Evidence map›Paper›PMID 40606058›Full record

ArticleOpen forum infectious diseases2025

Social Vulnerability Modifies the Effects of Geographic Proximity on Engagement in Latent Tuberculosis Infection Care in a United States Safety Net Healthcare Network.

Jeffrey I Campbell, Ariane Garing, Dorine Lavache, Sophia Bahad, Melissa Hofman, Jessica E Haberer, Meredith B Brooks, Pranay Sinha, Laura F White, Vishakha Sabharwal and 4 more

Abstract read
In one paragraph

Article in Open forum infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Uncovering Household Tuberculosis Infection Testing and Care Patterns Using a Novel Bioinformatics Linkage Strategy.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2026
    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

14 authors.

Jeffrey I CampbellSection of Pediatric Infectious Diseases, Boston Medical Center, Boston, Massachusetts, USA.ORCID https://orcid.org/0000-0001-7230-6285
Ariane GaringSection of Pediatric Infectious Diseases, Boston Medical Center, Boston, Massachusetts, USA.
Dorine LavacheSection of Pediatric Infectious Diseases, Boston Medical Center, Boston, Massachusetts, USA.
Sophia BahadBoston University Chobanian and Avedisian School of Medicine, Boston, Massachusetts, USA.ORCID https://orcid.org/0009-0001-0232-8670
Melissa HofmanResearch Informatics, Boston Medical Center, Boston, Massachusetts, USA.
Jessica E HabererCenter for Global Health, Massachusetts General Hospital, Boston, Massachusetts, USA.
Meredith B BrooksDepartment of Global Health, Boston University School of Public Health, Boston, Massachusetts, USA.
Pranay SinhaSection of Infectious Diseases, Boston Medical Center, Boston, Massachusetts, USA.
Laura F WhiteDepartment of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA.
Vishakha SabharwalSection of Pediatric Infectious Diseases, Boston Medical Center, Boston, Massachusetts, USA.ORCID https://orcid.org/0000-0002-5092-4442
Cynthia A TschamplThe Heller School for Social Policy and Management, Brandeis University, Waltham, Massachusetts, USA.ORCID https://orcid.org/0000-0001-8133-4683
C Robert HorsburghDepartments of Epidemiology, Biostatistics and Global Health, Boston University School of Public Health, Boston, Massachusetts, USA.
Helen E JenkinsDepartment of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA.
Karen R JacobsonSection of Infectious Diseases, Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine, Boston, Massachusetts, USA.

Funding

Tuberculosis in teens: a geospatial approach to predict community transmissionK01AI151083 · NIAID · HARVARD MEDICAL SCHOOL · PI Meredith Blair Brooks · 2021 to 2026
$698k
NIAID NIH HHS K01 AI151083
6 · The paper itself

Abstract

Background: Latent tuberculosis (TB) infection care often requires engagement with multiple teams in several locations throughout the diagnostic and treatment steps of the TB infection care cascade. The intersecting effects of geographic proximity and social drivers on care cascade retention have not been well examined. Methods: We conducted a retrospective cohort study of patients with a positive TB infection test between 2018-2019 within a health system in Boston, Massachusetts. The primary outcome was attendance at a TB clinic after a referral was placed. The primary exposure was geographic proximity, as measured by travel time by car. We assessed effect modification of proximity by Social Vulnerability Index (SVI), a composite measure of census tract social drivers. Results: We identified 1677 patients with positive TB infection tests; 1208 (72%) were referred to a TB clinic, of whom 748 (62%) completed referral. Longer travel times were associated with lower odds of referral completion (furthest vs nearest quartiles: adjusted odds ratio, 0.76 [95% confidence interval, .71-.82]). SVI significantly modified the effects of proximity: Increasing travel time was associated with decreasing probability of clinic attendance for patients in lower-vulnerability census tracts but had minimal effect on clinic attendance among patients in higher vulnerability census tracts. Conclusions: Additional support is needed for individuals referred to TB clinics that require long travel times to attend. Support should also account for other social drivers affecting care access for those living near TB clinics.

Indexed as

care cascadedistancelatent tuberculosis infectionSocial Vulnerability Indextravel time

Identifiers

PMID40606058
PMCPMC12216903

What OpenQuestion holds

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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.