ArticleIJTLD open2024
Mapping TB incidence across districts in Uganda to inform health program activities.
Article in IJTLD open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- AI-Driven Tuberculosis Hotspot Mapping to Optimize Active Case-Finding: Implementing the Epi-Control Platform in Uganda.Tropical medicine and infectious disease · 2026Article
- Combination interventions to control the tuberculosis epidemic in the Karamoja subregion of Uganda: A modelling analysis.PLOS global public health · 2026Article
- Spatio-temporal trends, distribution and prediction of tuberculosis incidence in Uganda (2020-2025).BMC infectious diseases · 2025Article
- Global status of policies and practices for systematic TB screening in high-burden countries.IJTLD open · 2025Article
- Importance of confirmatory test characteristics in optimizing community-based screening for tuberculosis: an epidemiological modeling analysis.BMC infectious diseases · 2025Article
- Molecular Epidemiology of Mycobacterium tuberculosis Across 3 Distinct Geographic Sites in South Africa.The Journal of infectious diseases · 2025Article
- Incidence and risk factors for tuberculosis at a rural HIV clinic in Uganda, 2012-2019; A retrospective cohort study.BMC public health · 2025Article
- Mobility patterns, activity locations, and TB in Nairobi, Kenya.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2025Article
- A qualitative exploration of community knowledge, attitudes, and practices towards tuberculosis in the Karamoja subregion, northeastern Uganda.BMC health services research · 2024Article
- Precision geospatial targeting for cost-effective tuberculosis case finding in Eastern Uganda: a quasi-experimental study.Therapeutic advances in infectious diseaseArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentifying 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.
methodsWe 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.
resultsEstimated 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.
conclusionA 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.