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

IF 1.9 4区医学 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Journal of Public Health Management and Practice Pub Date : 2025-09-16 DOI:10.1097/PHH.0000000000002238

Kim M Lersch, Timothy C Hart

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引用次数: 0

Abstract

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.

查看原文本刊更多论文

实时绘制过量用药风险：底特律911呼叫的风险地形建模分析，2022-2024。

背景：在美国，药物过量死亡仍然是可预防死亡的主要原因。现有的数据系统，如生命统计数据和医院记录，往往存在报告延迟和地理分辨率有限的问题，妨碍了及时的公共卫生反应。目的：利用风险地形建模（RTM）和公开的2022年至2024年911呼叫数据，确定密歇根州底特律过量相关紧急呼叫的高风险地点。设计：使用RTM进行回顾性地理空间分析，以评估过量用药事件与建筑环境特征之间的空间关系。背景：美国密歇根州底特律市。参与者：对过量用药相关事件的紧急呼叫数据（N = 18034）进行分析。没有使用个人水平的数据。干预：未进行干预。本研究采用RTM作为地理空间方法来识别环境风险因素并预测药物过量事件的高风险地点。主要结果测量：通过RTM生成的相对风险评分（RRS）来量化底特律250 × 250 m网格单元的过量风险。结果：药物过量相关的紧急呼叫在空间上较为集中。RTM确定了8个重要的风险因素，包括自动取款机、零售地点和宗教组织。相对风险评分范围为1 ~ 142.5 (mean = 9.77, SD = 8.55)，其中2.7%的地点属于非常高风险。结论：将RTM应用于911呼叫数据提供了一种及时、基于地点的方法来识别过量用药风险。公共卫生机构可使用这种方法确定减少危害战略的优先次序，并更有效地分配资源。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Public Health Management and Practice PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-

CiteScore

3.40

自引率

9.10%

发文量

287

期刊介绍： Journal of Public Health Management and Practice publishes articles which focus on evidence based public health practice and research. The journal is a bi-monthly peer-reviewed publication guided by a multidisciplinary editorial board of administrators, practitioners and scientists. Journal of Public Health Management and Practice publishes in a wide range of population health topics including research to practice; emergency preparedness; bioterrorism; infectious disease surveillance; environmental health; community health assessment, chronic disease prevention and health promotion, and academic-practice linkages.