Evidence map›Paper›PMID 38896648›Full record

ArticleRevista brasileira de epidemiologia = Brazilian journal of epidemiology2024

Tuberculosis incidence in Brazil: time series analysis between 2001 and 2021 and projection until 2030.

Marcus Tolentino Silva, Taís Freire Galvão

Abstract read
In one paragraph

Article in Revista brasileira de epidemiologia = Brazilian journal of epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Time series analysis (2015-2022): dengue risk scenario in Goiás, Brazil.Revista brasileira de epidemiologia = Brazilian journal of epidemiology · 2026
    Article
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  5. Article
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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

2 authors.

Marcus Tolentino SilvaUniversidade de Brasília, School of Health Sciences, Public Health Department - Brasília (DF), Brazil.ORCID http://orcid.org/0000-0002-7186-9075
Taís Freire GalvãoUniversidade Estadual de Campinas, School of Pharmaceutical Sciences - Campinas (SP), Brazil.ORCID http://orcid.org/0000-0003-2072-4834

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo assess the incidence of tuberculosis in Brazil between 2001 and 2022 and estimate the monthly incidence forecast until 2030.

methodsThis is a time-series study based on monthly tuberculosis records from the Notifiable Diseases Information System and official projections of the Brazilian population. The monthly incidence of tuberculosis from 2001 to 2022 was evaluated using segmented linear regression to identify trend breaks. Seasonal autoregressive integrated moving average (Sarima) was used to predict the monthly incidence from 2023 to 2030, deadline for achieving the sustainable development goals (SDGs).

resultsThere was a decrease in incidence between January/2001 and December/2014 (4.60 to 3.19 cases-month/100,000 inhabitants; β=-0.005; p<0.001), followed by an increase between January/2015 and March /2020 (β=0.013; p<0.001). There was a sharp drop in cases in April/2020, with the onset of the pandemic, and acceleration of the increase in cases since then (β=0.025; p<0.001). A projection of 124,245 cases in 2030 was made, with an estimated incidence of 4.64 cases-month/100,000 inhabitants, levels similar to those in the 2000s. The Sarima model proved to be robust, with error of 4.1% when removing the pandemic period.

conclusionThe decreasing trend in tuberculosis cases was reversed from 2015 onwards, a period of economic crisis, and was also impacted by the pandemic when there was a reduction in records. The Sarima model can be a useful forecasting tool for epidemiological surveillance. Greater investments in prevention and control need to be made to reduce the occurrence of tuberculosis, in line with the SDGs.

Indexed as

ForecastingTuberculosisBrazilHumansIncidenceLinear ModelsSeasonsTime Factors

Identifiers

PMID38896648
PMCPMC11182439

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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.