County-Level Factors Underlying Opioid Mortality in the United States

Abstract

Background: Mortality from overdoses involving opioids in the United States (U.S.) has reached epidemic proportions. More research is needed to examine the underlying factors contributing to opioid-related mortality regionally. This study's objective was to identify and examine the countylevel factors most closely associated with opioid-related overdose deaths across all counties in the U.S. Methods: Using a national cross-sectional ecological study design, we analyzed the relationships between 17 county-level characteristics in four categories (i.e. socio-economic, availability of medical care, health-related concerns, and demographics) with opioid mortality. Data were extracted from the Robert Wood Johnson County Health Rankings aggregate database and Centers for Disease Control and Prevention (CDC)'s Wide-ranging Online Data for Epidemiological Research (WONDER) system. Results: There were 1058 counties (33.67% of 3142 nationally) with reported opioid-related fatalities. Median opioid-related mortality was 15.61 per 100,000 persons. Multivariate regression results indicate that counties with the highest opioid-related mortality had increased rates of tobacco use, HIV, Non-Hispanic Caucasians, and females and were rural areas, but lower rates of food insecurity and uninsured adults. The rates of tobacco use and HIV had the strongest association with mortality. Availability of either mental health or primary care providers were not significantly associated with mortality. Severe housing problems, high school graduation rate, obesity, violent crime, and median household income also did not contribute to county-level differences in overdose mortality. Conclusions: Future health policies should fund further investigations and ultimately address the most influential and significant underlying county-level factors associated with opioid-related mortality.

Trends in heart failure-related cardiovascular mortality in rural versus urban United States counties, 2011–2018: A cross-sectional study

“…This dataset includes cause of death from death certificates for the 50 states and the District of Columbia. Similar to previous studies [16][17][18][19][20] [25]; and the Health Resources and Services Administration Area Health Resources File [26] and subsequently linked using county identifiers to the CDC WONDER mortality data. The rationale for our analytic approach is based on the recommendations by the United States Centers for Disease Control (CDC) and National Vital Statistics System (NVSS) who curate and provide access to these nationwide data and is consistent with prior CDC publications [27][28][29][30].…”

Disparities in Opioid-related Mortality Across United States Census Regions from 1999-2020

“…Several studies have described disparities in opioid-related mortality due to race/ethnicity, education, and geographical distribution (Buchanich et al, 2016;Cerdá et al, 2017;Langabeer, Chambers, Cardenas-Turanzas, & Champagne-Langabeer, 2022;Stewart, Cao, Hsu, Artigiani, & Wish, 2017). The difference in socioeconomic status, in particular, unemployment and homelessness/housing insecurity, lack of education, (Svendsen, Fredheim, Romundstad, Borchgrevink, & Skurtveit, 2014) lack of health insurance has been shown to be contributing to this disparity (Saloner & B, 2013).…”

Feasibility and outcomes from an integrated bridge treatment program for opioid use disorder

“…Regional variations in capacity, and lower prescribing levels for those who are waivered, could be one of many structural factors affecting opioid-related mortality. 11 Whereas some communities particularly in the northeast portion of the United States report average wait time for first appointment of <1 week, many other regions including Houston have significantly longer wait lists, making interim bridge programs more necessary. 6 In our study linkage to ongoing care despite long clinic wait times was associated with significant improvements in QOL.…”