Association Between Automotive Assembly Plant Closures and Opioid Overdose Mortality in the United States: A Difference-in-Differences Analysis

Abstract

Importance: Fading economic opportunity has been hypothesized to be an important factor associated with the US opioid overdose crisis. Automotive assembly plant closures are culturally significant events that substantially erode local economic opportunities.

Objective: To estimate the extent to which automotive assembly plant closures were associated with increasing opioid overdose mortality rates among working-age adults.

Design, setting, and participants: A county-level difference-in-differences study was conducted among adults aged 18 to 65 years in 112 manufacturing counties located in 30 commuting zones (primarily in the US South and Midwest) with at least 1 operational automotive assembly plant as of 1999. The study analyzed county-level changes from January 1, 1999, to December 31, 2016, in age-adjusted, county-level opioid overdose mortality rates before vs after automotive assembly plant closures in manufacturing counties affected by plant closures compared with changes in manufacturing counties unaffected by plant closures. Data analyses were performed between April 1, 2018, and July 20, 2019.

Exposure: Closure of automotive assembly plants in the commuting zone of residence.

Main outcomes and measures: The primary outcome was the county-level age-adjusted opioid overdose mortality rate. Secondary outcomes included the overall drug overdose mortality rate and prescription vs illicit drug overdose mortality rates.

Results: During the study period, 29 manufacturing counties in 10 commuting zones were exposed to an automotive assembly plant closure, while 83 manufacturing counties in 20 commuting zones remained unexposed. Mean (SD) baseline opioid overdose rates per 100 000 were similar in exposed (0.9 [1.4]) and unexposed (1.0 [2.1]) counties. Automotive assembly plant closures were associated with statistically significant increases in opioid overdose mortality. Five years after a plant closure, mortality rates had increased by 8.6 opioid overdose deaths per 100 000 individuals (95% CI, 2.6-14.6; P = .006) in exposed counties compared with unexposed counties, an 85% higher increase relative to the mortality rate that would have been expected had exposed counties followed the same outcome trends as unexposed counties. In analyses stratified by age, sex, and race/ethnicity, the largest increases in opioid overdose mortality were observed among non-Hispanic white men aged 18 to 34 years (20.1 deaths per 100 000; 95% CI, 8.8-31.3; P = .001) and aged 35 to 65 years (12.8 deaths per 100 000; 95% CI, 5.7-20.0; P = .001). We observed similar patterns of prescription vs illicit drug overdose mortality. Estimates for opioid overdose mortality in nonmanufacturing counties were not statistically significant.

Conclusions and relevance: From 1999 to 2016, automotive assembly plant closures were associated with increases in opioid overdose mortality. These findings highlight the potential importance of eroding economic opportunity as a factor in the US opioid overdose crisis.

Figure 1.. Sample Counties and Geographic Distribution of Automotive Assembly Plant Closures

The 112 manufacturing counties that comprised the study sample were defined as those in which the percentages of employed residents working in manufacturing are in the top quintile nationwide. The 29 exposed manufacturing counties (Closure) were located in the 10 commuting zones in which an automotive assembly plant closure occurred between 1999 and 2016. The 83 unexposed manufacturing counties (No closure) were located in the 20 commuting zones in which automotive assembly plants in operation as of 1999 remained open throughout the duration of the study period.

Figure 2.. Unadjusted Trends and Adjusted Difference-in-Differences Estimates of the Association Between Automotive Assembly Plant Closures and Opioid Overdose Mortality Rates

A, Unadjusted trends in county-level age-adjusted opioid overdose mortality rates among adults aged 18 to 65 years, separately for counties exposed and unexposed to automotive assembly plant closures. B, Adjusted difference-in-differences estimates (ie, the absolute adjusted difference between exposed and unexposed counties) for the same outcome (with the shaded areas representing 95% CIs) are plotted. In both panels, the x-axis represents the number of years relative to a plant closure, with event years 5 years or more years before exposure and 7 years or more years after combined into a single time point. The sample consisted of 2016 county-year observations, representing 29 exposed and 83 unexposed counties in 30 commuting zones followed from 1999 to 2016.

Figure 3.. Difference-in-Differences Estimates of the Association Between Automotive Assembly Plant Closures and Prescription Opioid Overdose Mortality and Illicit Opioid Overdose Mortality

A, Prescription opioid overdose mortality. B, Illicit opioid overdose mortality. Models are identical to those presented in Figure 2B, except here the dependent variables are opioid overdose mortality per 100 000 individuals aged 18 to 65 years from prescription opioids and illicit opioids. See Figure 2 caption for further details.

Figure 4.. Difference-in-Difference Estimates for Opioid Overdose Mortality for Non-Hispanic White Adults, Stratified by Sex-Age Subgroups

A, White men aged 18 to 34 years. B, White men aged 35 to 65 years. C, White women aged 18 to 34 years. D, White women aged 35 to 65 years. Models are identical to those in Figure 2B except here the dependent variable is opioid overdose mortality for each listed sex-age subgroup among non-Hispanic white adults. See Figure 2 caption for further details.