Evidence map›Paper›PMID 41360481›Full record

ArticleBMJ global health2025

Systematic differences in TB treatment outcomes across Brazil by patient- and area-related factors: an analysis of national disease registry data.

Do Kyung Ryuk, Daniele M Pelissari, Kleydson Alves, Luiza Ohana Harada, Patricia Bartholomay Oliveira, Fernanda D C Johansen, Ethel L N Maciel, Marcia C Castro, Ted Cohen, Mauro Sanchez and 1 more

Abstract read
In one paragraph

Article in BMJ global health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Do Kyung RyukDepartment of Global Health and Population, Harvard T H Chan School of Public Health, Boston, Massachusetts, USA.ORCID 0000-0001-8067-968X
Daniele M PelissariHealth and Environment Surveillance Secretariat, Ministry of Health, Brasília, Brazil.ORCID 0000-0002-0760-1875
Kleydson AlvesPan-American Health Organization, PAHO Brazil, Brasília, Brazil.
Luiza Ohana HaradaHealth and Environment Surveillance Secretariat, Ministry of Health, Brasília, Brazil.
Patricia Bartholomay OliveiraHealth and Environment Surveillance Secretariat, Ministry of Health, Brasília, Brazil.
Fernanda D C JohansenHealth and Environment Surveillance Secretariat, Ministry of Health, Brasília, Brazil.
Ethel L N MacielHealth and Environment Surveillance Secretariat, Ministry of Health, Brasília, Brazil.ORCID 0000-0003-4826-3355
Marcia C CastroDepartment of Global Health and Population, Harvard T H Chan School of Public Health, Boston, Massachusetts, USA.ORCID 0000-0003-4606-2795
Ted CohenDepartment of Epidemiology of Microbial Diseases, Yale University School of Public Health, New Haven, Connecticut, USA.
Mauro SanchezDepartment of Public Health, University of Brasília, Brasília, Brazil.
Nicolas A MenziesDepartment of Global Health and Population, Harvard T H Chan School of Public Health, Boston, Massachusetts, USA nmenzies@hsph.harvard.edu.ORCID 0000-0002-2571-016X

Funding

the National Institute of Health R01-AI146555
6 · The paper itself

Abstract

backgroundMany individuals initiating tuberculosis (TB) treatment do not successfully complete the regimen. Understanding variation in treatment outcomes could reveal opportunities to improve the effectiveness of TB treatment services.

methodsWe extracted data on treatment outcomes and patient covariates from Brazil's National Disease Notification Information System, for new TB patients diagnosed during 2015-2018. We analysed whether or not patients experienced an unsuccessful treatment outcome (any death on treatment, loss to follow-up or treatment failure). We constructed a statistical model (logistic regression with regularised two-way interactions) to predict treatment outcomes as a function of socio-demographic factors, co-prevalent health conditions, health behaviours, membership of vulnerable populations and form of TB disease. We used this model to decompose state- and municipality-level variation in treatment outcomes into differences attributable to patient-level and area-level factors.

resultsTreatment outcomes data for 259 449 individuals were used for the analysis. Across Brazilian states, variation in unsuccessful treatment due to patient-level factors was substantially less than variation due to area-level factors, with the difference between best and worst performing states (lowest and highest fraction with unsuccessful treatment, respectively) equal to 7.1 and 13.3 percentage points for patient-level and area-level factors. Similar results were estimated at the municipality level, with 9.3 percentage points separating best and worst performing municipalities according to patient-level factors, and 20.5 percentage points separating best and worst performing municipalities to area-level factors. Results were similar when we analysed loss to follow-up as an outcome.

conclusionsOur analysis revealed substantial variation in TB treatment outcomes across states and municipalities, with only a minority attributable to patient-level factors. Area-level variation likely reflects consequences of differences in health system organisation or socio-environmental factors not reflected in patient-level data. Further research on these factors is needed to identify effective approaches to TB care, reduce geographic disparities and improve treatment outcome.

Indexed as

Antitubercular AgentsTuberculosisAdolescentAdultAgedBrazilChildChild, PreschoolFemaleHumansMaleMiddle AgedRegistriesSocioeconomic FactorsTreatment OutcomeYoung AdultAntitubercular AgentsBrazilTreatmentTuberculosis

Identifiers

PMID41360481
PMCPMC12684155

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.