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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.
Zaidi, Syed Mohammad Asad; Mahfooz, Amna; Latif, Abdullah; Nawaz, Nainan; Fatima, Razia; Rehman, Fazal Ur; Reza, Tahira Ezra; Emmanuel, Faran.
Afiliação
  • Zaidi SMA; WHO Centre for Tuberculosis Research and Innovation, Institute for Global Health, University College London, London, UK syed.zaidi.22@ucl.ac.uk.
  • Mahfooz A; Center for Global Public Health, Islamabad, Pakistan.
  • Latif A; Mercy Corps, Islamabad, Pakistan.
  • Nawaz N; Mercy Corps, Islamabad, Pakistan.
  • Fatima R; Ministry of National Health Services Regulation and Coordination, Islamabad, Pakistan.
  • Rehman FU; Center for Global Public Health, Islamabad, Pakistan.
  • Reza TE; Center for Global Public Health, Islamabad, Pakistan.
  • Emmanuel F; Center for Global Public Health, Islamabad, Pakistan.
BMJ Open Respir Res ; 11(1)2024 Jul 11.
Article em En | MEDLINE | ID: mdl-38991950
ABSTRACT

INTRODUCTION:

Pakistan 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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Tuberculose / Inteligência Artificial Limite: Humans País/Região como assunto: Asia Idioma: En Revista: BMJ Open Respir Res Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Tuberculose / Inteligência Artificial Limite: Humans País/Região como assunto: Asia Idioma: En Revista: BMJ Open Respir Res Ano de publicação: 2024 Tipo de documento: Article