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Published in final edited form as: Am J Prev Med. 2023 Apr 8;65(4):618–626. doi: 10.1016/j.amepre.2023.03.019

Trends in Geographic Proximity to Substance Use Disorder Treatment

Kandice A Kapinos 1,2, Maria DeYoreo 3, Tadeja Gracner 3, Bradley D Stein 4, Jonathan Cantor 3
PMCID: PMC10524906  NIHMSID: NIHMS1899216  PMID: 37037326

Abstract

Introduction:

This study aims to assess trends in the number and characteristics of substance use disorder treatment (SUDT) facilities within the county of residence of adults aged 50+ over time.

Methods:

Using retrospective longitudinal data from the 1992–2018 Health and Retirement Study (HRS) merged to the county-level data on all licensed treatment facilities in the country, linear mixed models were estimated to calculate geographic accessibility to SUD treatment (SUDT), adjusted for person-level demographics, state-level controls, and calendar year fixed effects. Analysis conducted in 2022.

Results:

Overall, older adults experienced a decline in the average number of SUDT facilities within their counties of residence from 4.80 per 100,000 residents (95% CI= 4.69, 4.92) in 1992 to 4.50 (95% CI: 4.35, 4.64) in 2018. However, the number accepting Medicare increased from 0.26 (95% CI: 0.21, 0.30)) in 1992 to 1.88 (95% CI: 1.80, 1.96) facilities per 100,000 (42% of facilities); Medicaid increased from 0.20 (95% CI: 0.13, 0.26) in 1992 to 3.50 (95% CI: 3.39, 3.62) facilities per 100,000 (78% of facilities) in 2018. Older adults living in more rural areas experienced the most growth in SUDT facilities per capita in their counties but with less significant growth in facilities offering medication for opioid use disorder (OUD) relative to those living in more urban areas.

Conclusions:

Despite increases in the number of SUDT facilities in rural areas, there has been less growth in nearby facilities offering evidence-based medication treatment for OUD.

Introduction

From 1999 to 2019, the rate of drug-induced deaths (not including alcohol) increased by more than 383% among adults aged 55 or older from 4.07 to 19.69 deaths per 100,000 at a time when the rate of all other non-drug and non-alcohol deaths decreased by 30% (from 3,457.12 to 2,404.32 deaths per 100,000).1, 2 In contrast, the rate of drug-induced deaths among younger populations (aged 15–54) increased by 226% over the same period (from 10.37 to 32.34 deaths per 100,000).2

Drug overdoses due to opioids are a particularly pressing and salient problem. Twenty-eight percent of older adults receiving substance use disorder treatment (SUDT) use opioids, and the fraction of deaths attributable to opioids has increased to 5%, 1.7%, and 0.07% among adults aged 45–54, 55–64, and 65 plus, respectively.3 More than 70% of overdose deaths in the U.S. involve an opioid,4 and the opioid overdose death rate has increased the most for adults aged 55 to 64 (624%) since 1999. And while the number of treatment admissions for older adults with opioid use disorder (OUD) increased from 41.2% to 53.5% between 2004 and 2013, the percent of older adults with OUD with unmet needs is still high.5, 6

About 4.8 million adults aged 50 or older (more than 1 million of whom are 65+) experience substance use disorder (SUD) in a given year.7, 8 A study using claims data covering more than 43 million people found the prevalence of diagnosed opioid use disorder (OUD) peaks around two age bands: the first is between ages 20 and 26 at around 5.5 and 10 per 1000 for females and males, respectively, and the second occurs between ages 66 and 72 at 6 and 7 per 1,000 for females and males, respectively.9

There are several unique challenges to treating SUD and OUD, in particular, among older adults that may have influenced the development and supply of these services for older populations.10 First, the prevalence of multiple chronic conditions among this population is high: approximately 85% of older adults have at least one chronic condition, and 60% have at least two.11 Patients with multiple comorbidities tend to be treated with more prescription drugs, on average, and are medically more complex to treat.12, 13 In addition, aging affects the pharmacokinetics and hepatic metabolism of drugs which commonly increase the risk for adverse effects.14–16 A related challenge in treating older adults with SUD is that they are more likely to suffer chronic pain,17, 18 which is often treated with opioids, potentially increasing the risk for developing OUD.19, 20 The confluence of these challenges increases the complexity of SUD treatment for adults as they age in ways that are different from treating younger adults and adolescents, populations for whom substance use initiation is more likely21 and number of comorbidities is lower.

There has been a range of efforts to increase access to effective medications for OUD (MOUD) such as methadone and buprenorphine.22 One challenge with methadone is the requirement that treatment be dispensed through an opioid treatment program (OTP), where patients have historically been required to come to the clinic at least six days per week for at least the first three months of treatment.23 In contrast, buprenorphine could be prescribed for use at home, necessitating less frequent clinic visits and facilitating treatment in office-based settings, increasing potential access. Since buprenorphine’s approval in 2002 to treat OUD until recent changes as a result of the Consolidated Appropriations Act of 2023, it could only be prescribed by clinicians who had obtained a waiver (called the “X-waiver”) from the Drug Enforcement Administration,24 and many communities, particularly rural communities, did not have such a waivered prescriber.25–27

Historically, older adults may have faced significant challenges to receiving MOUD as OTPs28 or buprenorphine-waivered providers (whether in a treatment facility or office-based setting)29 were less likely to be located in rural areas. As a greater share of rural residents are older, lack of nearby providers might have generated greater barriers for older adults.30 The lack of nearby providers in rural areas may be more difficult for older adults who are more likely to face mobility and transportation challenges.30 Finally, prior to 2020, older adults covered by Medicare would not have had methadone covered, which meant reduced access to MOUD.31

To better understand older adults’ access to specialty SUD treatment, and how it has evolved over time, a longitudinal sample of older adults was studied. The nationally representative Health and Retirement Study (HRS) data allowed for examination of adults aged 50 years and older, as well as stratification of this group by those under age 64 and 65 years and older. The main outcomes were proximity to any SUD facility, to a facility that offers MOUD for treatment, and to a facility accepting Medicare and Medicaid as a form of payment.

Methods

Study Sample

The Health and Retirement Study (HRS), a nationally representative longitudinal study of U.S. adults aged 50+ from 1992 to 2018 was merged to the Mental health and Addiction Treatment Tracking Repository (MATTR) data. The MATTR was compiled longitudinally using the addresses of licensed SUDT facilities as reported in the National Directory of Drug and Alcohol Abuse Treatment Facilities directories.32–34 These directories are derived from facility responses to the National Survey of Substance Abuse Treatment Services (N-SSATS) for facilities that agree to be listed. Whereas the N-SSATS data is only available at the state level over time, the MATTR (available from the authors) collates the directories to follow treatment centers over time at the street address-level and includes information regarding forms of payment accepted and types of treatment offered (e.g., buprenorphine for OUD (available consistently in MATTR data since 2008), methadone maintenance treatment (hereafter methadone)). County-level data on the number of different types of treatment facilities were merged to the HRS to calculate geographic availability at the individual level.

Measures

The key outcome was the count of SUDT facilities per 100,000 residents within the HRS respondent’s county of residence. Counts of SUDT facilities by different insurance accepted and services offered were also examined. All count measures are presented as “per capita” rates defined by the total population in the county and not just the population of older adults because most facilities serve the total adult population.

To address heterogeneity in size and population density of counties, a vector of the USDA’s rural-urban continuum codes were included as covariates, which includes nine categories of county urbanicity.35 In addition, the following respondent-level covariates were included from the HRS: age, sex, race (White, Black/African American, or Other/Multiple), Hispanic origin, marital status (married versus not), type of health insurance coverage (own private coverage, spousal private coverage, Medicare, Medicaid, Veterans Affairs (VA), other coverage, or no coverage), labor force status (working, partially retired, completely retired), and survey year. All models included year fixed effects and state-level control variables, including an indicator for whether Medicaid had been expanded in the state for each year and state fixed effects. Year of Medicaid expansion was included because it is associated with increases in the availability of MOUD at SUDT facilities.36

Statistical Analysis

Linear mixed models were fit regressing the number of SUDT facilities in the respondent’s county of residence (per capita) on the covariates listed above, year, and state fixed effects. All models included respondent-level sampling weights, standard errors clustered at the person level and were estimated using xtreg with adjusted means generated using margins in Stata37 and figures generated using R. This study was deemed exempt by the RAND IRB.

Results

A total of 12,158 older adults from 1992 to 2018 (110,043 person-years) were included in the analytic sample with a mean age [SD] of 65.69 [6.57] and 54% were female. About 9% identified as Hispanic, 79% as White/Caucasian, 17% as Black/African American, and 4% as multiple or other categories. Across all years, approximately 39% were currently working, 54% were Medicare beneficiaries, and 6% were Medicaid enrollees (descriptive statistics are in Appendix).

Overall, the total number of SUDT facilities in older adults’ counties of residence declined slightly from 4.80 per 100,000 residents (95% CI= 4.69, 4.92) in 1992 to 4.50 (95% CI = 4.35, 4.64) in 2018 (Figure 1). Over the same time, older adults experienced increases in the average number of SUDT facilities per 100,000 accepting Medicaid or Medicare. In 1992, older adults had approximately 0.20 (95% CI: 0.13, 0.26) and 0.26 (95% CI: 0.21, 0.30) facilities per 100,000 accepting Medicaid and Medicare in their counties of residence, respectively. This translated to 4 to 5 percent of all facilities in a county, on average. By 2000, these numbers had increased to 2.98 (95% CI: 2.93, 2.53) and 2.25 (95% CI: 2.21, 2.30), respectively. The rates of Medicaid facilities have continued to increase to 3.5 facilities per 100,000 (95% CI: 3.39, 3.62) in 2018 (78% of all SUDT facilities in the county). The rates of Medicare facilities peaked in 2008 and then remained relatively stable thereafter at 1.88 per 100,000 (95% CI: 1.80, 1.96) in 2018 (on average approximately, 42% of all SUDT facilities in a county). Older adults have had approximately the same number of SUDT facilities offering methadone in their counties over the last 26 years (Figure 1, dotted line). By 2018, older adults had about 1.10 SUDT (95% CI: 1.06, 1.14) facilities offering buprenorphine per 100,000 residents in the county, on average, which represents a statistically significant increase since 2006.

Figure 1.

Figure 1.

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Average Per Capita Number of SUD Treatment Facilities within County of Residence Among HRS Respondents, from 1992 to 2018

Notes: Means derived post-estimation adjusted for respondent age, sex, race, Hispanic origin, marital status, employment and retirement status, health insurance coverage, the year the state of residence expanded Medicaid, and state and year fixed effects. 95% confidence intervals shown for the total number. The percentages reflect the share of all SUDT facilities in the respondents’ county of residence accepting Medicaid (from 4% to 78%) and Medicare (from 5% to 42%).

Results stratified by whether the respondent was less than 65 years of age or 65 and older are shown in Appendix Figure 1. Overall, rates were slightly lower for the younger group.

Figure 2 shows the extent to which older adults’ access to SUDT facilities varied by socio-demographic characteristics. The most notable difference is in comparing individuals by race: controlling for other factors, White respondents had about 4.52 SUDT (95% CI: 4.48, 4.56) facilities per 100,000 residents whereas Black respondents had 4.90 (95% 4.80, 5.00).

Figure 2.

Figure 2.

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Average Number of SUD Treatment Facilities within County of Residence Among HRS Respondents 1992–2018, by Respondent Characteristics

Notes: Means derived post-estimation adjusting for respondent age, gender, race, Hispanic origin, marital status, employment and retirement status, health insurance coverage, year state of residence expanded Medicaid, and state and year fixed effects. 95% CIs shown. Non-Hispanic and Hispanic are mutually exclusive. White/Caucasian, Black/African American, and Other Race are mutually exclusive categories. No Medicare and Medicare are mutually exclusive. No Medicaid and Medicaid are mutually exclusive. Population categories pertain to county-level and are mutually exclusive.

Comparing older adults by the rural-urbanicity of their resident county suggests that those living in large metropolitan areas (with populations of 1 million or more) have 3.95 (95% CI: 3.88, 4.02) SUDT facilities per 100,000 and this was relatively stable over time (top left panel in Figure 3). Overall, there was more variation over time among adults living in counties not in metropolitan areas (whether they are adjacent or not; bottom two rows in Figure 3). The largest growth occurred in the smallest counties, particularly those not near a metropolitan area.

Figure 3.

Figure 3.

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Trends in Average Number of SUD Treatment Facilities over Time, by Type of County of Residence (USDA Rural-Urban Continuum Codes)

Notes: Means derived post-estimation adjusting for respondent age, sex, race, Hispanic origin, marital status, employment and retirement status, health insurance coverage, whether the county is urban/rural based on the USDA codes, year state of residence expanded Medicaid, and state and year fixed effects.

Figure 4 depicts how the fraction of facilities offering two key MOUDs varied over time across the different county types. Among those living in metropolitan areas, there was significant growth in the percentage of facilities offering buprenorphine. There was slight growth in the percentage of facilities offering methadone, but this was less than 13% of facilities at most. In the smaller counties, measuring the percent was not possible in some years as the total count of facilities was zero. For adults living in these counties, the fraction offering buprenorphine varied over time, peaking around 2010. There was little variation in the percent offering methadone over time among the smaller counties (bottom two rows). These results are similar using counts of facilities instead of percentages (see Appendix Figure 2).

Figure 4.

Figure 4.

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Trends in Percentage of Facilities Offering Buprenorphine and Methadone, by Type of County of Residence

Notes: Means derived post-estimation adjusting for respondent age, sex, race, Hispanic origin, marital status, employment and retirement status, health insurance coverage, whether the county is urban/rural, year state of residence expanded Medicaid, and state and year fixed effects. In some of the smaller counties, many estimates of total number of facilities were near zero, which in made those years difficult to measure the fraction offering different types of treatment. Instead of labeling those as zero percent, those were left those blank in this figure.

Discussion

Older adults in this nationally representative longitudinal study witnessed a decline in the number of total SUDT facilities available within their counties of residence (whether adjusted for county population size or not) even though total counts of facilities have increased.38 Over the same time, the average HRS respondent experienced an increase in the number (or percent) accepting Medicaid or Medicare. The number of SUDT facilities offering buprenorphine has increased over time, particularly among those living in large, more metropolitan counties.

This is consistent with other studies39, 40 reporting rural residents face little or no local MOUD treatment options.26, 41 The average number of treatment facilities increased over time for older adults living in rural areas with rates being higher in some less populous counties than in large metropolitan areas. However, older rural area residents still have less access to facilities offering buprenorphine and methadone (and this is true whether access is measured as a percent of facilities offering medication or per capita counts), a concern as older adults are more likely to live in rural areas than younger adults,42 transportation is often a primary reason for Medicare beneficiaries to not receive OUD treatment,6 and some rural areas have high fatal opioid overdose rates.43, 44 Although less than 20% of the sample live in less populous areas (with <20,000 residents), CMS has shown that racial/ethnic disparities may be worse in rural areas suggesting that understanding barriers to access for rural residents is still important. For example, White Medicare Advantage beneficiaries are 10 percentage points more likely to initiate alcohol or drug dependence treatment in urban areas than their Hispanic counterparts, whereas in rural areas this gap is 24.3 percentage points.45

The finding on buprenorphine treatment should be caveated as buprenorphine-waivered physicians providing care in offices are not included in the data analyzed. This is an important limitation given physician office settings represent the largest share of buprenorphine treatment for OUD. For example, a SAMHSA report documented more than 21,000 patients treated with buprenorphine at an OPT relative to nearly 55,000 treated with buprenorphine outside of OPTs in 2015.46 The effects of being unable to identify the location of clinicians who have been able to prescribe buprenorphine is unclear, however, as many buprenorphine-waivered clinicians are not actively prescribing it,42, 47 many active prescribers treat few patients,47–49 and older adults with OUD have also historically been less likely to use buprenorphine relative to methadone for MOUD.50 Future research examining the impact of office-based buprenorphine prescribing to older adults is needed to better understand their access to MOUD, particularly after the passage of Consolidated Appropriations Act of 2023, which allows all clinicians who can prescribe controlled substances to prescribe buprenorphine to treat OUD.

There are additional legislative and policy changes that have altered the landscape in ways that likely impact access though the magnitude of these effects has yet to be determined. As noted, Medicare began covering methadone in 202051 but it is unclear the extent to which this translated into greater access for beneficiaries because more than 80% of beneficiaries carry supplement insurance which may have already covered methadone.52 One recent study found only a 2 percentage point increase in the number of counties with at least one SUDT facility accepting Medicare from 2020 to 2021, so whether there were significant improvements in access is unclear.31 Furthermore, in response to the pandemic, OTPs could allow “Take-Home” doses of up to 28 days and this exemption appears to be extended beyond the end of the public health emergency,53 potentially increasing access by decreasing daily attendance burdens for individuals on methadone.

It is worth considering more carefully how to define “older adults” as a population for the purposes of understanding and addressing the challenges to SUD treatment. Although there is not a clear consensus in the literature,10 this study focuses on individuals aged 50+ due to the sampling frame of the HRS. Changes in the risks from polypharmacy and comorbidities are often gradual and vary across individuals, making it difficult to set a firm age threshold. However, concerns regarding Medicare policies and coverage obviously do have a clear age threshold (e.g., age 65). Future work should examine the implications of these different age cut offs on SUD treatment and the extent to which specialized services for aging adults might improve outcomes.

Limitations

The following additional limitations apply. First, geographic access reflects only availability as measured by facilities within one’s county of residence. Access to care can be measured in other ways not captured by these data, including travel time, access to transportation, physical distance, wait time, whether the provider(s) at the facilities accept certain health insurance types, ability to get an appointment, quality, and affordability of care.54–57 Furthermore, older adults could travel to facilities in nearby counties, and although adjustments for both the population size and whether the county is in or adjacent to a large metropolitan area were made, the geographic access measures may suffer from measurement error. The per capita measures also do not account for demand, as the number of individuals needing or seeking treatment within a county are not observed.

In addition, the availability of naltrexone, another medication approved for treatment of OUD, albeit an opioid antagonist that works differently than methadone and buprenorphine, is not consistently available in the MATTR data for most of the study period. Relatedly, the extent to which findings reported here are generalizable to more recent years is unknown as HRS geo-codes are only available through 2018. This also prevents analysis of changes in 2020 allowing Medicare payment for methadone treatment.

Measures of SUD/OUD are also not consistently available in the HRS. Therefore, measures of geographic availability pertain to all older adults in the sample as restricting the sample to only individuals with a SUD or OUD is not possible. This could bias results if individuals with SUD or OUD select into counties or geographic areas based on whether they want to seek treatment or not. However, a priori, the direction of this bias is unclear. There is likely to be endogeneity between where SUD treatment facilities locate and where individuals with SUD or OUD choose to live.

Finally, the MATTR data are limited to treatment facilities that are both licensed and agree to be listed in the annual National Directories. As a result, results here are likely to be an underestimate of geographic proximity to treatment for SUD and OUD.

Conclusions

Despite these limitations, this work highlights many of the challenges older adults face in obtaining SUDT and, in particular, MOUD treatment. Although the number of SUDT facilities accepting Medicaid and Medicare has increased over time, there has been less growth in nearby facilities offering evidence-based medication treatment for OUD.

Supplementary Material

1

Acknowledgements:

The authors thank David Kravitz, Osonde Osoba, Zubin Jelveh, Adrian Salas, Jason Powers, Erin Leidy, Nupur Nanda, and Russell Hanson for research assistance in creating the facility-level data. This draft also benefited from participants at a brownbag seminar at UTSW, and the Associate for Public Policy Analysis and Management, including discussant, Willa Friedman. The funder had no role in the study design, analysis, interpretation of the data, writing of the manuscript or decision to publish.

Financial Support:

This study was supported by NIA 1R21AG068901-01. The content is solely the responsibility of the authors and does not necessarily represent the views of the National Institutes of Health. The funder had no role in the study design, analysis, interpretation of the data, writing of the manuscript or decision to publish.

Footnotes

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Conflicts of interest: None of the authors have any relevant conflicts of interest.

Financial disclosures: No financial disclosures have been reported by the authors of this paper.

CRediT author statement: Kandice Kapinos: Conceptualization, Acquisition of Data and Funding, Methodology, Formal Analysis, Writing- Original Draft. Maria DeYoreo: Data Curation, Writing- Review & Editing. Tadeja Gracner: Writing- Review & Editing. Bradley Stein: Writing- Original Draft, Writing- Review & Editing, Supervision. Jonathan Cantor: Conceptualization, Acquisition of Funding, Writing- Original Draft.

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