key: cord-1010294-1hpu2js8
authors: Tilhou, Alyssa Shell; Dague, Laura; Saloner, Brendan; Beemon, Daniel; Burns, Marguerite
title: Trends in Engagement With Opioid Use Disorder Treatment Among Medicaid Beneficiaries During the COVID-19 Pandemic
date: 2022-03-11
journal: JAMA Health Forum
DOI: 10.1001/jamahealthforum.2022.0093
sha: 167c4b1fa3c8b266ec9d9f7e2b80d5db186d260a
doc_id: 1010294
cord_uid: 1hpu2js8

IMPORTANCE: Disruptions in care during the COVID-19 pandemic may have decreased access to care for patients with opioid use disorder. OBJECTIVE: To examine trends in opioid use disorder treatment including buprenorphine possession, urine drug testing, and opioid treatment program services during the COVID-19 public health emergency. DESIGN, SETTING, AND PARTICIPANTS: This cohort study included 6453 parent and childless adult Medicaid beneficiaries, aged 18 to 64 years, with opioid use disorder and continuous enrollment from December 1, 2018, to September 30, 2020, in Wisconsin. Logistic regression compared differences in study outcomes before, early, and later in the COVID-19 public health emergency. Analyses were conducted from January 2021 to October 2021. EXPOSURES: Early (March 16, 2020, to May 15, 2020) and later (May 16, 2020, to September 30, 2020) in the public health emergency. MAIN OUTCOMES AND MEASURES: Person-week outcomes included possession of buprenorphine, completion of outpatient urine drug testing, and receipt of opioid treatment program services. RESULTS: The final cohort of 6453 participants included 3986 (61.8%) childless adults; 5741 (89%) were younger than 50 years, 3435 (53.2%) were women, 5036 (78.0%) White, and 22.0% were racial and ethnic minority groups (American Indian, 269 [4.2%]; Asian, 26 [0.4%]; Black, 458 [7.1%]; Hispanic, 292 [4.5%]; Pacific Islander, 1 [.02%]; Multiracial, 238 [3.7%]). Overall, 2858 (44.3%), 5074 (78.6%), and 2928 (45.4%) received buprenorphine, urine drug testing, or opioid treatment program services during the study period, respectively. Probability of buprenorphine possession did not change in the early or later part of the public health emergency. Probability of urine drug testing initially decreased (marginal effect [ME], –0.04; 95% CI, –0.04 to –0.03; P < .001) and then partially recovered in the later public health emergency (ME, –0.02; 95% CI, –0.03 to –0.02; P < .001). Probability of opioid treatment program services followed a similar pattern, with an early decrease (ME, –0.05; 95% CI, –0.05 to –0.04; P < .001) followed by partial recovery (ME, –0.02; 95% CI, –0.03 to –0.02; P < .001). CONCLUSIONS AND RELEVANCE: In a sample of continuously enrolled adult Medicaid beneficiaries, the COVID-19 public health emergency was not associated with decreased probability of buprenorphine possession, but was associated with decreased probability of urine drug testing and opioid treatment program services. These findings suggest patients in office-based settings retained access to buprenorphine despite decreased on-site services like urine drug tests, whereas patients at opioid treatment programs experienced greater disruption in care. Given the importance of medications for opioid use disorder in preventing overdose, policy makers should consider permanent policy changes based on lessons learned from the public health emergency to enable ongoing enhanced access to these medications.

Office Based Opioid Treatment We defined this binary variable for each person-week. We defined treatment as a prescription medication claim for buprenorphine or buprenorphine/naloxone. We determined the days supplied using the start and end dates of each prescription, and aligned those days supplied to each week. The OBOT variable takes on a value of 1 if the days supplied is >=1 in the person-week and 0 otherwise. We followed the methods published by Medicaid Outcomes Distributed Research Network (MODRN) to address overlap in medications. 1 Urine Drug Test We defined this binary variable for each person-week. We adopted the code set used by the MODRN to identify urine drug tests (shown below). 1 "80100" "80101" "80102" "80103" "80104" "80299" "80300" "80301" "80302" "80303" "80304" "80305" "80306" "80307" "80320" "80321" "80322" "80323" "80324" "80325" "80326" "80327" "80328" "80329" "80330" "80331" "80332" "80333" "80334" "80335" "80336" "80337" "80338" "80339" "80340" "80341" "80342" "80343" "80344" "80345" "80346" "80347" "80348" "80349" "80350" "80351" "80352" "80353" "80354" "80355" "80356" "80357" "80358" "80359" "80360" "80361" "80362" "80363" "80364" "80365" "80366" "80367" "80368" "80369" "80370" "80371" "80372" "80373" "80374" "80375" "80376" "80377" "82660" "83925" "83992" "84311" "G0430" "G0431" "G0434" "G0477" "G0478" "G0479" "G0480" "G0481" "G0482" "G0483" "G0659" "G6045" "G6046" "G6053" "G6056" "G6058" "H0003" "H0048" "H0049" "Z2103" "Z2104" "Z2105" "Z2106"

We assigned a value of 1 to the UDT outcome variable if the UDT occurred in an outpatient setting and zero otherwise to increase the likelihood of capturing only UDTs associated with OBOT.

We used the following method to identify the UDT setting. We defined the setting to be an OTP if the subject had an outpatient visit where the billing provider was an OTP on the same day as the UDT. We defined the source to be an ED if the subject had an ED visit on the same day as the UDT. We defined the source to be inpatient if the subject was hospitalized on the date of the UDT. We defined the source to be outpatient if the subject had an outpatient visit for which the billing provider was not an OTP. We defined the source as unknown (and presumed outpatient) if on the date of the UDT, the subject did not have an OTP, ED, inpatient or outpatient encounter. If the person had a health care encounter at more than one setting on the date of the UDT, we assigned the setting in this order: OTP, inpatient, ED, outpatient.

Use of Medications for Treatment of Opioid Use Disorder Among US Medicaid Enrollees in 11 States

We divided each month into four quarters with the aim of having the same number of days per quarter within the month to the extent possible. We assigned days that could not be evenly divided across quarters within the month as shown below. Since month-quarters can vary between 7 and 8 days, we controlled for this variation in our models. Results did not change for any of the outcomes. Thus, our final models are unscaled.