Evidence map›Paper›PMID 40431318›Full record

ArticleMicroorganisms2025

The Temporal Trends of Mortality Due to Tuberculosis in Brazil: Tracing the Coronavirus Disease 2019 (COVID-19) Pandemic's Effect Through a Bayesian Approach and Unmasking Disparities.

Reginaldo Bazon Vaz Tavares, Dulce Gomes, Thaís Zamboni Berra, Yan Mathias Alves, Antônio Carlos Vieira Ramos, Marcela Antunes Paschoal Popolin, André da Silva Abade, Nathalia Zini, Ariela Fehr Tártaro, Josilene Dália Alves and 7 more

Abstract read
In one paragraph

Article in Microorganisms, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Temporal Trends in Tuberculosis Incidence in the 1st Health Region of Alagoas, Brazil (2001-2022).International journal of environmental research and public health · 2025
    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

17 authors.

Reginaldo Bazon Vaz TavaresDepartment of Maternal-Infant and Public Health Nursing, Ribeirão Preto College of Nursing, University of São Paulo, Ribeirão Preto 14040-902, Brazil.ORCID 0000-0002-5378-6962
Dulce GomesDepartment of Mathematics, School of Science and Technology, University of Évora, 7000-671 Évora, Portugal.ORCID 0000-0001-8085-1945
Thaís Zamboni BerraDepartment of Maternal-Infant and Public Health Nursing, Ribeirão Preto College of Nursing, University of São Paulo, Ribeirão Preto 14040-902, Brazil.ORCID 0000-0002-4163-8719
Yan Mathias AlvesDepartment of Maternal-Infant and Public Health Nursing, Ribeirão Preto College of Nursing, University of São Paulo, Ribeirão Preto 14040-902, Brazil.ORCID 0000-0002-5596-0047
Antônio Carlos Vieira RamosDepartment of Nursing, State University of Minas Gerais, Passos 96010-440, Brazil.
Marcela Antunes Paschoal PopolinDepartment of Maternal-Infant and Public Health Nursing, Ribeirão Preto College of Nursing, University of São Paulo, Ribeirão Preto 14040-902, Brazil.ORCID 0000-0002-8111-4370
André da Silva AbadeDepartment of Computer Science, Federal Institute of Education, Science and Technology of Mato Grosso, Campus Barra do Garças, Barra do Garças 78600-000, Brazil.
Nathalia ZiniDepartment of Maternal-Infant and Public Health Nursing, Ribeirão Preto College of Nursing, University of São Paulo, Ribeirão Preto 14040-902, Brazil.ORCID 0000-0003-1891-1606
Ariela Fehr TártaroDepartment of Maternal-Infant and Public Health Nursing, Ribeirão Preto College of Nursing, University of São Paulo, Ribeirão Preto 14040-902, Brazil.
Josilene Dália AlvesDepartment of Nursing, Federal University of Mato Grosso, Barra do Garças, Cuiaba 78600-000, Brazil.
Fernanda Bruzadelli Paulino da CostaDepartment of Maternal-Infant and Public Health Nursing, Ribeirão Preto College of Nursing, University of São Paulo, Ribeirão Preto 14040-902, Brazil.ORCID 0000-0001-5310-5592
Maria Eduarda Pagano PelodanDepartment of Maternal-Infant and Public Health Nursing, Ribeirão Preto College of Nursing, University of São Paulo, Ribeirão Preto 14040-902, Brazil.
Beatriz Fornaziero VigatoDepartment of Maternal-Infant and Public Health Nursing, Ribeirão Preto College of Nursing, University of São Paulo, Ribeirão Preto 14040-902, Brazil.
Daniele de Moraes PinheiroDepartment of Maternal-Infant and Public Health Nursing, Ribeirão Preto College of Nursing, University of São Paulo, Ribeirão Preto 14040-902, Brazil.
Juliana Queiroz Rocha de PaivaDepartment of Maternal-Infant and Public Health Nursing, Ribeirão Preto College of Nursing, University of São Paulo, Ribeirão Preto 14040-902, Brazil.
Clara Ferreira de SouzaDepartment of Maternal-Infant and Public Health Nursing, Ribeirão Preto College of Nursing, University of São Paulo, Ribeirão Preto 14040-902, Brazil.
Ricardo Alexandre ArcêncioDepartment of Maternal-Infant and Public Health Nursing, Ribeirão Preto College of Nursing, University of São Paulo, Ribeirão Preto 14040-902, Brazil.ORCID 0000-0003-4792-8714

Funding

Coordenação de Aperfeicoamento de Pessoal de Nível Superior Finance Code 001National Council for Scientific and Technological Development 405902/2021-2 and 307014/2022-3the State of Sao Paulo Research Foundation 2023/16905-4, 2023/01758-6, 2024/04001-6 and 2022/08510-7
6 · The paper itself

Abstract

The COVID-19 pandemic disrupted tuberculosis (TB) control, increasing mortality and potentially worsening disparities. This study aimed to analyze the temporal trends of TB mortality in Brazil and to trace the COVID-19 pandemic's effect using a Bayesian approach, focusing on nationwide data. An ecological study of TB deaths recorded in the Mortality Information System (SIM) from 2012 to 2022 was conducted. Trends and percentage changes in the mortality were estimated. A Bayesian Structural Time Series model combined with an Autoregressive Integrated Moving Average model was used to assess the pandemic's effect on TB. A total of 51,809 TB deaths were identified, with a mortality rate of 2.27 per 100,000. Higher rates were found among the elderly (6.86), indigenous populations (5.58), and black individuals (4.21). The Bayesian model estimated a 9.9% (CI 8.8-11%) increase in TB mortality due to COVID-19. The Midwest region showed the highest increase (30%, 25-35%). Females experienced a greater post-pandemic monthly increase (2.80%) in mortality than males (0.72%). The Bayesian analysis revealed a significant rise in TB mortality during the COVID-19 pandemic, with notable disparities affecting females, the elderly, the indigenous, and the black populations. These findings highlight the pandemic's long-term impact on TB and stress the need for equity-focused, data-driven public health responses in Brazil.

Indexed as

Bayesian analysisdisparitiesecological studiesmortalitytime series studiestuberculosis

Identifiers

PMID40431318
PMCPMC12114275

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.