How to Prioritize County SUD Access Barriers by Payer
How can clinicians and program leaders use county-level payer patterns to identify where substance use disorder service access is most constrained?
Clinicians, health systems, and community programs often need to decide where to focus outreach, referral partnerships, and insurance-enrollment support for patients with substance use disorders. This guide applies to county-level service planning when local teams need a practical way to interpret whether low utilization may reflect insurance-related barriers rather than low need alone.
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Start with the county's overall SUD utilization level
Review the county's annual SUD health service utilization per 1,000 residents and compare it with the national county distribution reported in the study. Across 3,143 US counties, the mean was 5.2 per 1,000 residents; 23.3% of counties were below 3.0, 23.3% were 3.0 to 3.9, 25.3% were 4.0 to 5.9, 18.8% were 6.0 to 9.9, and 9.3% were 10.0 or higher. Treat very low-use counties as candidates for closer review of access and financing barriers.
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Compare local utilization with top- and bottom-quintile benchmarks
Use the study's top-versus-bottom 20% benchmarks to judge whether the county more closely resembles a high-use or low-use access environment. Overall SUD utilization was 10.4 per 1,000 residents in top-quintile counties versus 2.5 in bottom-quintile counties, a 4.2-fold difference. This helps frame whether local service use is markedly below the levels seen in higher-utilization counties.
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Check the county uninsured adult rate first
Prioritize the uninsured adult percentage as the first county-level access signal because it was the strongest correlate of lower SUD service utilization across models. In the overall model, uninsured rate alone accounted for 23.0% of the variance, and the adjusted association remained strongly negative in Model 4 with beta = -0.45. Top-use counties had 9.3% uninsured adults versus 19.7% in bottom-use counties.
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Interpret payer-specific utilization patterns
Examine whether local SUD service use is occurring mainly through self-pay, Medicaid, or private insurance, because the county patterns differed substantially by payer. The average annual county-level rates were 8.6 per 1,000 residents for self-pay, 6.9 per 1,000 Medicaid enrollees for Medicaid, and 2.8 per 1,000 privately insured residents for private insurance. Self-pay represented 43.0% of SUD utilizations but only 5.1% of expenditures, while Medicaid funded 52.7% of SUD expenditures and accounted for 37.7% of utilizations.
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Use unemployment to refine payer-specific concern
Interpret county unemployment as a payer-specific modifier rather than a uniform risk marker. Higher unemployment was positively associated with SUD utilization overall, for self-pay, and for Medicaid, with Model 4 beta values of 0.20, 0.20, and 0.25, respectively. For private insurance utilization, unemployment was negatively associated, with Model 4 beta = -0.12.
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Do not over-rely on health care resource counts alone
Avoid assuming that facility and workforce measures explain most county variation in SUD service use. After adjustment, health care resource indicators explained only an additional 2.3% of the variance overall, 2.0% for self-pay, 2.4% for Medicaid, and 0.6% for private insurance. In this study, insurance-related county characteristics were more influential than the measured resource indicators.
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Prioritize insurance-linked access interventions in low-use counties
When a county has low SUD utilization and a high uninsured rate, prioritize insurance enrollment, coverage stabilization, and referral pathways that reduce out-of-pocket dependence. The authors conclude that their findings quantify the critical role of health insurance in supporting access to and utilization of SUD services. This is especially relevant because self-pay accounted for a large share of utilization and out-of-pocket costs have been linked in the article's discussion to worse access, engagement, retention, and outcomes.
Clinical Considerations
- The study used county-level rather than individual-level data, so findings should not be applied as if they prove what is happening for any single patient.
- The analyses adjusted for county-level overdose mortality but could not adjust for county-level SUD prevalence, so low-utilization counties were not necessarily low-need counties.
- The study examined selected payers and could not assess other funding sources such as the Substance Abuse Prevention and Treatment Block Grant.
- The utilization data were from 2017 to 2019, so local service patterns may have changed with later telemedicine growth and financing changes.
Bottom Line
When county SUD service use is low, the uninsured adult rate is the most important county-level signal in this study to prioritize insurance-focused access strategies, more than measured health care resource counts alone.
