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

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.

Uses of Social Determinants of Health Data to Address Cardiovascular Disease and Health Equity: A Scoping Review

“…A complete extraction table of the studies may be found in Table S1 . 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 The study characteristics are summarized in Table 1 , the SDOH domains addressed by each study are summarized in Table 2 , and the primary uses of the SDOH data among each study are summarized in Table 3…”

“…Most studies used a cross‐sectional study design (n=17), retrospective cohort studies (n=13), and geographical and spatial analysis (n=12), whereas less common study designs included case studies (n=3), clinical trials (n=3), and randomized control trials (n=2). Thirty‐three studies used linear or logistic regression methods, 35 , 37 , 38 , 39 , 40 , 41 , 42 , 46 , 47 , 48 , 51 , 52 , 54 , 56 , 57 , 58 , 59 , 60 , 62 , 64 , 65 , 66 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 80 , 81 and 14 studies applied spatial analysis to understand the relationship between SDOH and CVD outcomes. 35 , 36 , 37 , 38 , 45 , 50 , 53 , 54 , 58 , 59 , 60 , 61 Machine learning, including data mining, gradient boosting machine, extreme gradient boosting, phase type random forests, Breiman's random forest, support vector machine, and tree‐based classification were used in 4 stu...…”

Demographic data in the built environment and human health studies

“…Medicaid and Medicare data can provide information on the health status, utilization, and costs of older adults and individuals with disabilities, while the data can provide detailed information on patient diagnoses, medications, and other health-related information. This allows researchers to examine particular health measures, like changes in left ventricular ejection fraction, that are otherwise difficult to capture without such data sets [52].…”

Social Determinants of Health Factors for Gene–Environment COVID‐19 Research: Challenges and Opportunities

“…[ 91 ] The potential role of epigenetic modulation along the renin‐apoptosis and pro‐inflammatory pathways activated by SARS‐CoV‐2 [ 92 , 93 , 94 ] has also been hypothesized to explain worse health outcomes in patients with pre‐existing comorbidities, such as Diabetes. [ 95 , 96 ] Diabetes elevates activation of the renin‐apoptosis and pro‐inflammatory pathways, which are targeted by SARS‐CoV‐2, [ 92 ] highlighting the hypothetical role of epigenetic modulation and why some patients may be predisposed to develop more severe COVID‐19 or experience the sequelae at higher rates once infected. Once exposed, SARS‐CoV‐2 activates its receptor and modulators, the ACE2 and TMPRSS2, eventually triggering the immune response, potentially multisystem inflammation, apoptosis, and further transmission to other hosts.…”

“…Additionally, there are multiple study designs and analytic models to consider in GxE analyses. [ 96 ] Chemical environmental exposures are often complex and multifaceted, requiring knowledge about the chemical(s) and their bioavailability, dose, route of exposure, metabolism/excretion, and timing of exposure. SDoH can be more complex, requiring knowledge about the society in which they occur, individual‐level and area‐level indicators, and timing of SDoH exposure with respect to the outcome being observed.…”