
- eDivision of Infectious Diseases, Massachusetts General Hospital, Boston, MA
- fHarvard Medical School, Cambridge, MA
- gDepartment of Health Metrics Sciences, University of Washington, Seattle, WA
- hInstitute for Health Metrics and Evaluation, Seattle, WA, USA
- iThe Centre for Neonatal and Paediatric Infection, Infection and Immunity Institute, St George's, University of London, London, UK
- jDivision of Allergy and Infectious Diseases, University of Washington, Seattle, WA, USA
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Abstract
BACKGROUND: Identifying spatial variation in TB burden can help national TB programs effectively allocate resources to reach and treat all people with TB. However, data limitations pose challenges for subnational TB burden estimation. METHODS: We developed a small-area modeling approach using geo-positioned prevalence survey data, case notifications, and geospatial covariates to simultaneously estimate spatial variation in TB incidence and case notification completeness across districts in Uganda from 2016-2019. TB incidence was estimated using 1) cluster-level data from the national 2014-2015 TB prevalence survey transformed to incidence, and 2) case notifications adjusted for geospatial covariates of health system access. The case notification completeness surface was fit jointly using observed case notifications and estimated incidence. RESULTS: Estimated pulmonary TB incidence among adults varied >10-fold across Ugandan districts in 2019. Case detection increased nationwide from 2016 to 2019, and the number of districts with case detection rates >70% quadrupled. District-level estimates of TB incidence were five times more precise than a model using TB prevalence survey data alone. CONCLUSION: A joint spatial modeling approach provides useful insights for TB program operation, outlining areas where TB incidence estimates are highest and health programs should concentrate their efforts. This approach can be applied in many countries with high TB burden.
TL;DRA small-area modeling approach using geo-positioned prevalence survey data, case notifications, and geospatial covariates to simultaneously estimate spatial variation in TB incidence and case notification completeness across districts in Uganda from 2016–2019 provides useful insights for TB program operation. (Semantic Scholar)