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

Shannon M. Monnat, PhD; David J. Peters, PhD; Mark T. Berg, PhD; Andrew Hochstetler, PhD

Disclosures

Am J Public Health. 2019;109(8):1084-1091. 

In This Article

Abstract and Introduction

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.

Introduction

Fatal drug overdose rates increased 250% in the United States from 6.1 deaths per 100 000 population in 1999 to 21.7 in 2017.[1] Opioids have been the primary contributor to this increase, accounting for more than 47 600 deaths in 2017 alone.[1] There is widespread geographic variation in fatal opioid overdose rates, with the highest rates concentrated in Appalachia, New England, Florida, eastern Oklahoma, and the desert Southwest.[2–5] In state-level analyses, prescription opioids, heroin, and fentanyl have been found to be differentially implicated in overdoses across different parts of the United States. For example, synthetic opioid deaths are strongly concentrated throughout the East,[6] whereas heroin overdoses are high in the Industrial Midwest and New Mexico.[7]

Our analysis of county-level variation in opioid mortality is grounded in literature emphasizing the importance of ecological factors on population health and reflects and embraces counties as important population health units of analysis.[8] Counties are small enough to reflect local economic and social conditions but large enough to be meaningful for policy.[9] County governments provide political and economic structure, which ultimately affects health and well-being. Moreover, the county is where most social and health services are delivered and where states administer funding for most social programs.[9] Counties are also largely responsible for bearing the costs of the drug crisis in the form of criminal justice, social services, and emergency service provider expenditures.

Census data can be an essential tool for understanding county-level variation in drug mortality rates and in driving policy responses to the crisis. Multiple previous studies have used census data to understand the roles of demographic, socioeconomic, and labor market conditions on county-level variation in life expectancy,[8] all-cause mortality,[10–13] premature mortality,[14,15] and cause-specific mortality from cardiovascular diseases,[16] cancers,[16] chronic obstructive pulmonary disease,[16] diabetes,[17] and unintentional injury.[18] Using county-level census data, Monnat[2] found that drug mortality rates varied across different types of labor markets and were higher in counties characterized by greater economic disadvantage. However, Monnat pooled 10 years of mortality data and did not examine how county characteristics were differentially associated with different classes of opioid mortality. Because previous research has primarily used an omnibus measure of drug mortality deaths, there is limited information about the ecological correlates of mortality linked to different types of opioids.

We extended previous research on geographic differences in opioid mortality by (1) describing the county-level demographic, economic, and labor market characteristics that are associated with overall drug mortality rates in 2014 to 2016 and (2) analyzing how these characteristics vary across "opioid mortality classes"—classes of counties characterized by differential levels and rates of growth in mortality from specific types of opioids. Our analyses were grounded in research showing the importance of various drug supply[19] and structural demand factors in driving the opioid overdose crisis, including population aging, economic distress, and employment restructuring.[4,20] In this structural approach to explaining the opioid crisis, we focused especially on economic dislocation and place-level distress among the working class.[21]

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