Evidence map›Paper›PMID 35982104›Full record

ArticleScientific reports2022

Geospatial analysis of reported activity locations to identify sites for tuberculosis screening.

Courtney M Yuen, Meredith B Brooks, Ana Karina Millones, Diana Acosta, Erika Del Águila-Rojas, Hortencia Campos, Sheyla Farroñay, Giannina Morales, Judith Ramirez-Sandoval, Tim C Nichols and 3 more

Abstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

13 authors.

Courtney M YuenDivision of Global Health Equity, Brigham and Women's Hospital, Boston, MA, USA. Courtney_Yuen@hms.harvard.edu.
Meredith B BrooksDepartment of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA.
Ana Karina MillonesSocios En Salud Sucursal Peru, Lima, Peru.
Diana AcostaSocios En Salud Sucursal Peru, Lima, Peru.
Erika Del Águila-RojasSocios En Salud Sucursal Peru, Lima, Peru.
Hortencia CamposSocios En Salud Sucursal Peru, Lima, Peru.
Sheyla FarroñaySocios En Salud Sucursal Peru, Lima, Peru.
Giannina MoralesSocios En Salud Sucursal Peru, Lima, Peru.
Judith Ramirez-SandovalSocios En Salud Sucursal Peru, Lima, Peru.
Tim C NicholsDepartment of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA.
Judith JimenezSocios En Salud Sucursal Peru, Lima, Peru.
Helen E JenkinsDepartment of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
Leonid LeccaDepartment of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA.

Funding

New approaches to optimizing the application and measuring the impact of community-based tuberculosis interventionsDP2MD015102 · NIMHD · BRIGHAM AND WOMEN'S HOSPITAL · PI YUEN, COURTNEY M. · 2019 to 2019
$2.4M
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 AI151083NIH HHS 1DP2MD015102NIH HHS K01AI151083NIMHD NIH HHS DP2 MD015102
6 · The paper itself

Abstract

Mobile screening units can help close tuberculosis case detection gaps. Placing screening units where people at high risk for undiagnosed tuberculosis preferentially spend time could make screening more resource-effective. We conducted a case-control study in Lima, Peru to identify locations where people with tuberculosis were more likely to spend time than community controls. We surveyed participants about activity locations over the past 6 months. We used density-based clustering to assess how patient and control activity locations differed, and logistic regression to compare location-based exposures. We included 109 tuberculosis patients and 79 controls. In density-based clustering analysis, the two groups had similar patterns of living locations, but their work locations clustered in distinct areas. Both groups were similarly likely to use public transit, but patients predominantly used buses and were less likely to use rapid transit (adjusted odds ratio [aOR] 0.31, 95% confidence interval [CI] 0.10-0.96) or taxis (aOR 0.42, 95% CI 0.21-0.85). Patients were more likely to have spent time in prison (aOR 11.55, 95% CI 1.48-90.13). Placing mobile screening units at bus terminals serving locations where tuberculosis patients have worked and within and around prisons could help reach people with undiagnosed tuberculosis.

Indexed as

TuberculosisCase-Control StudiesHumansMass ScreeningPrisonsTransportation

Identifiers

PMID35982104
PMCPMC9387880

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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.