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. Author manuscript; available in PMC: 2025 Aug 1.
Published in final edited form as: Prev Med. 2024 Jun 8;185:108034. doi: 10.1016/j.ypmed.2024.108034

Reducing Perceived Barriers to Scaling Up Overdose Education and Naloxone Distribution and Medications for Opioid Use Disorder in the United States in the HEALing (Helping End Addiction Long-term®) Communities Study

Hannah K Knudsen a, Daniel M Walker b, Nicole Mack c, Elizabeth N Kinnard d, Timothy R Huerta e, LaShawn Glasgow f, Louisa Gilbert g, Bryan R Garner h, Anindita Dasgupta i, Redonna Chandler j, Sharon L Walsh k, Yjuliana Tin l, Sylvia Tan m, Joel Sprunger n, Linda Sprague-Martinez o, Pamela Salsberry p, Merielle Saucier q, Maria Rudorf r, Sandra Rodriguez s, Carrie B Oser t, Emmanuel Oga u, Julie Nakayima v, Beth S Linas w, R Craig Lefebvre x, Sarah Kosakowski y, Rachel E Katz z, Timothy Hunt aa, Ari Holman bb, JaNae Holloway cc, Dawn Goddard-Eckrich dd, Naleef Fareed ee, Mia Christopher ff, Alison Aldrich gg, Joella W Adams hh, Mari-Lynn Drainoni ii
PMCID: PMC11269010  NIHMSID: NIHMS2004266  PMID: 38857770

Abstract

Background:

Scaling up overdose education and naloxone distribution (OEND) and medications for opioid use disorder (MOUD) is needed to reduce opioid overdose deaths, but barriers are pervasive. This study examines whether the Communities That HEAL (CTH) intervention reduced perceived barriers to expanding OEND and MOUD in healthcare/behavioral health, criminal-legal, and other/non-traditional venues.

Methods:

The HEALing (Helping End Addiction Long-Term®) Communities Study is a parallel, wait-list, cluster randomized trial testing the CTH intervention in 67 communities in the United States. Surveys administered to coalition members and key stakeholders measured the magnitude of perceived barriers to scaling up OEND and MOUD in November 2019-January 2020, May-June 2021, and May-June 2022. Multilevel linear mixed models compared Wave 1 (intervention) and Wave 2 (wait-list control) respondents. Interactions by rural/urban status and research site were tested.

Results:

Wave 1 respondents reported significantly greater reductions in mean scores for three outcomes: perceived barriers to scaling up OEND in Healthcare/Behavioral Health Venues (−0.26, 95% confidence interval, CI: −0.48, −0.05, p=0.015), OEND in Other/Non-traditional Venues (−0.53, 95% CI: − 0.84, −0.22, p=0.001) and MOUD in Other/Non-traditional Venues (−0.34, 95% CI: −0.62, −0.05, p=0.020). There were significant interactions by research site for perceived barriers to scaling up OEND and MOUD in Criminal-Legal Venues. There were no significant interactions by rural/urban status.

Discussion:

The CTH Intervention reduced perceived barriers to scaling up OEND and MOUD in certain venues, with no difference in effectiveness between rural and urban communities. More research is needed to understand facilitators and barriers in different venues.

Keywords: opioid overdose, opioid epidemic, overdose prevention, naloxone, medication for opioid use disorder, community engagement, implementation science

1. Introduction

Drug overdose deaths in the United States resulted in more than 110,000 lives lost in 2022 (Ahmad et al., 2023) with sharp increases in opioid overdose mortality rates among Black, Latino/a/e and Indigenous populations (Friedman and Hansen, 2022). The annual economic burden of overdose deaths and opioid use disorder (OUD) exceeds $1 trillion (Florence et al., 2021). Solutions to reduce demand and mitigate the harms of illicit opioid use are urgently needed.

Policy experts, researchers, and government representatives have underscored the need to scale up evidence-based practices, including overdose education and naloxone distribution (OEND) and medications for opioid use disorders (MOUD; i.e., methadone, buprenorphine, and naltrexone) across community settings (Pitt et al., 2018; Rao et al., 2021; U.S. Department of Health and Human Services, 2023). Increasing access to naloxone is associated with reductions in fatal opioid overdoses (Giglio et al., 2015; Smart et al., 2021; Tabatabai et al., 2023; Walley et al., 2013). MOUD is associated with decreases in non-fatal and fatal opioid overdoses (Burns et al., 2022; Larochelle et al., 2018; Lim et al., 2023). Priority settings for scaling up OEND and MOUD include healthcare, addiction treatment, criminal-legal settings, social service agencies, and community overdose hotspots.

The HEALing (Helping End Addiction Long-Term®) Communities Study (HCS) seeks to reduce opioid-related overdose deaths in Kentucky, Massachusetts, New York, and Ohio through the Communities That HEAL (CTH) intervention (HEALing Communities Study Consortium, 2020). Informed by the Communities That Care model for implementing drug use prevention interventions for youth (Hawkins et al., 2008), the CTH intervention is grounded in community engagement (HEALing Communities Study Consortium, 2020; Sprague Martinez et al., 2020), with coalitions working through a multi-phase process to prioritize strategies from a compendium called the Opioid-overdose Continuum of Care Approach (ORCCA). The ORCCA largely focuses on six strategies for OEND and ten MOUD strategies across 21 types of venues allowing for tailoring to community and organizational needs (Chandler et al., 2023; Winhusen et al., 2020). Coalitions and research teams develop communication campaigns for deployment across traditional media and social media to build support for MOUD and OEND while reducing OUD stigma (Lefebvre et al., 2020; Stein et al., 2023). Through technical assistance and funding, coalitions and partner organizations work to implement OEND and MOUD strategies across community venues. HCS study investigators meet weekly during the CTH to share information about study progress and implementation efforts.

During the CTH intervention, data-informed selection of OEND and MOUD strategies occurs during coalition meetings through four steps using locally tailored data dashboards that visualize trends in opioid overdose and availability of MOUD and naloxone (Sprague Martinez et al., 2020; Wu et al., 2020; Young et al., 2022). Initial implementation planning and monitoring occurs in coalition meetings. Given their involvement in monitoring progress, coalitions are uniquely suited to understanding whether barriers to scale-up are being overcome. It is anticipated that coalition members’ perceptions of the magnitude of barriers to OEND and MOUD will diminish over time, with greater reductions in CTH intervention communities relative to control communities.

Barriers to scaling up OEND and MOUD in the U.S. are sizable. Financial, regulatory, and stigma-related barriers have impeded OEND scale-up. (Smart and Davis, 2021; Winstanley et al., 2016). Many OEND programs are embedded within syringe service programs (SSPs) operated by local health departments with variable funding and grassroots organizations dependent on fundraising; unstable financial resources often limit how much naloxone they can purchase (Lambdin et al., 2020; Smart and Davis, 2021). OEND has not been widely implemented in addiction treatment or healthcare, and pharmacists report receiving limited training to identify and educate individuals at risk of overdose (Thakur et al., 2020). Moreover, naloxone receipt is riddled with disparities; people of color, unhoused individuals, and those involved in the criminallegal system encounter significant access barriers (Fine et al., 2022; Kinnard et al., 2021; Madden and Qeadan, 2020; Nolen et al., 2022; Rivera et al., 2022). Similar barriers to implementing OEND have been documented in other countries (Lacroix et al., 2018; Miller et al., 2023; Nielsen and Olsen, 2021; Sajwani and Williams, 2022).

MOUD access is limited in many U.S. communities (Carpenedo Mun et al., 2023), with fewer than 30% of individuals with past-year OUD receiving MOUD (Krawczyk et al., 2022; Mauro et al., 2022; Substance Abuse and Mental Health Services Administration, 2021a). The majority of U.S. counties lack opioid treatment programs (OTPs) that dispense methadone, and many rural counties lack buprenorphine providers (Andrilla and Patterson, 2022). Studies have documented a racialized MOUD treatment landscape, with lower access among racially minoritized populations reflecting patterns of systemic racism (Braveman et al., 2022; Goedel et al., 2020; Jones et al., 2023; Mauro et al., 2022).

Although the barriers to scaling up OEND and MOUD are substantial, settings may differ in the magnitude of barriers. The RE-AIM/PRISM implementation science framework guiding the HCS study (Feldstein and Glasgow, 2008; Glasgow et al., 2019; Glasgow et al., 1999) notes that implementation can be strongly impacted by organizational and environmental factors. Healthcare, behavioral health, and criminallegal organizations vary in their resources, structure, and culture. For example, MOUD implementation in criminal-legal settings is challenging given limited medical infrastructure, funding, and prioritization of public safety over public health (Belenko et al., 2013; Brinkley-Rubinstein et al., 2018; Grella et al., 2020); limited implementation has also been observed in other countries (Hayashi et al., 2017; Kouyoumdjian et al., 2018; Polonsky et al., 2015). While healthcare settings, such as emergency departments, have medical infrastructure, they may lack staff capacity for OEND and MOUD given the need to triage and respond to urgent medical needs and may face complex multi-level barriers, including negative staff attitudes (Drainoni et al., 2016; Duber et al., 2018; Lowenstein et al., 2019; Punches et al., 2020; Substance Abuse and Mental Health Services Administration, 2021b); similar barriers have been documented outside the U.S. (Wiercigroch et al., 2021).

External environmental factors, such as geographic location, may impact the magnitude of barriers to expanding MOUD and OEND. Rural communities have fewer healthcare organizations and clinicians who offer MOUD (Andrilla et al., 2019; Cantor et al., 2021; Mitchell et al., 2022). Rural pharmacies lag in naloxone access (Nguyen et al., 2020). These differences may reflect greater stigma towards individuals with OUD (Ezell et al., 2021; Franz et al., 2021; Thomas et al., 2020), harm reduction, and treatment services (Browne et al., 2016; Richard et al., 2020).

This manuscript has two objectives. First, we evaluate the CTH intervention’s impact on changes in perceived magnitude of barriers to OEND and MOUD. We hypothesize coalition members in Wave 1 communities will report greater reductions in barriers than Wave 2 communities. Second, we examine whether reductions in perceived barriers are moderated by rural/urban status or research site, addressing the role of external context in moderating the CTH’s impact.

2. Methods

2.1. Study design, sample, and data collection

The HCS is a multi-site, parallel group, cluster randomized wait-list controlled trial with 34 communities randomized to Wave 1 (intervention) and 33 communities randomized to Wave 2 (control). Full design details have been published (HEALing Communities Study Consortium, 2020). During the grant application process, research sites recruited communities highly impacted by the opioid epidemic and willing to implement MOUD and OEND across health care, behavioral health, and criminal-legal settings with ≥30% of communities being rural. Communities were counties in Kentucky and Ohio. In New York, 13 were counties and 3 were cities/towns. Massachusetts recruited individual cities/towns. Covariate constrained randomization was used within each state to balance communities on rural/urban status, population size, and opioid overdose death rate. One community withdrew post-randomization but pre-intervention; hence, no data were gathered (see Table S1 for community characteristics).

Three rounds of surveys were administered by the four sites to coalition members and key stakeholders in 66 communities. The four sites and the Data Coordinating Center (DCC) collaborated on the survey design (Knudsen et al., 2020). Email invitations and reminders were the primary mode for recruitment; if telephone numbers were available, reminder calls were made. Most surveys were completed via Research Electronic Data Capture (REDCap) web surveys (Harris et al., 2009); sites offered structured telephone interviews, mailed surveys, and surveys distributed during coalition meetings as options. All data were entered into the four sites’ REDCap systems, then securely transferred to the DCC. Three sites offered $50 incentives to participants. This study protocol (Pro00038088) met the guidelines for protection of human subjects and was approved by Advarra Inc., the HCS’s single Institutional Review Board.

The sample was expected to change over time as the early phase of the CTH was focused on developing opioid overdose-focused coalitions. It was anticipated some individuals would join or leave their coalition. Thus, the sample represents a longitudinal panel at the coalition-level, not the individual-level.

Data were collected from participants ≥18 years residing in Wave 1 and Wave 2 communities at three times before and during the active intervention in Wave 1; Wave 2 did not begin the intervention during this period. Time 1 (11/2019 to 1/2020) was baseline (i.e., prior to the intervention), Time 2 (5/2021 to 6/2021) was ~18 months into the CTH for Wave 1, and Time 3 (5/2022 to 6/2022) reflected the final months of the Wave 1 CTH intervention.

Participation criteria evolved as coalition formation was dynamic during this period. For Time 1, individuals were either members of existing coalitions (n=56 communities) or stakeholders knowledgeable about the HCS community if a new coalition was to be formed during the intervention (n=10 communities). Of 3,213 individuals invited, 1,044 individuals (32.5%) participated. For Time 2, Wave 1 individuals were required to have attended ≥2 coalition meetings since January 2020. For Wave 2 communities, a census-based sampling approach was used to invite members of pre-existing coalitions; in communities without a pre-existing coalition, sites recruited individuals likely to join the HCS-designated coalition during the Wave 2 intervention. Of 1,667 individuals invited, 784 participated (47.0%). For Time 3, Wave 1 individuals were required to attend ≥2 coalition meetings from January 2021 to March 2022. In Wave 2 communities, research sites invited potential members of the HCS-designated coalition to be formed during the intervention. Of 1,813 individuals invited, 682 participated (37.6%). Across the three time points, 1,864 unique individuals participated.

2.2. Measures

2.2.1. Venue-Based Magnitude of Perceived Barriers to OEND and MOUD Expansion.

Respondents were asked about the magnitude of barriers to expansion in 16 venues. The item read, “Given the current resources and characteristics of this community, how substantial are the barriers to expanding the delivery of overdose education and naloxone distribution (OEND) to reach more people through…” with 16 venues listed. This format was repeated for MOUD. Twelve venues represented physical locations (Supplemental Table S2). Street outreach, social network strategies, telemedicine, and mobile vans represented efforts to reach at-risk individuals outside brick-and-mortar locations. Respondents rated each venue on a 5-point Likert scale (1=not at all substantial, 5=very substantial).

2.2.2. Independent Variables.

The primary independent variables were wave of randomization (Wave 1 or 2) and timing of assessment (Time 1, 2, or 3).

2.2.3. Moderating Variables:

Two putative moderators were examined: rural/urban status and research site. In Kentucky, Ohio, and 13 New York communities, “metropolitan” counties were coded as urban, and “nonmetropolitan” counties were coded as rural (Ingram and Franco, 2014). In Massachusetts and 3 New York communities where communities were cities/towns, population density of <500 residents per square mile was coded as rural. The moderator for research site corresponded to the four states.

2.2.4. Demographics.

Participants were asked about age, ethnicity, race, gender, educational attainment, and community sector representation (see Table 1).

Table 1:

Baseline Descriptive Statistics of Coalition Members and Key Stakeholders in Kentucky, Massachusetts, New York, and Ohio Communities Participating in the HEALing Communities Study, November 2019-June 20221

Characteristic, statistic Intervention Wave Overall
Wave 1 Wave 2
Number of Respondents 695 763 1,458
Research Site, n (%)
 Kentucky (KY) 146 (21.0) 106 (13.9) 252 (17.3)
 Massachusetts (MA) 188 (27.1) 223 (29.2) 411 (28.2)
 New York (NY) 195 (28.1) 221 (29.0) 416 (28.5)
 Ohio (OH) 166 (23.9) 213 (27.9) 379 (26.0)
Rural-Urban Status, n (%)
 Rural 300 (43.2) 335 (43.9) 635 (43.6)
 Urban 395 (56.8) 428 (56.1) 823 (56.4)
Race/Ethnicity, n (%)
 Non-Hispanic White 619 (89.1) 693 (90.8) 1,312 (90.0)
 Other 76 (10.9) 70 (9.2) 146 (10.0)
Gender, n (%)
 Male 224 (32.2) 257 (33.7) 481 (33.0)
 Female 467 (67.2) 501 (65.7) 968 (66.4)
 Other 4 (0.6) 5 (0.7) 9 (0.6)
Education, n (%)
 <Bachelor’s Degree 136 (19.6) 144 (18.9) 280 (19.2)
 Bachelor’s Degree 192 (27.6) 213 (27.9) 405 (27.8)
 Graduate or Professional Degree 367 (52.8) 406 (53.2) 773 (53.0)
Age, n (%)
 18–34 Years 109 (15.7) 113 (14.8) 222 (15.2)
 35–54 Years 247 (35.5) 281 (36.8) 528 (36.2)
 50–64 Years 271 (39.0) 303 (39.7) 574 (39.4)
 65+ Years 68 (9.8) 66 (8.7) 134 (9.2)
Community Sector(s) of Representation (Collapsed)1,2, n (%)
 Substance Use Treatment 179 (25.8) 194 (25.4) 373 (25.6)
 Health Organizations 190 (27.3) 202 (26.5) 392 (26.9)
 Criminal-Legal System 91 (13.1) 136 (17.8) 227 (15.6)
Perceived Magnitude of Barriers, Mean (SD)
 OEND3 – Criminal-Legal Venues 3.3 (1.2) 3.0 (1.2) 3.1 (1.2)
 OEND - Healthcare/Behavioral Health Venues 2.6 (1.1) 2.7 (1.1) 2.7 (1.1)
 OEND – Other/Non-traditional Venues 2.9 (1.2) 3.1 (1.1) 3.0 (1.1)
 MOUD4 - Criminal-Legal Venues 3.6 (1.2) 3.4 (1.2) 3.5 (1.2)
 MOUD - Healthcare/Behavioral Health Venues 3.0 (1.1) 3.1 (1.0) 3.0 (1.1)
 MOUD - Other/Non-traditional Venues 3.4 (1.2) 3.4 (1.1) 3.4 (1.1)
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Notes:

1

Baseline characteristics defined as the first survey participant responded to (Time 1, November 2019-January 2020: 55.1%, Time 2, May-June 2021: 25.7%, Time 3, May-June 2022: 19.1%)

2

Percentages do not sum to 100% because participants could select multiple community sectors.

3

OEND: Overdose education and naloxone distribution

4

MOUD: Medication for opioid use disorder

2.3. Statistical Analysis

Factor analyses using Time 1 participants (N=931) were performed. Means of items loading onto each factor were used for the resulting scales and applied to subsequent timepoints. The comparative fit index and RMSEA assessed goodness-of-fit. Cronbach’s alpha assessed internal consistency.

Descriptive statistics for each outcome scale and independent variable were calculated using the first survey to which participants responded. The final sample included participants reporting all demographic characteristics and at least one barrier outcome.

To test our hypotheses, we estimated multilevel linear mixed models with random effects of community and time. The fixed effects included time (categorical), wave, and their interaction (tests of interest). Due to the use of constrained randomization (Moulton, 2004), we adjusted for research site, rural/urban status, and baseline opioid overdose death rate (HEALing Communities Study Consortium, 2020) as well as demographic characteristics.

Multilevel linear mixed models were extended to include three-way interactions (tests of interest) between time, wave, and either rural/urban status or research site and subsequent pairwise two-way interactions of each covariate. P-values were adjusted based on the false discovery rate (FDR) method (Benjamini and Hochberg, 1995). Rural-urban or site-specific results corresponding to the impact of the CTH intervention were only presented if the three-way interaction was statistically significant.

Statisticians at the DCC conducted per protocol analyses using SAS v9.4 (SAS Institute Inc., 2013). Tests were two-sided at the 0.05 significance level. Robust, small-sample corrected empirical standard error estimates were used to ensure valid inference (Ford and Westgate, 2017; Kauermann and Carroll, 2001).

3. Results

3.1. Characteristics of Survey Participants

Table 1 displays characteristics of 1,458 respondents using data from their first survey response. Respondents were approximately 90% non-Hispanic white and 66% female. About 85% of respondents were aged 35 years or older. Nearly 81% had at least a bachelor’s degree. Respondents in Wave 1 and Wave 2 communities were generally comparable.

3.2. Exploratory and Confirmatory Factor Analysis of Baseline Survey Items

Descriptive statistics for the 16 venues appear in Table S2. Exploratory factor analyses yielded 3-factor structures for OEND (Table S3) and MOUD (Table S4). Criminal-legal venues loaded onto one factor with internal consistency above α=0.89 for OEND and MOUD. Physical venues for healthcare, behavioral health, and other community-based organizations (henceforth, “Healthcare/Behavioral Health”) loaded onto a second factor with α=0.90 for OEND and MOUD. Non-traditional approaches (i.e., street outreach, social network strategies, telemedicine, mobile vans) loaded onto the final factor (“Other/Non-traditional Venues”) with α=0.82 for OEND and α=0.87 for MOUD. Confirmatory factor analyses resulted in reasonable goodness-of-fit statistics for the OEND (CFI=0.8733, RMSEA=0.1006) and MOUD (CFI=0.9163, RMSEA=0.0966) factor structures.

3.3. Impact of the CTH Intervention

Table 2 displays model estimated adjusted means, mean changes by wave, and differences in mean changes in the six outcomes from Time 1 to Time 3. Results were statistically significant for OEND in Healthcare/Behavioral Health Venues (p=0.015), OEND in Other/Non-traditional Venues (p=0.001), and MOUD in Other/Non-traditional Venues (p=0.020). Wave 1 communities had estimated mean reductions that were 0.26 units lower for OEND in Healthcare/Behavioral Health Venues [95% CI: (0.05, 0.48)], 0.53 units lower for OEND in Other/Non-traditional Venues [95% CI: (0.22, 0.84)] and 0.34 units lower for MOUD in Other/Non-traditional Venues [95% CI: (0.05, 0.62)]. These reductions represented differences of 10.2%, 17.9%, and 11.7%, respectively. There were no significant differences in mean changes from Time 1 to Time 2 (results not shown).

Table 2:

Adjusted Mean Changes in Perceived Barriers to Overdose Education and Naloxone Distribution (OEND) and Medication for Opioid Use Disorder (MOUD) Reported by Coalition Members and Key Stakeholders from Time 1 (Baseline, November 2019-January 2020) to Time 3 (End of Intervention, May-June 2022) in Kentucky, Massachusetts, New York and Ohio

Outcome:1 Perceived magnitude of barriers to…2 N3 Wave 1 Time 1 Adjusted Mean (SE)4 Wave 1 Time 3 Adjusted Mean (SE)4 Adjusted Mean Change (95% CI)5 Wave 2 Time 1 Adjusted Mean (SE)4 Wave 2 Time 3 Adjusted Mean (SE)4 Adjusted Mean Change (95% CI)5 Adjusted Difference in Mean Change (95% CI)6 P-value
OEND - Criminal-Legal Venues 1872 3.28 (0.08) 3.14 (0.12) −0.14 (−0.41,0.13) 2.95 (0.08) 3.04 (0.09) 0.09 (−0.09,0.27) −0.23 (−0.56,0.09) 0.161
OEND - Healthcare/Behavioral Health Venues 2018 2.66 (0.05) 2.39 (0.08) −0.27 (−0.45,−0.10) 2.61 (0.05) 2.60 (0.06) −0.01 (−0.13,0.11) −0.26 (−0.48,−0.05) 0.015
OEND - Other/Non-traditional Venues 1956 3.07 (0.07) 2.52 (0.09) −0.55 (−0.79,−0.32) 3.03 (0.07) 3.01 (0.06) −0.02 (−0.23,0.18) −0.53 (−0.84,−0.22) 0.001
MOUD - Criminal-Legal Venues 1791 3.67 (0.08) 3.53 (0.11) −0.13 (−0.37,0.11) 3.46 (0.08) 3.34 (0.10) −0.12 (−0.30,0.07) −0.02 (−0.32,0.29) 0.918
MOUD - Healthcare/Behavioral Health Venues 1879 2.99 (0.06) 2.78 (0.09) −0.21 (−0.42,−0.01) 3.02 (0.05) 3.00 (0.07) −0.02 (−0.17,0.13) −0.19 (−0.45,0.07) 0.148
MOUD - Other/Non-traditional Venues 1808 3.42 (0.06) 3.03 (0.09) −0.40 (−0.61,−0.18) 3.36 (0.06) 3.31 (0.07) −0.06 (−0.24,0.13) −0.34 (−0.62,−0.05) 0.020
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Notes:

1

Linear mixed-effect model adjusting for community-level covariates including research site (KY, MA, NY, OH), rural/urban status, and baseline opioid overdose death rate; for respondent-level covariates including race/ethnicity (Non-Hispanic White, Other), Gender (Male, Female, Other Identity), Education (<Bachelor’s Degree, Bachelor’s Degree, Graduate/Professional Degree), Age (18–34 Years, 35–49 Years, 50–64 Years, 65+ Years), and community sector of representation (Substance Use Treatment, Health Organization, Criminal- Legal System); for clustering of respondent within community over time; and for the clustering of data within respondents.

2

Higher scores represent more substantial barriers to OEND or MOUD, respectively (1=Not at all substantial to 5=Very substantial).

3

Number of respondent-timepoints included in modeling.

4

Model estimated marginal mean (standard error).

5

Model adjusted difference in mean community perceived barriers at Time 3 (end of evaluation; May-June 2022) minus mean community perceived barriers at Time 1 (baseline, November 2019-January 2020).

6

Model adjusted difference in mean change in Wave 1 minus mean change in Wave 2.

Tables 3 and 4 display model-adjusted estimates corresponding to statistically significant moderating effects. Differences in mean changes in perceived barriers for the Criminal-Legal sector varied across research sites for OEND (p=0.031) and MOUD (p<0.001). Only in Kentucky did the intervention significantly reduce perceived barriers in Criminal-Legal Venues (p=0.002) for OEND and MOUD. Kentucky’s Wave 1 communities had mean reductions that were 1.16 [95% CI: (0.54, 1.78)] and 1.01 [95% CI: (0.47, 1.55)] units lower than for Wave 2 communities for OEND and MOUD, respectively; these represented differences of 30.1% and 20.6%, respectively. There were no statistically significant moderators in the comparisons for OEND and MOUD from Time 1 to Time 2 (results not shown).

Table 3:

Subgroup Analyses by Research Site and Rural/Urban Status for Adjusted Mean Changes in Perceived Barriers to Overdose Education and Naloxone Distribution (OEND) Reported by Coalition Members and Key Stakeholders from Time 1 (Baseline, November 2019-January 2020) to Time 3 (End of Evaluation, May-June 2022) in Kentucky, Massachusetts, New York and Ohio

Outcome:1 Perceived magnitude of barriers to…2 Wave 1 Time 1 Adjusted Mean (SE)3 Wave 1 Time 3 Adjusted Mean (SE)3 Adjusted Mean Change (95% CI)4 Wave 2 Time 1 Adjusted Mean (SE)3 Wave 2 Time 3 Adjusted Mean (SE)3 Adjusted Mean Change (95% CI)4 Adjusted Difference in Mean Change (95% CI)5 P-value6 Test for Effect Modification7
OEND – Criminal-Legal Venues
KY 3.42 (0.16) 2.39 (0.24) −1.03 (−1.49,−0.57) 2.91 (0.16) 3.04 (0.14) 0.13 (−0.30,0.56) −1.16 (−1.78,−0.54) 0.002 0.031
MA 3.25 (0.14) 3.40 (0.24) 0.15 (−0.27,0.56) 3.00 (0.22) 2.83 (0.23) −0.17 (−0.59,0.25) 0.32 (−0.27,0.90) 0.460
NY 3.25 (0.16) 3.48 (0.10) 0.23 (−0.17,0.63) 2.95 (0.10) 3.05 (0.11) 0.10 (−0.09,0.29) 0.13 (−0.32,0.58) 0.658
OH 3.20 (0.09) 3.10 (0.21) −0.10 (−0.47,0.28) 2.91 (0.18) 3.23 (0.10) 0.32 (−0.04,0.68) −0.42 (−0.94,0.10) 0.317
Urban 3.28 (0.11) 3.11 (0.18) −0.17 (−0.55,0.22) 2.91 (0.08) 3.15 (0.10) 0.23 (−0.02,0.49) −0.40 0.579
Rural 3.29 (0.11) 3.16 (0.16) −0.13 (−0.52,0.27) 3.00 (0.15) 2.90 (0.15) −0.10 (−0.30,0.11) −0.03
OEND - Healthcare/Behavioral Health Venues
KY 2.72 (0.14) 2.41 (0.13) −0.31 (−0.65,0.03) 2.55 (0.14) 2.43 (0.16) −0.12 (−0.47,0.22) −0.19 0.263
MA 2.88 (0.04) 2.50 (0.18) −0.38 (−0.68,−0.08) 2.80 (0.10) 2.71 (0.11) −0.09 (−0.35,0.16) −0.29
NY 2.58 (0.11) 2.39 (0.13) −0.18 (−0.50,0.13) 2.45 (0.09) 2.45 (0.09) 0.00 (−0.24,0.24) −0.19
OH 2.50 (0.11) 2.25 (0.13) −0.24 (−0.62,0.13) 2.66 (0.09) 2.72 (0.11) 0.06 (−0.16,0.28) −0.30
Urban 2.68 (0.08) 2.31 (0.08) −0.38 (−0.60,−0.15) 2.56 (0.06) 2.64 (0.08) 0.08 (−0.11,0.27) −0.46 0.263
Rural 2.64 (0.08) 2.48 (0.12) −0.16 (−0.41,0.10) 2.68 (0.09) 2.56 (0.09) −0.12 (−0.28,0.03) −0.03
OEND – Other/Non-traditional Venues
KY 3.20 (0.18) 2.58 (0.21) −0.62 (−1.24,−0.01) 2.95 (0.18) 3.00 (0.11) 0.05 (−0.42,0.52) −0.67 0.579
MA 3.04 (0.09) 2.47 (0.18) −0.56 (−0.95,−0.18) 3.31 (0.11) 3.11 (0.12) −0.20 (−0.54,0.13) −0.36
NY 3.11 (0.12) 2.53 (0.17) −0.58 (−1.11,−0.05) 2.85 (0.15) 2.79 (0.15) −0.06 (−0.58,0.46) −0.52
OH 2.93 (0.11) 2.60 (0.20) −0.33 (−0.77,0.11) 3.01 (0.15) 3.10 (0.09) 0.08 (−0.27,0.43) −0.41
Urban 3.04 (0.10) 2.38 (0.13) −0.65 (−1.00,−0.31) 2.97 (0.10) 3.02 (0.07) 0.05 (−0.25,0.35) −0.70 0.579
Rural 3.12 (0.09) 2.68 (0.11) −0.45 (−0.75,−0.14) 3.11 (0.11) 2.99 (0.09) −0.12 (−0.37,0.12) −0.32
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Notes:

1

Linear mixed-effect model adjusting for community-level covariates including research site (KY, MA, NY, OH), rural/urban status, and baseline opioid overdose death rate; for respondent-level covariates including race/ethnicity (Non-Hispanic White, Other), Gender (Male, Female, Other Identity), Education (<Bachelor’s Degree, Bachelor’s Degree, Graduate/Professional Degree), Age (18–34 Years, 35–49 Years, 50–64 Years, 65+ Years), and community sector of representation (Substance Use Treatment, Health Organization, Criminal-Legal System); for clustering of respondent within community over time; and for the clustering of data within respondents. A separate model is fit for each potential modifier and includes fixed effects for the interaction between the potential modifier and the change in the effect of the intervention over time.

2

Higher scores represent more substantial barriers to OEND (1=Not at all substantial to 5=Very substantial).

3

Model estimated marginal mean (standard error).

4

Model adjusted difference in mean community perceived barriers to OEND at Time 3 (end of evaluation, May-June 2022) minus mean community perceived barriers to OEND at Time 1 (baseline, November 2019–2020).

5

Difference in mean changes of community perceived barriers calculated as adjusted mean difference in Wave 1 minus adjusted mean difference in Wave 2.

6

Adjusted p-values based on the Benjamini-Hochberg (1995) FDR adjustment to account for multiple tests. Reported only if test for effect modification is significant.

7

Adjusted p-values based on Benjamini-Hochberg (1995) FDR adjustment for the interaction between the potential modifier and the change in the effect of the intervention over time.

Table 4:

Subgroup Analyses by Research Site and Rural/Urban Status for Adjusted Mean Changes in Perceived Barriers to Medication for Opioid Use Disorder (MOUD) Reported by Coalition Members and Key Stakeholders from Time 1 (Baseline, November 2019-January 2020) to Time 3 (End of Evaluation, May-June 2022) in Kentucky, Massachusetts, New York and Ohio

Outcome:1 Perceived magnitude of barriers to…2 Wave 1 Time 1 Adjusted Mean (SE)3 Wave 1 Time 3 Adjusted Mean (SE)3 Adjusted Mean Change (95% CI)4 Wave 2 Time 1 Adjusted Mean (SE)3 Wave 2 Time 3 Adjusted Mean (SE)3 Adjusted Mean Change (95% CI)4 Adjusted Difference in Mean Change (95% CI)5 P-value6 Test for Effect Modification7
MOUD – Criminal-Legal Venues
KY 3.79 (0.11) 3.00 (0.23) −0.78 (−1.30,−0.27) 3.28 (0.08) 3.50 (0.07) 0.23 (0.03,0.43) −1.01 (−1.55,−0.47) 0.002 <0.001
MA 3.63 (0.22) 3.73 (0.17) 0.10 (−0.19,0.39) 3.50 (0.26) 2.98 (0.21) −0.52 (−0.91,−0.13) 0.62 (0.13,1.10) 0.068
NY 3.72 (0.16) 3.83 (0.15) 0.11 (−0.32,0.53) 3.58 (0.10) 3.38 (0.15) −0.19 (−0.53,0.14) 0.30 (−0.24,0.84) 0.460
OH 3.54 (0.12) 3.50 (0.20) −0.04 (−0.35,0.27) 3.36 (0.16) 3.53 (0.19) 0.16 (−0.16,0.49) −0.20 (−0.65,0.25) 0.514
Urban 3.65 (0.12) 3.49 (0.16) −0.16 (−0.56,0.24) 3.54 (0.08) 3.52 (0.11) −0.02 (−0.30,0.26) −0.14 0.606
Rural 3.68 (0.10) 3.57 (0.14) −0.11 (−0.37,0.15) 3.34 (0.15) 3.10 (0.17) −0.24 (−0.47,−0.01) 0.13
MOUD- Healthcare/Behavioral Health Venues
KY 2.90 (0.13) 2.59 (0.21) −0.31 (−0.80,0.18) 2.93 (0.12) 2.91 (0.12) −0.02 (−0.43,0.39) −0.29 0.234
MA 3.08 (0.11) 3.03 (0.21) −0.05 (−0.31,0.20) 3.19 (0.10) 3.06 (0.14) −0.13 (−0.40,0.14) 0.07
NY 3.09 (0.10) 2.78 (0.15) −0.31 (−0.73,0.11) 3.01 (0.13) 2.83 (0.14) −0.17 (−0.52,0.18) −0.14
OH 2.79 (0.14) 2.64 (0.20) −0.15 (−0.65,0.35) 2.91 (0.07) 3.12 (0.12) 0.21 (0.08,0.34) −0.36
Urban 3.02 (0.08) 2.70 (0.10) −0.33 (−0.58,−0.07) 3.04 (0.07) 3.06 (0.07) 0.02 (−0.19,0.23) −0.34 0.579
Rural 2.95 (0.09) 2.87 (0.16) −0.08 (−0.40,0.24) 3.00 (0.08) 2.93 (0.14) −0.07 (−0.30,0.16) −0.01
MOUD – Other/Non-traditional Venues
KY 3.51 (0.11) 2.92 (0.26) −0.59 (−1.14,−0.04) 3.22 (0.13) 3.25 (0.12) 0.03 (−0.20,0.26) −0.62 0.263
MA 3.43 (0.11) 3.12 (0.14) −0.31 (−0.51,−0.11) 3.55 (0.10) 3.32 (0.15) −0.23 (−0.56,0.11) −0.08
NY 3.39 (0.14) 3.13 (0.17) −0.26 (−0.77,0.25) 3.35 (0.14) 3.19 (0.14) −0.16 (−0.65,0.34) −0.10
OH 3.34 (0.11) 2.90 (0.20) −0.44 (−0.83,−0.05) 3.27 (0.09) 3.43 (0.14) 0.15 (0.02,0.29) −0.60
Urban 3.40 (0.10) 2.96 (0.13) −0.44 (−0.78,−0.10) 3.37 (0.08) 3.41 (0.08) 0.03 (−0.21,0.27) −0.47 0.606
Rural 3.45 (0.08) 3.09 (0.13) −0.36 (−0.62,−0.10) 3.35 (0.09) 3.17 (0.14) −0.18 (−0.46,0.10) −0.18
Open in a new tab

Notes:

1

Linear mixed-effect model adjusting for community-level covariates including research site (KY, MA, NY, OH), rural/urban status, and baseline opioid overdose death rate; for respondent-level covariates including race/ethnicity (Non-Hispanic White, Other), Gender (Male, Female, Other Identity), Education (<Bachelor’s Degree, Bachelor’s Degree, Graduate/Professional Degree), Age (18–34 Years, 35–49 Years, 50–64 Years, 65+ Years), and community sector of representation (Substance Use Treatment, Health Organization, and Criminal-Legal System); for clustering of respondent within community over time; and for the clustering of data within respondents. A separate model is fit for each potential modifier and includes fixed effects for the interaction between the potential modifier and the change in the effect of the intervention over time.

2

Higher scores represent more substantial barriers to MOUD (1=Not at all substantial to 5=Very substantial).

3

Model estimated marginal mean (standard error).

4

Model adjusted difference in mean community perceived barriers to MOUD at Time 3 (end of evaluation, May-June 2022) minus mean community perceived barriers to MOUD at Time 1 (baseline, November 2019-January 2020).

5

Difference in mean changes of community perceived barriers calculated as adjusted mean difference in Wave 1 minus adjusted mean difference in Wave 2.

6

Adjusted p-values based on the Benjamini-Hochberg (1995) FDR adjustment to account for multiple tests. Reported only if test for effect modification is significant.

7

Adjusted p-values based on Benjamini-Hochberg (1995) FDR adjustment for the interaction between the potential modifier and the change in the effect of the intervention over time.

4. Discussion

This study examined differences in trends of perceived magnitude of barriers to OEND and MOUD to evaluate the effect of the CTH intervention, which leveraged community engagement, health communication campaigns, training and technical assistance, funding, and other resources for community-level scale up OEND and MOUD. Wave 1 had significantly larger decreases in perceived barriers for OEND in Healthcare/Behavioral Health, OEND in Other/Non-traditional Venues, and MOUD in Other/Non-traditional Venues, although these decreases were modest. Significant moderating effects were found by research site, with significantly greater reductions in perceived barriers to OEND and MOUD in Kentucky for Criminal-Legal Venues relative to the other sites.

Wave 1 coalition members reported significant reductions in perceived barriers to implementing OEND in healthcare, behavioral health, other community-based organizations, and non-brick-and-mortar venues. These findings align with research documenting the feasibility of implementing OEND in non-traditional settings (Giglio et al., 2015; Lewis et al., 2016; Owczarzak et al., 2020; Walley et al., 2013; Waye et al., 2019) and health-oriented facilities (Behar et al., 2018; Jawa et al., 2020). Wave 1 coalitions used data-driven strategies to target traditional venues serving people at risk of overdose and community overdose hotspots overdoses where non-brick-and-mortar care systems could be impactful.

A core focus of the CTH intervention was increasing access to MOUD through traditional and non-traditional venues; however, significant reductions in the magnitude of perceived barriers were only observed for MOUD in non-traditional venues. Due to stigma and suboptimal infrastructure, scaling up MOUD in publicly funded addiction treatment and criminal-legal settings has been challenging (Grella et al., 2020; Knudsen et al., 2011; Stewart et al., 2021). Communities implemented novel efforts, including mobile vans and partnerships with faith-based organizations, the use of telehealth, and deployment of outreach workers, navigators, and recovery coaches. Engaging with novel partners may have represented opportunities that were less burdened by prior engagement or relationships. This community-based approach is critical to addressing concerns in rural communities’ perceptions that the healthcare system, particularly MOUD, disproportionately benefits wealthy white communities (Lister and Joudrey, 2023).

The CTH did not reduce perceived barriers related to scaling up OEND and MOUD in criminal-legal settings. Barriers to OEND and MOUD in criminal-legal settings include limited medical infrastructure, restrictive policies, costs, workforce challenges, stigma, and conflicting institutional logics (Alsan et al., 2023; Grella et al., 2020; Grella et al., 2021; Showalter et al., 2021). Some OEND implementation efforts within criminal-legal venues have been successful (Pearce et al., 2019; Wenger et al., 2019; Zucker et al., 2015). The Justice Community Opioid Innovation Network (JCOIN) is supporting several studies seeking to increase MOUD implementation (Ducharme et al., 2021). Significant effects of the CTH were observed in Kentucky for the criminal-legal sector. In their community action plans, Kentucky’s communities selected more strategies targeting the criminal-legal sector than each of the other sites (Chandler et al., 2023). Kentucky’s communities successfully implemented more OEND (n=31) and MOUD (n=29) strategies in the criminal-legal sector than the sum of criminal-legal OEND (n=30) and MOUD (n=28) strategies across the other three sites (Davis et al., 2024). Kentucky’s strategies were implemented across jails, community supervision, specialty drug courts, and pretrial services with support from a team of trained implementation facilitators. Implementing OEND and MOUD strategies across multiple criminal-legal settings may be necessary for coalitions to perceive meaningful reductions in barriers. Other sites experienced issues such as a lack of willingness to engage, lack of need because OEND or MOUD was already available in jails, or the implementation of concurrent policies (e.g., bail reform) that complicated efforts. Future analyses of qualitative interviews conducted with coalition members may further elucidate differences in implementation in criminal-legal settings by site.

The CTH’s impact on perceived barriers did not vary by rural/urban status. Many rural communities have faced challenges to scaling up OEND and MOUD (Andrilla et al., 2019; Cantor et al., 2021; Lister et al., 2020; Mitchell et al., 2022; Ziller and Milkowski, 2020), reflecting longstanding inequities related to healthcare infrastructure, financial resources, stigma, and workforce shortages (Kozhimannil and Henning-Smith, 2021; Ziller and Milkowski, 2020). Previous analyses of this sample found that community stigma toward people treated for OUD, MOUD, and naloxone was higher in rural communities at baseline (Davis et al., 2023). In evaluating the CTH’s impacts on community stigma, rural/urban status was not a significant moderator for stigma toward people treated for OUD or naloxone, but was a moderator for stigma toward MOUD; significant decreases in MOUD stigma were only observed in urban areas (Davis et al., 2024). Both rural and urban communities were successful in implementing OEND and MOUD strategies. The level of implementation achieved in rural communities was similar to their representation in the study, which may explain why rural/urban status was not a significant moderator.

Our findings were impacted by the COVID-19 pandemic, which began in the early phases of the CTH intervention. In many HCS communities, the same individuals tasked with responding to COVID-19 were involved in implementing the CTH, reducing their capacity to prioritize OEND and MOUD implementation. However, the CTH may have assisted some organizations in implementing novel OEND delivery models, such as mail-based OEND. Some communities fast-tracked OEND in jails which were rapidly releasing individuals to limit the spread of COVID-19 (Young et al., 2022). The pandemic brought about significant regulatory changes for MOUD that likely reduced barriers in all communities (Au-Yeung et al., 2021; Priest, 2020). These regulatory changes included exemptions for Drug Enforcement Agency waivers for buprenorphine (Spetz et al., 2022), relaxation of federal requirements of in-person initiation of buprenorphine (Substance Abuse and Mental Health Services Administration, 2020b) and follow-up visits for methadone (Substance Abuse and Mental Health Services Administration, 2020a), and expanded coverage for telehealth (Hughto et al., 2021). The broad scope of these policy changes may have outweighed the CTH’s impact.

This study has several limitations. First, respondents were likely to join coalitions due to professional roles or for personal reasons; their views may not be representative of overall community views. We sampled Wave 1 coalition members at Times 2 and 3 who were active in the coalition, measured by attendance of meetings. The processes of coalition formation, including adapting coalitions which relied heavily on pre-existing networks, resulted in a sample with limited representation from Black and Latino/a/e populations. Qualitative interviews in the 56 communities with pre-existing coalitions indicated that members valued diversity, but the interplay of historical emphases of membership based on professional and leadership roles and systemic racism contributed to coalitions lacking in racial diversity (Chen et al., 2023). The CTH was revised prior to deployment in Wave 2 to emphasize health equity and encourage more diverse HCS-designated coalitions (Chatterjee et al., 2022). The survey participation rate was low, which may reflect the impacts of limited relationships with communities at Time 1, COVID-19 disruptions at Times 2 and 3, and confusion about a simultaneous effort to conduct qualitative interviews at these three time points. Whether these findings generalize to other communities or countries is unknown, but deploying the CTH in new places is an important direction for future research.

The study has important strengths. Despite the relatively low response rate, the sample is likely reflective of coalition members invested in addressing the opioid epidemic; their responses reflect an important viewpoint about what is happening within these communities. In addition, obtaining perspectives from a variety of individuals across 66 communities in four different states may increase the generalizability of our findings. The differences found by venue clearly point to the importance of addressing implementation context.

Given the major disruptions of the COVID-19 pandemic, future research should consider whether similar findings of modest and venue-dependent changes are observed in the Wave 2 communities. The deployment of the CTH in Wave 2 communities occurred from July 2022 to December 2023 when the COVID-19 pandemic had subsided. Although such an analysis would be observational and less rigorous, such data would contribute to a better understanding of the value and limitations of the CTH.

5. Conclusions

To address the opioid overdose crisis, it is critical to scale up MOUD and OEND as rapidly as possible. Our finding of differences in the perceived reduction of barriers by venue indicates the importance of studying the facilitators and implementation strategies for scaling up OEND and MOUD in both traditional and non-traditional settings. It is likely that effective strategies for adoption, implementation, and scale-up vary widely between settings. Future research should focus on how entities can use community engagement and implementation strategies to modify and reduce barriers to implementing OEND and MOUD.

Supplementary Material

1

Highlights.

  • Scaling up overdose education and naloxone distribution (OEND) is challenging.

  • Barriers to scaling up medication for opioid use disorder (MOUD) are substantial.

  • The Communities That HEAL (CTH) intervention reduced perceived barriers to scaling up OEND and MOUD in some venues.

  • The CTH’s impact’s on perceived barriers did not vary by rural-urban status.

Acknowledgements

We wish to acknowledge the participation of the HEALing Communities Study communities, community coalitions, community partner organizations and agencies, and Community Advisory Boards and state government officials who partnered with us on this study.

Disclosure of Funding and Conflicts of Interest

This research was supported by the National Institutes of Health and the Substance Abuse and Mental Health Services Administration through the NIH HEAL (Helping to End Addiction Long-termSM) Initiative under award numbers UM1DA049394, UM1DA049406, UM1DA049412, UM1DA049415, UM1DA049417 (ClinicalTrials.gov Identifier: NCT04111939). Dr. Chandler was substantially involved in UM1DA049394, UM1DA049406, UM1DA049412, UM1DA049415, and UM1DA049417, consistent with her role as Scientific Officer. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the Substance Abuse and Mental Health Services Administration or the NIH HEAL Initiative®.

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 paper.

Footnotes

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Trial Registration

ClinicalTrials.gov (NCT04111939).

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