Harnessing electronic health records to study emerging environmental disasters: a proof of concept with perfluoroalkyl substances (PFAS)

Abstract

Environmental disasters are anthropogenic catastrophic events that affect health. Famous disasters include the Seveso disaster and the Fukushima-Daiichi nuclear meltdown, which had disastrous health consequences. Traditional methods for studying environmental disasters are costly and time-intensive. We propose the use of electronic health records (EHR) and informatics methods to study the health effects of emergent environmental disasters in a cost-effective manner. An emergent environmental disaster is exposure to perfluoroalkyl substances (PFAS) in the Philadelphia area. Penn Medicine (PennMed) comprises multiple hospitals and facilities within the Philadelphia Metropolitan area, including over three thousand PFAS-exposed women living in one of the highest PFAS exposure areas nationwide. We developed a high-throughput method that utilizes only EHR data to evaluate the disease risk in this heavily exposed population. We replicated all five disease/conditions implicated by PFAS exposure, including hypercholesterolemia, thyroid disease, proteinuria, kidney disease and colitis, either directly or via closely related diagnoses. Using EHRs coupled with informatics enables the health impacts of environmental disasters to be more easily studied in large cohorts versus traditional methods that rely on interviews and expensive serum-based testing. By reducing cost and increasing the diversity of individuals included in studies, we can overcome many of the hurdles faced by previous studies, including a lack of racial and ethnic diversity. This proof-of-concept study confirms that EHRs can be used to study human health and disease impacts of environmental disasters and produces equivalent disease-exposure knowledge to prospective epidemiology studies while remaining cost-effective.

Associations between PFAS occurrence and multimorbidity as observed in an electronic health record cohort

“…This binary classification approach mirrors that taken by previous analyses of PFAS using electronic health records. 27 Although testing for the UCMR3 (2013–2015) occurred toward the end of our study period, the long half-lives of PFAS chemicals suggests that historical concentrations were if anything higher in the past.…”

“…Thus, our study provides significant improvements though both studies were able to replicate several previously observed associations. 27 In this study, we examined a random sample of individuals; however, they are still all hospital patients and thus are more likely to be older and have pre-existing health conditions than the general population. Despite this, associations observed here match associations seen in other large, population-based epidemiologic studies highlighting the generalizability of PFAS health effects and the potential utility of EHRs for PFAS studies.…”

Translational Bioinformatics to Enable Precision Medicine for All: Elevating Equity across Molecular, Clinical, and Digital Realms

“…These algorithms are designed to estimate a patient's risk of developing a particular phenotype or requiring a specific type of clinical care. Some recent applications of predictive algorithms include preterm birth [36], mortality of preterm infants [37], cardiovascular events [38], COVID-19 outcomes [39], critical illness [40], impact of environmental disasters [41], acute kidney injury [42], length of hospital stay [42], 30-day hospital readmission [43], retention of care [44], and postoperative in-hospital mortality [45]. Two important considerations described in these studies are interpretability and transportability.…”