Authors: Jane Clougherty*, Drexel University Dornsife School of Public Health, Ellen J. Kinnee, Department of Environmental and Occupational Health | University of Pittsburgh, Fernando Holguin, University of Colorado
Topics: Medical and Health Geography, Spatial Analysis & Modeling, Urban Geography
Keywords: RCT, GIS, exposure, asthma, effect modification
Session Type: Paper
Start / End Time: 3:05 PM / 4:45 PM
Room: Tyler, Marriott, Mezzanine Level
Presentation File: No File Uploaded
Randomized clinical trials (RCTs), generally considered the gold standard of evidence in medical research, randomize and balance treatment and control arms, thereby maximizing internal validity, and reducing between-group biases resulting from variation in individual patient characteristics. This assumption may well hold true for the population accurately represented by the trial cohort, however, RCTs do not generally examine variation by environmental exposures and socioeconomic position (SEP), which may impact clinical outcomes, treatment response, and generalizability.
To assess whether variation in SEP and environmental exposures across an asthma RCT cohort modify treatment response, we developed GIS-based metrics to characterize residential exposures for the 221 children in the Step Up Yellow Zone Inhaled Corticosteroids to Prevent Exacerbations (STICS) study.
On average, across the cohort, there was no significant effect of 5x increased corticosterone dose on a range of outcomes (e.g., exacerbation rate, albuterol use). We found, however, that higher roadway densities conferred a greater number of exacerbation events and unscheduled medical visits. Further, children in areas of greater poverty and home vacancy had significantly shorter times to first corticosteroid use. We are now examining near-residence roadway noise, violent crime exposure, and access to health care on variation in observed treatment efficacy.
Using spatial analysis and GIS to understand the lived context of RCT participants – better accounting for socioeconomic and environmental factors – may help to improve the interpretation of RCT results, to better identify subpopulations for whom an intervention may be most effective, and to inform on the generalizability of RCT results.