A novel approach for exposing and sharing clinical data: the Translator Integrated Clinical and Environmental Exposures Service

Abstract

Objective This study aimed to develop a novel, regulatory-compliant approach for openly exposing integrated clinical and environmental exposures data: the Integrated Clinical and Environmental Exposures Service (ICEES). Materials and Methods The driving clinical use case for research and development of ICEES was asthma, which is a common disease influenced by hundreds of genes and a plethora of environmental exposures, including exposures to airborne pollutants. We developed a pipeline for integrating clinical data on patients with asthma-like conditions with data on environmental exposures derived from multiple public data sources. The data were integrated at the patient and visit level and used to create de-identified, binned, “integrated feature tables,” which were then placed behind an OpenAPI. Results Our preliminary evaluation results demonstrate a relationship between exposure to high levels of particulate matter ≤2.5 µm in diameter (PM2.5) and the frequency of emergency department or inpatient visits for respiratory issues. For example, 16.73% of patients with average daily exposure to PM2.5 >9.62 µg/m3 experienced 2 or more emergency department or inpatient visits for respiratory issues in year 2010 compared with 7.93% of patients with lower exposures (n = 23 093). Discussion The results validated our overall approach for openly exposing and sharing integrated clinical and environmental exposures data. We plan to iteratively refine and expand ICEES by including additional years of data, feature variables, and disease cohorts. Conclusions We believe that ICEES will serve as a regulatory-compliant model and approach for promoting open access to and sharing of integrated clinical and environmental exposures data.

Translator Exposure APIs: Open Access to Data on Airborne Pollutant Exposures, Roadway Exposures, and Socio-Environmental Exposures and Use Case Application

“…We successfully used the open APIs to extract exposures data on 100% of geocoded participants within an EPR cohort, and we integrated the exposures data with EPR data at the participant level. Importantly, we applied the data to a proof-of-concept asthma use case and demonstrated an association between asthma exacerbations, as measured by participant self-report of ED or urgent care visit for asthma, and sex, race, smoking history, obesity, median household income, and exposure to airborne particulate matter, thus largely supporting our hypothesis that the Translator Exposures APIs could be used to replicate our prior findings [24][25][26]35,36].…”

“…† Significance level was set at p < 0.10. ‡ Binned using pandas cut function to facilitate comparison with [24] and account for the granularity of the data, with airborne pollutant exposure estimates obtained at a resolution of 12 km-squared and provisioned at a resolution of the US Census Track centroid, and median household income estimates provisioned at the US Census Block Group level.…”

FHIR PIT: an open software application for spatiotemporal integration of clinical data and environmental exposures data

“…FHIR PIT is a complex, custom, open-source software application that uses geocodes and time stamps of varying resolution (e.g., hour, day, year) to automatically integrate multiple sources of spatiotemporal data, irrespective of the degree to which the data depend on space and time. FHIR PIT was motivated by our research and development of the Integrated Clinical and Environmental Exposures Service [ICEES [16]. ICEES was developed as part of the Biomedical Data Translator program in response to a need to openly expose clinical data that have been integrated at the patient and visit level with environmental exposures data [17,18].…”

Clinical Data: Sources and Types, Regulatory Constraints, Applications

“…ICEES was designed by Translator team members as a novel extension of COHD . Specifically, ICEES permits open access to observational patient data from the UNC Health Care System that have been integrated at the patient and visit level with environmental exposures data (e.g., airborne and roadway pollutants, socioeconomic factors) derived from multiple public sources.…”