Understanding the role of urban social and physical environment in opioid overdose event using found geospatial data

Keywords: Opioid overdose epidemic, neighborhood context, social determinants of health, street view images, machine learning
Session Type: Virtual Paper Abstract
Day: Tuesday
Session Start / End Time: 3/1/2022 09:40 AM (Eastern Time (US & Canada)) - 3/1/2022 11:00 AM (Eastern Time (US & Canada))
Room: Virtual 16

Abstract

Abstract: Opioid overdose is a serious public health crisis in the United States. Opioid overdose events (OOEs) vary within and across communities and there is growing evidence that this variation is partially rooted in community-level social and economic conditions. The lack of high spatial resolution, timely data has hampered research into the associations between social and physical environments and the occurrence of OOEs. We explore the use of non-traditional, “found” geospatial data collected for other purposes as indicators of urban social-environmental conditions and their relationships with OOEs at the neighborhood level. We evaluate the use of Google street view images and non-emergency “311” service requests, along with US Census data, as indicators of social and physical conditions in community neighborhoods. We estimate Poisson regression models with opioid overdose event data from first responders in Columbus, Ohio, USA between January 1, 2016, and December 31, 2017. Our result adds to the scientific evidence that the opioid crisis is rooted in social inequality, distress, and underinvestment. It also shows the previously underutilized data sources hold promise for providing insights into this complex problem to help inform the development of population-level interventions and harm reduction policies.