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

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.

COVID-19 economic impact payments and opioid overdose deaths.

BACKGROUND: Given the global economic recessions mediated by the COVID-19 pandemic and that many countries have implemented direct income support programs, we investigated the timing of the COVID-19 economic impact payments and opioid overdose deaths. METHODS: A longitudinal, observational study design that included data from the Ohio Department of Health was utilized. Statistical change point analyses were conducted to identify significant changes in weekly number of opioid overdose deaths from January 1 of 2018 to August 1 of 2020. Additional analyses including difference-in-difference, time series tests, interrupted time series regression analysis and Granger causality test were performed. RESULTS: A single change point was identified and occurred at week 16, 2020. For 2020, the median opioid overdose deaths numbers for weeks 1-16 and weeks 17-32 were 68.5 and 101, respectively. The opioid overdose deaths numbers from weeks 17-32 of 2020 were significantly higher than those in weeks 1-16 of 2020 and those in 2018 and 2019 (before and after week 16). The interrupted time series regression analysis indicated more than 203 deaths weekly for weeks 17-32 of 2020 compared to all other weeks. The result of the Granger causality test found that the identified change point (week 16 of 2020) directly influenced the increase in opioid overdose deaths in weeks 17-32 of 2020. CONCLUSION: The identified change point may refer to the timing of many factors, not only the economic payments and further research is warranted to investigate the potential relationship between the COVID-19 economic impact payments and overdose deaths.

During the COVID-19 Pandemic, Opioid Overdose Deaths Revert to Previous Record Levels in Ohio.

OBJECTIVE: During the COVID-19 pandemic, states have had to confront a drug overdose problem associated with the pandemic. The objective of this study was to identify the impact of the COVID-19 pandemic on the opioid epidemic in the state of Ohio by describing the changes in the quarterly opioid overdose deaths (OOD) over the last 10 years. METHODS: This longitudinal study included OOD data from death records obtained through the Ohio Department of Health. Temporal trend analysis and visualizations were performed on the OOD death rate per 100,000 quarterly from 2010 to 2020. Age, sex, and ethnicity were also analyzed. RESULTS: The OOD rate of 11.15 in Q2 of 2020 was statistically equivalent to the previous peak level of 10.87 in Q1 of 2017. There was a significant increase in the OOD rate from Q1 to Q2 of 2020 and a significant difference between the actual Q2 of 2020 OOD rate and the predicted OOD rate. The poisoning indicator fentanyl was present in 94% of OOD during Q2 of 2020. The total number of OOD remains highest in the White population. There was no significant difference between the actual and predicted OOD rates in the Black population of Q2 of 2020 based on the trend line. However, the OOD rate of 14.29 in Q2 of 2020 was significantly higher than the previous peak level of 8.34 in Q2 of 2017. The Q2 of 2020 OOD rates for 18 to 39 and 40+ age groups were significantly higher from what would be expected from the trend predictions. CONCLUSIONS: Based on these findings, Ohio has entered a COVID-19 pandemic mediated fourth wave in the opioid epidemic. These findings further suggest that as efforts are made to address the worldwide COVID-19 pandemic, states need to maintain their vigilance toward combating the local opioid epidemic.