Evaluation of associations between asthma exacerbations and distance to roadways using geocoded electronic health records data

Abstract

Background Asthma exacerbations in children often require medications, urgent care, and hospitalization. Multiple environmental triggers have been associated with asthma exacerbations, including particulate matter 2.5 (PM2.5) and ozone, which are primarily generated by motor vehicle exhaust. There is mixed evidence as to whether proximity to highways increases risk of asthma exacerbations. Methods To evaluate the impact of highway proximity, we assessed the association between asthma exacerbations and the distance of child’s primary residence to two types of roadways in Durham County, North Carolina, accounting for other patient-level factors. We abstracted data from the Duke University Health System electronic health record (EHR), identifying 6208 children with asthma between 2014 and 2019. We geocoded each child’s distance to roadways (both 35 MPH+ and 55 MPH+). We classified asthma exacerbation severity into four tiers and fitted a recurrent event survival model to account for multiple exacerbations. Results There was a no observed effect of residential distance from 55+ MPH highway (Hazard Ratio: 0.98 (95% confidence interval: 0.94, 1.01)) and distance to 35+ MPH roadway (Hazard Ratio: 0.98 (95% confidence interval: 0.83, 1.15)) and any asthma exacerbation. Even those children living closest to highways (less 0.25 miles) had no increased risk of exacerbation. These results were consistent across different demographic strata. Conclusions While the results were non-significant, the characteristics of the study sample – namely farther distance to roadways and generally good ambient environmental pollution may contribute to the lack of effect. Compared to previous studies, which often relied on self-reported measures, we were able to obtain a more objective assessment of outcomes. Overall, this work highlights the opportunity to use EHR data to study environmental impacts on disease.

Environmental and clinical data utility in pediatric asthma exacerbation risk prediction models

“…We identified each child’s zip code of residence and linked data from the American Community Survey to calculate the Agency for Healthcare Research and Quality (AHRQ) socioeconomic status (SES) index, generating a score between 0 and 100, with higher scores indicative of greater deprivation [ 11 ]. We additionally calculated distance to major roadways with speed limits greater than 55 MPH as described previously, and distance to parks, and tree cover for the census block associated with each address [ 12 ]. Briefly, we used ArcGIS to calculate straight-line distance to roadways for each geocoded address within our dataset.…”

Characterizing Patient Phenotypes and Emerging Trends in Application of Spatial Analysis in Individual-Level Health Data

“…The possibilities for applying spatial analysis of individual-level EHR-derived data are beyond geocoding, basic mapping, or external data linkage. For instance, spatial network analysis examines proximity to the sources of pollution [4], measures accessibility to healthcare facilities [5], and optimizes resource allocations to mitigate health disparities [6]. Spatial clustering pinpoints statistically signi cant spatial and spatiotemporal hotspots and cold spots [7], especially when considering longitudinal EHR data.…”