Evidence map›Paper›PMID 35193581›Full record

ArticleBMC medical informatics and decision making2022

An electronic medical records study of population obesity prevalence in El Paso, Texas.

Jennifer J Salinas, Jon Sheen, Navkiran Shokar, Justin Wright, Gerardo Vazquez, Ogechika Alozie

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical informatics and decision making, 2022. 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. US public health surveillance, reimagined.Learning health systems · 2024
    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

6 authors.

Jennifer J SalinasDepartment of Molecular and Translational Medicine, Texas Tech Health Sciences Center El Paso, 5001 El Paso Dr., El Paso, TX, 79905, USA. jennifer.salinas@ttuhsc.edu.
Jon SheenDepartment of Molecular and Translational Medicine, Texas Tech Health Sciences Center El Paso, 5001 El Paso Dr., El Paso, TX, 79905, USA.
Navkiran ShokarDepartment of Family and Community Medicine, Texas Tech Health Sciences Center El Paso, El Paso, TX, USA.
Justin WrightDepartment of Family and Community Medicine, Texas Tech Health Sciences Center El Paso, El Paso, TX, USA.
Gerardo VazquezDepartment of Family and Community Medicine, Texas Tech Health Sciences Center El Paso, El Paso, TX, USA.
Ogechika AlozieDel Sol Medical Center, El Paso, TX, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn this study, we determine the feasibility of using electronic medical record (EMR) data to determine obesity prevalence at the census tract level in El Paso County, Texas, located on the U.S.-Mexico border.

methods2012-2018 Body Mass Index (BMI kg/m

resultsEMR data represented all but one El Paso census tract. Representation ranged from 0.7% to 34.9%. Greatest representation were among census tracts in and around clinics. The mean EMR data BMI (kg/m

conclusionsEMR data use for obesity surveillance is feasible in El Paso County, Texas, a U.S.-Mexico border community. Findings indicate substantial obesity prevalence variation between census tracts within El Paso County that may be associated with population distributions related to socioeconomics.

Indexed as

Electronic Health RecordsHispanic or LatinoHumansMexicoObesityPrevalenceTexasBody Mass IndexElectronic medical recordsGeographic information systemsMexican AmericansObesity

Identifiers

PMID35193581
PMCPMC8861479

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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.