key: cord-0943017-r1cmth80
authors: Hughes, Phillip M.; Verrastro, Genevieve; Fusco, Carriedelle Wilson; Wilson, Courtenay Gilmore; Ostrach, Bayla
title: An examination of telehealth policy impacts on initial rural opioid use disorder treatment patterns during the COVID‐19 pandemic
date: 2021-03-15
journal: J Rural Health
DOI: 10.1111/jrh.12570
sha: 1348734635f0c13ab4f8914fe2cf4003ea473bc3
doc_id: 943017
cord_uid: r1cmth80

PURPOSE: Tracking changes in care utilization of medication for opioid use disorder (MOUD) services before, during, and after COVID‐19‐associated changes in policy and service delivery in a mixed rural and micropolitan setting. METHODS: Using a retrospective, open‐cohort design, we examined visit data of MOUD patients at a family medicine clinic across three identified periods: pre‐COVID, COVID transition, and COVID. Outcome measures include the number and type of visits (in‐person or telehealth), the number of new patients entering treatment, and the number of urine drug screens performed. Distance from patient residence to clinic was calculated to assess access to care in rural areas. Goodness‐of‐Fit Chi‐Square tests and ANOVAs were used to identify differences between time periods. FINDINGS: Total MOUD visits increased during COVID (436 pre vs. 581 post, p < 0.001), while overall new patient visits remained constant (33 pre vs. 29 post, p = 0.755). The clinic's overall catchment area increased in size, with new patients coming primarily from rural areas. Length of time between urine drug screens increased (21.1 days pre vs. 43.5 days post, p < 0.001). CONCLUSIONS: The patterns of MOUD care utilization during this period demonstrate the effectiveness of telehealth in this area. Policy changes allowing for MOUD to be delivered via telehealth, waiving the need for in‐person initiation of MOUD, and increased Medicaid compensation for MOUD may play a valuable role in improving access to MOUD during the COVID‐19 pandemic and beyond.

treatment to avoid withdrawal and increased overdose risk if the availability of nonmedical opioids is disrupted. 8 In anticipation of this increased demand, the Drug Enforcement Agency (DEA) and Substance Abuse and Mental Health Service Administration (SAMHSA) altered the guidelines for providing MOUD early in the pandemic.

These changes allowed buprenorphine induction via telehealth (including by phone) and waived the Ryan Haight Act's in-person visit requirement for prescribing controlled substances. 9 Crucially, compensation for telehealth services via Medicaid was also expanded to match the rates for in-person visits, making telehealth a viable alternative for providers. 10 Based on the projected increase in demand for MOUD services during COVID and the policy changes to allow MOUD to be administered via telehealth, we anticipate increases in the number of total visits and the number of new patients seen during COVID. Further, expanded availability of telehealth for treating OUD may increase access to these services, especially in remote or rural areas where providers with the required credentials, such as the DEA "X"-waiver, have historically been scarce. [11] [12] [13] [14] As such, we expect to see an increase in the use of MOUD services by patients from rural areas after the COVID-related changes; however, it is also known that Internet and mobile phone service coverage are each more limited in rural areas, making it unclear to what extent people in rural areas who could benefit from telehealthprovided MOUD will be able to access this modality. 15 In this study, we aim to explore changes associated with the telehealth policy change in terms of the patient population seen for MOUD, access to MOUD services for rural patients, overall MOUD clinic volume, and changes to the provision of MOUD.

We extracted data for patient visits from the electronic health records system at a single family medicine clinic with a high concentration of providers that offer office-based opioid treatment (OBOT) services in a primarily rural and micropolitan region with a high overdose rate in the Appalachian Mountains. Patients were included if they had ever been prescribed a buprenorphine-containing medication and had an ICD-10 diagnosis code for OUD (F11. 20 16 Data from November and December of 2019 were used to identify those patients who were established in treatment prior to the start of our pre-COVID period in order for us to appropri-ately identify new patients entering OBOT. This study was approved by the Mission Health Institutional Review Board.

We collected patient demographics, including age, race, sex, i ZIP Code of residence, and insurance type. For each visit, we extracted the date of the visit, type of visit (in-person, lab, or telehealth), and whether or not a urine drug screen (UDS) was performed. We also obtained the total number of visits that the entire family medicine clinic had during these three time periods. Several additional variables were calculated from the extracted data. First, the distance that patients traveled to reach the clinic was estimated based on the distance between the cen- Finally, the Rural-Urban Continuum Codes (RUCC) produced by the US Department of Agriculture were used to classify ZIP Codes as rural if they had a nonmetro RUCC code (4 or more). 17

We used SAS v9.4 (SAS Institute Inc., Cary, NC) for all data cleaning and analysis. Goodness of fit Chi-square tests and ANOVAs were used to evaluate changes in the proportions and averages, respectively, of outcomes across all three time periods. For goodness of fit Chisquare tests, the expected proportions were assumed to be 40% for pre-COVID and COVID, and 20% for the transition phase based on their respective amount of study time (2, 1, and 2 months, respectively).

Prior administrative data for the clinic from 2018 and 2019 closely followed this 40/20/40 distribution from January to June, suggesting that this distribution was appropriate and neither cyclic trends nor seasonality were of concern. Maps were made in ArcGIS Desktop version 10.7.1 (Esri, Redlands, CA) for the purpose of visualizing catchment area changes between time periods.

Over the 5-month study period, a total of 242 patients had at least one visit for OBOT. Of those, 196 were seen pre-COVID, 171 during the 1-month transition phase, and 221 post-policy change. A higher percentage of the patients had a visit during the transition phase than would be expected based on the proportion of time (29.1%, p < 0.001).

The patients had a mean age of 37.5 (SD = 11.1), 57.0% were females 

There were not significant differences in the average distance from the clinic per patient or by visit type between time periods (Table 1) 

The implementation of telemedicine and reduction in face-to-face visits being scheduled during the transition and COVID phases resulted in a significant shift in visit locations from office to telemedicine (p < 0.001). During the COVID period, there were also more lab visits (n = 13) scheduled than during the transition (n = 1) or pre-COVID (n = 3; p < 0.001). For a full examination of visit types over time, see The MOUD policy changes made in response to the COVID-19

pandemic have been vocally championed by substance use and harm reduction experts as a step toward much-needed policy reform, especially with regard to administering MOUD via telehealth. [18] [19] [20] The patterns of MOUD care utilization we observed during this 

i Assigned sex at birth (ASAB) as indicated in the electronic health record (EHR); this practice allows providers to identify a patient's correct sex and/or gender manually through an override process; however, by and large, a data pull will reflect ASAB. We acknowledge this limitation of the EHR likely results in an under-reflection of noncisgender OUD patients-an important consideration as LGBTQ populations are at increased structural risk for SUD and overdose, particularly in the South.

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An examination of telehealth policy impacts on initial rural opioid use disorder treatment patterns during the COVID-19 pandemic