Implementation Outcomes of Low Threshold Care for Persons with Opioid Use Disorders.

Abstract

Low-threshold care (LTC) practices for prescribing medication for opioid use disorder (MOUD) systematically remove treatment barriers, increasing access to lifesaving MOUD. Despite its promise, LTC operationalization is unclear and heterogeneous, and lacks standardized measures. To develop and test LTC composite measures as useful predictors of implementation outcomes. This prospective cohort study was embedded within a California state MOUD practice change collaborative involving safety-net primary care clinics. Data were collected at baseline, midpoint, and endpoint from 20 clinics. Clinics received a multifaceted implementation-support package designed to improve MOUD delivery. Four LTC scales (LTC12, LTC5, LTC3, LTC2) were developed and tested using team-reported one to five Likert items. Implementation outcomes included Reach (monthly new MOUD patients), Retention (monthly new MOUD patients engaged in treatment after initial diagnosis), and Adoption (active MOUD prescribers). Analyses included repeated-measures ANOVA for LTC change and Poisson GEE for incidence rate ratios, adjusting for panel size, medically underserved area designation, and time. Clinics showed significant improvements in LTC scores over time. The LTC12 scale demonstrated the largest effect size (d = 1.18, p = .003). A 1-point increase on the LTC3 index was associated with a 37% increase in new patients receiving MOUD (IRR = 1.37, 95% CI [1.01,1.86], p = .047). A 1-point increase on LTC2 was associated with a 24% increase (IRR = 1.24, 95% CI [1.01,1.53], p = 0.049). Our findings provide preliminary empirical support for a replicable measure of LTC in primary care settings. Longer scales showed greater internal consistency and sensitivity to change, while brief scales predicted patient reach outcomes. These measures may be useful for clinical programs to gauge the extent to which their MOUD services align with low threshold care principles and to guide quality improvement efforts. Future research should validate these scales in larger, diverse cohorts and test causal impact.