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Deaths from opioid-use disorders (OUDs) have become a major drug issue in the United States. OUD deaths have risen by 430 percent since 1999; and today nearly 70 percent of drug overdose deaths involve opioids (CDC 2018). However, mortality has increased at a much faster pace in rural versus urban areas, increasing by 185 percent in large central metro areas, 693 percent in micropolitans, and 725 percent in noncore/rural areas (CDC 2017). Further, the crisis is in constant flux as the type of opioids have changed. The so-called first wave included prescription opioids starting in the late 1990s, followed by heroin second wave in 2010, and the current third wave that include synthetic opioids and mixes of all three drugs. Our analysis seeks to identify OUD mortality clusters by type of drug at the county level; and to describe the socioeconomic and spatial correlates of these clusters. Using restricted mortality data from NCHS-CDC from 1999 to 2016 for counties in the contiguous U.S., we employ latent profile analysis to class counties into unique OUD mixtures using Bayes posterior probabilities. We then model membership in these classes using multinomial logistic regression with socioeconomic correlates taken from secondary data sources. We identify several epidemic clusters (prescription opioids, heroin, and emerging heroin) and two syndemic clusters involving multiple opioids. Regression analysis suggests certain classes are driven by four trajectories: the rural left behind, polarized large cities, declining micropolitans, and suburban drug use.
David Peters, Iowa State University
Shannon Monnat, Syracuse University
Andy Hochstetler, Iowa State University