ArticleBMJ open respiratory research2024
Geographical targeting of active case finding for tuberculosis in Pakistan using hotspots identified by artificial intelligence software (SPOT-TB): study protocol for a pragmatic stepped wedge cluster randomised control trial.
Article in BMJ open respiratory research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06017843 (Impact Evaluation of Use of MATCH AI Predictive Modelling for Identification of Hotspots for TB Active Case Finding in Pakistan), which is not on this map. Cited by 5 papers.
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Impact Evaluation of Use of MATCH AI Predictive Modelling for Identification of Hotspots for TB Active Case Finding in Pakistan: a Pragmatic Stepped Wedge Cluster Randomized Trial
Who cites it
5 citing papers in PubMed.
- Artificial Intelligence in the Diagnosis and Management of Pulmonary Tuberculosis: A Review of Current Applications and Future Perspectives.Therapeutics and clinical risk management · 2026Review
- Adaptive bandit algorithms increase efficiency of mobile tuberculosis screening programs.Scientific reports · 2025Article
- Global status of policies and practices for systematic TB screening in high-burden countries.IJTLD open · 2025Article
- Detection of pulmonary tuberculosis through mobile X-ray based active case-finding in Pakistan: a retrospective analysis from programmatic screening of1 214 289 individuals from 2017 to 2021.BMJ global health · 2025Article
- Review
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionPakistan has significantly strengthened its capacity for active case finding (ACF) for tuberculosis (TB) that is being implemented at scale in the country. However, yields of ACF have been lower than expected, raising concerns on its effectiveness in the programmatic setting. Distribution of TB in communities is likely to be spatially heterogeneous and targeting of ACF in areas with higher TB prevalence may help improve yields. The primary aim of SPOT-TB is to investigate whether a policy change to use a geographically targeted approach towards ACF supported by an artificial intelligence (AI) software, MATCH-AI, can improve yields in Pakistan. METHODS AND ANALYSIS: SPOT-TB will use a pragmatic, stepped wedge cluster randomised design. A total of 30 mobile X-ray units and their field teams will be randomised to receive the intervention. Site selection for ACF in the intervention areas will be guided primarily through the use of MATCH-AI software that models subdistrict TB prevalence and identifies potential disease hotspots. Control areas will use existing approaches towards site selection that are based on staff knowledge, experience and analysis of historical data. The primary outcome measure is the difference in bacteriologically confirmed incident TB detected in the intervention relative to control areas. All remaining ACF-related procedures and algorithms will remain unaffected by this trial. ETHICS AND DISSEMINATION: Ethical approval has been obtained from the Health Services Academy, Islamabad, Pakistan (7-82/IERC-HSA/2022-52) and from the Common Management Unit for TB, HIV and Malaria, Ministry of Health Services, Regulation and Coordination, Islamabad, Pakistan (26-IRB-CMU-2023). Findings from this study will be disseminated through publications in peer-reviewed journals and stakeholder meetings in Pakistan with the implementing partners and public-sector officials. Findings will also be presented at local and international medical and public health conferences. TRIAL REGISTRATION NUMBER: NCT06017843.
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