Evidence map›Paper›PMID 33215073›Full record

ArticleJAMIA open2020

Using electronic health records for population health sciences: a case study to evaluate the associations between changes in left ventricular ejection fraction and the built environment.

Yiye Zhang, Mohammad Tayarani, Subhi J Al'Aref, Ashley N Beecy, Yifan Liu, Evan Sholle, Arindam RoyChoudhury, Kelly M Axsom, Huaizhu Oliver Gao, Jyotishman Pathak and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JAMIA open, 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. Big data and psychiatry: advances, constraints and future directions.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2026
    Article
  2. Article
  3. Article
  4. 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

11 authors.

Yiye ZhangDepartment of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York City, New York, USA.
Mohammad TayaraniSchool of Civil and Environmental Engineering, Cornell University, Ithaca, New York, USA.
Subhi J Al'ArefDivision of Cardiology, Department of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.
Ashley N BeecyDivision of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, New York, USA.
Yifan LiuDepartment of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York City, New York, USA.
Evan SholleDepartment of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York City, New York, USA.
Arindam RoyChoudhuryDepartment of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York City, New York, USA.
Kelly M AxsomColumbia University Irving Medical Center, New York, New York, USA.
Huaizhu Oliver GaoSchool of Civil and Environmental Engineering, Cornell University, Ithaca, New York, USA.
Jyotishman PathakDepartment of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York City, New York, USA.
Jessica S AnckerDepartment of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York City, New York, USA.

Funding

Clinical and Translational Science CenterUL1TR000457 · NCATS · WEILL MEDICAL COLL OF CORNELL UNIV · PI IMPERATO-MCGINLEY, JULIANNE L · 2012 to 2016
$46.0M
Outcome-driven Order Set Content Development, Management, and EvaluationK01LM013257 · NLM · WEILL MEDICAL COLL OF CORNELL UNIV · PI ZHANG, YIYE · 2019 to 2021
$541k
NCATS NIH HHS UL1 TR000457NLM NIH HHS K01 LM013257
6 · The paper itself

Abstract

objectiveElectronic health record (EHR) data linked with address-based metrics using geographic information systems (GIS) are emerging data sources in population health studies. This study examined this approach through a case study on the associations between changes in ejection fraction (EF) and the built environment among heart failure (HF) patients. MATERIALS AND

methodsWe identified 1287 HF patients with at least 2 left ventricular EF measurements that are minimally 1 year apart. EHR data were obtained at an academic medical center in New York for patients who visited between 2012 and 2017. Longitudinal clinical information was linked with address-based built environment metrics related to transportation, air quality, land use, and accessibility by GIS. The primary outcome is the increase in the severity of EF categories. Statistical analyses were performed using mixed-effects models, including a subgroup analysis of patients who initially had normal EF measurements.

resultsPreviously reported effects from the built environment among HF patients were identified. Increased daily nitrogen dioxide concentration was associated with the outcome while controlling for known HF risk factors including sex, comorbidities, and medication usage. In the subgroup analysis, the outcome was significantly associated with decreased distance to subway stops and increased distance to parks.

conclusionsPopulation health studies using EHR data may drive efficient hypothesis generation and enable novel information technology-based interventions. The availability of more precise outcome measurements and home locations, and frequent collection of individual-level social determinants of health may further drive the use of EHR data in population health studies.

Indexed as

built environmentcardiovascular diseaseselectronic health recordsgeographic information systempublic health informatics

Identifiers

PMID33215073
PMCPMC7660965

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.