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. Issue 3 (28th October 2020)

• Record Type: Journal Article
• Authors: Zhang, Yiye; Tayarani, Mohammad; Al'Aref, Subhi J; Beecy, Ashley N; Liu, Yifan; Sholle, Evan; RoyChoudhury, Arindam; Axsom, Kelly M; Gao, Huaizhu Oliver; Pathak, Jyotishman; Ancker, Jessica S
• Is Part Of: JAMIA open. Volume 3:Issue 3(2020)
• Page Start: 386
• Page End: 394
• Publication Date: 2020-10-28
• Subjects: cardiovascular diseases -- built environment -- public health informatics -- geographic information system -- electronic health records; Medical informatics -- Periodicals; 610.285
• DOI: 10.1093/jamiaopen/ooaa038
• Languages: English
• ISSNs: 2574-2531

Abstract

Abstract: Objective: Electronic 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 Methods: We 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. Results: Previously 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. Conclusions: Population 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.