Rural and frontier access to mental health prescribers and nonprescribers: A geospatial analysis in Oregon Medicaid.

OBJECTIVE: Medicaid enrollees in rural and frontier areas face inadequate access to mental health services, but the extent to which access varies for different provider types is unknown. We assessed access to Medicaid-participating prescribing and nonprescribing mental health clinicians, focusing on Oregon, which has a substantial rural population. METHODS: Using 2018 Medicaid claims data, we identified enrollees aged 18-64 with psychiatric diagnoses and specialty mental health providers who billed Medicaid at least once during the study period. We measured both 30- and 60-minute drive time to a mental health provider, and a spatial access score derived from the enhanced 2-step floating catchment area (E2SFCA) approach at the level of Zip Code Tabulation Areas (ZCTAs). Results were stratified for prescribers and nonprescribers, across urban, rural, and frontier areas. RESULTS: Overall, a majority of ZCTAs (68.6%) had at least 1 mental health prescriber and nonprescriber within a 30-minute drive. E2SFCA measures demonstrated that while frontier ZCTAs had the lowest access to prescribers (84.3% in the lowest quintile of access) compared to other regions, some frontier ZCTAs had relatively high access to nonprescribers (34.3% in the third and fourth quartiles of access). CONCLUSIONS: Some frontier areas with relatively poor access to Medicaid-participating mental health prescribers demonstrated relatively high access to nonprescribers, suggesting reliance on nonprescribing clinicians for mental health care delivery amid rural workforce constraints. Efforts to monitor network adequacy should consider differential access to different provider types, and incorporate methods, such as E2SFCA, to better account for service demand and supply.

State Policy and the Breadth of Buprenorphine-Prescriber Networks in Medicaid Managed Care.

Provider networks in Medicaid Managed Care (MMC) play a crucial role in ensuring access to buprenorphine, a highly effective treatment for opioid use disorder. Using a difference-in-differences approach that compares network breadth across provider specialties and market segments within the same state, we investigated the association between three Medicaid policies and the breadth of MMC networks for buprenorphine prescribers: Medicaid expansion, substance use disorder (SUD) network adequacy criteria, and SUD carveouts. We found that both Medicaid expansion and SUD network adequacy criteria were associated with substantially increased breadth in buprenorphine-prescriber networks in MMC. In both cases, we found that the associations were largely driven by increases in the network breadth of primary care physician prescribers. Our findings suggest that Medicaid expansion and SUD network adequacy criteria may be effective strategies at states' disposal to improve access to buprenorphine.

Provider and Patient-panel Characteristics Associated With Initial Adoption and Sustained Prescribing of Medication for Opioid Use Disorder.

OBJECTIVES: Limited information is available regarding provider- and patient panel-level factors associated with primary care provider (PCP) adoption/prescribing of medication for opioid use disorder (MOUD). METHODS: We assessed a retrospective cohort from 2015 to 2018 within the Pennsylvania Medicaid Program. Participants included PCPs who were Medicaid providers, with no history of MOUD provision, and who treated ≥10 Medicaid enrollees annually. We assessed initial MOUD adoption, defined as an index buprenorphine/buprenorphine-naloxone or oral/extended release naltrexone fill and sustained prescribing, defined as ≥1 MOUD prescription(s) for 3 consecutive quarters from the PCP. Independent variables included provider- and patient panel-level characteristics. RESULTS: We identified 113 rural and 782 urban PCPs who engaged in initial adoption and 36 rural and 288 urban PCPs who engaged in sustained prescribing. Rural/urban PCPs who issued increasingly larger numbers of antidepressant and antipsychotic medication prescriptions had greater odds of initial adoption and sustained prescribing (P < 0.05) compared to those that did not prescribe these medications. Further, each additional patient out of 100 with opioid use disorder diagnosed before MOUD adoption increased the adjusted odds for initial adoption 2% to 4% (95% confidence interval [CI] = 1.01-1.08) and sustained prescribing by 4% to 7% (95% CI = 1.01-1.08). New Medicaid providers in rural areas were 2.52 (95% CI = 1.04-6.11) and in urban areas were 2.66 (95% CI = 1.94, 3.64) more likely to engage in initial MOUD adoption compared to established PCPs. CONCLUSIONS: MOUD prescribing adoption was concentrated among PCPs prescribing mental health medications, caring for those with OUD, and new Medicaid providers. These results should be leveraged to test/implement interventions targeting MOUD adoption among PCPs.

Patterns of clinic switching and continuity of medication for opioid use disorder in a Medicaid-enrolled population.

BACKGROUND: Many persons with opioid use disorder (OUD) initiate medication for opioid use disorder (MOUD) with one clinic and switch to another clinic during their course of treatment. These switches may occur for referrals or for unplanned reasons. It is unknown, however, what effect switching MOUD clinics has on continuity of MOUD treatment or on overdoses. OBJECTIVE: To examine patterns of switching MOUD clinics and its association with the proportion of days covered (PDC) by MOUD, and opioid-related overdose. DESIGN: Cross-sectional retrospective analysis of Pennsylvania Medicaid claims data. MAIN MEASURES: MOUD clinic switches (i.e., filling a MOUD prescription from a prescriber located in a different clinic than the previous prescriber), PDC, and opioid-related overdose. RESULTS: Among 14,107 enrollees, 43.2 % switched clinics for MOUD at least once during the 270 day period. In multivariate regression results, enrollees who were Non-Hispanic black (IRR = 1.43; 95 % CI = 1.24-1.65; p < 0.001), had previous methadone use (IRR = 1.32; 95 % CI = 1.13-1.55; p < 0.001), and a higher total number of office visits (IRR = 1.01; CI = 1.01-1.01; p < 0.001) had more switches. The number of clinic switches was positively associated with PDC (OR = 1.12; 95 % CI = 1.10-1.13). In secondary analyses, we found that switches for only one MOUD fill were associated with lower PDC (OR = 0.97; 95 % CI = 0.95-0.99), while switches for more than one MOUD fill were associated with higher PDC (OR = 1.40; 95 % CI = 1.36-1.44). We did not observe a relationship between opioid-related overdose and clinic switches. CONCLUSIONS: Lack of prescriber continuity for receiving MOUD may not be problematic as it is for other conditions, insofar as it is related to overdose and PDC.