TY - JOUR
T1 - A place-based spatial analysis of racial inequities in overdose in St. Louis County Missouri, United States
AU - Marotta, Phillip L.
AU - Leach, Benjamin CB
AU - Hutson, William D.
AU - Caplan, Joel M.
AU - Lohmann, Brenna
AU - Hughes, Charlin
AU - Banks, Devin
AU - Roll, Stephen
AU - Chun, Yung
AU - Jabbari, Jason
AU - Ancona, Rachel
AU - Mueller, Kristen
AU - Cooper, Ben
AU - Anasti, Theresa
AU - Dell, Nathaniel
AU - Winograd, Rachel
AU - Heimer, Robert
N1 - Publisher Copyright:
© 2024
PY - 2024/12
Y1 - 2024/12
N2 - 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.
AB - 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.
KW - Opioid use disorders
KW - Overdose prevention
KW - Racial equity
KW - Spatial modeling
KW - Stimulant use disorders
UR - http://www.scopus.com/inward/record.url?scp=85207881213&partnerID=8YFLogxK
U2 - 10.1016/j.drugpo.2024.104611
DO - 10.1016/j.drugpo.2024.104611
M3 - Article
C2 - 39488868
AN - SCOPUS:85207881213
SN - 0955-3959
VL - 134
JO - International Journal of Drug Policy
JF - International Journal of Drug Policy
M1 - 104611
ER -