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2020 opioid use disorder and treatment needs. West Virginia
2020 opioid use disorder and treatment needs. West Virginia
- Collection:
- Health Policy and Services Research
- Author(s):
- Clemans-Cope, Lisa, author
Wissoker, Douglas A., author
Epstein, Marni, author - Contributor(s):
- Johns Hopkins University, issuing body.
Urban Institute, issuing body. - Publication:
- [Washington, DC] : Urban Institute, [2021]
- Language(s):
- English
- Format:
- Text
- Subject(s):
- Analgesics, Opioid
Opioid Epidemic -- statistics & numerical data
Opioid-Related Disorders
West Virginia
United States - Genre(s):
- Technical Report
- Abstract:
- Data sources. We used 2016–19 county drug overdose death data (for people ages 12 and older) from all drugs and from opioids from the CDC WONDER Multiple Cause of Death data. Data on languages spoken are from the Agency for Healthcare Research and Quality’s Social Determinants of Health database. Driving estimates show the estimated driving time from 2016 county population–weighted centroids (published by the Missouri Census Data Center) and DC ward centroids (estimated from DC block centroids from Open Data DC) to the nearest opioid treatment program (OTP) and buprenorphine-waivered prescriber from the Drug Enforcement Administration (DEA) Active Controlled Substances Act Registrants Database. Opioid use disorder (OUD) estimates. To estimate the demand for treatment, we calculated county OUD rates by averaging two estimates based on different methodological approaches. For the first, we started with substate estimates of past-year pain reliever use disorder (PUD) and heroin use for people 12 and older from the combined 2016 to 2018 National Survey on Drug Use and Health (NSDUH) substate data. We adjusted these estimates for recent trends and the share of people who have heroin use only but not PUD. We then used regression models to predict county-level rates as a function of explanatory variables that have an empirical relationship with OUD (Alzeer et al. 2017; Paulozzi et al. 2017). For the second OUD estimates, we multiplied the estimated NSDUH-based county estimates by a scalar representing the relationship between an NSDUH-based OUD rate, known to be biased downward, and a more accurate OUD rate based on a capture-recapture analysis of seven linked Massachusetts administrative databases (Barocas et al. 2018). We averaged these two estimates to compute county OUD rates and counts.
- Copyright:
- Reproduced with permission of the copyright holder. Further use of the material is subject to CC BY-NC-DC license. (More information)
- Extent:
- 1 online resource (1 PDF file (2 unnumbered pages))
- Illustrations:
- Illustrations
- NLM Unique ID:
- 9918368087606676 (See catalog record)
- Permanent Link:
- http://resource.nlm.nih.gov/9918368087606676