The Digital Divide and Tele-MOUD: A Qualitative Study of Opioid Community Coalition Perspectives During COVID-19

Abstract

Background:

The rapid expansion of telehealth-delivered medication for opioid use disorder (MOUD) during the COVID-19 pandemic highlighted critical digital divide issues in communities. How community context influences the digital divide remains unclear, creating uncertainty about ameliorating the gaps in access to tele-MOUD.

Methods:

We qualitatively examined the perspectives of 315 opioid community coalition members who were part of the HEALing Communities Study (HCS) to understand how the digital divide created access barriers in urban and rural communities. Primary coding for all interviews used a deductive approach with codes derived from the Reach, Effectiveness, Adoption, Implementation, Maintenance/Practical Robust Implementation and Sustainability Model overarching HCS framework. Secondary coding used the nine determinants of Lythreatis’s 2022 digital divide framework, and inductive thematic analysis was used to identify themes with each of the nine determinants.

Results:

Shared issues across communities related to the digital divide, including trust, social support, technological infrastructure, digital literacy, policy changes, and pandemic-related disruptions, critically influenced telehealth expansion and effectiveness. Rural communities reported specific barriers around infrastructure and socioeconomics, whereas urban communities reported specific barriers around sociodemographic factors.

Conclusions:

To address these digital divide issues, policymakers should continue to invest in rural infrastructure and improve internet access for underserved populations. Clear guidelines are also needed for when tele-MOUD is appropriate versus in-person visits and when urine drug screening is necessary. Additionally, emphasizing patient choice and maintaining in-person care is important to support equitable access to these services.

Introduction

The digital divide refers to inequities in accessing, using, and benefiting from internet technology.¹ While 85% of Americans now have access to broadband services at 25/3 Mbps, the definition of high-speed broadband defined by the Federal Communications Commission, significant disparities in digital access remain^1
–3 and current standards have shifted that definition to 100/20, which will likely create greater disparities.⁴ Beyond technology infrastructure, factors such as financial constraints, digital literacy, and insufficient access to equipment further hinder access.¹ These disparities significantly impact disadvantaged groups (e.g., based on socioeconomic status, race/ethnicity, rurality).⁵

A recent systematic review by Lythreatis and colleagues broke down factors driving the digital divide and synthesized the results into a framework with nine determinants: sociodemographic, personal elements, rights, infrastructure, socioeconomic, social support, type of technology, digital training, and large-scale events.¹ The framework’s authors noted a lack of studies on the divide’s impact during COVID-19 and how it amplified inequities, highlighting the need for further research.

These inequities came to light in March 2020 during COVID-19 as policies shifted to expand telehealth. The Coronavirus Preparedness and Response Supplemental Appropriations Act 2020 (PL 116-123) mandated that telehealth services be reimbursed at the same rate as in-person medical services.⁶ Specifically, one critical health service transformed by pandemic-era telehealth policies was the regulation of medication for opioid use disorder (MOUD) or tele-MOUD.⁷

MOUD encompasses the prescribing of opioid agonists such as methadone or buprenorphine, or opioid antagonists such as naltrexone for those with opioid use disorder (OUD).⁸ Agonist MOUD treatment is highly regulated with strict federal and state policies around dosing, drug testing, and counseling.^9,10 Before COVID-19, regulations generally required an in-person examination to initiate agonist MOUD, creating barriers for patients in underserved communities.^10,11 During the pandemic, the Substance Abuse and Mental Health Services Administration (SAMHSA) suspended the requirement for in-person visits for buprenorphine consultations, thereby allowing telehealth visits.^12,13 SAMHSA also adjusted take-home policies for methadone, allowing up to 28 days of take-home doses and waiving the requirement that doses must be consumed under observation.⁷ Treatment centers and health care systems quickly implemented policies to expand tele-MOUD, including more telehealth visits for counseling and dose adjustments, increased take-home methadone, and suspension of drug testing or counseling requirements.^9,14,15

These efforts represent significant steps toward increased access to tele-MOUD, but it remains unclear how the digital divide complicated tele-MOUD expansion differently in urban and rural settings. Understanding how the nine determinants create unique access barriers in rural and urban communities can inform post-pandemic telehealth policies and may improve equitable access. This qualitative analysis of the HEALing Communities Study (HCS) community stakeholders uses Lythreatis et al.’s framework to describe how the digital divide impacts tele-MOUD in urban and rural communities.¹

Methods

STUDY DESIGN

HCS is part of the broader National Institutes of Health (NIH) Helping to End Addiction Long-term (HEAL) Initiative® and launched in 2019 as a wait-listed community-level cluster randomized implementation trial aimed at reducing opioid overdose deaths in 67 high-burden communities across MA, NY, KY, and OH. One OH community withdrew prior to data collection. Communities were randomized into two waves: Wave 1 started the intervention in Year 1 (2020), and Wave 2 as the wait-list control began the intervention in Year 3 (2022). HCS used local community coalitions as the primary strategy to implement the Communities That Heal intervention, which included three menus of evidence-based practices (EBPs): opioid education and naloxone distribution (OEND), MOUD, and safer prescribing. HCS also developed communication campaigns to support awareness of the EBPs and reduce stigma in the communities.^5,16 Details regarding communities, eligibility, and participation criteria have been previously published.^17,18 The HCS study protocol (Pro00038088) was approved by Advarra Inc., the HCS single institutional review board (IRB).

SAMPLE SELECTION AND RECRUITMENT

Researchers obtained coalition rosters with members’ occupations to inform interviewee selection and recruitment. This allowed them to purposively sample relevant sectors, including health care, behavioral health, and criminal justice/legal perspectives. Additionally, we prioritized the perspectives of the coalition chairperson, coordinator, and intervention champions.^19,20

DATA COLLECTION INSTRUMENT

Interview guides were based on the Reach, Effectiveness, Adoption, Implementation, Maintenance/Practical Robust Implementation and Sustainability Model (RE-AIM/PRISM).^19,20 Further details about the creation of the interview guides, recruitment, and coding methods are outlined in previous manuscripts.^19,20

DATA COLLECTION

During the study, there were qualitative interviews at four timepoints (baseline, follow-up 1, follow-up 2, and follow-up 3). Follow-up 2, conducted between May 2022 and June 2022, was selected for this analysis because it allowed for analysis of persistent digital divide challenges that were not fully addressed in the earlier telehealth expansion phase. Trained research team members from each of the four states conducted qualitative interviews remotely using Zoom meeting software.^19,20 For all interviews, verbal consent was obtained, and a waiver of consent documentation was approved by the IRB.^19,20 Interviews were audio recorded with permission granted by the participants.^19,20 Participants in KY, MA, and NY were compensated with $50 gift cards unless they opted out; participants in OH did not receive compensation.^19,20

DATA ANALYSIS

The research team (in-house) or a professional transcription company transcribed the interview audio recordings. The research team reviewed and corrected transcripts to ensure accuracy. The qualitative software NVIVO 12.0 was used to facilitate analysis. Primary coding for all transcripts used a deductive approach with codes derived from the RE-AIM/PRISM HCS overarching study framework. Details about the primary coding process have been published.^19,20 Following primary coding, the secondary analysis focused only on relevant codes for the research question: Health Services Environment (HSE), COVID-19, Community Risk, Race and Racism, Activism in Response to Racism, and Policy. To examine the relevant code in greater depth, a team of four researchers (S.C., S.B-H., M.B., D.G.-E.)—representing three different study sites—used the nine determinants of the digital divide framework to subcode.¹ The qualitative codebook for the nine determinants is presented in Table 1.

Table 1.

Qualitative Codebook Based on Lythreatis’ Digital Divide Framework

DIGITAL DIVIDE DETERMINANTS	CODEBOOK DEFINITION
Sociodemographic	Sociodemographic includes attributes that may impact access to digital virtual services, such as age, race/ethnicity, gender, geographic disparity, and urban/rural factors. Includes descriptions of the community in rural or urban settings. Double code with other relevant codes such as infrastructure (e.g., internet/broadband), large-scale events (e.g., COVID-19)
Socioeconomics	Socioeconomics includes attributes that may impact access to virtual services such as educational differences, employment, average income in a community, individual level of accessibility impacted by financial hardships, affordability of internet, internet-connected devices, and paying for cell phone minutes. Include comments about socioeconomics having a greater influence than sociodemographic in specific communities. Double code with other relevant codes, such as large-scale events (e.g., COVID-19)
Personal elements	Personal elements include attributes focusing on trust, motivations, privacy, concerns, risk perceptions, and beliefs about virtual services. Attitudes about distrust of virtual services compared to in-person visits, lack of accountability, and lack of engagement are included. Examples could include reports about people lying about current drug use during virtual visits and lack of personal connection between patient and provider. Double code with other relevant codes, such as large-scale events (e.g., COVID-19)
Social support	Social support includes access to peers who have knowledge and can offer support and guidance about virtual services. These peers serve as technical support sources or mental support that assist in accessing virtual services. Include tension codes such as increased social isolation during quarantine impacting attendance of virtual visits, including OUD online support groups. Double code with other relevant codes, such as large-scale events (e.g., COVID-19)
Type of technology	Type of technology includes internet devices or related devices needed to access virtual services such as computers, smartphones, web cameras, headphones, and other videoconferencing equipment. Can double code with socioeconomic when describing the lack of affordability of these items. Double code with other relevant codes, such as large-scale events (e.g., COVID-19)
Digital training	Digital training includes digital literacy and knowledge of virtual services. Includes a focus on telehealth and virtual service delivery training. Includes statements about not knowing how to access technology and virtual services, including lack of training. Includes statements about virtual services being badly designed, badly implemented, or having a bad user experience. Double code with other relevant codes, such as large-scale events (e.g., COVID-19) Excludes statements about internal coalition training for coalition members and remote meetings for coalition members. Instead, focus on OUD virtual services and telehealth.
Rights	Rights include policy, civil liberties, political rights, and net neutrality. Include restrictions around the delivery of specific medications over telehealth, such as methadone. Include changes in telehealth policy during COVID-19 and changes in reimbursement for visits. Also, include organizations’ internal policies that expand or limit access to virtual services. Double code with other relevant codes, such as large-scale events (e.g., COVID-19)
Infrastructure	Infrastructure includes electricity, cell service, and broadband. Also, profit-based discrimination by broadband carriers that choose not to enter specific communities. Includes a lack of options for internet providers, low internet speed, poor cell service, and other larger system-wide issues that impact access to virtual services. Can double code with financial services if lack of broadband carriers’ options impact affordability. Double code with other relevant codes, such as large-scale events (e.g., COVID-19)
Large-scale events	This includes large-scale events that either enhance or diminish the digital divide, such as COVID-19’s impact on virtual service delivery, including policy changes and the effects of social isolation due to quarantines.

Based on Lythreatis’s Digital Divide Framework.

OUD, opioid use disorder.

Inductive thematic analysis was used to identify themes within the nine determinants and explore how the community context of rural or urban areas impacted access to tele-MOUD during the COVID-19 pandemic.¹ The coding team met throughout this process to discuss alignment and themes within the framework. Any differences in categorization or themes were resolved through a consensus-oriented process.

Results

STUDY PARTICIPANTS

Among 315 interviewees, KY made up 79 (25.08%), MA 78 (24.76.%), NY 76 (24.13%), and OH 82 (26.03%). Across all sites, most participants were between 35–49 years old (113 [35.87%]) and 50–64 years old (111 [35.24%]). Most self-identified as either male (111 [35.24%]) or female (185 [58.73%]). Additionally, 284 interviewees (90.16%) identified as non-Hispanic and 269 (85.40%) identified as Caucasian/White. The interview sample also consisted of 15 (4.76%) African American/Black individuals, 2 (0.63%) American Indian/Alaska Native individuals, and 2 (0.63%) Asian individuals. There was representation from a variety of educational backgrounds, but more than half of the interviewees had either a bachelor’s degree (22.86%) or a master’s degree (42.22%). Of the interviewees,135 (42.86%) represented rural communities, while 180 (57.14%) represented urban communities. See Table 2 for participants’ characteristics.

Table 2.

Participant Characteristics

CHARACTERISTIC	TOTAL PARTICIPANTS (N = 315)
Research site, n (%)
Kentucky	79 (25.08%)
Massachusetts	78 (24.76%)
New York	76 (24.13%)
Ohio	82 (26.03%)
Age, n (%)
18–34 years	40 (12.70%)
35–49 years	113 (35.87%)
50–64 years	111 (35.24%)
65–74 years	30 (9.52%)
75 years or older	3 (0.95%)
Refused	1 (0.32%)
Missing	17 (5.40%)
Ethnicity, n (%)
Non-Hispanic or Non-Latino/a	284 (90.16%)
Hispanic or Latino/a	13 (4.13%)
Refused	1 (0.32%)
Missing	17 (5.40%)
Race,^a n (%)
African American/Black	15 (4.76%)
American Indian/Alaska Native	2 (0.63%)
Asian	2 (0.63%)
Caucasian/White	269 (85.40%)
Native Hawaiian or Other Pacific Islander	0 (0.00%)
Other	8 (2.54%)
Refused	3 (0.95%)
Missing	18 (5.71%)
Gender, n (%)
Male	111 (35.24%)
Female	185 (58.73%)
Trans male/Trans man	1 (0.32%)
Trans female/Trans woman	0 (0.00%)
Genderqueer	0 (0.00%)
Other identity	0 (0.00%)
Missing	18 (5.71%)
Education, n (%)
Less than HS diploma	1 (0.32%)
HS degree or equivalent	9 (2.86%)
Some college, no degree	20 (6.35%)
Associate degree	15 (4.76%)
Bachelor’s degree	72 (22.86%)
Master’s degree	133 (42.22%)
Professional degree	22 (6.98%)
Doctorate degree	22 (6.98%)
Refused	2 (0.63%)
Missing	19 (6.03%)

Participant characteristics collected during HEALing Communities Study Qualitative Round 3 (follow-up 2) data collection from May 2022 to June 2022.

Data pulled on March 21, 2024.

Percentages may not add up to 100 due to participants being able to select more than one race.

THEMATIC SIMILARITIES BETWEEN RURAL AND URBAN COMMUNITIES

After comparing rural and urban communities, several overarching themes emerged around the shared experience of tele-MOUD during the pandemic. Specifically, commonalities became evident within the digital divide determinants of personal elements, social support, type of technology, digital training, rights (civil liberties, political rights, policy), and large-scale events (see Table 3 for exemplary quotes for each theme).

Table 3.

Thematic Similarities Between Rural and Urban Communities Participating in the HEALing Communities Study

THEME	EXEMPLARY QUOTES
Personal elements: Coalition members noted perceiving lower levels of trust and engagement among community members using tele-MOUD compared with those who had experienced in-person MOUD visits.	“I’ve literally had people tell me that the second they knew they weren’t being drug tested, there was no way anybody would know. And they went, and they relapsed because that accountability was no longer there. And they needed that…I think that telehealth can be good for long-distance stuff, but I think that they still need to have some accountability given.” (NY #5157—Rural)“Telephonic outreach does not work when you are trying to reach out to someone in the throes of addiction. Right? Are they going to pick up the phone? Anybody’s guess. Probably not. That’s what’s changed. I think that’s part of what’s made things worse.”(MA #4301—Urban)
Social support: Coalition members believed that pandemic isolation from lockdowns and quarantines worsened the opioid epidemic, but tele-MOUD helped to mitigate the gaps.	“At the same time, it gave rise to a few different groups [that] offer telehealth models now that have stepped in to fill some of that gap and some of that need for folks who don’t have direct access to a local community of support... I think the overall impact was negative obviously for all the obvious reasons, but I also think it gave rise to some innovation in terms of treatment accessibility as well.” (OH #2208—Rural) “Everybody’s been so isolated. I think that isolation has really increased the risky behavior throughout our region, throughout this nation I would expect. Treatment facilities are still looking at online options. I will say this, this is a good thing about the COVID 19 that we did find, for some of our programs, because we were providing treatment resources online. It gave some of our treatment providers the opportunity to, this is a little sneaky, to have a little bit of a look at the environment that patients living in…” (KY #3771—Urban)
Type of technology: Coalition members felt that service providers and their communities lacked the technological equipment to fully implement tele-MOUD operations, but innovative approaches arose to address the problem.	“And we found that people weren’t getting access because they don’t have phones, they don’t have Wi-Fi. So, I think that the state and many of the organizations did a phenomenal job at providing those for people. The tablets, and we still actually have the program running where they can come and get their phones or a $20 tablet, so that they’re able to do the telehealth appointments.” (MA #3634—Rural)“Well, I think because treatment centers are doing more virtual visits, but the person has to come to their air, come to their place of business and use their computer it really hasn’t been all that helpful to go virtual.”(OH #6097—Urban)
Digital training: Coalition members described gaps in digital knowledge and training in their communities that impacted tele-MOUD.	“I think that, you know, again, because of the isolation and lack of knowledge about Telehealth, I think that people haven’t been seeking assistance.” (OH #0391—Rural)“A lot of people had never done Zoom. They don’t even have internet—some of these folks didn’t—so they didn’t know how to go about getting on a Zoom meeting and talking to someone and getting the help that they needed... I mean, it really hurt them.”(KY #3834—Urban)
Rights: Coalition members noted that the policy to expand tele-MOUD during the COVID-19 pandemic overall offered benefits to their communities but was hindered due to the digital divide.	“New York State really did a lot with telehealth. They loosened up the rules on things people can do to keep people, you know, able to be seen and able to be accessed and everything. Not everybody has access to the Internet, not everybody has a telephone. It’s still very much appreciated what New York did. So, you know, that made it a challenge.” (NY #0479—Rural)“I’d argue to say in some ways it has helped access because now we are able to see patients via telehealth. And these are patients that normally I would be like, ‘Huh, can’t get here. I don’t know what I’m going to do.’ Like, I have my license says, ‘I have to see you and that I have to do an in-clinic induction for Suboxone.’ And now that we can do home inductions, we can do telehealth visits.”(MA #3905—Urban)
Large-scale events: Despite the strain on their communities, coalition members reported seeing the expansion of tele-MOUD as a silver lining of the pandemic.	“Some has been for the better and some for the worse. I do think that as far as treatment services that the entire SUD [substance use disorder] industry had to just kind of flip on its side. I mean, I know that we did. I know that we were really blessed to have put in a whole bunch of things that we didn’t know that we would need the way that we needed them right before COVID. And so, we started a really successful telehealth practice here.” (KY #3252—Rural) “I am hopeful that the telehealth piece will stay, even beyond COVID. I think that as a whole on within the OUD field here in Ohio we’ve been able to show the benefit. You know, if you do if you do a cost benefit analysis, we’re going to come out on the benefit use of that. Especially for folks who don’t have transportation and that telehealth pieces is very beneficial. Or for people who work during our OTP normal hours, and we have a Zoom video group that they’re able to attend. So, I think that the telehealth piece will maintain beyond COVID.” (OH #6599—Urban)

Authors’ qualitative thematic analysis.

MOUD, medication for opioid use disorder.

DETERMINANT: PERSONAL ELEMENTS

Coalition members noted lower levels of trust and engagement quality among those using tele-MOUD compared with in-person MOUD visits.

Participants reported that tele-MOUD resulted in less accountability and engagement for persons with OUD. They expressed concern that without any in-person visits, those with OUD might be more likely to lie during virtual appointments or even relapse. They noted that the loss of in-person connections hampered service providers’ abilities to accurately monitor treatment.

DETERMINANT: SOCIAL SUPPORT

Coalition members believed that pandemic isolation from lockdowns and quarantines worsened the opioid epidemic, but tele-MOUD helped to mitigate the gaps.

Participants noted that the lack of social support due to isolation and no in-person visits during the pandemic seemed to be a catalyst that worsened the opioid epidemic. Comments suggested that they did not view tele-MOUD as a replacement for in-person social support but, instead, as a stopgap implemented during the height of the crisis. Once the pandemic waned, their perceptions of tele-MOUD shifted toward this modality as a useful supplement to in-person services.

DETERMINANT: TYPE OF TECHNOLOGY

Coalition members felt that service providers and their communities lacked the technological equipment to fully implement tele-MOUD operations, but innovative approaches arose to address the problem.

During the pandemic, short-term supply chain issues and longstanding difficulties with access to technology limited telehealth expansion. Some coalition members described providers in their communities struggling to find webcams and noted that people lacked internet-enabled devices, phones, and other equipment required for virtual visits. In response, some agencies deployed efforts to supply no or low-cost internet-enabled devices to help mitigate these deficits.

DETERMINANT: DIGITAL TRAINING

Coalition members described gaps in digital knowledge and training in their communities that impacted tele-MOUD.

Coalition members described knowledge limitations in accessing and using virtual platforms and noted that these limitations impeded the full utilization of tele-MOUD. They reflected that it was difficult for new technology to become ubiquitous in their communities without adequate corresponding training or support.

DETERMINANT: RIGHTS (CIVIL LIBERTIES, POLITICAL RIGHTS, POLICY)

Coalition members noted that the policy to expand tele-MOUD during the COVID-19 pandemic overall offered benefits to their communities but was hindered due to the digital divide.

The abrupt loosening of policies to expand access to tele-MOUD dramatically changed how organizations offered services to those with OUD. Interviewed coalition members noted the tele-MOUD helped people attend more appointments by removing barriers like transportation, taking time off work, and arranging childcare.

DETERMINANT: LARGE-SCALE EVENTS

Despite the strain on their communities, coalition members reported seeing the expansion of tele-MOUD as a silver lining of the pandemic.

The multitude of challenges experienced during the COVID-19 pandemic and its subsequent lockdowns dramatically shifted the landscape of tele-MOUD. Despite these challenges, coalition members maintained a resilient and realistic outlook on the long-term impacts of providing virtual services to those with OUD in their communities. They described rapid changes in the HSE as their communities as tele-MOUD expanded in their communities and expressed hope that services would continue to adapt to. Their communities’ specific needs for better utilization.

THEMATIC DIFFERENCES: RURAL AND URBAN

Interviewees from urban and rural communities reported notable differences within their communities when considering the infrastructure, socioeconomic, and sociodemographic digital divide determinants. These community differences highlighted disparities that impacted access and expansion to tele-MOUD as described further below (see Table 4 for exemplary quotes for each theme).

Table 4.

Thematic Differences Between Rural and Urban Communities Participating in the HEALing Communities Study

THEME	RURAL EXEMPLARY QUOTE	URBAN EXEMPLARY QUOTE
Sociodemographic and socioeconomic: Rural participants reported that economic barriers were the main drivers of the digital divide, while urban participants more often emphasized the impact of disparities tied to race or ethnicity in their communities.	“Now that’s a problem for some people out here because some people just don’t have computers, or they don’t have a cell phone that works. There’s no reception or they can’t afford it. And we try to get the word out that there are extremely discounted services for cable and things like that. But I think it has made it more difficult for some people to get to appointments. They are kind of overwhelmed, but it has also provided other options.” (MA-#4040—Rural)	“But still, folks go to libraries to get on the computer because they don’t have the technology at home. And so again, race and class are going to be intimately tied to the ability to receive services.” (NY #0126—Urban)
Infrastructure: Rural coalition members spoke of widespread issues with broadband and cell service infrastructure that contributed to the underutilization of MOUD virtual services in their communities.	“It’s spotty. And being in the rural counties, to think that here we are in 2022, there are still people who don’t have access to broadband or Internet …it’s become a privilege. I know that the county is looking to invest in broadening that bandwidth so that more people can have access to it, because again that’s one of the things that came out of the pandemic is, we want to provide the services and we can do it here, but you can’t receive it, and it’s just not helpful. So, it’s definitely hindered, I think, service delivery.” (NY #0658—Rural)	“For example, into the communities that, heck a lot, a lot of our outlying, outside of [city] area, especially heading toward [rural] county. There’s no Internet service, for example. So, to even provide telehealth to those areas is difficult to do.” (OH #0338—Urban)

Authors’ qualitative thematic analysis.

DETERMINANTS: SOCIODEMOGRAPHIC VERSUS SOCIOECONOMIC

Rural participants reported that economic barriers were the main drivers of the digital divide, while urban participants more often emphasized the impact of disparities tied to race or ethnicity in their communities.

Rural participants described socioeconomic factors impacting their communities’ ability to equitably access virtual services compared to demographic factors. Specifically, the costs of telecommunication services, including internet service, computers, smartphones, and webcams, create significant financial barriers in rural communities. In contrast, urban coalition members were more likely to include descriptions of sociodemographic barriers linked to race and class statuses with equitable access in their communities.

DETERMINANT: INFRASTRUCTURE

Rural coalition members spoke of widespread issues with broadband and cell service infrastructure that contributed to the underutilization of MOUD virtual services in their communities.

Rural participants described infrastructure support for internet access as playing a significant role in impacting the accessibility of tele-MOUD in their communities. Interviewees described issues such as lack of high-speed access and unreliable connections as barriers to accessing tele-MOUD. Alternatively, urban participants recognized that infrastructure was better in their communities and acknowledged that broadband was less reliable in more rural areas.

Discussion

This study aimed to identify how the digital divide determinants complicated tele-MOUD expansion in rural and urban communities. The findings reveal how these determinants create access barriers in both communities. By analyzing coalition members perspectives in these communities in the HCS, we identified key themes within each determinant of Lythreatis et al.’s digital divide framework.¹

Our findings highlight both shared challenges and distinct differences across communities. Shared issues related to trust, social support, technology access, digital training, policy, and pandemic-related disruptions influenced telehealth expansion and effectiveness. However, some determinants revealed unique barriers specific to rural and urban communities.

Across communities, coalition members highlighted mistrust of patients with OUD when care was exclusively provided through telehealth, perceiving these patients with OUD as less accountable and less engaged. This issue of mistrust likely stems from stigma toward individuals with OUD.²¹ Evidence from related analysis from the HCS suggests that shifting attitudes toward MOUD may be accomplished through community-engaged approaches involving education, facilitation, data-driven decision-making, and communications campaigns.²²

Subject matter experts have recently called for broadband internet access to be considered a social determinant of health.²³ Rural coalition members expressed specific frustrations with tele-MOUD expansion, highlighting a systemic oversight regarding digital readiness across different populations and organizations. These findings are consistent with previous research on rural–urban disparities in telehealth adoption, particularly regarding limited access in rural areas.^3,24 Our findings support this assertion, as rural and underserved communities appeared most affected by limited internet access and digital tools that were not appropriate or easy to use. In contrast, urban coalition members more frequently highlighted racial and ethnic disparities in the delivery of tele-MOUD, reflecting broader health equity concerns in urban settings.²⁵ Despite these challenges, coalition members across rural and urban areas viewed the pandemic-driven expansion of tele-MOUD as a positive development that complemented in-person care and addressed scheduling and transportation barriers.

IMPLICATIONS FOR POLICY AND PRACTICE

The Centers for Disease Control and Prevention declared the COVID-19 Public Health Emergency Declaration in 2023.²⁶ While telemedicine was widely utilized during the pandemic, concerns about physical examinations and the digital divide led to a decreased preference for virtual visits postpandemic.²⁷ Nonetheless, the Drug Enforcement Agency and the Department of Health and Human Services have expanded the COVID-19 telemedicine flexibilities through December 2025, with the final set of regulations under development.²⁸ Despite broader access to tele-MOUD, critical gaps remain in the areas of trust, tele-MOUD guidelines, and infrastructure.

Coalition members identified the lack of trust in tele-MOUD compared with in-person care as a barrier. This mistrust reported among participants highlights the need to build trust with clear guidelines on when tele-MOUD is appropriate versus in-person visits and when urine drug screening (UDS) is necessary. The American Telemedicine Association provides guidance for physicians to establish rapport and routine UDS has been adapted to telehealth-based opioid treatment platforms.^29,30 Developing clear guidelines and protocols for virtual patient interactions and remote UDS will be essential for expanding trust in tele-MOUD. Additionally, emphasizing patient choice and maintaining in-person care is important to support equitable access to tele-MOUD.³¹

Tele-MOUD expansion was constrained by poor infrastructure and digital literacy. While the Infrastructure Investment and Jobs Act (2021) allocated nearly $65 billion to support expanding the broadband infrastructure, particularly in rural areas, these efforts should be continued and expanded.³² Building off the CARES Act, which invested in internet accessibility in communities, policymakers must continue to prioritize infrastructure investment in rural areas and subsidize low-income populations to acquire equipment and internet services.³³ A comprehensive approach is necessary to ensure access and optimal health outcomes across socioeconomic, racial, and ethnic lines, starting with collaborating with trusted community stakeholders who can guide decisions about how to scale and promote tele-MOUD services.³⁴

Next, telehealth treatment providers should engage in technology training³⁵ and include telemedicine in Notices of Privacy Practices.³⁶ Technology vendors need to continue to prioritize developing telehealth platforms that protect patient privacy and information security.^37,38 By addressing these challenges and capitalizing on telehealth opportunities, health care providers and policymakers can improve access to OUD treatment and reduce the disparities between communities.

Our findings imply that specific community needs must be considered to support increased access to tele-MOUD. The opioid settlement funds present states with an opportunity to enact this approach.^39,40 These funds have resulted from litigation with opioid manufacturers and pharmacies and represent a considerable windfall to states and local communities hardest hit by the opioid epidemic. Many states, such as OH, are establishing local boards to make decisions about allocating these resources.⁴¹ This approach will help communities overcome the persistent inequities that form the digital divide.³¹

LIMITATIONS

This study has several limitations. As a qualitative analysis, our findings may not be generalizable to all communities. The perspectives captured are those of opioid coalition members from a variety of backgrounds rather than only patients or providers directly involved in tele-MOUD services. Additionally, the interview guide focused on treatment services broadly rather than focusing on specific challenges such as the delivery of physician appointments or unique challenges for buprenorphine or methadone.

CONCLUSIONS

The expansion of tele-MOUD during the COVID-19 pandemic revealed both opportunities and challenges, such as expanded access to treatment and the critical need to further close the digital divide. Future research should explore patient and provider experiences with tele-MOUD, quantify the impact of digital divide factors on treatment outcomes, and evaluate the effectiveness of interventions designed to address telehealth disparities. Longitudinal studies are needed to examine the long-term impacts of ongoing investments in broadband infrastructure and expanded tele-MOUD access on opioid-related outcomes. While the rapid expansion of telehealth during COVID-19 improved access to tele-MOUD, the digital divide remains a significant barrier to equitable care. Addressing these disparities through targeted interventions and policy changes is essential to maximize the potential of telehealth in addressing the ongoing opioid crisis.

Footnotes

Acknowledgments

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

Authors’ Contributions

A.A.: Writing—original draft and writing—reviewing and editing; S.A.-H.: Reviewing; S.B.-H.: Formal analysis; M.B.: Formal analysis, writing—original draft, and writing—reviewing and editing; J.L.B.: Writing—reviewing and editing; S.C.: Conceptualization, formal analysis, writing—original draft, writing—reviewing and editing; J.D.: Writing—reviewing and editing; M.-L.D.: Writing—reviewing, and editing; D.G.-E.: Writing—original draft and writing—reviewing and editing; M.G.: Writing—original draft and writing—reviewing and editing; D.G.: Formal analysis, writing—original draft, and writing—reviewing and editing; T.R.H.: Reviewing and editing; A.S.M.: Reviewing and editing; S.R.: Writing—reviewing and editing; D.M.W.: Writing—reviewing and editing and supervision.

Disclaimer

The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, SAMHSA, or the NIH HEAL Initiative.

Disclosure Statement

The authors report no conflicts of interest.

Funding Information

This research was supported by the NIH and the SAMHSA through the NIH HEAL (Helping to End Addiction Long-term) Initiative under award numbers UM1DA049394, UM1DA049406, UM1DA049412, UM1DA049415, and UM1DA049417 (ClinicalTrials.gov Identifier: NCT04111939).

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