Using Census Data to Understand County-Level Differences in Overall Drug Mortality and Opioid-Related Mortality by Opioid Type

Abstract

Objectives. To examine associations of county-level demographic, socioeconomic, and labor market characteristics on overall drug mortality rates and specific classes of opioid mortality. Methods. We used National Vital Statistics System mortality data (2002–2004 and 2014–2016) and county-level US Census data. We examined associations between several census variables and drug deaths for 2014 to 2016. We then identified specific classes of counties characterized by different levels and rates of growth in mortality from specific opioid types between 2002 to 2004 and 2014 to 2016. We ran multivariate and multivariable regression models to predict probabilities of membership in each “opioid mortality class” on the basis of county-level census measures. Results. Drug mortality rates overall are higher in counties characterized by more economic disadvantage, more blue-collar and service employment, and higher opioid-prescribing rates. High rates of prescription opioid overdoses and overdoses involving both prescription and synthetic opioids cluster in more economically disadvantaged counties with larger concentrations of service industry workers. High heroin and “syndemic” opioid mortality counties (high rates across all major opioid types) are more urban, have larger concentrations of professional workers, and are less economically disadvantaged. Syndemic opioid counties also have greater concentrations of blue-collar workers. Conclusions. Census data are essential tools for understanding the importance of place-level characteristics on opioid mortality. Public Health Implications. National opioid policy strategies cannot be assumed universally applicable. In addition to national policies to combat the opioid and larger drug crises, emphasis should be on developing locally and regionally tailored interventions, with attention to place-based structural economic and social characteristics.

The Opioid Hydra: Understanding Overdose Mortality Epidemics and Syndemics Across the Rural‐Urban Continuum

“…For employment , manufacturing job shares dropped by −7.3 percentage points in both synthetic + Rx and prescription opioid class counties, almost double that experienced by low overdose communities. This finding is consistent with previous research implicating industrial decline for the rise in opioid deaths (Monnat et al ). More generally, non‐metro counties impacted by opioid overdoses have falling labor force participation rates, indicating general economic distress as jobs have disappeared or people have stopped looking for work.…”

“…They are also less connected to interstate highways and are located in regions with low mountainous, making these places more remote and isolated. Remoteness can hinder economic development and result in local economies dominated by resource‐based industries that have high rates of workplace injury and disability (BLS ; Pender et al ), placing these communities at risk for prescription opioid use and potential overdose fatality (Monnat et al ). However, remoteness also limits the presence of DTOs that supply illicit opioids like heroin and synthetics, leaving narcotics supplied by local pharmacies as the primary source (DEA ).…”

“…Recent literature on the opioid overdose epidemic points to both compositional and contextual features of communities that are associated with spatial variation in fatal drug overdose rates. Compositional risk factors contributing to higher overdose rates include high rates of poverty, unemployment, disability, single parent families, divorce, and lower educational attainment (Monnat et al ; Paulozzi and Annest ; Ruhm ). This work also finds that certain vulnerable demographic groups tend to have higher overdose rates, such as the elderly, military veterans, and Native Americans (Monnat ).…”

Trends in U.S. Working-Age non-Hispanic White Mortality: Rural–Urban and Within-Rural Differences

“…5 ). In New England, the Mid-Atlantic, EN Central, and South Atlantic, most of the increase occurred in the 2010s, owing to the mid-2010s surge in fentanyl availability in those regions (Monnat et al 2019 ; Peters et al In Press). Conversely, in Appalachia, the drug mortality rate increased more in the 1990s and 2000s, perhaps sparked by the increased availability of prescription opioids and then heroin in this region during this period.…”

Trends of the incidence of drug use disorders from 1990 to 2017: an analysis based on the Global Burden of Disease 2017 data

“…unsafe sex and drug injection), and increased risk of infectious diseases (e.g. human immunodeficiency virus and hepatitis) (Swendsen and Merikangas, 2000; Compton et al , 2003; Kessler, 2004; O'Brien et al , 2004; Degenhardt and Hall, 2012; Ghimire et al , 2013; Monnat et al , 2019; Guo et al , 2019).…”