This data visualization presents provisional counts for drug overdose deaths based on a current flow of mortality data in the National Vital Statistics System. Counts for the most recent final annual data are provided for comparison. National provisional counts include deaths occurring within the 50 states and the District of Columbia as of the date specified and may not include all deaths that occurred during a given time period. Provisional counts are often incomplete and causes of death may be pending investigation (see Technical notes) resulting in an underestimate relative to final counts. To address this, methods were developed to adjust provisional counts for reporting delays by generating a set of predicted provisional counts (see Technical notes).
From 2010 to 2019, the drug-related death rate among never-married prime-age white men increased some 125 percent: from 52 deaths per 100,000 to 117 (including 2020 would show an even steeper rise, but the pandemic affected Census data collection). If single and divorced prime-age white men had seen opioid deaths rise by only the same rate as those deaths rose among their married counterparts, the U.S. would have seen 38,800 fewer deaths from drug-related causes over the past decade just among this demographic group.
A marriage certificate is no prophylactic against the scourge of drug overdoses, of course. Marital status is correlated with income, race, and age; while death certificates don’t report income, we know that married decedents are more likely to be white, older, and better-educated. Controlling for those factors still shows single men to be at greater risk of dying from drug-related causes than married ones.
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