Suspected heroin-related overdoses incidents in Cincinnati, Ohio: A spatiotemporal analysis


Autoři: Zehang Richard Li aff001;  Evaline Xie aff002;  Forrest W. Crawford aff001;  Joshua L. Warren aff001;  Kathryn McConnell aff006;  J. Tyler Copple aff007;  Tyler Johnson aff007;  Gregg S. Gonsalves aff007
Působiště autorů: Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America aff001;  Yale College, New Haven, Connecticut, United States of America aff002;  Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut, United States of America aff003;  Department of Statistics & Data Science, Yale University, New Haven, Connecticut, United States of America aff004;  Yale School of Management, New Haven, Connecticut, United States of America aff005;  Yale School of Forestry & Environmental Studies, New Haven, Connecticut, United States of America aff006;  Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America aff007;  Yale Law School, New Haven, Connecticut, United States of America aff008
Vyšlo v časopise: Suspected heroin-related overdoses incidents in Cincinnati, Ohio: A spatiotemporal analysis. PLoS Med 16(11): e32767. doi:10.1371/journal.pmed.1002956
Kategorie: Research Article
doi: 10.1371/journal.pmed.1002956

Souhrn

Background

Opioid misuse and deaths are increasing in the United States. In 2017, Ohio had the second highest overdose rates in the US, with the city of Cincinnati experiencing a 50% rise in opioid overdoses since 2015. Understanding the temporal and geographic variation in overdose emergencies may help guide public policy responses to the opioid epidemic.

Methods and findings

We used a publicly available data set of suspected heroin-related emergency calls (n = 6,246) to map overdose incidents to 280 census block groups in Cincinnati between August 1, 2015, and January 30, 2019. We used a Bayesian space-time Poisson regression model to examine the relationship between demographic and environmental characteristics and the number of calls within block groups. Higher numbers of heroin-related incidents were found to be associated with features of the built environment, including the proportion of parks (relative risk [RR] = 2.233; 95% credible interval [CI]: [1.075–4.643]), commercial (RR = 13.200; 95% CI: [4.584–38.169]), manufacturing (RR = 4.775; 95% CI: [1.958–11.683]), and downtown development zones (RR = 11.362; 95% CI: [3.796–34.015]). The number of suspected heroin-related emergency calls was also positively associated with the proportion of male population, the population aged 35–49 years, and distance to pharmacies and was negatively associated with the proportion aged 18–24 years, the proportion of the population with a bachelor's degree or higher, median household income, the number of fast food restaurants, distance to hospitals, and distance to opioid treatment programs. Significant spatial and temporal heterogeneity in the risks of incidents remained after adjusting for covariates. Limitations of this study include lack of information about the nature of incidents after dispatch, which may differ from the initial classification of being related to heroin, and lack of information on local policy changes and interventions.

Conclusions

We identified areas with high numbers of reported heroin-related incidents and features of the built environment and demographic characteristics that are associated with these events in the city of Cincinnati. Publicly available information about opiate overdoses, combined with data on spatiotemporal risk factors, may help municipalities plan, implement, and target harm-reduction measures. In the US, more work is necessary to improve data availability in other cities and states and the compatibility of data from different sources in order to adequately measure and monitor the risk of overdose and inform health policies.

Klíčová slova:

Built environment – Census – Critical care and emergency medicine – Heroin – Open data – Opioids – Public and occupational health – Spatial epidemiology


Zdroje

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2019 Číslo 11

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