Evidence map›Paper›PMID 33676092›Full record

ArticleEpidemics2021

Bayesian evidence synthesis to estimate subnational TB incidence: An application in Brazil.

Melanie H Chitwood, Daniele M Pelissari, Gabriela Drummond Marques da Silva, Patricia Bartholomay, Marli Souza Rocha, Mauro Sanchez, Denise Arakaki-Sanchez, Philippe Glaziou, Ted Cohen, Marcia C Castro and 1 more

Abstract read
In one paragraph

Article in Epidemics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Trends in tuberculosis incidence and mortality coefficients in Brazil, 2011-2019: analysis by inflection points.Revista panamericana de salud publica = Pan American journal of public health · 2023
    Article
  7. Article
  8. Article
  9. 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.

Melanie H ChitwoodDepartment of Epidemiology of Microbial Diseases, Yale School of Public Health, 60 College Street, New Haven CT 06510 United States. Electronic address: melanie.chitwood@yale.edu.
Daniele M PelissariChronic and Airborne Disease Surveillance Coordination, Ministry of Health, SRTVN Qd. 701, Via W5 Norte, Lote D, Ed. PO 700, Brasília Brazil.
Gabriela Drummond Marques da SilvaChronic and Airborne Disease Surveillance Coordination, Ministry of Health, SRTVN Qd. 701, Via W5 Norte, Lote D, Ed. PO 700, Brasília Brazil.
Patricia BartholomayChronic and Airborne Disease Surveillance Coordination, Ministry of Health, SRTVN Qd. 701, Via W5 Norte, Lote D, Ed. PO 700, Brasília Brazil.
Marli Souza RochaChronic and Airborne Disease Surveillance Coordination, Ministry of Health, SRTVN Qd. 701, Via W5 Norte, Lote D, Ed. PO 700, Brasília Brazil.
Mauro SanchezDepartment of Tropical Medicine, University of Brasília, Campus Universitário Darcy Ribeiro, s/n Asa Norte, Brasília Brazil.
Denise Arakaki-SanchezChronic and Airborne Disease Surveillance Coordination, Ministry of Health, SRTVN Qd. 701, Via W5 Norte, Lote D, Ed. PO 700, Brasília Brazil.
Philippe GlaziouWorld Health Organization, Avenue Appia 20, Geneva Switzerland.
Ted CohenDepartment of Epidemiology of Microbial Diseases, Yale School of Public Health, 60 College Street, New Haven CT 06510 United States.
Marcia C CastroDepartment of Global Health and Population, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston MA, 02115 United States.
Nicolas A MenziesDepartment of Global Health and Population, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston MA, 02115 United States.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Optimal targeting for individual and population-level TB preventionR01AI146555 · NIAID · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI MENZIES, NICOLAS A · 2020 to 2024
$2.5M
NCATS NIH HHS UL1 TR001863NIAID NIH HHS R01 AI146555World Health Organization 001
6 · The paper itself

Abstract

backgroundEvidence on local disease burden and the completeness of case detection represent important information for TB control programs. We present a new method for estimating subnational TB incidence and the fraction of individuals with incident TB who are diagnosed and treated in Brazil.

methodsWe compiled data on TB notifications and TB-related mortality in Brazil and specified an analytic model approximating incidence as the number of individuals exiting untreated active disease (sum of treatment initiation, death before treatment, and self-cure). We employed a Bayesian inference approach to synthesize data and adjust for known sources of bias. We estimated TB incidence and the fraction of cases treated, for each Brazilian state and the Federal District over 2008-2017.

findingsFor 2017, TB incidence was estimated as 41.5 (95 % interval: 40.7, 42.5) per 100 000 nationally, and ranged from 11.7-88.3 per 100 000 across states. The fraction of cases treated was estimated as 91.9 % (89.6 %, 93.7 %) nationally and ranged 86.0 %-94.8 % across states, with an estimated 6.9 (5.3, 9.2) thousand cases going untreated in 2017. Over 2008-2017, incidence declined at an average annual rate of 1.4 % (1.1 %, 1.9 %) nationally, and -1.1%-4.2 % across states. Over this period there was a 0.5 % (0.2 %, 0.9 %) average annual increase in the fraction of incident TB cases treated.

interpretationTime-series estimates of TB burden and the fraction of cases treated can be derived from routinely-collected data and used to understand variation in TB outcomes and trends.

Indexed as

TuberculosisBayes TheoremBrazilHumansIncidenceBayesianEstimationIncidenceSubnationalTuberculosis

Identifiers

PMID33676092
PMCPMC8252152

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.