A place-based spatial analysis of racial inequities in overdose in St. Louis County Missouri, United States

Phillip L Marotta; Benjamin Cb Leach; William D Hutson; Joel M Caplan; Brenna Lohmann; Charlin Hughes; Devin Banks; Stephen Roll; Yung Chun; Jason Jabbari; Rachel Ancona; Kristen Mueller; Ben Cooper; Theresa Anasti; Nathaniel Dell; Rachel Winograd; Robert Heimer

doi:10.1016/j.drugpo.2024.104611

A place-based spatial analysis of racial inequities in overdose in St. Louis County Missouri, United States

Int J Drug Policy. 2024 Nov 2:134:104611. doi: 10.1016/j.drugpo.2024.104611. Online ahead of print.

Authors

Phillip L Marotta¹, Benjamin Cb Leach², William D Hutson³, Joel M Caplan⁴, Brenna Lohmann⁵, Charlin Hughes⁵, Devin Banks⁶, Stephen Roll⁷, Yung Chun⁷, Jason Jabbari⁷, Rachel Ancona⁸, Kristen Mueller⁹, Ben Cooper¹⁰, Theresa Anasti⁷, Nathaniel Dell⁶, Rachel Winograd¹¹, Robert Heimer¹²

Affiliations

¹ Washington University in St. Louis, MO, USA; Brown School, Washington University in St. Louis, St. Louis, MO, USA; Social Policy Institute, Washington University in St. Louis, MO, USA. Electronic address: [email protected].
² Washington University in St. Louis, MO, USA; Brown School, Washington University in St. Louis, St. Louis, MO, USA; University of California San Francisco, Department of Medicine, Division of Health Equity and Society, San Francisco, California, United States.
³ Washington University in St. Louis, MO, USA; Brown School, Washington University in St. Louis, St. Louis, MO, USA; School of Medicine, Washington University in St. Louis, MO, USA; Department of Psychiatry, Washington University in St. Louis, MO, USA.
⁴ Simsi, Inc. NJ, USA; Rutgers University School of Criminal Justice Center on Public Security Newark, NJ, USA.
⁵ St Louis County - Circuit Attorney's Office Law Enforcement Assisted Diversion Program (LEAD) St. Louis, MO, USA.
⁶ Washington University in St. Louis, MO, USA; School of Medicine, Washington University in St. Louis, MO, USA; Department of Psychiatry, Washington University in St. Louis, MO, USA.
⁷ Washington University in St. Louis, MO, USA; Brown School, Washington University in St. Louis, St. Louis, MO, USA; Social Policy Institute, Washington University in St. Louis, MO, USA.
⁸ Washington University in St. Louis, MO, USA; School of Medicine, Washington University in St. Louis, MO, USA; Institute for Informatics, Washington University in St. Louis, MO, USA.
⁹ Washington University in St. Louis, MO, USA; School of Medicine, Washington University in St. Louis, MO, USA; Department of Psychiatry, Washington University in St. Louis, MO, USA; Department of Emergency Medicine, Washington University in St. Louis, USA.
¹⁰ Washington University in St. Louis, MO, USA; School of Medicine, Washington University in St. Louis, MO, USA; Institute for Informatics, Washington University in St. Louis, MO, USA; Public Health Data & Training Center, Institute for Public Health Washington University in St. Louis, St. Louis, Missouri, United States.
¹¹ Department of Psychological Sciences, University of Missouri, St. Louis, USA.
¹² Department of the Epidemiology of Microbial Diseases and the Center for Interdisciplinary Research on AIDS, Yale School of Public Health, New Haven, CT, USA.

PMID: 39488868
DOI: 10.1016/j.drugpo.2024.104611

Abstract

Objective: The objective of this study was to identify place features associated with increased risk of drug-involved fatalities and generate a composite score measuring risk based on the combined effects of features of the built environment.

Methods: We conducted a geospatial analysis of overdose data from 2022 to 2023 provided by the St. Louis County Medical Examiner's Office to test whether drug-involved deaths were more likely to occur near 54 different place features using Risk Terrain Modeling (RTM). RTM was used to identify features of the built environment that create settings of heightened overdose risk. Risk was estimated using Relative Risk Values (RRVs) and a composite score measuring Relative Risk Scores (RRS) across the county was produced for drugs, opioids, and stimulants, as well as by Black and White decedents.

Results: In the model including all drugs, deaths were more likely to occur in close proximity to hotels/motels (RRV=39.65, SE=0.34, t-value=10.81 p<.001), foreclosures (RRV=4.42, SE=0.12, t-value = 12.80, p<.001), police departments (RRV=3.13, SE=0.24, t-score=4.86, p<.001), and restaurants (RRV=2.33, SE=0.12, t-value=7.16, p<.001). For Black decedents, deaths were more likely to occur near foreclosures (RRV=9.01, SE=0.18, t-value =11.92, p<.001), and places of worship (RRV= 2.51, SE=0.18, t-value = 11.92, p<.001). For White decedents, deaths were more likely to occur in close proximity to hotels/motels (RRV=38.97, SE=0.39, t-value=9.30, p<.001) foreclosures (RRV=2.57, SE=0.16, t-value =5.84, p<.001), restaurants (RRV=2.52, SE=0.17, t-value=5.33, p<.001) and, auto painting/repair shops (RRV=0.04, SE=0.18, t-value =3.39, p<.001).

Conclusion: These findings suggest that places of worship, the hospitality industry, and housing authorities may be physical features of the environment that reflect social conditions that are conducive to overdose. The scaling up of harm reduction strategies could be enhanced by targeting places where features are co-located.

Keywords: Opioid use disorders; Overdose prevention; Racial equity; Spatial modeling; Stimulant use disorders.

Grants and funding

K08 DA058080/DA/NIDA NIH HHS/United States