Background
Opioid use disorder and overdose deaths is a public health crisis in the United States. In the year of 2019, over 70% of all drug overdose deaths involved an opioid, including prescription opioids, heroin, and synthetic opioid like fentanyl (National Institute of Drug Abuse, 2021). Ohio is among the states hit hardest by the “opioid epidemic” with the rise in the misuse and abuse of prescription opioid pain relievers like OxyContin and Fentanyl and non-prescription opioids like heroin. Franklin County, which includes City of Columbus and the surrounding suburbs, experienced 547 drug overdose deaths in 2019 the largest number of any region in the state, and representing a 14.9% increase over the previous year (Ohio Department of Health, 2020).
There is increasing recognition that crisis’s etiology is rooted in part by social determinants such as poverty, isolation and social upheaval. This places attention on the health effects of upstream social factors such as economic, education, and demographic that shape downstream factors such as behavior, economic stability, stress levels, support networks, neighborhood and physical environment, and access to healthy food and health care. Limiting research and policy interventions is the low temporal and spatial resolution of publicly available administrative data such as census data. A lack of timely, high-resolution data hampers research into the neighborhood social determinants of opioid use disorder. We explore the use of nontraditional municipal service requests (also known as “311” requests) as high resolution spatial and temporal indicators of neighborhood social distress and opioid misuse. These are public data that are frequently updated (in many cases, daily) and have high spatial resolution (latitude and longitude).
Results
We analyze the spatial associations between georeferenced opioid overdose event (OOE) data from emergency medical service responders and 311 service request data from the City of Columbus, OH, USA for the time period 2008–2017. We find 10 out of 21 types of 311 requests (abandoned vehicles, animal complaints, code violation, law enforcement, public health, refuse trash litter, street lighting, street maintenance, traffic signs, and water sewers drains) spatially associate with OOEs and also characterize neighborhoods with lower socio-economic status in the city, both consistently over time. We also demonstrate that the 311 indicators are capable of predicting OOE hotspots at the neighborhood-level: our results show code violation, public health, and street lighting were the top three accurate predictors with predictive accuracy as 0.92, 0.89 and 0.83, respectively.
Figure (a) shows the actual spatial distribution of OOE hot spots and cold spots in Columbus, 2017. The remaining maps show the three most accurate predictors based on predict accuracy: code violation (b), public health (c) and street lighting (d). Figure also shows the three most inaccurate predictions: traffic signs, street maintenance, and waters sewers drains in Fig. e–g, respectively. (Li et al., 2020)
Implications
The results from this study support the view that opioid crisis is rooted in social and neighborhood distress. We show such spatial characteristics can be used along with 311 data itself to predict the trends of opioid overdose hotspots when OOEs data is not available. Since 311 requests are publicly available and with high spatial and temporal resolution, they can be effective as opioid overdose surveillance indicators for basic research and applied policy. It is worth mentioning that our research is not a predictive policing tool. An appropriate use is to help think strategically about where to allocate outreach, programs and resources to at-risk individuals and how to alleviate the underlying social and environmental stressors in our city.
Yuchen Li, PhD Candidate
References
Li, Y., Hyder, A., Southerland, L. T., Hammond, G., Porr, A., & Miller, H. J. (2020). 311 Service Requests As Indicators of Neighborhood Distress and Opioid Use Disorder. Scientific Reports, 10(1), 1–11. https://doi.org/10.1038/s41598-020-76685-z
National Institute of Drug Abuse. (2021). Overdose Death Rates. Retrieved May 13, 2021, from National Institute on Drug Abuse website: https://www.drugabuse.gov/drug-topics/trends-statistics/overdose-death-rates
Ohio Department of Health. (2020). 2019 Ohio Drug Overdose Data: General Findings. Retrieved from https://odh.ohio.gov/wps/wcm/connect/gov/0a7bdcd9-b8d5-4193-a1af-e711be4ef541/2019_OhioDrugOverdoseReport_Final_11.06.20.pdf?MOD=AJPERES&CONVERT_TO=url&CACHEID=ROOTWORKSPACE.Z18_M1HGGIK0N0JO00QO9DDDDM3000-0a7bdcd9-b8d5-4193-a1af-e711be4ef541-nmv3qSt