Loading…
Impact of strategic public health interventions to reduce tuberculosis incidence in Brazil: a Bayesian structural time-series scenario analysisResearch in context
Background: Despite government efforts, tuberculosis (TB) remains a major public health threat in Brazil. In 2023, TB incidence was 39.8 cases per 100,000 population, far above the WHO’s target of 6.7 cases per 100,000. Using national-level datasets, we investigated and forecasted the potential impa...
Saved in:
Published in: | Lancet Regional Health - Americas (Online) 2025-01, Vol.41, p.100963 |
---|---|
Main Authors: | , , , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | |
---|---|
cites | |
container_end_page | |
container_issue | |
container_start_page | 100963 |
container_title | Lancet Regional Health - Americas (Online) |
container_volume | 41 |
creator | Klauss Villalva-Serra Beatriz Barreto-Duarte Moreno M. Rodrigues Artur T.L. Queiroz Leonardo Martinez Julio Croda Valeria C. Rolla Afrânio L. Kritski Marcelo Cordeiro-Santos Timothy R. Sterling Mariana Araújo-Pereira Bruno B. Andrade |
description | Background: Despite government efforts, tuberculosis (TB) remains a major public health threat in Brazil. In 2023, TB incidence was 39.8 cases per 100,000 population, far above the WHO’s target of 6.7 cases per 100,000. Using national-level datasets, we investigated and forecasted the potential impact of proposed public health interventions aimed at reducing TB incidence in Brazil. Methods: Monthly TB surveillance data (January 2018–December 2023) were collected from Brazilian national reporting systems: SINAN-TB (TB cases), SITE-TB (TB drug resistance), and IL-TB (preventive therapy). These data were used to create a multivariable Bayesian Structural Time-Series (BSTS) model, with 5000 Monte-Carlo simulations, which identified key predictors of TB incidence and forecasted these rates from 2024 to 2030 under various scenarios. Findings: Vulnerabilities including incarceration, TB-HIV coinfection and TB-diabetes mellitus, as well as coverages of directly observed therapy (DOT), contact investigation and preventive treatment (TPT) completion rates, were identified as key predictors of TB incidence. Under current trends, we forecasted TB incidence in Brazil to be 42.1 [34.1–49.8] per 100,000 person-years by 2030 (mean [95% prediction intervals]). A scenario considering decreases in TB cases among vulnerable populations resulted in an absolute reduction of −10.6 [−9.4 to −12.0] in projected TB incidence. Additional reductions were seen with increased coverage of DOT, TPT adherence, and contact investigation rates (−14.4 [−13 to −16.2]), and by combining these with efforts to reduce TB cases among vulnerable populations (−23.6 [−26.3 to −41.4]), potentially lowering incidence to 18.5 [7.8–28.4] per 100,000, though still above WHO targets. Interpretation: Our findings demonstrate that interventions focused on enhancing health policies focused on decreasing TB cases among vulnerable populations, such as individuals with TB-HIV coinfection, incarcerated populations, and those with TB-diabetes comorbidity, along with improvements in health management indicators such as DOT implementation, contact investigation coverage, and TPT completion rates, are effective in reducing TB incidence nationwide. Funding: Oswaldo Cruz Foundation. |
format | article |
fullrecord | <record><control><sourceid>doaj</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_0dbefffb777a476caf66a662bd85c836</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_0dbefffb777a476caf66a662bd85c836</doaj_id><sourcerecordid>oai_doaj_org_article_0dbefffb777a476caf66a662bd85c836</sourcerecordid><originalsourceid>FETCH-doaj_primary_oai_doaj_org_article_0dbefffb777a476caf66a662bd85c8363</originalsourceid><addsrcrecordid>eNqtjUFKxEAQRYMgOOjcoS4QiIl2Z1yOKM5WXLgLlU71TA2d7lDVEcfjeFIz4hFcPXh83r8oVrUxtrzdNO9XxVr1WFVV3dqmqTar4ns3TugyJA-aBTPt2cE092HBgTDkA3DMJB8UM6eokBMIDbMjyHNP4uaQlHUZOR4oLpojbAW_ODwAwhZPpIzxHJ9dngUDZB6pVBImBXUUUTgBRgynJfRKSiju_AouLc-f-aa49BiU1n-8LnbPT2-PL-WQ8NhNwiPKqUvI3a9Isu9QMrtAXTX05L3vrbV4Z41DbwwaU_dDe-_axjT_2foBIz96lA</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Impact of strategic public health interventions to reduce tuberculosis incidence in Brazil: a Bayesian structural time-series scenario analysisResearch in context</title><source>ScienceDirect (Online service)</source><source>PubMed Central</source><creator>Klauss Villalva-Serra ; Beatriz Barreto-Duarte ; Moreno M. Rodrigues ; Artur T.L. Queiroz ; Leonardo Martinez ; Julio Croda ; Valeria C. Rolla ; Afrânio L. Kritski ; Marcelo Cordeiro-Santos ; Timothy R. Sterling ; Mariana Araújo-Pereira ; Bruno B. Andrade</creator><creatorcontrib>Klauss Villalva-Serra ; Beatriz Barreto-Duarte ; Moreno M. Rodrigues ; Artur T.L. Queiroz ; Leonardo Martinez ; Julio Croda ; Valeria C. Rolla ; Afrânio L. Kritski ; Marcelo Cordeiro-Santos ; Timothy R. Sterling ; Mariana Araújo-Pereira ; Bruno B. Andrade</creatorcontrib><description>Background: Despite government efforts, tuberculosis (TB) remains a major public health threat in Brazil. In 2023, TB incidence was 39.8 cases per 100,000 population, far above the WHO’s target of 6.7 cases per 100,000. Using national-level datasets, we investigated and forecasted the potential impact of proposed public health interventions aimed at reducing TB incidence in Brazil. Methods: Monthly TB surveillance data (January 2018–December 2023) were collected from Brazilian national reporting systems: SINAN-TB (TB cases), SITE-TB (TB drug resistance), and IL-TB (preventive therapy). These data were used to create a multivariable Bayesian Structural Time-Series (BSTS) model, with 5000 Monte-Carlo simulations, which identified key predictors of TB incidence and forecasted these rates from 2024 to 2030 under various scenarios. Findings: Vulnerabilities including incarceration, TB-HIV coinfection and TB-diabetes mellitus, as well as coverages of directly observed therapy (DOT), contact investigation and preventive treatment (TPT) completion rates, were identified as key predictors of TB incidence. Under current trends, we forecasted TB incidence in Brazil to be 42.1 [34.1–49.8] per 100,000 person-years by 2030 (mean [95% prediction intervals]). A scenario considering decreases in TB cases among vulnerable populations resulted in an absolute reduction of −10.6 [−9.4 to −12.0] in projected TB incidence. Additional reductions were seen with increased coverage of DOT, TPT adherence, and contact investigation rates (−14.4 [−13 to −16.2]), and by combining these with efforts to reduce TB cases among vulnerable populations (−23.6 [−26.3 to −41.4]), potentially lowering incidence to 18.5 [7.8–28.4] per 100,000, though still above WHO targets. Interpretation: Our findings demonstrate that interventions focused on enhancing health policies focused on decreasing TB cases among vulnerable populations, such as individuals with TB-HIV coinfection, incarcerated populations, and those with TB-diabetes comorbidity, along with improvements in health management indicators such as DOT implementation, contact investigation coverage, and TPT completion rates, are effective in reducing TB incidence nationwide. Funding: Oswaldo Cruz Foundation.</description><identifier>EISSN: 2667-193X</identifier><language>eng</language><publisher>Elsevier</publisher><subject>Directly observed therapy ; Forecasting ; Public health ; Tuberculosis ; Tuberculosis preventive therapy ; Vulnerabilities</subject><ispartof>Lancet Regional Health - Americas (Online), 2025-01, Vol.41, p.100963</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784</link.rule.ids></links><search><creatorcontrib>Klauss Villalva-Serra</creatorcontrib><creatorcontrib>Beatriz Barreto-Duarte</creatorcontrib><creatorcontrib>Moreno M. Rodrigues</creatorcontrib><creatorcontrib>Artur T.L. Queiroz</creatorcontrib><creatorcontrib>Leonardo Martinez</creatorcontrib><creatorcontrib>Julio Croda</creatorcontrib><creatorcontrib>Valeria C. Rolla</creatorcontrib><creatorcontrib>Afrânio L. Kritski</creatorcontrib><creatorcontrib>Marcelo Cordeiro-Santos</creatorcontrib><creatorcontrib>Timothy R. Sterling</creatorcontrib><creatorcontrib>Mariana Araújo-Pereira</creatorcontrib><creatorcontrib>Bruno B. Andrade</creatorcontrib><title>Impact of strategic public health interventions to reduce tuberculosis incidence in Brazil: a Bayesian structural time-series scenario analysisResearch in context</title><title>Lancet Regional Health - Americas (Online)</title><description>Background: Despite government efforts, tuberculosis (TB) remains a major public health threat in Brazil. In 2023, TB incidence was 39.8 cases per 100,000 population, far above the WHO’s target of 6.7 cases per 100,000. Using national-level datasets, we investigated and forecasted the potential impact of proposed public health interventions aimed at reducing TB incidence in Brazil. Methods: Monthly TB surveillance data (January 2018–December 2023) were collected from Brazilian national reporting systems: SINAN-TB (TB cases), SITE-TB (TB drug resistance), and IL-TB (preventive therapy). These data were used to create a multivariable Bayesian Structural Time-Series (BSTS) model, with 5000 Monte-Carlo simulations, which identified key predictors of TB incidence and forecasted these rates from 2024 to 2030 under various scenarios. Findings: Vulnerabilities including incarceration, TB-HIV coinfection and TB-diabetes mellitus, as well as coverages of directly observed therapy (DOT), contact investigation and preventive treatment (TPT) completion rates, were identified as key predictors of TB incidence. Under current trends, we forecasted TB incidence in Brazil to be 42.1 [34.1–49.8] per 100,000 person-years by 2030 (mean [95% prediction intervals]). A scenario considering decreases in TB cases among vulnerable populations resulted in an absolute reduction of −10.6 [−9.4 to −12.0] in projected TB incidence. Additional reductions were seen with increased coverage of DOT, TPT adherence, and contact investigation rates (−14.4 [−13 to −16.2]), and by combining these with efforts to reduce TB cases among vulnerable populations (−23.6 [−26.3 to −41.4]), potentially lowering incidence to 18.5 [7.8–28.4] per 100,000, though still above WHO targets. Interpretation: Our findings demonstrate that interventions focused on enhancing health policies focused on decreasing TB cases among vulnerable populations, such as individuals with TB-HIV coinfection, incarcerated populations, and those with TB-diabetes comorbidity, along with improvements in health management indicators such as DOT implementation, contact investigation coverage, and TPT completion rates, are effective in reducing TB incidence nationwide. Funding: Oswaldo Cruz Foundation.</description><subject>Directly observed therapy</subject><subject>Forecasting</subject><subject>Public health</subject><subject>Tuberculosis</subject><subject>Tuberculosis preventive therapy</subject><subject>Vulnerabilities</subject><issn>2667-193X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2025</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNqtjUFKxEAQRYMgOOjcoS4QiIl2Z1yOKM5WXLgLlU71TA2d7lDVEcfjeFIz4hFcPXh83r8oVrUxtrzdNO9XxVr1WFVV3dqmqTar4ns3TugyJA-aBTPt2cE092HBgTDkA3DMJB8UM6eokBMIDbMjyHNP4uaQlHUZOR4oLpojbAW_ODwAwhZPpIzxHJ9dngUDZB6pVBImBXUUUTgBRgynJfRKSiju_AouLc-f-aa49BiU1n-8LnbPT2-PL-WQ8NhNwiPKqUvI3a9Isu9QMrtAXTX05L3vrbV4Z41DbwwaU_dDe-_axjT_2foBIz96lA</recordid><startdate>20250101</startdate><enddate>20250101</enddate><creator>Klauss Villalva-Serra</creator><creator>Beatriz Barreto-Duarte</creator><creator>Moreno M. Rodrigues</creator><creator>Artur T.L. Queiroz</creator><creator>Leonardo Martinez</creator><creator>Julio Croda</creator><creator>Valeria C. Rolla</creator><creator>Afrânio L. Kritski</creator><creator>Marcelo Cordeiro-Santos</creator><creator>Timothy R. Sterling</creator><creator>Mariana Araújo-Pereira</creator><creator>Bruno B. Andrade</creator><general>Elsevier</general><scope>DOA</scope></search><sort><creationdate>20250101</creationdate><title>Impact of strategic public health interventions to reduce tuberculosis incidence in Brazil: a Bayesian structural time-series scenario analysisResearch in context</title><author>Klauss Villalva-Serra ; Beatriz Barreto-Duarte ; Moreno M. Rodrigues ; Artur T.L. Queiroz ; Leonardo Martinez ; Julio Croda ; Valeria C. Rolla ; Afrânio L. Kritski ; Marcelo Cordeiro-Santos ; Timothy R. Sterling ; Mariana Araújo-Pereira ; Bruno B. Andrade</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-doaj_primary_oai_doaj_org_article_0dbefffb777a476caf66a662bd85c8363</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2025</creationdate><topic>Directly observed therapy</topic><topic>Forecasting</topic><topic>Public health</topic><topic>Tuberculosis</topic><topic>Tuberculosis preventive therapy</topic><topic>Vulnerabilities</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Klauss Villalva-Serra</creatorcontrib><creatorcontrib>Beatriz Barreto-Duarte</creatorcontrib><creatorcontrib>Moreno M. Rodrigues</creatorcontrib><creatorcontrib>Artur T.L. Queiroz</creatorcontrib><creatorcontrib>Leonardo Martinez</creatorcontrib><creatorcontrib>Julio Croda</creatorcontrib><creatorcontrib>Valeria C. Rolla</creatorcontrib><creatorcontrib>Afrânio L. Kritski</creatorcontrib><creatorcontrib>Marcelo Cordeiro-Santos</creatorcontrib><creatorcontrib>Timothy R. Sterling</creatorcontrib><creatorcontrib>Mariana Araújo-Pereira</creatorcontrib><creatorcontrib>Bruno B. Andrade</creatorcontrib><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Lancet Regional Health - Americas (Online)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Klauss Villalva-Serra</au><au>Beatriz Barreto-Duarte</au><au>Moreno M. Rodrigues</au><au>Artur T.L. Queiroz</au><au>Leonardo Martinez</au><au>Julio Croda</au><au>Valeria C. Rolla</au><au>Afrânio L. Kritski</au><au>Marcelo Cordeiro-Santos</au><au>Timothy R. Sterling</au><au>Mariana Araújo-Pereira</au><au>Bruno B. Andrade</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Impact of strategic public health interventions to reduce tuberculosis incidence in Brazil: a Bayesian structural time-series scenario analysisResearch in context</atitle><jtitle>Lancet Regional Health - Americas (Online)</jtitle><date>2025-01-01</date><risdate>2025</risdate><volume>41</volume><spage>100963</spage><pages>100963-</pages><eissn>2667-193X</eissn><abstract>Background: Despite government efforts, tuberculosis (TB) remains a major public health threat in Brazil. In 2023, TB incidence was 39.8 cases per 100,000 population, far above the WHO’s target of 6.7 cases per 100,000. Using national-level datasets, we investigated and forecasted the potential impact of proposed public health interventions aimed at reducing TB incidence in Brazil. Methods: Monthly TB surveillance data (January 2018–December 2023) were collected from Brazilian national reporting systems: SINAN-TB (TB cases), SITE-TB (TB drug resistance), and IL-TB (preventive therapy). These data were used to create a multivariable Bayesian Structural Time-Series (BSTS) model, with 5000 Monte-Carlo simulations, which identified key predictors of TB incidence and forecasted these rates from 2024 to 2030 under various scenarios. Findings: Vulnerabilities including incarceration, TB-HIV coinfection and TB-diabetes mellitus, as well as coverages of directly observed therapy (DOT), contact investigation and preventive treatment (TPT) completion rates, were identified as key predictors of TB incidence. Under current trends, we forecasted TB incidence in Brazil to be 42.1 [34.1–49.8] per 100,000 person-years by 2030 (mean [95% prediction intervals]). A scenario considering decreases in TB cases among vulnerable populations resulted in an absolute reduction of −10.6 [−9.4 to −12.0] in projected TB incidence. Additional reductions were seen with increased coverage of DOT, TPT adherence, and contact investigation rates (−14.4 [−13 to −16.2]), and by combining these with efforts to reduce TB cases among vulnerable populations (−23.6 [−26.3 to −41.4]), potentially lowering incidence to 18.5 [7.8–28.4] per 100,000, though still above WHO targets. Interpretation: Our findings demonstrate that interventions focused on enhancing health policies focused on decreasing TB cases among vulnerable populations, such as individuals with TB-HIV coinfection, incarcerated populations, and those with TB-diabetes comorbidity, along with improvements in health management indicators such as DOT implementation, contact investigation coverage, and TPT completion rates, are effective in reducing TB incidence nationwide. Funding: Oswaldo Cruz Foundation.</abstract><pub>Elsevier</pub><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | EISSN: 2667-193X |
ispartof | Lancet Regional Health - Americas (Online), 2025-01, Vol.41, p.100963 |
issn | 2667-193X |
language | eng |
recordid | cdi_doaj_primary_oai_doaj_org_article_0dbefffb777a476caf66a662bd85c836 |
source | ScienceDirect (Online service); PubMed Central |
subjects | Directly observed therapy Forecasting Public health Tuberculosis Tuberculosis preventive therapy Vulnerabilities |
title | Impact of strategic public health interventions to reduce tuberculosis incidence in Brazil: a Bayesian structural time-series scenario analysisResearch in context |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T04%3A07%3A09IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-doaj&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Impact%20of%20strategic%20public%20health%20interventions%20to%20reduce%20tuberculosis%20incidence%20in%20Brazil:%20a%20Bayesian%20structural%20time-series%20scenario%20analysisResearch%20in%20context&rft.jtitle=Lancet%20Regional%20Health%20-%20Americas%20(Online)&rft.au=Klauss%20Villalva-Serra&rft.date=2025-01-01&rft.volume=41&rft.spage=100963&rft.pages=100963-&rft.eissn=2667-193X&rft_id=info:doi/&rft_dat=%3Cdoaj%3Eoai_doaj_org_article_0dbefffb777a476caf66a662bd85c836%3C/doaj%3E%3Cgrp_id%3Ecdi_FETCH-doaj_primary_oai_doaj_org_article_0dbefffb777a476caf66a662bd85c8363%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |