California County Fact Sheets: Treatment Gaps in Opioid-Agonist Medication Assisted Therapy (OA-MAT) and Estimates of How Many Additional Prescribers Are Needed
Note: An earlier version of this analysis used a methodology that differs from the current analysis’s methodology in two ways. First, it used a broad estimate of opioid misuse as a proxy for opioid use disorder (OUD). Second, it estimated treatment needs assuming 20 percent of people with OUD seek treatment. The current analysis separates the rate of OUD from the rate of opioid misuse and assumes that all people with OUD seek treatment. These changes affect the estimated number of people with OUD, number of prescribers needed, and treatment gap per county. We have revised the content of this web page, along with the county fact sheets and the methodological appendix, to incorporate our clearer, more comprehensive estimates.
This analysis presents county-level estimates of opioid use disorder and treatment needs in California counties. Estimated rates of OUD, which we define as opioid abuse or dependence, are based on California, regional, and national estimates from the National Survey on Drug Use and Health. We estimate the demand for treatment in each county based on several data sources, assuming all people with OUD seek opioid-agonist treatment (i.e., buprenorphine or methadone). We estimate each county’s opioid-agonist treatment capacity based on data from the Drug Enforcement Administration and the state, as well as opioid treatment program data from the Substance Abuse and Mental Health Services Administration. Using a range of estimates of patients per waivered buprenorphine provider, we estimate the number of additional prescribers needed per county to fill the estimated treatment gap. We present strategies to meet demand for treatment, showing a range based on lower and upper estimates of the treatment gap and the treatment capacity. Fact sheets can be downloaded individually or as one document, and a separate methodological appendix describes the data, assumptions, and methodology.