Opioid-related deaths have severely increased since 2000 in the United States. This crisis has been declared a public health emergency, and among the most affected states is Ohio. We used statewide vital statistic data from the Ohio Department of Health (ODH) and demographics data from the U.S. Census Bureau to analyze opioid-related mortality from 2010 to 2016. We focused on the characterization of the demographics from the population of opioid-related fatalities, spatiotemporal pattern analysis using Moran’s statistics at the census-tract level, and comorbidity analysis using frequent itemset mining and association rule mining. We found higher rates of opioid-related deaths in white males aged 25–54 compared to the rest of Ohioans. Deaths tended to increasingly cluster around Cleveland, Columbus and Cincinnati and away from rural regions as time progressed. We also found relatively high co-occurrence of cardiovascular disease, anxiety or drug abuse history, with opioid-related mortality. Our results demonstrate that state-wide spatiotemporal and comorbidity analysis of the opioid epidemic could provide novel insights into how the demographic characteristics, spatiotemporal factors, and/or health conditions may be associated with opioid-related deaths in the state of Ohio.

Objective: Our study focused on identifying socioeconomic factors associated with death by opioid overdose in Ohio communities at the census tract level. Materials and Methods: A large-scale vital statistic dataset from Ohio Department of Health (ODH) and U.S. Census datasets were used to obtain opioid-related death rate and socioeconomic characteristics for all census tracts in Ohio. Regression analysis was performed to identify the relationships between socioeconomic factors of census tracts and the opioid-related death rate for both urban and rural tracts. Results: In Ohio from 2010-2016, whites, males, and people aged 25-44 had the highest opioid-related death rates. At the census tract level, higher death rates were associated with certain socioeconomic characteristics (e.g. percentage of the census tract population living in urban areas, percentage divorced/separated, percentage of vacant housing units). Predominately rural areas had a different population composition than urban areas, and death rates in rural areas exhibited fewer associations with socioeconomic characteristics. Discussion: Predictive models of opioid-related death rates based on census tract-level characteristics held for urban areas more than rural ones, reflecting the recently observed rural- to- urban geographic shift in opioid-related deaths. Future research is needed to examine the geographic distribution of opioid abuse throughout Ohio and in other states. Conclusion: Regression analysis identified associations between population characteristics and opioid-related death rates of Ohio census tracts. These analyses can help government officials and law official workers prevent, predict and combat opioid abuse at the community level.

Objective: We aimed to identify (1) differences in opioid poisoning mortality among population groups, (2) geographic clusters of opioid-related deaths over time, and (3) health conditions co-occurring with opioid-related death in Ohio by computational analysis. Materials and Methods: We used a large-scale Ohio vital statistic dataset from the Ohio Department of Health (ODH) and U.S. Census data from 2010-2016. We surveyed population differences with demographic profiling and use of relative proportions, conducted spatiotemporal pattern analysis with spatial autocorrelation via Moran statistics at the census tract level, and performed comorbidity analysis using frequent itemset mining and association rule mining. Results: Our analyses found higher rates of opioid-related death in people aged 25-54, whites, and males. We also found that opioid-related deaths in Ohio became more spatially concentrated during 2010-2016, and tended to be most clustered around Cleveland, Columbus and Cincinnati. Drug abuse, anxiety and cardiovascular disease were found to predict opioid-related death. Discussion: Comprehensive data-driven spatiotemporal analysis of opioid-related deaths provides essential identification of demographic, geographic and health factors related to opioid abuse. Future research should access personal health information for more detailed comorbidity analysis, as well as expand spatiotemporal models for real-time use. Conclusion: Computational analyses revealed demographic differences in opioid poisoning, changing regional patterns of opioid-related deaths, and health conditions co-occurring with opioid overdose for Ohio from 2010-2016, providing essential knowledge for both government officials and caregivers to establish policies and strategies to best combat the opioid epidemic.