Evidence map›Paper›PMID 40036593›Full record

ArticleClinical infectious diseases : an official publication of the Infectious Diseases Society of America2026

Effects of the COVID-19 Pandemic on Tuberculosis Outcomes in the United States: A Bayesian Analysis.

Nicole A Swartwood, Ted Cohen, Suzanne M Marks, Andrew N Hill, Garrett R Beeler Asay, Julie Self, Pei-Jean I Feng, C Robert Horsburgh, Joshua A Salomon, Nicolas A Menzies

Abstract read
In one paragraph

Article in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Nicole A SwartwoodDepartment of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.ORCID 0000-0003-4116-9243
Ted CohenDepartment of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, USA.ORCID 0000-0002-8091-7198
Suzanne M MarksDivision of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.ORCID 0000-0003-3024-1940
Andrew N HillDivision of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.ORCID 0000-0002-7418-6456
Garrett R Beeler AsayDivision of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
Julie SelfDivision of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.ORCID 0000-0001-9584-7041
Pei-Jean I FengDivision of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
C Robert HorsburghDepartments of Epidemiology, Biostatistics, Global Health and Medicine, Boston University Schools of Public Health and Medicine, Boston, Massachusetts, USA.ORCID 0000-0001-6838-7895
Joshua A SalomonDepartment of Health Policy, Stanford University School of Medicine, Stanford, California, USA.ORCID 0000-0003-3929-5515
Nicolas A MenziesDepartment of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.ORCID 0000-0002-2571-016X

Funding

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
CDC, National Center for HIV, Viral Hepatitis, STD, and TB Prevention Epidemiologic and Economic Modeling Agreement grant number 5NU38PS004644Intramural CDC HHS CC999999NCHHSTP CDC HHS U38 PS004644NIAID NIH HHS R01 AI146555NIH HHS R01AI146555-02
6 · The paper itself

Abstract

backgroundTuberculosis (TB) notifications and deaths in the United States fluctuated substantially during the coronavirus disease 2019 (COVID-19) pandemic. We analyzed multiple data sources to understand the factors contributing to these changes and estimated future TB trends.

methodsWe identified 4 mechanisms potentially contributing to observed TB trends during 2020-2023: immigration, respiratory contact rates, rates of accurate diagnosis and treatment initiation, and mortality rates for persons experiencing TB disease. We employed a Bayesian approach to synthesize evidence on how these mechanisms changed during the pandemic and how they might have combined to produce observed 2020-2023 TB data, using a transmission-dynamic model to link mechanisms to TB outcomes. We also simulated a no-pandemic-counterfactual scenario that assumed mechanisms followed pre-pandemic trends. We estimated TB outcomes associated with the pandemic until 2035 to capture lagged effects. We evaluated additional scenarios to estimate the individual effect of each mechanism.

resultsOver 2020-2035, we estimate an additional 2784 (95% uncertainty interval: 2164-3461) TB notifications and 1138 (1076-1201) TB deaths in the United States associated with changes occurring during the COVID-19 pandemic. Mechanisms had offsetting effects-decreases in TB diagnosis rates led to more TB deaths and notifications, while reductions in contact rates reduced TB deaths and notifications. Immigration changes initially reduced TB deaths, but increased deaths and notifications over time. Higher TB mortality rates increased TB deaths but decreased TB notifications.

conclusionsAlthough direct impacts of the COVID-19 pandemic occurred between 2020 and 2023, these changes may continue to influence TB incidence and mortality in future years.

Indexed as

COVID-19TuberculosisBayes TheoremHumansPandemicsSARS-CoV-2United StatesCOVID-19infectious diseasesmathematical modelingtuberculosisUnited States

Identifiers

PMID40036593
PMCPMC12353213

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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.