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A spatial-mechanistic model to estimate subnational tuberculosis burden with routinely collected data: An application in Brazilian municipalities

Reliable subnational estimates of TB incidence would allow national policy makers to focus disease control resources in areas of highest need. We developed an approach for generating small area estimates of TB incidence, and the fraction of individuals missed by routine case detection, based on avai...

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Autores principales: Chitwood, Melanie H., Alves, Layana C., Bartholomay, Patrícia, Couto, Rodrigo M., Sanchez, Mauro, Castro, Marcia C., Cohen, Ted, Menzies, Nicolas A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10021638/
https://www.ncbi.nlm.nih.gov/pubmed/36962578
http://dx.doi.org/10.1371/journal.pgph.0000725
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author Chitwood, Melanie H.
Alves, Layana C.
Bartholomay, Patrícia
Couto, Rodrigo M.
Sanchez, Mauro
Castro, Marcia C.
Cohen, Ted
Menzies, Nicolas A.
author_facet Chitwood, Melanie H.
Alves, Layana C.
Bartholomay, Patrícia
Couto, Rodrigo M.
Sanchez, Mauro
Castro, Marcia C.
Cohen, Ted
Menzies, Nicolas A.
author_sort Chitwood, Melanie H.
collection PubMed
description Reliable subnational estimates of TB incidence would allow national policy makers to focus disease control resources in areas of highest need. We developed an approach for generating small area estimates of TB incidence, and the fraction of individuals missed by routine case detection, based on available notification and mortality data. We demonstrate the feasibility of this approach by creating municipality-level burden estimates for Brazil. We developed a mathematical model describing the relationship between TB incidence and TB case notifications and deaths, allowing for known biases in each of these data sources. We embedded this model in a regression framework with spatial dependencies between local areas, and fitted the model to municipality-level case notifications and death records for Brazil during 2016–2018. We estimated outcomes for 5568 municipalities. Incidence rate ranged from 8.6 to 57.2 per 100,000 persons/year for 90% of municipalities, compared to 44.8 (95% UI: 43.3, 46.8) per 100,000 persons/year nationally. Incidence was concentrated geographically, with 1% of municipalities accounting for 50% of incident TB. The estimated fraction of incident TB cases receiving diagnosis and treatment ranged from 0.73 to 0.95 across municipalities (compared to 0.86 (0.82, 0.89) nationally), and the rate of untreated TB ranged from 0.8 to 72 cases per 100,000 persons/year (compared to 6.3 (4.8, 8.3) per 100,000 persons/year nationally). Granular disease burden estimates can be generated using routine data. These results reveal substantial subnational differences in disease burden and other metrics useful for designing high-impact TB control strategies.
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spelling pubmed-100216382023-03-17 A spatial-mechanistic model to estimate subnational tuberculosis burden with routinely collected data: An application in Brazilian municipalities Chitwood, Melanie H. Alves, Layana C. Bartholomay, Patrícia Couto, Rodrigo M. Sanchez, Mauro Castro, Marcia C. Cohen, Ted Menzies, Nicolas A. PLOS Glob Public Health Research Article Reliable subnational estimates of TB incidence would allow national policy makers to focus disease control resources in areas of highest need. We developed an approach for generating small area estimates of TB incidence, and the fraction of individuals missed by routine case detection, based on available notification and mortality data. We demonstrate the feasibility of this approach by creating municipality-level burden estimates for Brazil. We developed a mathematical model describing the relationship between TB incidence and TB case notifications and deaths, allowing for known biases in each of these data sources. We embedded this model in a regression framework with spatial dependencies between local areas, and fitted the model to municipality-level case notifications and death records for Brazil during 2016–2018. We estimated outcomes for 5568 municipalities. Incidence rate ranged from 8.6 to 57.2 per 100,000 persons/year for 90% of municipalities, compared to 44.8 (95% UI: 43.3, 46.8) per 100,000 persons/year nationally. Incidence was concentrated geographically, with 1% of municipalities accounting for 50% of incident TB. The estimated fraction of incident TB cases receiving diagnosis and treatment ranged from 0.73 to 0.95 across municipalities (compared to 0.86 (0.82, 0.89) nationally), and the rate of untreated TB ranged from 0.8 to 72 cases per 100,000 persons/year (compared to 6.3 (4.8, 8.3) per 100,000 persons/year nationally). Granular disease burden estimates can be generated using routine data. These results reveal substantial subnational differences in disease burden and other metrics useful for designing high-impact TB control strategies. Public Library of Science 2022-09-21 /pmc/articles/PMC10021638/ /pubmed/36962578 http://dx.doi.org/10.1371/journal.pgph.0000725 Text en © 2022 Chitwood et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Chitwood, Melanie H.
Alves, Layana C.
Bartholomay, Patrícia
Couto, Rodrigo M.
Sanchez, Mauro
Castro, Marcia C.
Cohen, Ted
Menzies, Nicolas A.
A spatial-mechanistic model to estimate subnational tuberculosis burden with routinely collected data: An application in Brazilian municipalities
title A spatial-mechanistic model to estimate subnational tuberculosis burden with routinely collected data: An application in Brazilian municipalities
title_full A spatial-mechanistic model to estimate subnational tuberculosis burden with routinely collected data: An application in Brazilian municipalities
title_fullStr A spatial-mechanistic model to estimate subnational tuberculosis burden with routinely collected data: An application in Brazilian municipalities
title_full_unstemmed A spatial-mechanistic model to estimate subnational tuberculosis burden with routinely collected data: An application in Brazilian municipalities
title_short A spatial-mechanistic model to estimate subnational tuberculosis burden with routinely collected data: An application in Brazilian municipalities
title_sort spatial-mechanistic model to estimate subnational tuberculosis burden with routinely collected data: an application in brazilian municipalities
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10021638/
https://www.ncbi.nlm.nih.gov/pubmed/36962578
http://dx.doi.org/10.1371/journal.pgph.0000725
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