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

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.