naloxone among people initiating opioid agonist treatment in British Columbia

Journal Pre-proof Determinants of selection into buprenorphine/naloxone among people initiating opioid agonist treatment in British Columbia F. Homayra, N. Hongdilokkul, M. Piske, L.A. Pearce, C. Zhou, J.E. Min, E. Krebs, B. Nosyk

PII:

S0376-8716(19)30575-7

DOI:

https://doi.org/10.1016/j.drugalcdep.2019.107798

Reference:

DAD 107798

To appear in:

Drug and Alcohol Dependence

Received Date:

20 August 2019

Revised Date:

26 November 2019

Accepted Date:

27 November 2019

Please cite this article as: Homayra F, Hongdilokkul N, Piske M, Pearce LA, Zhou C, Min JE, Krebs E, Nosyk B, Determinants of selection into buprenorphine/naloxone among people initiating opioid agonist treatment in British Columbia, Drug and Alcohol Dependence (2019), doi: https://doi.org/10.1016/j.drugalcdep.2019.107798

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.

Working title: Determinants of selection into buprenorphine/naloxone among people initiating opioid agonist treatment in British Columbia Authors: Homayra F[1], Hongdilokkul N[1], Piske M[1], Pearce LA[1], Zhou C[1], Min JE[1], Krebs E [1], Nosyk B[1,2]

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1. BC Centre for Excellence in HIV/AIDS, St. Paul’s Hospital 613-1081 Burrard Street Vancouver, BC, Canada V6Z 1Y6 2. Faculty of Health Sciences, Simon Fraser University Blusson Hall, Room 9706 8888 University Drive Burnaby, BC, Canada, V5A 1S6

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Bohdan Nosyk, PhD BC Centre for Excellence in HIV/AIDS St. Paul’s Hospital 613-1081 Burrard St. Vancouver, BC, Canada V6Z 1Y6 E: [email protected] T: 604-806-8649 F: 604-806-8464

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Corresponding Author:

Word Count: 3988/4000 (excluding references)

Figures: 1 Appendix Tables: 7

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Tables: 4

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Running Title: Determinants of selection into buprenorphine/naloxone

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Disclaimer: All inferences, opinions, and conclusions drawn in this study are those of the authors, and do not reflect the opinions or policies of the Data Steward

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Highlights    

Individual and provider factors influence buprenorphine/naloxone selection Provider-related differences accounted for 48.8% of selection variance Individual-related differences accounted for 28.2% of selection variance Physician prescribing preferences strongly associated with the selection

Keywords: opioid agonist treatment; buprenorphine/naloxone; methadone; administrative data; population-level; instrumental variables

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Abstract [250/250 words]

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Background: Studies assessing the comparative effectiveness of methadone versus buprenorphine/naloxone for opioid use disorder in real-world settings are rare - challenged by structural differences in delivery across settings and factors influencing treatment selection. We identified determinants of selection into buprenorphine/naloxone and quantified contributions of individual and provider-level covariates in a setting delivering both medications within the same healthcare settings.

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Methods: Utilizing linked health administrative datasets, we conducted a retrospective cohort study of people with opioid use disorder (PWOUD) receiving opioid agonist treatment (OAT) in British Columbia, Canada, from 2008-2017. Determinants of buprenorphine/naloxone selection were identified using a generalized linear mixed model with random intercept terms for providers and individuals. We determined the influence of individual demographics, clinical history, measures of provider experience and preference, and dates of key policy changes.

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Results: A total of 39,605 individuals experienced 178,976 OAT episodes (methadone:139,439(77.9%);buprenorphine/naloxone:39,537(22.1%)). Male sex, less OAT experience, younger age, mental health conditions and chronic pain were associated with higher odds of buprenorphine/naloxone prescription. For providers, higher client-attachment, more complex OAT case-mixes, and higher buprenorphine/naloxone prescribing-preference were also associated with higher odds of buprenorphine/naloxone prescription. Observed individual-level covariates explained 9.7% of variance in odds of buprenorphine/naloxone selection, while observed provider-level covariates explained 20.0%. Controlling for covariates, residual unmeasured between-individual variance accounted for 18.5% of the explained variation in the odds of buprenorphine/naloxone selection, while unmeasured between-provider variance accounted for 28.4%. Conclusion: Provider characteristics were more influential in selection of buprenorphine/naloxone over methadone informing subsequent analyses of comparative effectiveness of these regimens.

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1.0 Introduction Engagement and sustained retention in opioid agonist treatment (OAT) has been a key focus of public health efforts to reduce the risk of mortality for people with opioid use disorder (PWOUD) in both the United States and Canada (National Institutes of Health, 2018; Nosyk et al., 2013; Stotts et al., 2009). However, notable differences in clinical guidelines and delivery of these medications exist across jurisdictions. In the United States, methadone can only be prescribed in opioid treatment programs, while buprenorphine/naloxone is mainly prescribed in office-based settings (Blanco and Volkow, 2019). Insurance coverage also determines OAT medication

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selection in the United States (Blanco and Volkow, 2019). These restrictions, in contrast, are not applicable in British Columbia (BC), Canada, due to its single-payer health system and availability

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of both methadone and buprenorphine/naloxone in office-based settings. Current BC clinical practice guidelines (British Columbia Centre on Substance Use, 2017c) recommend buprenorphine/naloxone as the first-line OAT medication and methadone, formerly the preferred

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first-line OAT in BC, as a second-line treatment with no explicit restrictions in OAT medication selection. These recommendations were later adopted in Canada’s national clinical practice

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guideline (Bruneau et al., 2018). In contrast, American Society of Addiction Medicine (ASAM) and Substance Abuse and Mental Health Services Administration (SAMHSA) guidelines do not explicitly endorse either medication as a preferred first-line treatment (American Society of

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Addiction Medicine, 2015; Center for Substance Abuse, 2005). Differences in BC and US (ASAM and SAMHSA) clinical guidelines for treatment of opioid use

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disorder (OUD) have largely resulted from a lack of consensus in the trade-offs between desired treatment outcomes (safety vs. retention) stemming from a lack of real-world evidence on the comparative effectiveness of OAT medications. In a population-based, observational study from 2009, buprenorphine/naloxone was found to have a superior safety profile, with the risk of overdose death six times lower compared to methadone (Marteau et al., 2015). In terms of retention, results from a systematic review of randomized controlled trials (RCTs) concluded that

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methadone demonstrated superior retention when dosing was flexible (RR=0.83; 95% CI: 0.73, 0.95) (Mattick et al., 2014). Nevertheless, these differences were not apparent in clinical trials when dosages were fixed at medium (RR=0.87; 95% CI: 0.69, 1.10); and high doses (RR=0.79; 95%; CI: 0.20, 3.16), and there was no difference between buprenorphine/naloxone and methadone according to urine drug testing and self-reported heroin use (Mattick et al., 2014). Additionally, methadone does not appear to be superior to buprenorphine/naloxone in terms of OAT retention and opioid use in clinical trials among individuals with prescription opioid

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dependence (Holbrook, 2015; Potter et al., 2013). However, heterogeneity across RCTs in participant selection, dosing protocols, observation times, and methodologies used to measure retention limit applicability to key populations in other contexts. Further complicating this comparison is a range of complex healthcare needs of PWOUD, who commonly present to treatment with concurrent mental health conditions, infectious diseases, pain, and other substance use disorders, in addition to the influence of extra-individual factors such as the site of treatment provision (Bell et al., 2009). Medication selection for PWOUD should involve both individual and provider and carefully

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consider which treatment option is most likely to provide the greatest benefits to the individual (Blanco and Volkow, 2019). Generating high-quality evidence on the comparative effectiveness

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of available treatment options in observational settings and across subgroups of individuals is therefore a key priority to inform clinical guidance for new and existing forms of OAT. This priority

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has taken on a greater sense of urgency during an era of elevated risk of mortality due to the contamination of the illicit drug supply with fentanyl and its analogues in many settings across North America, including BC. Despite high internal validity, RCTs examining the causal effect of

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OAT medications on retention cannot determine the effects across subpopulations due to limited numbers of participants and restrictive inclusion criteria. In addition to individuals’ preferences

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and responses to previous treatment, understanding individual characteristics associated with increased retention in treatment can help inform personalized treatment for each individual (Blanco and Volkow, 2019).

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Studies of comparative effectiveness using real-world data, where selection of OAT medication is not randomized, require advanced statistical techniques to address selection bias and confounding. High-dimensional propensity scores, for example, reduce unmeasured confounding by identifying a set of covariates that are highly predictive of the probability of receiving one form of OAT over another (Schneeweiss et al., 2009). An alternative approach, instrumental variable

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(IV) (Petitjean et al.) analysis, requires an instrument – a variable or combination of variables that satisfy three conditions: (1) the instrument is associated with the medication selection, (2) the instrument does not affect OAT retention except through medication selection, and (3) the instrument does not share any causes with OAT retention (Swanson and Hernan, 2013). These conditions are potentially satisfied by measures capturing provider prescribing preference. Physicians' previous prescriptions have been used as an instrument in comparative effectiveness studies in other disease areas (Boef et al., 2016; Brookhart et al., 2006; Davies et al., 2013a; Davies et al., 2013b; Joffe, 2000; Nelson et al., 2013; Rassen et al., 2009; Wang et al., 2005) to 4

indirectly control for potential selection bias (Davies et al., 2013a; Davies et al., 2013b; Hernan and Robins, 2006). Both techniques require the identification of individual-level and provider-level determinants of selection into competing forms of OAT, particularly provider OAT prescribing preference, which has yet to be investigated (Blanco and Volkow, 2019). To inform future comparative effectiveness research, our primary objective was to identify determinants of selection into buprenorphine/naloxone within the population of OAT recipients in British Columbia, Canada, following the introduction of buprenorphine/naloxone in 2008. We further quantified the relative contribution of individual-level, provider-level, and policy change

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variables in the treatment selection decision and assessed the suitability of provider prescribing

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preference as an IV.

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2.0 Methods 2.1 Study setting In BC, all residents are eligible for single-payer insurance plans for medical service (Medical Service

Plan:

MSP)

and

prescription

drugs

(PharmaCare).

Methadone

or

buprenorphine/naloxone can be prescribed by any physician or nurse practitioners and dispensed by community-based pharmacies (College of Pharmacists of British Columbia, 2018). Physicians and pharmacists are required to receive College of Physicians and Surgeons of British Columbia authorization for prescribing and dispensing methadone; however, this authorization is not

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required for buprenorphine/naloxone prescription since July 2016 (College of Physicians and Surgeons of British Columbia, 2016). Buprenorphine/naloxone was included as a PharmaCare

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regular benefit as of 13 October 2015 (Government of British Columbia, 2015). PharmaCare covers methadone and buprenorphine/naloxone under the income-based Fair PharmaCare plan.

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PharmaCare provides full coverage for OAT to BC residents receiving income assistance (PharmaCare Plan C), those who demonstrate clinical and financial need (confirmed by the

(Government of British Columbia, 2019).

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2.2 Data and Cohort definition

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physician or nurse practitioner), and are registered residents under First Nations Health Benefits

This study was based on a provincial-level linkage of four health administrative databases: BC PharmaNet (capturing drug dispensations), the Discharge Abstract Database (DAD; records of hospitalizations), Medical Services Plan (MSP; physician billing records under BC public health

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insurance), and BC Vital Statistics (capturing deaths and their underlying causes). Data linkage was performed based on a unique provincial health number recorded in all databases. Each of the component databases included in our analyses is described in further detail in Table A1. We identified all individuals with at least one OAT medication dispensation in PharmaNet from 1 2008

to

30

November

2017,

corresponding

to

the

period

in

which

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January

buprenorphine/naloxone was available in BC. The type of OAT medication prescribed was determined using the Drug Identification Number (DIN) in PharmaNet (Table A2). 2.3 Variables

Our outcome variable of interest was an indicator of initiating an OAT episode with buprenorphine/naloxone on the episode start date, as opposed to initiation with methadone. Slowrelease oral morphine and injectable forms of OAT were introduced in BC towards the end of

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study follow-up (Socías et al., 2018) and in limited quantity. Therefore, episodes initiated with these alternative forms of medication were excluded from this analysis. According to provincial guidelines, if prescribed doses are missed consecutively for 5 days for methadone and 6 days for buprenorphine/naloxone upon an in-person reassessment, individuals have to be restarted on initial dose (British Columbia Centre on Substance Use, 2017c). Therefore, we summarized OAT episodes, defined as continuous periods of dispensed medication without interruptions in prescribed doses ≥5 days for methadone and ≥6 days for buprenorphine/naloxone. We adjusted for hospitalization in OAT episode construction, combining OAT dispensations and

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hospitalizations with gaps less than the threshold into one OAT episode, based on the assumption that those who were on OAT at the time of hospitalization continued treatment while in hospital.

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OAT episodes ongoing at the end of follow-up (30 November 2017) were censored.

We categorized covariates into three groups: individual characteristics, provider characteristics,

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and indicators of OAT-related policy changes. We identified individual- and provider-level covariates which may be associated with treatment selection according to prior observational studies (Boef et al., 2016; Brookhart et al., 2006; Davies et al., 2013a; Davies et al., 2013b; Joffe,

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2000; Knudsen et al., 2005; Murphy et al., 2014; Nelson et al., 2013; Rassen et al., 2009; Ridge et al., 2009; Rieckmann et al., 2011; Wang et al., 2005; Werb et al., 2008). Individual

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characteristics included sex, age at OAT episode start date (<24 years, 25-34 years, 35-44 years, and >44 years), health authority of residence (HA; geographic healthcare delivery region), receipt of income assistance (enrollment in PharmaCare plan C), OUD-related comorbidities (human

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immunodeficiency virus (HIV), hepatitis C virus (HCV), mental health conditions (MH), substance use disorder (SUD) other than OUD, alcohol use disorder (Petitjean et al.) (Petitjean et al.), and chronic pain, prior receipt of buprenorphine/naloxone, and cumulative time on OAT prior to the episode start date (no prior OAT, 0-24 months, ≥24 months). An individual could be assigned to different providers in different episodes but could only be assigned to one provider in each episode

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at the episode start date (provider on the date of OAT episode initiation). Provider characteristics included the years of OAT prescribing experience (measured as years since first OAT prescription) and variables measured based on the records during the 12 month period prior to each individual’s OAT episode initiation, including the number of clients on OAT, average Chronic Disease Score (CDS) (Clark DO et al., 1995) of clients on OAT, percentage of clients retained in OAT at least 12 months, the percentage of clients receiving the recommended minimum

effective

OAT

dose

for

at

least

four

weeks

(methadone:

60

mg/day,

buprenorphine/naloxone: 12 mg/day) (British Columbia Centre on Substance Use, 2017c), and 7

provider-individual attachment, calculated as the percentage of an individual’s billing records attributed to the OAT prescribing provider among all physician billing records (Parent et al., 2018). Our provider characteristics also included provider prescribing preference, used as an IV in previous comparative effectiveness studies (Boef et al., 2016; Brookhart et al., 2006; Davies et al., 2013a; Davies et al., 2013b; Joffe, 2000; Nelson et al., 2013; Rassen et al., 2009; Wang et al., 2005) to mitigate selection bias (Joffe, 2000). Physicians’ previous prescriptions has been used as an instrument and empirically shown to satisfy all three instrumental conditions (Davies et al., 2013b). Traditionally, physicians’ most recent prescription has been used as an instrument,

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but may inflate the variance of IV estimates from dichotomization and noise from a single previous individual’s characteristics (Abrahamowicz et al., 2011; Davies et al., 2013b). Based on previous

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studies, we assessed alternative IVs, including whether the provider had ever written a new buprenorphine/naloxone prescription to a client; the time since the provider had written their last

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buprenorphine/naloxone prescription to a new client (Boef et al., 2016); and the percentage of new buprenorphine/naloxone prescriptions among all new OAT episodes they had initiated in the

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past 12 months (Brookhart et al., 2006; Davies et al., 2013b).

Finally, we included indicators of OAT episode initiation after OAT-related policy changes, including the date that buprenorphine/naloxone was included in PharmaCare as a regular benefit

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(13 October 2015) (Government of British Columbia, 2015); the provincial declaration of the opioid overdose public health emergency (14 April 2016) (Government of British Columbia, 2016); the exemption of authorization for prescribing buprenorphine/naloxone (1 July 2016) (College of

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Physicians and Surgeons of British Columbia, 2016); and buprenorphine/naloxone becoming the preferred first-line OAT medication in BC (5 June 2017) (British Columbia Centre on Substance Use, 2017c).

2.4 Statistical Analyses

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We fit a generalized linear mixed model with logit link function (McCulloch et al., 2008) to identify the determinants of selection of buprenorphine/naloxone at individuals’ first and subsequent OAT episodes. Using the complete sample of buprenorphine/naloxone and methadone episodes initiated between 1 January 2008 and 30 November 2017, we fit a three-level logistic regression model with random intercept terms for providers and individuals (model 1). We assumed a crossclassified model where a provider may have multiple individuals and vice versa. We ran two additional, restricted models to assess the robustness of our results. First, we restricted our sample to the OAT episodes initiated after buprenorphine/naloxone became the 8

preferred first-line OAT medication according to the provincial guidelines in BC (5 June 2017; model 2). Second, we restricted the sample to include only individuals’ first OAT episodes (model 3) using a two-level logistic model with a random intercept for providers. We further quantified the relative contribution of individual characteristics, provider characteristics and OAT-related policy changes by fitting a null model with no covariates and incorporated individual- and provider-specific random effects, capturing unmeasured confounding that is fixed in time. We then compared McKelvey and Zavoina pseudo R-square values (RMZ) (Hox, 2010) with models including only individual characteristics, only provider characteristics and only OAT-

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related policy change indicators, respectively. RMZ indicates the proportion of variance explained by the covariates and are lower than adjusted R-square values. An RMZ of between 0.2 and 0.4

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indicates a good prediction (Hox, 2010).

Sources of variance in treatment selection include measured covariates, unmeasured provider-

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and individual-level differences, and other independent random errors. By determining the relative contribution of the unmeasured between-provider and between-individual variance that persist

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after adjusting for all measured covariates, we estimated the effect of the unmeasured providerand individual-level differences on selection into buprenorphine/naloxone. To quantify the relative contributions of each, we used the variance partition coefficient (VPC) for hierarchical structures

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(Austin and Merlo, 2017), representing the proportion of the total observed individual variation in the underlying propensity of the outcome attributable to between-provider and between-individual variation (Austin and Merlo, 2017). The parameters of interest were the variance of the provider-

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and individual-level random effects. We presented the scale-corrected proportional change (Hox, 2010) in the random effect variance in the fitted model compared to the null model to examine whether the respective random effect variance decreased after adjusting for the corresponding covariates.

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All analyses were conducted using SAS enterprise guide 7.1 and R version 3.5.0.

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3.0 Results Among 39,605 individuals who received OAT during the study period, 7,164 (18.1%) received only buprenorphine/naloxone, 24,293 (61.3%) received only methadone, and 8,148 (20.6%) received both medications for a total of 178,976 unique OAT episodes (buprenorphine/naloxone: 39,537; methadone: 139,439). Among 1,592 unique OAT providers, 462 (29.0%) initiated individuals on methadone only, 496 (31.2%) to buprenorphine/naloxone only, and 634 (39.2%) to both medications.

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The majority of OAT episodes were experienced among males diagnosed with SUD, MH, or chronic pain (Table 1). More than 34% of all OAT episodes were among individuals 25 to 34 years old. Compared to buprenorphine/naloxone episodes, methadone episodes were more likely to be

and those receiving income assistance (38.7% vs. 53.9%).

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received by individuals with more than 24 months (22.4% vs 44.1%) of cumulative OAT exposure

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For both medication types, it was most common for providers to have 10 or more years of experience prescribing OAT (buprenorphine/naloxone:45.4%, methadone:50.7%), to have low

methadone:66.4%),

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individual attachment in the past 12 months (<25% attachment; buprenorphine/naloxone:73.3%, modest

12-month

OAT

retention

rates

(25%-50%;

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buprenorphine/naloxone:49.7%, methadone: 44.5%), and high percentages of clients receiving the minimum effective dose in the past 12 months (50%-75%; buprenorphine/naloxone:71.7%, methadone:69.8%) (Table 2). Most buprenorphine/naloxone episodes (58.7%) were initiated by providers who initiated a new client on buprenorphine/naloxone within the past week, while 42.2% methadone

episodes

were

initiated

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by

providers

who

did

not

write

a

new

buprenorphine/naloxone prescription to a new client in their practice in the past 3 months. Most methadone episodes (80.4%) were initiated by providers having low preference (<25%) for prescribing buprenorphine/naloxone to new clients in the past 12 months. In contrast, 39.0% of buprenorphine/naloxone episodes were initiated by providers having moderate preference (25-

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50%) for prescribing buprenorphine/naloxone to new clients in the past 12 months.

Table 3 and Figure 1 provide results of the generalized linear mixed effect models used to identify determinants of selection into buprenorphine/naloxone. In the full model (model 1; all OAT episodes during follow-up), younger age, male sex, and indications of mental health conditions and chronic pain were all associated with higher odds of buprenorphine/naloxone prescription. Individuals having less OAT experience (no prior OAT, adjusted odds ratio=4.54 [4.20, 4.91]; 024 months OAT: 1.97 [1.85, 2.10]; reference >24 months OAT) were associated with increased 10

odds of receiving buprenorphine/naloxone, while receiving income assistance was associated with decreased odds of receiving buprenorphine/naloxone (0.48 [0.45,0.51]). Providers who previously prescribed buprenorphine/naloxone prescription to new clients (6.63 [5.62, 7.82]), demonstrated higher preference for prescribing buprenorphine/naloxone to new clients in the past 12 months (25-50%: 2.15 [2.02, 2.29]; >50%: 4.85 [4.36, 5.40]; reference 0-25%). Providers with more experience treating individuals with complex comorbidities, and with higher attachment with the client were associated with higher odds of buprenorphine/naloxone prescription. In contrast, community health care providers, providers with larger client load and high percentage of clients

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receiving the minimum effective dose, and no recent new buprenorphine/naloxone prescription were associated with lower odds of buprenorphine/naloxone prescription. we

restricted

the

analysis

to

include

only

OAT

episodes

initiated

after

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When

buprenorphine/naloxone became the preferred first-line OAT medication in BC (5 June 2017;

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model 2) we did not observe an association between age or chronic pain and receipt of buprenorphine/naloxone. In contrast to the full model, providers with less experience were associated with higher odds of prescribing buprenorphine/naloxone (<1 year of experience: 4.38

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[2.68, 7.15], reference >10 years of experience), and providers with more experience in treating individuals with complex comorbidities (0.81 [0.67, 0.97]) were associated with lower odds of

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buprenorphine/naloxone prescription. In model 3, in which the sample was restricted to individuals’ first OAT episode only, results were similar to that of the full model. Table 4 provides results from variance decomposition analysis of the multilevel logistic regression

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model to identify determinants of selection into buprenorphine/naloxone compared to methadone. The null model, with provider- and individual-level random effects equal to zero on the logit scale, generated a crude estimate of the probability of selection into buprenorphine/naloxone of 0.25 [0.21, 0.29], and 28.4% and 57.8% of the unadjusted residual variance in the underlying probability of the outcome were due to the unmeasured between-individual and between-provider

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differences, respectively. However, after adjusting for the observed individual- and provider-level covariates and the key policy changes, 18.5% of the residual variance in the outcome was attributable to unmeasured between-individual differences, and 28.4% was attributable to unmeasured between-provider differences (Table 3). After incorporating covariates in the null model, the variance of the individual-level random effect decreased by 49.4% (Table 4, model A2) and the provider-level random effect decreased by 41.0% (Table 4, model A3). After incorporating policy change indicators in the null model, the variance of the provider-level random effect decreased by 18.8%. It is noteworthy that these covariates explained a substantial amount 11

of variance of the underlying propensity of the outcome (provider-level 20.0%, individual-level 9.7%, policy indicators 4.8%) The RMZ (34.1%) (Table 4, Model 1) indicated a good prediction of

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the selection into buprenorphine/naloxone (see supplementary appendix for further details).

4.0 Discussion

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This study aimed to identify the determinants of selection into buprenorphine/naloxone among individuals receiving OAT in BC. In the absence of structural restrictions in OAT delivery, individuals who were younger, male, with less OAT experience, and who experienced mental

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health conditions and chronic pain were more likely to receive buprenorphine/naloxone over methadone. Providers with larger and more complex caseloads, higher client attachment, and

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stronger prescribing preference for buprenorphine/naloxone were associated with higher odds of buprenorphine/naloxone prescription. Observed provider-level covariates accounted for 20.0% of

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the underlying variation in the selection of buprenorphine/naloxone, while observed individuallevel covariates accounted for 9.7%. After controlling for individual- and provider-level covariates and policy changes, we found that unmeasured systematic differences among providers contributed 1.5 times more than unmeasured systematic differences among individuals. These

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findings therefore highlight the substantial influence of individual and particularly provider-level factors upon OAT initiation that must be accounted for in subsequent comparative effectiveness analyses utilizing observational data.

To our knowledge, this study is the first to identify determinants of OAT medication selection using

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population-level data; however, the factors behind these choices are multifactorial. Other studies have reported higher odds of buprenorphine/naloxone prescription among providers with less OAT experience (Knudsen et al., 2005; Werb et al., 2008), higher OAT caseloads (Rieckmann et al., 2011), and among individuals with mental health conditions (Murphy et al., 2014; Ridge et al., 2009) and chronic pain (Murphy et al., 2014), further supporting our findings. Relative to methadone, buprenorphine/naloxone features a lower risk of drug interactions and improved safety profile with an option for take-home dosing, properties that make it a safer and more convenient option for many individuals (British Columbia Centre on Substance Use, 2017c).

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Findings from this study and other studies indicating higher odds of buprenorphine/naloxone prescription among younger individuals and those with less OAT experience may be explained partially by confounding based on opioid experience. Individuals with long histories of OUD are more likely to develop opioid physical dependence (Kanof et al., 1991), and therefore more likely to experience relatively severe symptoms during partial opioid withdrawal (Rosado et al., 2007); in such cases buprenorphine/naloxone may not be the appropriate treatment regimen (British Columbia Centre on Substance Use, 2017a). The validity of provider prescribing preferences as IVs for future comparative effective studies

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were supported by our findings that unobserved provider prescribing preferences were associated with medication selection for PWOUD after controlling for observed individual- and provider-level

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characteristics. The percentage of new buprenorphine/naloxone prescriptions among all new OAT episodes initiated by a provider yielded a relatively more accurate estimate of provider preference as it was estimated from all individuals treated over a longer time period

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(Abrahamowicz et al., 2011; Davies et al., 2013b; Rassen et al., 2009). Following current methodological standards for selection, validation, and reporting of IVs (Swanson and Hernan,

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2013), a next step for this analysis is to empirically assess whether our prospective instruments do not affect OAT retention except through medication selection and do not share any causes

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with OAT retention. These two conditions are empirically supported if the instruments and determinants (or measured confounders) of OAT retention are not associated (Brookhart et al., 2006; Davies et al., 2013a; Davies et al., 2013b; Swanson and Hernan, 2013).

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Threats to validity may arise if unobserved previous OAT training known to correlate with both physician prescribing preferences and OAT retention (Knudsen et al., 2005; Rieckmann et al., 2011) vary greatly across physicians. Although all physicians in BC are required to receive province-wide standardized education and training prior to prescribing OAT, physicians with longer histories of OAT prescribing may have received older versions of this training. We therefore

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proxy previous OAT training by years of OAT prescribing experience in our future studies. Evidence

of

an

improved

buprenorphine/naloxone

safety

compared

profile to

allowing

methadone

for

more

catalyzed

flexible

policy

dosing

changes

with

towards

buprenorphine/naloxone as the preferred first line treatment in BC, a change further supported by evidence of public safety and comparable efficacy (British Columbia Centre on Substance Use, 2017c; Ye et al., 2018). The influence of these policy changes are evidenced in the increased rates of buprenorphine/naloxone prescription, particularly following PharmaCare coverage of buprenorphine/naloxone as a regular benefit in BC. In the past, practitioners were required to 13

obtain an exemption from Health Canada for prescribing buprenorphine/naloxone and methadone. Following the exemption of buprenorphine/naloxone from this policy in July 2016 (College of Physicians and Surgeons of British Columbia, 2016), a substantial increase in buprenorphine/naloxone prescriptions was observed. These findings indicate the success of a policy change that aimed to increase access to care for OUD care by reducing prescriber-level restrictions. However, we did not observe a significant increase in the odds of buprenorphine/naloxone episodes after buprenorphine/naloxone was recommended as the firstline OAT medication after June 5 2017. This may be due to the limited observation period after

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this policy change and possibility of prevalence bias given the relatively small number of episodes within this short period, likely attenuating associations after adjusting for covariates.

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The linked health administrative datasets used in this study are perhaps the most comprehensive available in North America, capturing nearly all health care interactions of the entire population of

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BC. Nonetheless, we note several limitations. First, several determinants of treatment selection that have been suggested to be influential in existing literature were not available in our data. These factors include provider-level characteristics such as education and previous training

re

(Knudsen et al., 2005); as well as individual preferences, treatment goals, willingness to receive treatment, and drug use severity (Ridge et al., 2009), though the latter may be proxied by OAT

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experience. Unmeasured individual- level characteristics suggested in previous literature include knowledge of available treatment options, previous OAT experience; which was the most important predictor of OAT medication selection, and fear of stigmatization (Ridge et al., 2009;

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Yarborough et al., 2016). Second, our results may have been affected by the introduction of slow release oral morphine in June 2017 (British Columbia Centre on Substance Use, 2017c) and injectable OAT (hydromorphone) in October 2017 (British Columbia Centre on Substance Use, 2017b) since these alternative OAT medications drew individuals with high drug use severity and multiple relapses out of the pool of potential methadone and buprenorphine/naloxone recipients.

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In conclusion, we identified heterogeneity between providers accounted for almost 50% of the variation of buprenorphine/naloxone selection in a population-based setting with office-based OAT prescription. During a period of increasing opioid-related morbidity and mortality, identifying effective treatment options for key populations is critical to inform guidelines on the clinical management of OUD. We found measures of prescriber preference may be a useful IV to control for selection bias in subsequent comparative effectiveness of buprenorphine/naloxone and methadone.

14

Author Disclosures Funding Source: Health Canada Substance Use and Addictions Program award no. 1819-HQ000036. Role of the funding source: The funding source was independent of the design of this study and did not have any role during its execution, analyses, interpretation of the data, writing, or decision to submit results. All authors had full access to the results in the study and take responsibility for the integrity of the data and accuracy of analysis.

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Contributors: FH led the statistical analysis and wrote the first draft of the manuscript. NH and BN conceptualized the study. NH, MP, LP, EK, BN reviewed and edited the manuscript. CZ and JM supported the statistical analysis. FH, NH, MP, LP and EK conducted literature reviews. BN

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supervised the work and secured funding for this study. All authors reviewed and approved the

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submitted draft of the manuscript

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Conflict of Interest: No conflicts declared.

Acknowledgements

1819-HQ-000036.

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This work was supported by a Health Canada Substance Use and Addictions Program award no.

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Tables and Figures Table 1: Opioid agonist treatment (OAT) episodes among people who received OAT† Characteristics

buprenorphine/naloxone

methadone

Number of episodes

N (%) 39,537 (22.1)

N (%) 139,439 (77.9)

Number of person months on OAT (median, IQR)

2.6 (0.4-8.5)

18.3 (2.8-50.8)

Male

27,133 (68.6)

90,356 (64.8)

Female

12,393 (31.3)

49,083 (35.2)

≤24

6,144 (15.5)

12,740 (9.1)

25-34

14,668 (37.1)

35-44

10,124 (25.6)

Sex

≥45 Health authority

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8,601 (21.8)

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Age

a

a

46,580 (33.4) 42,567 (30.5) 37,552 (26.9)

5,788 (14.6)

16,583 (11.9)

Vancouver Coastal Health

10,023 (25.4)

38,911 (27.9)

6,233 (15.8)

17,073 (12.2)

16,676 (42.2)

63,462 (45.5)

811 (2.1)

3,391 (2.4)

24,225 (61.3)

18,439 (13.2)

9,136 (23.1)

21,153 (15.2)

21,536 (54.5)

56,724 (40.7)

8,865 (22.4)

61,562 (44.1)

489 (1.2)

2,668 (1.9)

513 (1.3)

2,122 (1.5)

6,504 (16.5)

21,539 (15.4)

35,106 (88.8)

1,18,593 (85.1)

Mental health condition

27,897 (70.6)

88,704 (63.6)

Chronic pain

22,137 (56.0)

67,397 (48.3)

15,304 (38.7)

75,215 (53.9)

BNX listed in PharmaCare regular benefit (13 Oct, 2015)

22,529 (57.0)

37,321 (26.8)

Declaration of public health emergency in BC (14 Apr, 2016) Exemption of authorization for BNX prescription (1 Jul, 2016)

19,443 (49.2) 17,702 (44.8)

29,274 (21.0) 25,791 (18.5)

BNX became the first-line OAT in BC (5 Jun, 2017)

6,680 (16.9)

9,876 (7.1)

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Interior Vancouver Island Fraser b

Cumulative time on OAT

b

No prior OAT 0-24 months >24 months OUD related comorbidity

a

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Human immunodeficiency virus

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Prior BNX receipt

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Northern

Hepatitis C virus

Alcohol use disorder

Substance use disorder

c

Recipient of income assistance

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OAT episodes initiated after key policy changes

†

British Columbia provincial estimates from January 1, 2008 to November 30, 2017. Abbreviations: OAT: opioid agonist treatment; BNX: buprenorphine/naloxone; OUD: opioid use disorder Number of unique individuals = 39,605; Number of episodes = 178,976; a. Age, Health authority, comorbidities and OAT clinical history are observed at the start date of each episode. b. prior to current OAT episode. c. substance use disorder other than OUD and alcohol use.

22

Table 2: Characteristics of providers initiating individuals on opioid agonist treatment (OAT)† buprenorphine/naloxone

methadone

Number of episodes Community health service providers

N (%) 39,537 (22.1) 1,261 (3.2)

N (%) 1,39,439 (77.9) 4,752 (3.4)

Experience prescribing OAT <1 year 1-5 years 5-10 years >10 years

3,699 (9.4) 9,227 (23.3) 8,663 (21.9) 17,948 (45.4)

8,495 (6.1) 27,710 (19.9) 32,543 (23.3) 70,691 (50.7)

Provider-Individual attachment a <25% 25-50% 50-75% >75%

28,980 (73.3) 2,440 (6.2) 3,203 (8.1) 4,914 (12.4)

92,650 (66.4) 8,256 (5.9) 13,418 (9.6) 25,115 (18.0)

Number of OAT clients b <200 201-500 >500

15,212 (38.5) 16,357 (41.4) 7,968 (20.2)

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Prior new BNX prescription d Time since last new BNX prescription e ≤1 week 1 week to ≤ 1 month

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65,617 (47.1) 56,454 (40.5) 17,368 (12.5)

12,547 (31.7) 19,637 (49.7) 7,281 (18.4) 72 (0.2)

30,780 (22.1) 61,994 (44.5) 44,977 (32.3) 1,688 (1.2)

2,136 (5.4) 4,132 (10.5) 28,347 (71.7) 4,922 (12.4)

4,240 (3.0) 10,749 (7.7) 97,353 (69.8) 27,097 (19.4)

23,377 (59.1) 16,160 (40.9)

97,569 (70.0) 41,870 (30.0)

38,671 (97.8)

91,536 (65.6)

23,199 (58.7) 10,877 (27.5)

36,969 (26.5) 28,489 (20.4)

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% of clients received minimum effective dose b <25% 25-50% 50-75% >75% Average CDS of OAT client caseload b <4 ≥4 Provider Prescribing Preference c

1 month to ≤ 3 months >3 months

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b

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% of clients retained ≥12 months <25% 25-50% 50-75% >75%

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Characteristics

3,532 (8.9) 11,929 (4.9)

15,172 (10.9) 58,809 (42.2)

% of new BNX prescription f <25% 25-50%

>50%

14,163 (35.8) 15,421 (39.0) 99,953 (25.2)

1,12,096 (80.4) 23,313 (16.7) 44,030 (2.9)

†

British Columbia provincial estimates from January 1, 2008 to November 30, 2017. Abbreviations: OAT: opioid agonist treatment; BNX: buprenorphine/naloxone; CDS: Chronic Disease Score Total number of episodes = 178,976; Number of unique providers=1,592; a. The percentage of an individual’s billing records attributed to the OAT prescribing provider among all outpatient billing records in the past 12 months prior to the OAT episode initiation ; b. In the past 12 months prior to OAT episode initiation, c. Provider prescribing preference is estimated using the following instrumental variables. d. Ever written BNX prescription to a new client prior to the episode initiation; e. The time since the provider had written their last buprenorphine/naloxone prescription to a new client; f. The percentage of new BNX prescriptions among all new OAT episodes initiated in the past 12 months

23

Table 3. Generalized linear mixed effect model to identify determinants of selection into buprenorphine/naloxone compared to methadone among people who received opioid agonist treatment† Full model (Model 1) After BNX first line OAT (Model 2) 39605 10617 1592 812 178976 16556 Adjusted OR Adjusted OR (95% CI) (95% CI)

of

1.57 (1.32-1.87)** 1.07 (0.99-1.15) 14.17 (11.57-17.36)** -23.33 (17.26-31.54)** 2.99 (2.42-3.68)** 0.56 (0.28-1.11) 0.64 (0.35-1.19) 1.27 (0.95-1.69) 1.35 (1.10-1.65)* 1.25 (1.04-1.49)* 0.88 (0.74-1.04) 0.36 (0.30-0.43)**

0.88 (0.61-1.26) 0.91 (0.70-1.17) 1.15 (1.02-1.30)* 0.90 (0.82-0.99)* 1.41 (1.30-1.53)** 1.16 (1.07-1.25)** 0.51 (0.47-0.55)**

-

1.47 (1.25-1.73)** 1.52 (1.24-1.85)** 1.35 (1.03-1.77)*

0.51 (0.38-0.69)** 0.54 (0.34-0.86)*

0.84 (0.74-0.94)* 1.08 (0.90-1.28)

0.81 (0.67-0.97)*

1.04 (0.95-1.15)

2.24 (0.80-6.21)

3.88 (3.04-4.95)**

0.86 (0.73-1.02) 0.92 (0.69-1.23) 1.45 (0.86-2.43)

0.86 (0.79-0.93)** 0.65 (0.57-0.73)** 0.28 (0.23-0.34)**

2.43 (1.76-3.36)** 13.85 (8.89-21.57)**

2.11 (1.92-2.32)** 4.39 (3.73-5.18)**

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Parameter Individual characteristics ұ Sex (Reference: female) Male 1.28 (1.20-1.37)** Prior BNX receipt (Reference: never) 4.82 (4.54-5.12)** Cumulative time on OAT (Reference: >24 months) No prior OAT 4.54 (4.20-4.91)** 0-24 months 1.97 (1.85-2.10)** OUD-related comorbidity (Reference: negative disease status) Human immunodeficiency virus 0.82 (0.62-1.07) Hepatitis C virus 0.87 (0.68-1.11) a Substance use disorder 1.14 (1.03-1.26)* Alcohol use disorder 0.97 (0.89-1.06) Mental health condition 1.54 (1.43-1.66)** Chronic pain 1.23 (1.14-1.31)** Income assistance (Reference: non-recipient) 0.48 (0.45-0.51)** Provider characteristics ұ Provider-Individual attachment b (Reference: <25%) 25-50% 1.35 (1.23-1.47)** 50-75% 1.09 (1.00-1.18) >75% 0.85 (0.78-0.91)** Number of OAT clients c (Reference: <200) 201-500 0.88 (0.81-0.95)* >500 0.99 (0.88-1.11)‡ Average CDS of OAT client caseload c (Reference: <4) ≥4 1.10 (1.04-1.16)* Provider prescribing preference d Prior new BNX prescription e (Reference: 6.63 (5.62-7.82)** none) Time since last new BNX prescription f(Reference: <1 week) 1 week to ≤ 1 month 0.83 (0.79-0.87)** 1 month to ≤ 3 months 0.61 (0.57-0.66)** >3 months 0.25 (0.22-0.28)** % of new BNX prescriptions g (Reference: <25%) 25-50% 2.15 (2.02-2.29)** >50% 4.85 (4.36-5.40)**

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Number of individuals Number of providers Number of episodes

First ever OAT episode (Model 3) 30289 973 30289 Adjusted OR (95% CI)

†

British Columbia provincial estimates from January 1, 2008 to November 30, 2017. Abbreviations: OAT: opioid agonist treatment; BNX: buprenorphine/naloxone; OUD: opioid use disorder; CDS: Chronic Disease Score; Legend: (* p-value <0.05, ** p-value <0.001, ‡ p-value =0.05); a. Substance use disorder other than OUD and alcohol use. b. The percentage of an individual’s billing records attributed to the OAT prescribing provider among all outpatient billing records in the past 12 months prior to the OAT episode initiation ; c. In the past 12 months prior to OAT episode initiation; d. Provider prescribing preference is estimated using the following instrumental variables. e. Ever written BNX prescription to a new client prior to the episode initiation; f. The time since the provider had written their last buprenorphine/naloxone prescription to a new client; g. The percentage of new buprenorphine/naloxone prescriptions among all new OAT episodes initiated in the past 12 months; ұ See Appendix Table A4 for all other covariates included in the models.

24

Table 4: Variance decomposition of null and restricted generalized linear mixed effect models of determinants of selection into buprenorphine/naloxone compared to methadone among people who received opioid agonist treatment† Null model

Individual characteristics

Provider characteristics

Policy change

Full model

Model A1

Model A2

Model A3

Model A4

Model 1

0.10

0.20

0.05

0.34

2.60

1.53

2.55

2.57

1.79

Proportional change (scalecorrected)

Reference

0.65 (0.49)

0.04 (-0.08)

0.03 (-0.13)

0.53 (0.35)

Variance partition coefficient

0.28

0.14

0.31

0.32

0.18

3.71

3.02

2.69

Proportional change (scalecorrected)

Reference

0.34 (0.03)

Variance partition coefficient

0.58

0.56

Scale correction factor

Reference

1.46

Parameter

Variance attributable to covariates a Variance attributable to random effect Standard deviation b

b

†

3.09

2.21

0.47 (0.41)

0.30 (0.19)

0.64 (0.51)

0.34

0.47

0.28

1.17

1.38

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Standard deviation

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Provider level

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Individual level

1.12

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British Columbia provincial estimates from January 1, 2008 to November 30, 2017. Legend: Model A1: individual- and provider-level random effects; Model A2: Individual characteristics + individual- and provider-level random effects; Model A3: Provider characteristics + individual- and provider-level random effects; Model A4: Health care policy change indicators + individual- and provider-level random effects; Model 1: All covariates+ individual- and provider-level random effects (see supplementary appendix for further details). a. McKelvey and Zavoina pseudo R-square; b. Proportional change of the variance of the random effect.

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Figure legend †

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Figure 1: Adjusted odds ratio of the generalized mixed effect model to identify determinants of selection of buprenorphine/naloxone, among people who received opioid agonist treatment†

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British Columbia provincial estimates from January 1, 2008 to November 30, 2017. Abbreviations: CDS: Chronic Disease Score; BNX: buprenorphine/naloxone; OAT: opioid agonist treatment; Last BNX prescription: The time since the provider had written their last buprenorphine/naloxone prescription to a new client; % of BNX prescription: The percentage of new BNX prescriptions among all new OAT episodes initiated in the past 12 months prior to the OAT episode initiation; Provider-individual attachment: The percentage of an individual’s billing records attributed to the OAT prescribing provider among all outpatient billing records in the past 12 months prior to the OAT episode initiation; Full model: Individual- and provider-level random effects, individual- and provider-specific covariates, and policy change indicators, number of episodes = 1,78,976; 1 January 2008 to 30 November 2017; 1st OAT episode: Provider-level random effect, individual- and provider-specific covariates, and policy change indicators, number of episodes = 30,289; 1 January 2008 to 30 November 2017; 1st line BNX : Individual- and provider-level random effects, individual- and provider-specific covariates, number of episodes (initiated after buprenorphine/naloxone became preferred first-line drug for opioid agonist treatment in British Columbia) = 16,556; 5 June 2017 to 30 November 2017.

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Appendix for the manuscript “Determinants of selection into buprenorphine/naloxone among people initiating opioid agonist treatment in British Columbia” Table A1. Descriptions of databases used for cohort selection Description All records of medical services provided by fee-for-service practitioners to individuals covered by the public health insurance Medical Services Plan (MSP) in BC. All residents in BC are eligible for MSP and it covers the cost of medically necessary insured doctor services (fee-for-service, provincially funded community health clinics).

Key data Service dates, service provider, diagnose, types of outpatient care delivered, fee items, costs billed to the provincial Ministry of Health.

British Columbia (BC) PharmaNet Database

All prescription drug and medical supplies dispensation records from community pharmacies in BC, provider’s office, clinic, hospital outpatient pharmacies for individual use at home, or emergency department visit. This data excluded opioid agonist treatment administered in hospital, to federally insured individuals, including Royal Canadian Mounted Police, Canadian Forces, veterans, and Status First Nations (under Non-Insured Health Benefits Program prior to October 1, 2017) All inpatient and day surgery records of discharges, transfers, and deaths from acute care hospitals in BC and data on BC residents who are admitted to a hospital outside BC.

Demographic data, pharmacy information, practitioner’s information, drug information, medication-dispensing information.

BC’s central demographic database on all vital statistics registered in BC.

Date of birth, date of stillbirth, date of marriage, date of death (including ICD-9-CA and ICD10-CA* codes of cause of death).

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Discharge Abstract Database (DAD)

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Database Medical Service Plan (MSP) Database

Demographic data, dates and duration of hospitalization, up to 25 diagnostic codes (ICD-9-CA and ICD-10-CA*), admit category, discharge status.

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Abbreviations: *ICD-9/10-CA: The International Statistical Classification of Diseases and Related Health Problems (ICD), Ninth and Tenth Revisions, Canada.

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Table A2. ICD-9/10-CA and drug identification numbers for identification of opioid use disorder Database

Code no.*

Description

PharmaNet

999792, 66999990, 66999991, 66999992,66999993, 66999997, 66999998, 66999999, 67000000, 67000001,67000002, 67000003, 67000004

DIN/PIN for methadone

PharmaNet

2295695, 2295709, 2408090, 2408104, 2424851, 2424878, 2453908, 2453916, 2468085, 2468093

DIN/PIN for buprenorphine/naloxone

PharmaNet

2242963, 2242964, 66999995, 66999996

DIN/PIN for buprenorphine

PharmaNet

22123349, 22123346, 22123347, 22123348

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DIN/PIN for slow-release oral morphine PharmaNet 66123367, 2146126, 22123340 DIN/PIN for injectable OAT** MSP 39, 15039 Fee items related to OAT MSP/DAD 304.0, 304.7, 305.5 ICD-9-CA for opioid use disorder ICD-9-CA for opioid MSP/DAD 965.0, E850.0-E858.2 poisoning MSP/DAD/VS F11 ICD-10-CA for opioid use disorder MSP/DAD/VS ICD-10-CA for opioid X42&(T40.0‐T40.4 or T40.6), X62&(T40.0‐T40.4 or T40.6), Y12&(T40.0‐ poisoning T40.4 or T40.6) Abbreviations: ICD-9/10-CA: The International Statistical Classification of Diseases and Related Health Problems (ICD), Ninth and Tenth Revisions, Canada; DAD: Discharge Abstract Database; MSP: Medical services Plan; DIN: Drug Identification Numbers; PIN: Product Identification Numbers; OAT: Opioid agonist treatment; *PharmaNet database: DIN/PIN used for identification; MSP, DAD, Vital statistics databases, ICD-9/10-CA codes used for cohort identification; ** Diacetylmorphine or hydromorphone dispensed in certain pharmacies.

Table A3. Identification of concurrent chronic conditions* Diagnosis code

Mental health†

ICD-9-CA from DAD and MSP: 295-298, 300, 301, 308, 309, 311, 314; ICD-10-CA from DAD: F20-F25, F28-F34, F38-F43, F48.8, F48.9, F60-F61, F69, F90; MSP additional diagnostic code 50B ICD-9-CA from DAD and MSP: 042‐044, 079.53, 795.8, V08; ICD-10-CA from DAD: B20‐B24, B97.35, F02.4, O98.7, Z21; MSP fee item: 13015, 13105, 33645, 36370 ICD-9-CA from DAD and MSP: 70.41, 70.51, 70.44, 70.54, 70.7; ICD-10 from DAD B17.1, B18.2, B19.2; AHFS category: 8:18.40 ICD-9-CA from DAD and MSP: 291, 303, 305.0, 357.5, 425.5, 535.3, 571.0-571.3, 655.4, 760.71,V65.42; ICD-10CA from DAD: F10, Z50.2, Z71.4, Z72.1,G31.2, G62.1, G72.1, I42.6, K29.2, K70, K86.0, O35.4, P04.3, Q86.0; DIN: 2293269, 2158655, 2213826, 2444275, 2451883, 2534, 2542, 2041375, 2041391, 66124089, 66124085, 66124087 ICD-9-CA from DAD and MSP: 292, 304.x (1-6,8,9), 305.x (2-4,6-9), 969.x (4,6,7), 970.81, E853.2, E854.1,E854.2, 648.3, 760.73, 760.75, 779.5; ICD-10-CA from DAD: F12-F16, F19, X42, X62, Y12, T40.5, T40.7T40.9, T42.4, T43.6, Z50.3, Z71.5, Z72.2, P04.4, P96.1

Hepatitis C virus Alcohol use disorder Substance use disorder‡

ICD-9-CA from DAD and MSP: 338.2, 338.4, 307.80, 307.89, 338.0, 719.41, 719.45-719.47, 719.49, 720.0, 720.2, 720.9, 721.0-721.4, 721.6, 721.8, 721.9, 722, 723.0, 723.1, 723.3-723.9, 724.0-724.6, 724.70, 724.79, 724.8, 724.9, 729.0-729.2, 729.4, 729.5, 350, 352-357, 344.0, 344.1, 997.0, 733.0, 733.7, 733.9, 781; ICD-10-CA from DAD: F45.4, G89.0, G89.2, G89.4, M08.1, M25.50, M25.51, M25.55-M25.57, M43.2-M43.6, M45, M46.1, M46.3, M46.4, M46.9, M47, M48.0, M48.1, M48.8, M48.9, M50.8, M50.9, M51, M53.1-M53.3, M53.8, M53.9, M54, M60.8, M60.9, M63.3, M79.0-M79.2, M79.6, M79.7, M96.1, G50, G52-G64, G82, G97, M89, R29

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Chronic pain§

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Diseases

Abbreviations: DAD: Discharge Abstract Database for hospitalization; MSP: Medical Service Plan for physician billing; OAT: Opioid agonist treatment; ICD-9/10-CA: The International Statistical Classification of Diseases and Related Health Problems (ICD), Ninth and Tenth Revisions, Canada; DIN: Drug Identification Number; AHFS: American Hospital Formulary Service from PharmaNet. *To minimize misclassification due to errors in the coding of physician billing records, we applied a case finding algorithm based on the presence of at least 1 hospitalization, more than 3 physician billing records, or medication receipt for alcohol use disorder; †Any indication of depression, anxiety, psychotic illness, personality disorders, attention-deficit/hyperactivity disorders, or bipolar disorders; ‡Any indication of non-opioid drug use, poisoning (accidental or intentional), or substance use counselling or rehab, excluding alcohol use disorder; §non-cancer chronic pain.

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Table A4: Generalized linear mixed effect model to identify determinants of selection into buprenorphine/naloxone compared to methadone among people who received opioid agonist treatment in 2008-2017 Full model (Model 1) After BNX first line OAT (Model 2)

First ever OAT initiation episode (Model 3)

Number of Individuals

39,605

10,617

30,289

Number of providers

1,592

812

973

Number of episodes

1,78,976

16,556

30,289

Parameter

adjusted OR

adjusted OR

adjusted OR

(95% CI)

(95% CI)

(95% CI)

1.72 (1.55-1.90)** 1.15 (1.06-1.24)** 1.05 (0.97-1.13)

0.93 (0.69-1.24) 0.74 (0.60-0.91)* 0.90 (0.73-1.11)

1.63 (1.47-1.82)** 1.21 (1.11-1.33)** 1.11 (1.00-1.23)Ɨ

1.31 (1.14-1.51)** 1.19 (1.10-1.28)** 0.99 (0.87-1.14) 1.02 (0.79-1.32)

1.91 (1.34-2.74)** 1.15 (0.91-1.46) 0.75 (0.53-1.06) 1.68 (0.95-2.98)

1.13 (0.95-1.35) 1.26 (1.13-1.40)** 1.22 (1.02-1.45)* 0.98 (0.74-1.30)

Community health service provider (Reference: all other 0.48 (0.32-0.70)** providers) Experience of prescribing OAT (Reference: >10 years) <1 year 0.98 (0.81-1.19) 1-5 years 0.73 (0.63-0.85)** 5-10 years 1.03 (0.92-1.14) % of clients retained ≥12 months a (Reference: <25%) 25-50% 0.94 (0.86-1.02) 50-75% 1.03 (0.92-1.15) >75% 1.16 (0.74-1.81) % of clients’ received minimum effective dose b (Reference: <25%) 25-50% 0.20 (0.16-0.25)** 50-75% 0.17 (0.14-0.22)** >75% 0.15 (0.12-0.19)** OAT episode initiated after key policy changes (Reference: before) BNX listed in PharmaCare regular benefit (13 Oct, 2015) 2.19 (2.01-2.38)** Declaration of public health emergency in BC (14 Apr, 2016) 1.20 (1.07-1.36)* Exemption of authorization for prescribing BNX (1 Jul, 2016) 0.93 (0.83-1.03) BNX became the preferred first-line OAT in BC (5 Jun, 2017) 0.52 (0.48-0.56)**

0.25 (0.15-0.42)**

0.56 (0.40-0.78)**

4.38 (2.68-7.15)** 1.57 (1.05-2.34)* 0.77 (0.51-1.18)

1.74 (1.38-2.17)** 1.07 (0.89-1.29) 1.25 (1.09-1.44)*

0.38 (0.28-0.53)** 0.28 (0.17-0.45)** 0.20 (0.05-0.84)*

0.79 (0.70-0.90)** 0.67 (0.56-0.79)** 1.21 (0.61-2.37)

0.02 (0.01-0.07)** 0.02 (0.01-0.05)** 0.02 (0.01-0.05)**

0.19 (0.14-0.25)** 0.21 (0.16-0.27)** 0.20 (0.15-0.26)**

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Age (Reference: ≥45) ≤24 25-34 35-44 Health Authority (Reference: Fraser) Interior Vancouver Coastal Health Vancouver Island Northern Provider characteristics

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Individual characteristics

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1.99 (1.74-2.27)** 1.44 (1.17-1.77)** 1.12 (0.93-1.34) 0.44 (0.39-0.50)**

Abbreviations: BNX: buprenorphine/naloxone; OAT: opioid agonist treatment; OR: odds ratio; * p-value <0.05, ** p-value <0.001, ‡ p-value =0.05; a. the percentage of individual’s retained on OAT at least 12 months in the past 12 months prior to the OAT episode initiation. b. the percentage of clients receiving the recommended minimum effective OAT dose for at least four weeks (methadone: 60 mg/day, buprenorphine/naloxone: 12 mg/day) in the past 12 months prior to the OAT episode initiation.

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Table A5: Generalized linear mixed effect model of determinants of selection into buprenorphine/naloxone compared to methadone among people who received opioid agonist treatment in 2008-2017; restricted model with individual-level covariates. Model A2: Individual characteristics + individual-level random effect + provider-level random effect Adjusted odds ratio (95 % CI)

Parameter Individual characteristics

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1.34 (1.22,1.46)** 0.96 (0.89,1.03) 0.93 (0.87,0.99)*

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1.18 (1.04,1.35)* 1.05 (0.98,1.13) 0.84 (0.74,0.95)* 1.02 (0.81,1.29) 9.30 (8.83,9.80)**

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Cumulative time on OAT (Reference: >24 Months) No prior OAT 0-24 months OUD related comorbidity (Reference: negative disease status) Human immunodeficiency virus Hepatitis C virus Substance use disorder other than OUD and alcohol Alcohol use disorder Mental health condition Chronic pain Income assistance (Reference: non-recipient)

1.28 (1.21,1.36)**

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Sex (Reference: Female) Male Age (Reference: ≥45) ≤24 25-34 35-44 Health Authority (Reference: Fraser) Interior Vancouver Coastal Health Vancouver Island Northern Prior buprenorphine/naloxone receipt (Reference: never)

3.75 (3.51,4.02)** 1.52 (1.44,1.61)** 0.71 (0.56,0.90)* 0.86 (0.69,1.07) 1.39 (1.27,1.51)** 1.01 (0.93,1.08) 1.41 (1.32,1.51)** 1.17 (1.10,1.24)** 0.50 (0.47,0.53)**

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Abbreviations: OAT: opioid agonist treatment; OUD: opioid use disorder * p-value <0.05, ** p-value <0.001; Individual N=39,605; Provider N=1,592; Episode N=178,976.

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Table A6: Generalized linear mixed effect model of determinants of selection into buprenorphine/naloxone compared to methadone among people who received opioid agonist treatment in 2008-2017; restricted model with provider-level covariates. Parameter

Model A3: Provider characteristics + individual-level random effect+ provider-level random effect Adjusted odds ratio (95 % CI)

Provider characteristics Community health service provider (Reference: other providers) Experience of prescribing OAT (Reference: >10 years) <1 year 1-5 years 5-10 years Provider-individual attachment a (Reference: <25%)

0.62 (0.40,0.97)*

25-50% 50-75% >75% Number of OAT clients b (Reference: <200) 201-500 >500 % of clients retained ≥12 months b (Reference: <25%) 25-50% 50-75% >75% % of clients received minimum effective dose b (Reference: <25%) 25-50% 50-75% >75% Average CDS of OAT client caseload b reference: <4) ≥4 Provider prescribing preference c Prior new BNX prescription d (Reference: none) Time since last new BNX prescription e (Reference: <1 week) 1 week to 1 month 1 - 3 months >3 months % of new BNX prescription f (Reference: <25%) 25-50% >50%

1.28 (1.17,1.40)** 0.96 (0.88,1.04) 0.62 (0.58,0.67)**

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0.58 (0.48,0.72)** 0.48 (0.42,0.56)** 0.87 (0.78,0.98)*

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0.88 (0.81,0.96)* 0.93 (0.83,1.05)

0.82 (0.75,0.90)** 0.83 (0.74,0.93)* 0.84 (0.53,1.32) 0.15 (0.12,0.19)** 0.13 (0.11,0.17)** 0.12 (0.09,0.15)** 1.10 (1.04,1.17)* 8.46 (7.12,10.05)** 0.77 (0.73,0.81)** 0.53 (0.49,0.57)** 0.19 (0.17,0.22)** 3.28 (3.09,3.48)** 10.03 (9.03,11.15)**

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Abbreviations: BNX: buprenorphine/naloxone; OAT: opioid agonist treatment; CDS: Chronic Disease Score;* p-value <0.05, ** pvalue <0.001; Individual N=39,605; Provider N=1,592; Episode N=178,976; a. The percentage of an individual’s billing records attributed to the OAT prescribing provider among all outpatient billing records in the past 12 months prior to the OAT episode initiation; b. In the past 12 months prior to the OAT episode initiation; c. Provider prescribing preference is estimated using the following instrumental variables; d. Ever written BNX prescription to a new client prior to the OAT episode initiation; e. Time since the provider had written their last buprenorphine/naloxone prescription to a new client; f. The percentage of new buprenorphine/naloxone prescriptions among all new OAT episodes they had initiated in the past 12 months.

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Table A7: Generalized linear mixed effect model of determinants of selection into buprenorphine/naloxone compared to methadone among people who received opioid agonist treatment in 2008-2017; restricted model with key policy change indicators. Parameter

Model A4: Health care policy change indicators + individuallevel random effect+ providerlevel random effect; Adjusted odds ratio (95 % CI)

OAT Episode initiated after policy change date (Reference: before) BNX listed in PharmaCare regular benefit (13 Oct, 2015) Declaration of public health emergency in BC (14 Apr, 2016) Exemption of authorization for prescribing BNX (1 Jul, 2016) BNX became first-line OAT in BC (5 Jun, 2017)

4.31 (3.96,4.69)** 1.68 (1.48,1.90)** 1.45 (1.30,1.62)** 0.69 (0.64,0.75)**

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Abbreviations: BNX: buprenorphine/naloxone; OAT: opioid agonist treatment; * p-value <0.05, ** p-value <0.001; Individual N=39,605; Provider N=1,592; Episode N=178,976.

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