Evidence map›Paper›PMID 42241460›Full record

ArticlePLoS medicine2026

Comparative impacts and cost-effectiveness of tuberculosis systematic screening strategies in prisons in Brazil, Colombia, and Peru: A mathematical modeling study.

Yiran E Liu, José Victor Bortolotto Bampi, Ronan F Arthur, Argita D Salindri, Caroline Busatto, Pedro Avedillo Jiménez, Daniele Maria Pelissari, Fernanda Dockhorn Costa Johansen, Robert Arana-Narvaez, Alvaro Fernando Moreno Roca and 14 more

Abstract readComparative Study
In one paragraph

Article in PLoS medicine, 2026. 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

5 · Who and what money

Authors and funding

24 authors.

Yiran E LiuDivision of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, California, United States of America.ORCID https://orcid.org/0000-0001-7964-712X
José Victor Bortolotto BampiFederal University of Mato Grosso do Sul, Campo Grande, Brazil.ORCID https://orcid.org/0000-0002-1410-7461
Ronan F ArthurDivision of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, California, United States of America.ORCID https://orcid.org/0000-0001-8513-8348
Argita D SalindriDivision of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, California, United States of America.ORCID https://orcid.org/0000-0003-0962-0348
Caroline BusattoNational Tuberculosis Program, Ministry of Health, Brasília, Brazil.
Pedro Avedillo JiménezPan American Health Organization, Communicable Diseases Prevention, Control and Elimination, Washington, District of Columbia, United States of America.
Daniele Maria PelissariNational Tuberculosis Program, Ministry of Health, Brasília, Brazil.
Fernanda Dockhorn Costa JohansenNational Tuberculosis Program, Ministry of Health, Brasília, Brazil.
Robert Arana-NarvaezDirección de Prevención y Control de la Tuberculosis, Ministerio de Salud, Lima, Peru.ORCID https://orcid.org/0000-0002-7801-1872
Alvaro Fernando Moreno RocaDirección de Prevención y Control de la Tuberculosis, Ministerio de Salud, Lima, Peru.
Wilfredo Santos Solís TupesDirección de Prevención y Control de la Tuberculosis, Ministerio de Salud, Lima, Peru.ORCID https://orcid.org/0009-0009-3477-3451
Esther Mori JiuDirección de Prevención y Control de la Tuberculosis, Ministerio de Salud, Lima, Peru.ORCID https://orcid.org/0009-0000-6177-2213
Christian Alfredo Moreno RocaDirección de Prevención y Control de la Tuberculosis, Ministerio de Salud, Lima, Peru.ORCID https://orcid.org/0009-0000-3471-1538
Erika Albertina Abregú ContrerasDirección de Prevención y Control de la Tuberculosis, Ministerio de Salud, Lima, Peru.
Valentina Antonieta Alarcón GuizadoDirección de Prevención y Control de la Tuberculosis, Ministerio de Salud, Lima, Peru.ORCID https://orcid.org/0000-0002-6381-5843
Julián Trujillo TrujilloMinisterio de Salud y Protección Social, Subdirección de Enfermedades Transmisibles, Bogotá, Colombia.
Belkys MarcelinoPan American Health Organization, Communicable Diseases Prevention, Control and Elimination, Washington, District of Columbia, United States of America.
Mónica Alonso GonzalezPan American Health Organization, Communicable Diseases Prevention, Control and Elimination, Washington, District of Columbia, United States of America.
Mayra Cecilia Córdova AyllonInstituto Penitenciário Nacional, Lima, Peru.
Ted CohenDepartment of Epidemiology of Microbial Diseases, School of Public Health, Yale University, New Haven, Connecticut, United States of America.ORCID https://orcid.org/0000-0002-8091-7198
Moises A HuamanDivision of Infectious Diseases, Department of Internal Medicine, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America.ORCID https://orcid.org/0000-0002-6839-5809
Jeremy D Goldhaber-FiebertDepartment of Health Policy, Stanford University School of Medicine, Stanford, California, United States of America.ORCID https://orcid.org/0000-0002-4007-5192
Julio CrodaFederal University of Mato Grosso do Sul, Campo Grande, Brazil.ORCID https://orcid.org/0000-0002-6665-6825
Jason R AndrewsDivision of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, California, United States of America.ORCID https://orcid.org/0000-0002-5967-251X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIncarceration is a leading driver of tuberculosis in Latin America. Systematic screening in prisons may reduce tuberculosis burden, but optimal strategies and cost-effectiveness remain uncertain. We examined the population-wide health impacts and cost-effectiveness of systematic screening in prisons in Brazil, Colombia, and Peru, comparing different timepoints, frequencies, and screening algorithms. METHODS AND

findingsUsing dynamic transmission models calibrated to Brazil, Colombia, and Peru, we simulated annual or biannual (twice-yearly) prison-wide screening, alone or combined with entry and exit screening from 2026 to 2035. We evaluated four algorithms: (1) symptom screening, (2) chest X-ray with computer-aided detection (CXR-CAD), (3) symptoms and CXR-CAD (follow-up testing if either is positive), and (4) GeneXpert Ultra (Xpert) with pooled sputum. Individuals screening positive then received individual Xpert. We projected impacts on within-prison and population-level tuberculosis incidence in 2035, along with discounted costs (2023 US dollars) and disability-adjusted life years (DALYs). Model projections showed that combined entry, exit, and biannual screening with CXR-CAD was highly impactful and cost-effective across countries, reducing tuberculosis incidence by 61%-87% in prisons and 18%-28% population-wide. Compared to only biannual CXR-CAD (the next best strategy), the incremental cost per DALY averted of adding entry and exit screening was $2,984 (Brazil), $2,925 (Colombia), and $645 (Peru). Adding symptom screening to CXR-CAD marginally increased benefit and was only cost-effective in Peru's higher-incidence prisons. Biannual screening alone remained cost-effective at prison incidence levels well below national averages, as well as at far lower willingness-to-pay thresholds. In settings without CXR-CAD, pooled Xpert was an impactful, cost-effective alternative. Key limitations include the model's simplified representation of tuberculosis disease states and lack of stratification by age, gender/sex, HIV, or drug resistance.

conclusionsThese modeling results support immediate national-level adoption of prison-wide tuberculosis screening twice-yearly and at entry and exit, using CXR-CAD or pooled Xpert.

Indexed as

Mass ScreeningPrisonsTuberculosisBrazilColombiaCost-Effectiveness AnalysisFemaleHealth Services for PrisonersHumansIncidenceMaleModels, TheoreticalPeruPrisoners

Identifiers

PMID42241460
PMCPMC13258141

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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.