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Background: Opioid overdose deaths in the United States remain a major public health crisis. Little is known about counties with high rates of opioid overdose mortality but low availability of opioid use disorder (OUD) treatment facilities. We sought to identify characteristics of United States (US) counties with high rates of opioid overdose mortality and low rates of opioid treatment facilities. Methods: Rates of overdose mortality from 3,130 US counties were compared with availability of opioid treatment facilities that prescribed or allowed medications for OUD (MOUD), from 2018-2019. The outcome variable, "risk-availability mismatch" county, was a binary indicator of a high rate (above national average) of opioid overdose mortality with a low (below national average) rate of opioid treatment facilities. Covariates of interest included county-level sociodemographics and rates of insurance, unemployment, educational attainment, poverty, urbanicity, opioid prescribing, depression, heart disease, Gini index, and Theil index. Multilevel logistic regression, accounting for the clustering of counties within states, was used to determine associations with being a "risk-availability mismatch" county. Results: Of 3,130 counties, 1,203 (38.4%) had high rates of opioid overdose mortality. A total of 1,098 counties (35.1%) lacked a publicly-available opioid treatment facility in 2019. In the adjusted model, counties with an additional 1% of: white residents (odds ratio, OR, 1.02; 95% CI, 1.01-1.03), unemployment (OR, 1.11; 95% CI, 1.05-1.19), and residents without insurance (OR, 1.04; 95% CI, 1.01-1.08) had increased odds of being a mismatch county. Counties that were metropolitan (versus non-metropolitan) had an increased odds of being a mismatch county (OR, 1.85; 95% CI, 1.45-2.38). Conclusion: Assessing mismatch between treatment availability and need provides useful information to characterize counties that require greater public health investment. Interventions to reduce overdose mortality are unlikely to be effective if they do not take into account diverse upstream factors, including sociodemographics, disease burden, and geographic context of communities. [ABSTRACT FROM AUTHOR]