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

Results

A map of county-level drug mortality rates for 2014 to 2016 is shown in Appendix C (available as a supplement to the online version of this article at http://www.ajph.org). High rates are concentrated throughout New England, Central Appalachia, parts of the Industrial Midwest, eastern Oklahoma, and the desert Southwest. Low rates are observed throughout the Southern Black Belt, Texas, and the Northern Great Plains.

Regression models revealed that several demographic, socioeconomic, and labor market characteristics are associated with drug mortality rates (Table 1). Each of the economic disadvantage indicators and percentages of workers in various service occupations and industries are associated with significantly higher drug mortality rates. Higher shares of new residents; workers employed in executive or managerial and farming, fishing, or forestry occupations; and workers employed in agriculture, fishing, forestry, business or professional, finance, insurance, and real estate, manufacturing, and wholesale trade industries are associated with significantly lower drug mortality rates. Counties with higher opioid-prescribing rates have significantly higher drug mortality rates. Sensitivity models showing regression results using the 2000 Census and 2012 to 2016 ACS are presented in Appendix E (available as a supplement to the online version of this article at http://www.ajph.org).

Turning to the LPA results (Appendix B), most counties (1701; 55%) are in the low opioid overdose class. These counties have comparatively low mortality rates and change between 2002 to 2004 and 2014 to 2016 from each of the specific opioid types. The LPA classified 270 counties (8.8%) into the high prescription opioid class, characterized by above average rates of prescription opioid mortality in 2014 to 2016 and rates of growth since 2002 to 2004. The high heroin class (n = 165; 5.4%) is characterized by sharply rising heroin mortality rates between 2002 to 2004 and 2014 to 2016, with rates in 2014 to 2016 the highest in the nation. The emerging heroin class (n = 447; 14.5%) incorporates counties with slightly lower and slower-growing heroin mortality rates than the high heroin class, but the average heroin mortality rate in the emerging class still outpaced most other classes.

There were 2 classes that involved high rates of fatal overdose from multiple opioid drugs. First, counties in the synthetic+ class (n = 211; 6.9%) have above average and fast-growing mortality rates from synthetic opioids alone or in combination with prescription opioids and, to a lesser extent, heroin. Counties in the final class (n = 129; 4.2%) are in the depths of the opioid crisis, having very high and rapidly growing mortality rates from all types of opioids: heroin, prescription, synthetic, and combinations. We termed this class the syndemic opioid class because it reflects an aggregation of multiple concurrent or sequential epidemics, wherein the combination of high death rates from multiple opioids greatly exacerbates the crisis.[28] The remaining 156 counties (5.1%) were unclassified.

The geographic distribution of opioid classes is shown in Figure 1, with maps showing levels of mortality and rates of growth for specific opioids in Appendix B. The overlap between these maps gives us confidence that our LPA accurately classified counties. The patterns are also consistent with those in a recent study of state-level growth and levels of mortality from specific opioids[6] and with the map of overall drug mortality rates shown in Appendix C. High prescription opioid counties are concentrated in southern Appalachia, eastern Oklahoma, parts of the desert Southwest and Mountain West, and sprinkled throughout the Great Plains. High and emerging heroin counties are concentrated throughout parts of New York, the Industrial Midwest, central North Carolina, and the Southwest and Northwest. The synthetic+ and syndemic classes are concentrated throughout New England, central Appalachia, and central New Mexico.

Figure 1.

The Geographic Distribution of Opioid Mortality Classes by County: United States
Note. Classes are derived from absolute mortality rates in 2014–2016 and the change in rates between 2002–2004 and 2014–2016. Counties in the synthetic+ class have above average and fast-growing mortality rates from synthetic opioids alone or in combination with prescription opioids and, to a lesser extent, heroin.

Mean overall drug and opioid-specific mortality rates and means of all county characteristics by opioid class are shown in Appendix F. Overall drug mortality and opioid-specific mortality rates in 2014 to 2016 were highest in the syndemic class (41.3 overall drug and 31.7 opioid deaths per 100 000 population), followed by synthetic+ (30.3, 20.9), high prescription opioid (27.3, 16.9), high heroin (25.1, 16.7), emerging heroin (19.3, 10.8), and low overdose (11.0, 3.5). On average, the low overdose class is less White and more rural than are the high opioid classes. However, nonmetropolitan counties are most heavily represented in the prescription opioid class; 73% of counties in the high prescription opioid class are nonmetropolitan compared with 35% in the syndemic class. The prescription opioid class counties are also the most economically disadvantaged, have the highest blue-collar and service economy index scores, and have the lowest urban professional index score. These counties also have the highest average prescribing rates. The emerging heroin and syndemic class counties have the highest urban professional scores. The 2 heroin classes have the lowest average opioid-prescribing rates.

Relative risk ratios of opioid mortality class membership (compared with the low overdose class) are presented in Table 2. Economic disadvantage is associated with significantly greater odds of being in the synthetic+ class and significantly lower odds of being in the high heroin class versus the low overdose class. The blue-collar index and service economy index are associated with significantly greater odds of being in any of the 5 opioid classes versus the low overdose class. The urban professional index is associated with significantly greater odds of being in the heroin, synthetic+, or syndemic classes versus the low overdose class. High opioid prescribing significantly increases odds of membership in the prescription opioid and syndemic classes. Results were relatively unaffected by substituting opioid prescribing for 2006 to 2008 and 2012 to 2014 (see Appendix G, available as a supplement to the online version of this article at http://www.ajph.org).

To enable comparisons across all classes, predicted probabilities of opioid class membership across levels of the 4 indices are presented in Figure 2. Probabilities are from fully adjusted models with all covariates held at their means. Higher county economic disadvantage (1) is associated with lower probability of membership in both heroin classes but higher probability of membership in the prescription opioid and synthetic+ classes. Higher blue-collar worker presence (2) is associated with higher probabilities of membership in the emerging heroin and syndemic classes. Higher values on the urban professional index (3) are related to rapidly rising probability of membership in the syndemic class. Finally, higher values on the service economy index (4) are associated with rising probability of membership in each of the 5 high opioid classes. Appendix H (available as a supplement to the online version of this article at http://www.ajph.org) shows that opioid prescribing is associated only with increased probability of membership in the high prescription opioid class and, to a much smaller extent, the syndemic class.

Figure 2.

Predicted Probabilities of Opioid Mortality Class Membership by Levels of Census-Variable Derived Indices (a) Economic Disadvantage Index, (b) Blue-Collar Index, (c) Urban Professional Index, and (d) Service Economy Index: United States
Note. Avg = average (mean). Predicted probabilities are calculated from fully adjusted multinomial logistic regression models with all covariates held at means.

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