LAST YEAR was a woeful time for people suffering from a drug addiction. Government shutdowns brought job losses and social isolation—conditions that make a transportive high all the more enticing. Those who had previously used drugs with others did so alone; if they overdosed, no one was around to call for help or administer naloxone, a medication that reverses opioid overdoses.
Fatal overdoses were marching upwards before the pandemic. But they leapt in the first part of last year as states locked down, according to provisional data from the Centres for Disease Control and Prevention. Deaths from synthetic opioids—the biggest killer—were up by 52% year-on-year in the 12 months to August, the last month for which data are available. Those drugs killed nearly 52,000 Americans during the period; cocaine and heroin killed about 16,000 and 14,000, respectively (see chart). Once fatalities are fully tallied for 2020, in a few months’ time, it is likely to be the deadliest year yet in America’s opioid epidemic.
The dataset summarized in this article is a combination of several of U.S. federal data resources for the years 2006-2013, containing county-level variables for opioid pill volumes, demographics (e.g. age, race, ethnicity, income), insurance coverage, healthcare demand (e.g. inpatient and outpatient service utilization), healthcare infrastructure (e.g. number of hospital beds or hospices), and the supply of various types of healthcare providers (e.g. medical doctors, specialists, dentists, or nurse practitioners). We also include indicators for states which permitted opioid prescribing by nurse practitioners. This dataset was originally created to assist researchers in identifying which factors predict per capita opioid pill volume (PCPV) in a county, whether early state Medicaid expansions increased PCPV, and PCPV’s association with opioid-related mortality. Missing data were imputed using regression analysis and hot deck imputation. Non-imputed values are also reported.
Taken together, our data provide a new level of precision that may be leveraged by scholars, policymakers, or data journalists who are interested in studying the opioid epidemic. Researchers may use this dataset to identify patterns in opioid distribution over time and characteristics of counties or states which were disproportionately impacted by the epidemic. These data may also be joined with other sources to facilitate studies on the relationships between opioid pill volume and a wide variety of health, economic, and social outcomes.
Author(s): Kevin N. Griffith, Yevgeniy Feyman, Samantha G. Auty, Erika L. Crable, Timothy W. Levengood