Evidence map›Paper›PMID 41863536›Full record

ArticleEmerging infectious diseases2026

Tuberculosis before and during COVID-19 Pandemic, United States, 2010-2023.

Pei-Jean I Feng, Christina R Phares, Robert Pratt, Julie L Self

Abstract read
In one paragraph

Article in Emerging infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Pei-Jean I Feng
Christina R Phares
Robert Pratt
Julie L Self

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

After a steady decline in tuberculosis (TB) during 2010-2019, the United States reported a sharp drop in 2020 and increases during 2021-2023. We assessed whether TB cases during 2020-2023 differed from what was expected in the absence of the COVID-19 pandemic. Using data from the Centers for Disease Control and Prevention National TB Surveillance System and Electronic Disease Notification system, we constructed Poisson regression models to predict frequencies of TB cases, persons receiving TB diagnosis within 1 year of arrival, and persons for whom postarrival TB follow-up was recommended on the basis of 2010-2019 trends. We observed lower than predicted TB cases (7,170 observed, 8,822 predicted), persons receiving diagnosis within 1 year of arrival (208 observed, 259 predicted), and persons with class B TB (4,827 observed, 7,169 predicted) in 2020. Migration changes and COVID-19-related factors likely contributed to the decrease in TB in 2020 and increases during 2021-2023.

Indexed as

COVID-19TuberculosisHumansPandemicsSARS-CoV-2United StatesCOVID-19epidemiologypublic health surveillancerespiratory infectionsSARS-CoV-2severe acute respiratory syndrome coronavirus 2tuberculosis and other mycobacteriaUnited Statesviruses

Identifiers

PMID41863536
PMCPMC13016024

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.