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Quantifying the potential epidemiological impact of a 2-year active case finding for tuberculosis in rural Nepal: a model-based analysis

OBJECTIVES: Active case finding (ACF) is an important tuberculosis (TB) intervention in high-burden settings. However, empirical evidence garnered from field data has been equivocal about the long-term community-level impact, and more data at a finer geographic scale and data-informed methods to qua...

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Autores principales: Shrestha, Sourya, Mishra, Gokul, Hamal, Mukesh, Dhital, Raghu, Shrestha, Suvesh, Shrestha, Ashish, Shah, Naveen Prakash, Khanal, Mukti, Gurung, Suman, Caws, Maxine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10626874/
https://www.ncbi.nlm.nih.gov/pubmed/37914308
http://dx.doi.org/10.1136/bmjopen-2022-062123
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author Shrestha, Sourya
Mishra, Gokul
Hamal, Mukesh
Dhital, Raghu
Shrestha, Suvesh
Shrestha, Ashish
Shah, Naveen Prakash
Khanal, Mukti
Gurung, Suman
Caws, Maxine
author_facet Shrestha, Sourya
Mishra, Gokul
Hamal, Mukesh
Dhital, Raghu
Shrestha, Suvesh
Shrestha, Ashish
Shah, Naveen Prakash
Khanal, Mukti
Gurung, Suman
Caws, Maxine
author_sort Shrestha, Sourya
collection PubMed
description OBJECTIVES: Active case finding (ACF) is an important tuberculosis (TB) intervention in high-burden settings. However, empirical evidence garnered from field data has been equivocal about the long-term community-level impact, and more data at a finer geographic scale and data-informed methods to quantify their impact are necessary. METHODS: Using village development committee (VDC)-level data on TB notification and demography between 2016 and 2017 in four southern districts of Nepal, where ACF activities were implemented as a part of the IMPACT-TB study between 2017 and 2019, we developed VDC-level transmission models of TB and ACF. Using these models and ACF yield data collected in the study, we estimated the potential epidemiological impact of IMPACT-TB ACF and compared its efficiency across VDCs in each district. RESULTS: Cases were found in the majority of VDCs during IMPACT-TB ACF, but the number of cases detected within VDCs correlated weakly with historic case notification rates. We projected that this ACF intervention would reduce the TB incidence rate by 14% (12–16) in Chitwan, 8.6% (7.3–9.7) in Dhanusha, 8.3% (7.3–9.2) in Mahottari and 3% (2.5–3.2) in Makwanpur. Over the next 10 years, we projected that this intervention would avert 987 (746–1282), 422 (304–571), 598 (450–782) and 197 (172–240) cases in Chitwan, Dhanusha, Mahottari and Makwanpur, respectively. There was substantial variation in the efficiency of ACF across VDCs: there was up to twofold difference in the number of cases averted in the 10 years per case detected. CONCLUSION: ACF data confirm that TB is widely prevalent, including in VDCs with relatively low reporting rates. Although ACF is a highly efficient component of TB control, its impact can vary substantially at local levels and must be combined with other interventions to alter TB epidemiology significantly.
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spelling pubmed-106268742023-11-07 Quantifying the potential epidemiological impact of a 2-year active case finding for tuberculosis in rural Nepal: a model-based analysis Shrestha, Sourya Mishra, Gokul Hamal, Mukesh Dhital, Raghu Shrestha, Suvesh Shrestha, Ashish Shah, Naveen Prakash Khanal, Mukti Gurung, Suman Caws, Maxine BMJ Open Epidemiology OBJECTIVES: Active case finding (ACF) is an important tuberculosis (TB) intervention in high-burden settings. However, empirical evidence garnered from field data has been equivocal about the long-term community-level impact, and more data at a finer geographic scale and data-informed methods to quantify their impact are necessary. METHODS: Using village development committee (VDC)-level data on TB notification and demography between 2016 and 2017 in four southern districts of Nepal, where ACF activities were implemented as a part of the IMPACT-TB study between 2017 and 2019, we developed VDC-level transmission models of TB and ACF. Using these models and ACF yield data collected in the study, we estimated the potential epidemiological impact of IMPACT-TB ACF and compared its efficiency across VDCs in each district. RESULTS: Cases were found in the majority of VDCs during IMPACT-TB ACF, but the number of cases detected within VDCs correlated weakly with historic case notification rates. We projected that this ACF intervention would reduce the TB incidence rate by 14% (12–16) in Chitwan, 8.6% (7.3–9.7) in Dhanusha, 8.3% (7.3–9.2) in Mahottari and 3% (2.5–3.2) in Makwanpur. Over the next 10 years, we projected that this intervention would avert 987 (746–1282), 422 (304–571), 598 (450–782) and 197 (172–240) cases in Chitwan, Dhanusha, Mahottari and Makwanpur, respectively. There was substantial variation in the efficiency of ACF across VDCs: there was up to twofold difference in the number of cases averted in the 10 years per case detected. CONCLUSION: ACF data confirm that TB is widely prevalent, including in VDCs with relatively low reporting rates. Although ACF is a highly efficient component of TB control, its impact can vary substantially at local levels and must be combined with other interventions to alter TB epidemiology significantly. BMJ Publishing Group 2023-11-01 /pmc/articles/PMC10626874/ /pubmed/37914308 http://dx.doi.org/10.1136/bmjopen-2022-062123 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Epidemiology
Shrestha, Sourya
Mishra, Gokul
Hamal, Mukesh
Dhital, Raghu
Shrestha, Suvesh
Shrestha, Ashish
Shah, Naveen Prakash
Khanal, Mukti
Gurung, Suman
Caws, Maxine
Quantifying the potential epidemiological impact of a 2-year active case finding for tuberculosis in rural Nepal: a model-based analysis
title Quantifying the potential epidemiological impact of a 2-year active case finding for tuberculosis in rural Nepal: a model-based analysis
title_full Quantifying the potential epidemiological impact of a 2-year active case finding for tuberculosis in rural Nepal: a model-based analysis
title_fullStr Quantifying the potential epidemiological impact of a 2-year active case finding for tuberculosis in rural Nepal: a model-based analysis
title_full_unstemmed Quantifying the potential epidemiological impact of a 2-year active case finding for tuberculosis in rural Nepal: a model-based analysis
title_short Quantifying the potential epidemiological impact of a 2-year active case finding for tuberculosis in rural Nepal: a model-based analysis
title_sort quantifying the potential epidemiological impact of a 2-year active case finding for tuberculosis in rural nepal: a model-based analysis
topic Epidemiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10626874/
https://www.ncbi.nlm.nih.gov/pubmed/37914308
http://dx.doi.org/10.1136/bmjopen-2022-062123
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