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Published in final edited form as: Drug Alcohol Depend. 2024 Feb 14;256:111125. doi: 10.1016/j.drugalcdep.2024.111125

Expanding Access to Medication Treatment for Opioid Use Disorders: Findings from the Washington State Hub and Spoke Effort

Maureen T Stewart 1, Shay M Daily 1, Cindy Parks Thomas 1, Lee Panas 1, Grant Ritter 1, Sharon Reif 1
PMCID: PMC10922849  NIHMSID: NIHMS1970038  PMID: 38368666

Abstract

Background:

Opioid use disorder (OUD) is a leading cause of preventable death and injury nationwide. Efforts to increase the use of medication for opioid use disorder (MOUD) are needed. In 2017, Washington State implemented a Hub and Spoke (HS) model of care with the primary goal of expanding access to MOUD. We examined changes in MOUD utilization among Washington State Medicaid beneficiaries before and after HS implementation.

Methods:

We used Medicaid claims data to examine longitudinal changes in MOUD use for beneficiaries with OUD. We conducted a comparative interrupted time series analysis to examine the association between HS policy implementation and rates of MOUD utilization, overall and by type of medication.

Results:

Between 2016 and 2019, a period of increasing OUD prevalence, rates of MOUD utilization among Washington Medicaid beneficiaries increased overall from 39.7 in 2016 to 50.5. Following HS implementation, rates of MOUD use grew at a significantly greater rate in the HS cohort than in the non-HS cohort (β=0.54, SE=0.02, p< 0.0001, 95% CI 0.49, 0.59). Analyses by medication type show that this rate increase was primarily due to buprenorphine use (β= 0.61, SE= 0.02, p< 0.0001, 95% CI 0.57, 0.65).

Conclusion:

Improved systems of care are needed to make MOUD accessible to all patients in need. The Washington HS model is one strategy that may facilitate and expand MOUD use, particularly buprenorphine. Over the study period, Washington State saw increased use of buprenorphine, which was an emphasis of their HS model.

Keywords: Opioid Use Disorder, Medication for Opioid Use Disorder, Medicaid, MOUD Utilization, Hub and Spoke

1. Introduction

Opioid-related mortality and addiction are leading causes of preventable death and injury (Mattson et al., 2021). In the United States, more than 100,000 people died of a drug overdose in 2022, with approximately 75% of deaths due to opioid use (Ahmad et al., 2023). Three medications are approved for opioid use disorder (OUD) treatment: buprenorphine, methadone, and naltrexone. All three medications for OUD (MOUD) are effective in helping people reduce opioid use (Krupitsky et al., 2011; Mattick et al., 2014, 2009; NASEM, 2019); buprenorphine and methadone are also associated with a reduced risk of fatal overdose (Larochelle et al., 2018). Despite MOUD effectiveness, most people do not receive it; MOUD treatment rates range from 11% among privately insured individuals (Morgan et al., 2022), to 19% in a household survey (Wu et al., 2016), and 25% among Medicaid enrollees (Donohue et al., 2021). There are a range of reasons for low MOUD uptake. The required waiver for providers to prescribe buprenorphine is one barrier that was recently removed (SAMHSA, 2023), yet many obstacles to MOUD use remain, including stigma, poor treatment availability, and lack of integration into primary care (Hollander et al., 2021; Hughto et al., 2022; NASEM, 2019; Quest et al., 2012; Wakeman and Rich, 2018). Evidence supporting strategies to improve MOUD use is needed.

In 2017, Washington State used federal Opioid State Treatment Response (STR) funding to support the development of six Hub and Spoke (HS) networks (Reif et al., 2020). Washington’s HS model built on previous innovative programs that increased the availability of MOUD treatment in primary and specialty care settings, including the Vermont Hub and Spoke model and the Massachusetts Collaborative Care model (Alford et al., 2011; Brooklyn and Sigmon, 2017; LaBelle et al., 2016; Rawson et al., 2019a, 2019b; Stoller, 2015). In the Washington HS model, MOUD-experienced providers – including primary care providers and addiction treatment programs – served as hubs, while community-based providers – such as mental health clinics, law enforcement, emergency departments, and jails – served as spokes. MOUD initiation and maintenance could occur at either hubs or spokes to maximize MOUD utilization. Three of Washington’s hubs are primary care organizations, two are specialty behavioral health organizations, and one is an opioid treatment program. Four hubs are in urban areas, with two servicing rural areas. The hub and spoke networks together included 6 opioid treatment programs, 37 sites offering buprenorphine, and 33 sites providing naltrexone (see Reif 2020 for details). Based on program-reported treatment use data, Washington State increased MOUD use by an estimated 200% from 2010–2019 compared to a 106% increase nationally (Krawczyk et al., 2022). Additional analyses of changes by HS participation and by MOUD type may help identify opportunities to improve OUD treatment systems further.

This study examines the longitudinal patterns of MOUD utilization among Washington Medicaid beneficiaries with OUD receiving MOUD in the HS model versus receiving MOUD elsewhere. We hypothesized that during the study period, (1) MOUD uptake increased substantially for Medicaid beneficiaries in both cohorts, and (2) MOUD uptake was greater for Medicaid beneficiaries in the HS cohort versus non-HS. We explore trends by type of MOUD, given potential policy implications.

2. Methods

2.1. Data source and study population

We used Medicaid health care claims data from the Washington State Health Care Authority, which included services and prescriptions (Washington Health Care Authority, 2023). The analytic sample included all non-dual-eligible Washington State Medicaid beneficiaries aged 18–64 years with OUD from 1/1/2016 – 6/30/2019 who were continuously enrolled in Medicaid for at least six months. Using ICD-10 diagnosis codes (Table S1), we identified 115,911 people with OUD over the study period and aggregated their OUD and MOUD claims by month. This study was approved by the University’s Institutional Review Board (IRB) and the Washington Department of Social and Health Services IRB.

2.3. Research design and measures

We used comparative interrupted time series (ITS) to model the association between HS policy implementation and monthly rates of MOUD utilization among Medicaid beneficiaries (Penfold and Zhang, 2013). The Washington HS program was implemented on 7/1/2017. The analytic period from 1/1/2016 – 6/30/2019 provides 18 months pre- and 24 months post-implementation to fit the study’s quasi-experimental longitudinal design.

Among Washington Medicaid beneficiaries with OUD, we differentiated two cohorts: those who received MOUD through the HS model and those who received MOUD outside the HS model (non-HS). The HS cohort was identified using a roster provided by Washington State, originally generated by the HS programs. These data were linked with a unique research identifier to Medicaid claims data to capture each person’s MOUD treatment. The non-HS cohort consisted of Medicaid beneficiaries with OUD who were not linked to the HS roster during the study period.

Three types of MOUD were identified (buprenorphine, methadone, naltrexone) using National Drug Codes and Health Care Common Procedural codes (Table S2). We defined MOUD use within each month, overall and by medication type, as having at least one MOUD claim. We generated counts of people with OUD and MOUD for each month. Monthly utilization rates were calculated for each cohort. The numerator was the count of Medicaid beneficiaries with a MOUD claim during the month, and the denominator was the count of those identified with OUD over the past six months, enabling an analysis of medication use among those with clinically identified OUD who are likely to benefit from MOUD. Three-month rolling averages were calculated and analyzed to smooth each OUD cohort’s observed MOUD rates. Naltrexone was included only in the any MOUD variable and excluded from analyses by medication type due to low utilization across all periods.

Independent variables included (1) a sequential time series indicator (e.g., 1, 2, … 42th) to model the overall time trend, (2) an interruption indicator to demonstrate the initial effect of the HS implementation (July 1, 2017) where the pre-period was coded zero and post-period coded one, and (3) a time after implementation indicator where the pre-period was coded zero and post-period as a sequential time series (e.g., 1, 2… 24th).

2.4. Statistical analysis

Univariate and bivariate statistics were used to describe MOUD utilization rates among Medicaid beneficiaries with OUD, overall, by HS cohort and by MOUD type. Comparative ITS models were conducted to examine changes in MOUD use over time. These models assessed the longitudinal patterns of MOUD use, in particular, by the slope reflecting the possible increase in MOUD use across the analytic period, the change in MOUD use among the HS cohort immediately following implementation, and the change in slope of MOUD use among the HS cohort in the post-period (See Linden, 2015). ITS results are reported as beta coefficients with standard errors, 95% confidence limits (95% CL) for the estimated betas, and p-values. Statistical significance was determined with an alpha-level set to 0.05 using a two-tailed distribution. For every unit change in an indicator (e.g., time), the beta coefficient describes the respective change in MOUD utilization (Bzovsky et al., 2022). All analyses were completed using SAS 9.4® (SAS Institute Inc., 2019).

3. Results

Between the pre- and post-periods, the number of people with OUD and the rate of any MOUD increased (Table 1). Rates of buprenorphine use increased between the pre- and post-periods among both cohorts, but to a greater degree among HS beneficiaries.

Table 1.

Washington State Medicaid OUD population and MOUD average monthly utilization rates for three-month rolling averages.

OUD Population Hub and Spoke Non-Hub and Spoke
Pre-Period Post-Period Pre-Period Post-Period Pre-Period Post-Period
Model Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
Rate* Monthly OUD Population 28030 1618 32982 1794 1243 147 2547 606 26787 1472 30435 1212
Any MOUD 39.73 1.72 50.49 3.77 39.71 2.42 60.27 9.12 39.73 1.70 49.59 3.25
Buprenorp hine 13.36 2.04 24.68 4.46 16.30 2.49 35.86 8.01 13.23 2.01 23.70 4.04
Methadone 25.90 0.89 24.60 1.29 22.38 0.70 21.54 1.45 26.06 0.91 24.82 1.41
Naltrexone 0.54 0.23 1.37 0.17 1.13 0.58 3.33 0.64 0.51 0.21 1.21 0.13
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Notes.

*

=3 month rolling average MOUD Utilization SD= Standard Deviation. Pre-Period= January 01, 2016 – June 30, 2017, Post-Period= July 01, 2017 – June 30, 2019. Any MOUD includes buprenorphine, methadone, and naltrexone.

ITS model estimates are reported in Table S4. Rates of any MOUD use for the overall OUD population significantly increased during the pre-period (β= 0.34, SE= 0.04, p< 0.0001 95% CL [LCL= 0.27, UCL= 0.41]) in support of hypothesis (1). ITS trends are displayed in Figure 1. The rate of MOUD use grew in the post-period at a significantly greater rate than in the pre-period (β= 0.54, SE= 0.02, p< 0.0001, 95% CL [LCL= 0.49, UCL= 0.59]). Buprenorphine trends suggest that increased use of buprenorphine is the primary driver of rates in any MOUD use (β= 0.61, SE= 0.02, p< 0.0001, 95% CL [LCL= 0.57, UCL= 0.65]). At the same time, methadone demonstrated a significant decrease in the rate of use before (β= −0.11, SE= 0.02, p< 0.0001, 95% CL [LCL= −.15, UCL= −0.06]), but not after HS implementation. Neither medication type showed statistically significant change immediately following HS implementation.

Figure 1.

Figure 1.

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Single and Comparative ITS analysis of MOUD treatment rates in Washington Medicaid OUD Population overall and by Hub and Spoke Participation.

Descriptive analyses (Table S3) and the ITS models (Table S4) suggest the two cohorts were comparable prior to the intervention with no significant differences in intercepts (β= −1.88, SE= 2.40, p>0.05, 95% CL [LCL= −6.58, UCL= 2.81]) or pre-period growth (β= 0.16, SE= 0.18, p= 0.38, 95% CL [LCL= −0.19, UCL= 0.50]). As also illustrated in Figure 1, over the analytic period, a positive differential trend within the HS treatment cohort was found for any MOUD treatment (β= 0.60, SE= 0.25, p= 0.0172, 95% CL [LCL= 0.12, UCL= 1.08]) in support of hypothesis (2). Models of buprenorphine and methadone separately showed an increase in use of each medication in the post-period (buprenorphine: β= 0.44, SE= 0.21, p= 0.049, 95% CL [LCL= 0.03, UCL= 0.85]; methadone: β= 0.35, SE= 0.12, p= 0.0032, 95% CL [LCL= 0.13, UCL= 0.58]), similar to those for any MOUD.

4. Discussion

MOUD utilization among Washington Medicaid beneficiaries increased over time, as did the prevalence of OUD. After implementation, Medicaid beneficiaries in the HS cohort were significantly more likely to utilize MOUD than the non-HS cohort. Analyses by medication type show that the rate of buprenorphine use increased for both cohorts: for the HS cohort, we observe a 50% increase in the number of person-months with buprenorphine; in the non-HS cohort, there is a 10% increase. The number of people using methadone also increased, but the rate of methadone use slightly declined because the denominator (number of people with OUD) increased at a greater rate.

Our findings are an example of one HS program. According to a SAMHSA report to Congress, Hub and Spoke efforts have been implemented in 24 states (Substance Abuse and Mental Health Services Administration, 2020), but few are described in the literature. Similar to our findings, analyses of the HS model in Vermont identified increased use of MOUD in association with HS implementation (Brooklyn and Sigmon, 2017; Rawson et al., 2019a). Preliminary analyses using data reported by HS programs suggest the California program is associated with an increase in MOUD use (Darfler et al., 2020). In California, identified barriers to effective HS implementation included provider attitudes, stigma, and lack of training (Darfler et al., 2020).

Our results build on previous studies of changes in MOUD utilization resulting from programmatic efforts to expand its use. MOUD use for patients with and without Medicaid treated in specialty SUD facilities increased following Medicaid expansion (Johnson et al., 2022). Similarly, change in MOUD use over time is consistent with that observed by Hawkins and colleagues (2021) examining a quality improvement effort to expand MOUD use in non-SUD Veterans Administration (VA) clinics where MOUD use did not increase immediately but did in the following year. Analyses by medication type provide a more complete picture of changes in MOUD use. Our buprenorphine results differ from an analysis in North Carolina, a state that did not expand Medicaid which found, among both Medicaid and privately insured people, the proportion of people with OUD who initiated MOUD declined each year from 2014 to 2017 because of the increases in the prevalence of diagnosed OUD (Gertner et al., 2022).

During the study period and paralleling national trends (Donohue et al., 2021), the number of Medicaid beneficiaries with OUD increased. Despite improvements in MOUD rates from 40% in the pre-period to 51% in the post-period, underutilization of MOUD remains a problem. Findings that among the HS cohort, MOUD use increased to a greater extent than in the non-HS cohort (39% pre to 60% post) suggest the value of the HS network model that incorporates specialty and non-specialty providers and that the intentional emphasis on MOUD can impact utilization.

This study emphasized population patterns in MOUD uptake; future research is needed to investigate differences in MOUD use by organizational characteristics. Further research is also needed to assess trends in MOUD use for different population groups and associated policy implications. Future research will incorporate additional data to examine the types of programs that generated the most substantial increases in MOUD use in Washington and to examine implementation of the HS models.

4.1. Limitations

Hubs created networks and were selected through a competitive application process with the state. These facilities were likely already interested in increasing MOUD; therefore, findings may not be representative of people treated elsewhere. Washington State, and the nation overall, was focused on addressing opioid use during the study period and changes other than the HS model happened simultaneously; we cannot disentangle the effects of other policy changes from HS model implementation. However, it is unlikely that other changes were implemented only for the HS cohort, so the comparative ITS design with comparison is a robust framework for these analyses. We used Medicaid claims data, which may underestimate the number of people with OUD and do not include information on services not billed to insurance. Further, our approach to estimating OUD prevalence in the claims data was conservative, and may underestimate the true prevalence, compared to other approaches. However, our approach provides a consistent estimate of prevalence over time, thus providing a reliable estimate of trends.

4.2. Conclusion

Improved systems of care are needed to make MOUD more accessible. The Washington HS model is one strategy to facilitate and expand MOUD use. We found that the HS model is associated with increased use of buprenorphine, which was an emphasis of Washington’s implementation plan. Further research is needed to understand differences in HS vs non-HS organizations and to assess differences in beneficiary characteristics to determine whether the HS effort disproportionately benefited specific groups of people. Our findings provide support for federal and state efforts to facilitate expanding access to MOUD, and future work should examine whether more targeted efforts are also needed.

Supplementary Material

1

Highlights:

  • Hub and Spoke (HS) care models may improve MOUD access.

  • Between 2016 and 2019, MOUD use among Washington Medicaid beneficiaries increased.

  • Rates of MOUD use were higher in the HS cohort compared to the non-HS cohort.

  • The Washington State HS model may facilitate and expand MOUD use.

Acknowledgments:

We are grateful to Washington State’s Health Care Authority for permission to access Washington Medicaid data for these analyses, its Division of Behavioral Health and Recovery for overseeing the state’s hub and spoke intervention and for providing the hub and spoke data, and to the Research and Data Analytics team at the Washington State Department of Social and Health Services for providing the data and technical support. We thank Madeline Brown of Brandeis University for project management.

Funding:

This research was supported by grants R33DA045851 and P30DA035772 from the National Institute on Drug Abuse (NIDA) at the National Institutes of Health.

Role of Funding Source:

The funding agency had no role in study design, data collection, analysis, or interpretation of data, writing or review of the manuscript, or decision to submit the manuscript for publication. The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. 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.

Declaration of competing interest:

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this manuscript.

CRediT authorship contribution statement: Maureen Stewart: conceptualization, project administration, methodology, funding acquisition, writing - original draft, writing - review & editing, supervision. Shay Daily: methodology, data curation, formal analysis, writing - original draft, writing - review & editing, visualization. Cindy Parks Thomas: conceptualization, writing - original draft, writing - review & editing. Lee Panas: data curation, formal analysis, supervision. Grant Ritter: formal analysis, methodology, supervision. Sharon Reif: conceptualization, project administration, funding acquisition, writing - review & editing, supervision.

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