Spatial clustering of heroin-related overdose incidents: a case study in Cincinnati, Ohio

Abstract

Background: Drug overdose is one of the top leading causes of accidental death in the U.S., largely due to the opioid epidemic. Although the opioid epidemic is a nationwide issue, it has not affected the nation uniformly.

Methods: We combined multiple data sources, including emergency medical service response, American Community Survey data, and health facilities datasets to analyze distributions of heroin-related overdose incidents in Cincinnati, Ohio at the census block group level. The Ripley's K function and the local Moran's I statistics were performed to examine geographic variation patterns in heroin-related overdose incidents within the study area. Then, conditional cluster maps were plotted to examine a relationship between heroin-related incident rates and sociodemographic characteristics of areas as well as the resources for opioid use disorder treatment.

Results: The global spatial analysis indicated that there was a clustered pattern of heroin-related overdose incident rates at every distance across the study area. The univariate local spatial analysis identified 7 hot spot clusters, 27 cold spot clusters, and 1 outlier cluster. Conditional cluster maps showed characteristics of neighborhoods with high heroin overdose rates, such as a higher crime rate, a high percentage of the male, a high poverty level, a lower education level, and a lower income level. The hot spots in the Southwest areas of Cincinnati had longer distances to opioid treatment programs and buprenorphine prescribing physicians than the median, while the hot spots in the South-Central areas of the city had shorter distances to those health resources.

Conclusions: Our study showed that the opioid epidemic disproportionately affected Cincinnati. Multi-phased spatial clustering models based on various data sources can be useful to identify areas that require more policy attention and targeted interventions to alleviate high heroin-related overdose rates.

Keywords: Clustering; Drug overdose; Emergency medical service response (EMS); Geospatial analysis; Heroin-related incident; Socioeconomic factors.

Fig. 1

Distribution of heroin-related overdose incident rates by census block groups in Cincinnati, Ohio, from January 2015 to December 2020

Fig. 2

Ripley’s K functions measuring overall spatial clustering across Cincinnati, Ohio for heroin-related incident rates. (Obs: observed; Theo: theoretical)

Fig. 3

Univariate LISA cluster map identifying significant local clusters of heroin-related incident rates, Cincinnati, Ohio, 2015–2020: High-High: high incident rate within a block and high incident rate of neighboring blocks, Low-Low: low incident

Fig. 4

LISA conditional cluster maps for combinations of two variables

Fig. 5

Socio-demographic characteristics of hot spots and cold spots identified from LISA

Fig. 6

LISA conditional cluster maps of the distance to (A) hospitals, (B) the distance to Opioid Treatment Programs, (C) the distance to buprenorphine prescribing physicians