Mapping Overdose Risk in Real Time: A Risk Terrain Modeling Analysis of 911 Calls in Detroit, 2022-2024

Context: Drug overdose deaths in the United States remain a leading cause of preventable mortality. Existing data systems, such as vital statistics and hospital records, often suffer from reporting delays and limited geographic resolution, hindering timely public health responses. Objectives: To identify high-risk locations for overdose-related emergency calls in Detroit, Michigan, using Risk Terrain Modeling (RTM) and publicly available 911 call data from 2022 to 2024. Design: A retrospective geospatial analysis using RTM was conducted to evaluate the spatial relationship between overdose incidents and built environment features. Setting: City of Detroit, Michigan, USA. Participants: Emergency call data for overdose-related incidents (N = 18 034) were analyzed. No individual-level data were used. Intervention: No intervention was implemented. The study employed RTM as a geospatial method to identify environmental risk factors and predict high-risk locations for overdose events. Main Outcome Measure: Relative Risk Scores (RRS) generated from RTM to quantify overdose risk across 250 × 250 m grid cells in Detroit. Results: Overdose-related emergency calls were spatially concentrated. RTM identified 8 significant risk factors, including ATMs, retail locations, and religious organizations. Relative Risk Scores ranged from 1 to 142.5 (mean = 9.77, SD = 8.55), with 2.7% of locations classified as very high risk. Conclusions: RTM applied to 911 call data offers a timely, place-based approach to identifying overdose risk. Public health agencies may use this method to prioritize harm reduction strategies and allocate resources more effectively.