An assessment of opioid overdose mortality risk across the urban–rural continuum: Integrating satellite-derived and socioeconomic indicators

Abstract

Opioid-related overdose is a public health crisis, shaped by socio-built environmental factors. Overdose has increased in rural areas; however, commonly used census-based classifications overlook fine-grained variations across areas, which obscure local variation, settlement patterns, and introduce ecological bias. We address this using remote sensing data, capturing built environment features to examine fatal opioid overdoses. We performed a retrospective ecological study analyzing fatal opioid overdoses within census block groups in Cook County, Illinois, from 2018 to 2023. Urbanicity was classified using satellite data to measure environmental indicators (built-up intensity, vegetative greenness, nighttime light intensity (NLI)), then combined with census-based measures of neighborhood deprivation to characterize physical and social conditions. A Bayesian spatiotemporal model was estimated, yielding neighborhood-level estimates of overdose risk. There were 9,249 opioid-related deaths, with clustering and strong associations with socio-built environmental factors. Neighborhood disadvantage had a dose-response relationship, with high deprivation areas experiencing over four times the risk of overdose. NLI had a 10.24-fold increase in overdose mortality, while vegetative greenness showed no significant protective effects. Urban areas had the highest absolute risk, yet increasing temporal trends occurred across all regions, including 25% of rural and transitional areas. Remote sensing data provides more granular and consistent characterization of the socio-built environment, enabling public health officials and urban planners to identify high-risk environments and implement place-based interventions.

Taylor Harrington

PhD Candidate, Methodologist

Taylor Harrington, MS, is a PhD candidate in Public Health at The Ohio State University. To investigate how multi-level factors shape health behaviors and outcomes, she integrates advanced computational and geospatial methods, including machine learning (e.g., computer vision, random forests, structured topic modeling, text mining) and geospatial modeling (e.g., Bayesian spatial modeling, remote sensing). Through this work, she aims to generate actionable evidence to inform equitable health policy and community-level interventions.

Dr. Gia Elise Barboza-Salerno

Director

I am an interdisciplinary scholar whose research integrates law, public health, social work, and advanced statistics to study childhood adversity, violence-related harm, and neighborhood-level inequities. I lead the ISSUES Lab at Ohio State, translating spatial and legal analysis into policy and practice.

Dr. Keith Warren

Faculty Affiliate

Dr. Keith Warren is a Faculty Affiliate in the ISSUES Lab and an Associate Professor at The Ohio State University.