Evidence map›Paper›PMID 32957943›Full record

ArticleBMC public health2020

Spatial distribution of tuberculosis incidence in Los Angeles County.

Adam Readhead, Alicia H Chang, Jo Kay Ghosh, Frank Sorvillo, Julie Higashi, Roger Detels

Abstract read
In one paragraph

Article in BMC public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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

6 authors.

Adam ReadheadDepartment of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, USA. adamreadhead@ucla.edu.ORCID http://orcid.org/0000-0003-3186-9027
Alicia H ChangTB Control Program, Los Angeles County Department of Public Health, Los Angeles, USA.
Jo Kay GhoshIndependent Researcher, Los Angeles, USA.
Frank SorvilloDepartment of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, USA.
Julie HigashiTB Control Program, Los Angeles County Department of Public Health, Los Angeles, USA.
Roger DetelsDepartment of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn Los Angeles County, the tuberculosis (TB) disease incidence rate is seven times higher among non-U.S.-born persons than U.S.-born persons and varies by country of birth. But translating these findings into public health action requires more granular information, especially considering that Los Angeles County is more than 4000 mile

methodsData on 5447 TB cases from Los Angeles County were combined with stratified population estimates available from the 2005-2011 Public Use Microdata Survey. TB disease incidence rates stratified by country of birth and Public Use Microdata Area were calculated and spatial smoothing was applied using a conditional autoregressive model. We used Bayesian Poisson models to investigate spatial patterns adjusting for age, sex, country of birth and years since initial arrival in the U.S.

resultsThere were notable differences in the crude and spatially-smoothed maps of TB disease rates for high-risk subgroups, namely persons born in Mexico, Vietnam or the Philippines. Spatially-smoothed maps showed areas of high incidence in downtown Los Angeles and surrounding areas for persons born in the Philippines or Vietnam. Areas of high incidence were more dispersed for persons born in Mexico. Adjusted models suggested that the spatial distribution of TB disease could not be fully explained using age, sex, country of birth and years since initial arrival.

conclusionsThis study highlights areas of high TB incidence within Los Angeles County both for U.S.-born cases and for cases born in Mexico, Vietnam or the Philippines. It also highlights areas that had high incidence rates even when accounting for non-spatial error and country of birth, age, sex, and years since initial arrival in the U.S. Information on spatial distribution provided here complements other descriptions of local disease burden and may help focus ongoing efforts to scale up testing for TB infection and treatment among high-risk subgroups.

Indexed as

TuberculosisBayes TheoremHumansIncidenceLos AngelesMexicoPhilippinesVietnam

Identifiers

PMID32957943
PMCPMC7507739

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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.