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. Author manuscript; available in PMC: 2023 Jul 1.
Published in final edited form as: J Psychoactive Drugs. 2021 Aug 17;54(3):241–249. doi: 10.1080/02791072.2021.1962577

Unmet Need in Relation to Mental Healthcare and Past-Month Drug Use among People with Mental Illness in the United States

Benjamin E Reid 1, Joseph J Palamar 1
PMCID: PMC8911933  NIHMSID: NIHMS1733886  PMID: 34402414

Abstract

Individuals with mental illness are at risk of developing co-occurring substance use disorders (SUDs). We assessed whether unmet need for mental health treatment in the past year was a risk factor for past-month use of marijuana, cocaine, methamphetamine, and misuse of prescription opioids in this population. Data from adults diagnosed with mental illness who were not diagnosed with SUD were examined from the 2015–2018 National Survey on Drug Use and Health (N = 33,104). An estimated 20.8% (95% CI: 20.1–21.5) of adults in the US with mental illness have experienced unmet need in the past year. Those reporting marijuana use (29.7% vs. 19.5%, p < .001) and/or prescription opioid misuse (35.7% vs. 20.5%, p < .001) were more likely to report unmet need than those not reporting use. In multivariable models, unmet need remained a risk factor for marijuana use (aOR = 1.37, 95% CI: 1.24–1.54) and prescription opioid misuse (aOR = 1.65, 95% CI: 1.29–2.13). Unmet need was not a risk factor for cocaine or methamphetamine use. Cost as a barrier to treatment was a risk factor for marijuana use (aOR = 1.37, 95% CI: 1.19–1.58) and prescription opioid misuse (aOR = 1.95, 95% CI: 1.43–2.64). Policies aimed at improving mental healthcare access may be effective in reducing substance use in this population.

Keywords: treatment need, substance use disorder, marijuana, opioids

Introduction

The high prevalence of co-occurring substance use disorders (SUDs) and mental health disorders has been well-documented in the literature (Grant et al. 2004; Han et al. 2017; Rush et al. 2008). Recent reports estimate that up to 8 million adults in the United States (US) suffer from comorbid SUDs and mental health disorders, with 45–60% of adults with SUD diagnosed with a co-occurring mental health disorder (Han et al. 2017; Manuel, Stebbins & Wu 2018). Co-occurring SUD and mental health disorder poses a significant public health burden, as such individuals are at increased risk for a number of adverse outcomes, including relapse, disability, unemployment, homelessness, incarceration, and suicide, compared to those diagnosed with either an SUD or mental health disorder alone (Merikangas et al. 1998; Nam, Matejkowski & Lee 2017; Rush et al. 2008). In addition, individuals with co-occurring disorders generally experience worse treatment outcomes compared to those without co-occurrence (Han et al. 2017; Nam, Matejkowski & Lee 2017; Novak et al. 2019; Rush et al. 2008).

Self-reported unmet need for mental healthcare is positively associated with substance use. Unmet need refers to when an individual perceives a need for mental health treatment or counseling that has not been satisfied, regardless of whether mental health services were utilized. Recent studies have reported that unmet need is associated with use of alcohol, marijuana, other illegal drugs, and nonmedical use of prescription opioids, tranquilizers, and stimulants (Smith et al. 2017; Wang et al. 2019). Previous research has also shown unmet need to be higher in individuals with co-occurring SUD and mental health disorder than in those with either a substance use or mental health disorder alone. This has been reported among those with co-occurring opioid use disorder, alcohol use disorder, and more generally, those with co-occurring SUD involving one or more illegal drugs (Chen et al. 2013; Choi, DiNitto & Marti 2014; Novak et al. 2019).

However, no recent studies have investigated the association between unmet need for mental healthcare and use of specific substances among individuals with mental illness that have not developed a co-occurring SUD. Focusing on this population is important for several reasons. First, previous research has reported that in the majority of individuals suffering from co-occurring SUD and a mental health disorder, the onset of the mental health disorder typically preceded that of the SUD (Harris & Edlund 2005; Kessler et al. 1996; Merikangas et al. 1998; Robinson et al. 2011). While any causal effect of mental health disorders on the development of co-occurring substance use remains unclear, this finding suggests that the presence of a mental health disorder places individuals at higher risk for developing SUD. Thus, investigating the association between unmet need for mental healthcare and recent or current substance use in this population may enable identification of an addressable risk factor for the development of co-occurring SUD in this high-risk group.

Elucidating the association between unmet mental healthcare needs and current or recent substance use in this population may also inform intervention strategies. If unmet need is associated with substance use in those who have not developed SUD, improving access to mental health services may curtail the development of co-occurring SUD in many cases (Harris & Edlund 2005). There is also the need to characterize the various barriers that have prevented this population from obtaining the mental healthcare that they need, and to explore the degree to which specific barriers are associated with substance use in this population. It is important to investigate these associations specific to discrete drugs or drug classes, given the unique characteristics of various substances with regard to effects, addictiveness, availability, and prevalence of use (Johnston LD et al. 2020; Nutt et al. 2007; Nutt, King & Phillips 2010). While many studies have explored the relationship between unmet need and substance use, most have been limited by relatively small sample sizes and by grouping the use of several drugs (e.g., illegal drugs other than marijuana) into a single response variable (Chen et al. 2013; Choi, DiNitto & Marti 2014; Harris & Edlund 2005; Smith et al. 2017; Wang et al. 2019).

This study explored the following questions: First, in those with mental illness, is perceived unmet need for mental healthcare in the past year associated with current substance use? Second, what barriers are preventing this population from obtaining the mental health services they need, and which barriers are associated with increased substance use? This was examined with regard to past-month use of marijuana, cocaine, and methamphetamine, and misuse of prescription opioids, separately, in a nationally representative sample of adults in the US.

Methods

Procedure

The National Survey on Drug Use and Health (NSDUH) is a nationally representative annual cross-sectional survey of non-institutionalized individuals in the US (Center for Behavioral Health Statistics and Quality 2019). Individuals ages ≥12 are eligible and the survey is conducted in the 50 US states and the District of Columbia using a multistage sampling design. Surveys are administered to participants via computer-assisted interviewing conducted by an interviewer and audio computer-assisted self-interviewing. Sample weights (provided by NSDUH) are used to account for the complex survey design, non-response, selection probability, and population distribution. Response rates ranged from 66.6–69.7%. Analyses focused on adult participants (ages ≥18) from the 2015–2018 cohorts.

Measures

Participants were asked about demographic characteristics including age, sex, race/ethnicity, household income, education, marital status, sexual identity, whether they have health insurance, and past-year receipt of governmental program assistance (i.e., food stamps, supplemental security income, cash assistance, non-cash assistance). In addition, participants were asked to self-rate their level of overall health. Participants were also asked about past-month use of marijuana, cocaine, and methamphetamine, and about misuse of prescription opioids. NSDUH defines misuse as using in any way not directed by a doctor, including use without a prescription, more often, in greater amounts, or longer than directed to use them, or use in any other way not directed to use (Center for Behavioral Health Statistics and Quality 2019). Participants reporting use or misuse of a drug were asked to answer Diagnostic Statistical Manual of Mental Disorders Fourth Edition (DSM-IV) questions to gauge potential ‘abuse’ or ‘dependence’ associated with use (American Psychiatric Association 2000). Those proxy-diagnosed with either were coded as having substance use disorder (SUD) (Han, Ko & Palamar 2019).

With respect to mental illness, NSDUH includes an indicator developed and validated by the Substance Abuse and Mental Health Services Administration and National Institute of Mental Health, which is based on responses to specific questions (Substance Abuse and Mental Health Services Administration 2015). The items query level of depression, emotional distress, functional impairment, and suicidal thoughts. Responses are coded by NSDUH investigators to indicate mild, moderate, and severe mental illness. With regard to perception of unmet need, participants were asked, “During the past 12 months, was there any time when you needed mental health treatment or counseling for yourself but didn’t get it?” Response options were ‘yes’ and ‘no’. Those reporting unmet need were asked to check off reasons for not receiving care. Based on work by Alang (Alang 2015) we recoded these responses to indicate reasons including stigma (e.g., concerned about opinions of neighbors), cost (e.g., could not afford care), structural barriers (e.g., did not know where to obtain services), minimization of need (e.g., did not think needed care), and low perception of effectiveness (e.g., did not think treatment would help).

Analysis

We limited participants in the analytic sample to adults with any level of mental illness (ranging from mild to severe) who were not proxy-diagnosed with SUD (for any illegal drug queried by NSDUH) (n=33,104). We first estimated sample characteristics in a univariable manner and estimated prevalence of unmet need. We then compared sample characteristics according to whether unmet need was reported. These bivariable comparisons were conducted using Rao-Scott chi-square (Heeringa, West & Berglund 2010). Next, all variables for sample characteristics were fit into a multivariable logistic regression model in order to determine correlates of unmet need with all else being equal. Since prevalence of the outcome was above 10%, we used generalized linear model using Poisson and log link to generate adjusted prevalence ratios (aPRs) for each covariate.

We then examined unmet need as a correlate of past-month use of each of the four drugs or drug classes in a bivariable and multivariable manner. Specifically, we first assessed whether there were differences in unmet need according to past-month drug use using Rao-Scott chi-square, and then we assessed whether unmet need was a significant correlate of past-month use while controlling for all covariates in multivariable models. This was done using logistic regression and these models produced adjusted odds ratios (aORs) for each variable. These analyses were repeated examining reasons for unmet need as independent variables. We also conducted sensitivity tests in which we repeated bivariable and multivariable models with marijuana use as the outcome limited to those who did not report having marijuana recommended by a doctor in the past year. We used Stata 13 SE (StataCorp 2013) to analyze all data, we used sample weights in all analyses, and we used Taylor series estimation methods to provide accurate standard errors (Heeringa, West & Berglund 2010). This secondary analysis was exempt from review by New York University Langone Medical Center’s institutional review board.

Results

We estimated that a fifth of adults (20.8%, 95% CI: 20.1–21.5) in the US with diagnosed mental illness that do not have SUD experienced unmet need in the past year with regard to mental health treatment or counseling. Table 1 presents associations between participant characteristics and unmet need. With all else being equal, older individuals were at lower risk for reporting unmet need with risk of unmet need reducing in a dose-response-like manner among those in older age groups. Compared to males, females were at increased risk for reporting unmet need (aPR = 1.38, 95% CI: 1.29–1.48), and compared to white individuals, those identifying as black (aPR = 0.80, 95% CI: 0.71–0.90), Hispanic (aPR = 0.86, 95% CI: 0.79–0.94), or Asian (aPR = 0.70, 95% CI: 0.59–0.84) were at lower risk for reporting unmet need. Compared to those with less than a high school education, those with some college (aPR = 1.23, 95% CI: 1.12–1.34) or a college degree (aPR = 1.51, 95% CI: 1.35–1.68) were at higher risk for reporting unmet need, and compared to those identifying as heterosexual, those identifying as gay or lesbian (aPR = 1.30, 95% CI: 1.13–1.49) or bisexual (aPR = 1.24, 95% CI: 1.16–1.32) were at higher risk for reporting unmet need. Compared to those reporting excellent health, those reporting poorer health were at higher risk for reporting unmet need in a dose-response-like manner. Finally, compared to those diagnosed with mild mental illness, those diagnosed with moderate (aPR = 1.86, 95% CI: 1.73–1.99) or severe (aPR = 3.06, 95% CI: 2.87–3.26) mental illness were at higher risk for reporting unmet need.

Table 1 –

Sample characteristics and bivariable and multivariable associations with unmet need

Univariable Bivariable Comparisons Multivariable Model
Full Sample Weighted % (95% CI) No Unmet Need Weighted % Unmet Need Weighted % aPR (95% CI)
Age, years c
 18–25 16.7 (16.2–17.2) 68.1 (67.0–69.1) 31.9 (30.9–33.0) 1.00
 26–34 20.3 (19.8–20.8) 74.7 (73.6–75.8) 25.3 (24.2–26.4) 0.78 (0.73–0.82) c
 35–49 26.9 (26.2–27.6) 79.2 (78.0–80.3) 20.8 (19.7–22.0) 0.64 (0.59–0.70) c
 ≥50 36.1 (35.2–37.0 86.9 (85.6–88.1) 13.1 (11.9–14.4) 0.43 (0.39–0.48) c
Sex c
 Male 36.9 (36.1–37.6) 84.1 (83.1–85.1) 15.9 (14.9–16.9) 1.00
 Female 63.1 (62.4–63.9) 76.3 (75.4–77.2) 23.7 (22.8–24.6) 1.38 (1.29–1.48) c
Race/ethnicity c
 Non-Hispanic White 69.4 (68.6–70.1) 78.1 (77.3–79.0) 21.9 (21.0–22.7) 1.00
 Non-Hispanic Black 9.85 (9.29–10.4) 82.7 (80.7–84.5) 17.3 (15.5–19.3) 0.80 (0.71–0.90) c
 Hispanic 13.4 (12.7–14.0) 80.8 (79.0–82.4) 19.2 (17.6–21.0) 0.86 (0.79–0.94) b
 Asian 4.04 (3.64–4.47) 83.8 (80.0–87.0) 16.2 (13.0–20.0) 0.70 (0.59–0.84) c
 Other or Mixed 3.36 (3.09–3.67) 79.2 (76.0–82.1) 20.8 (17.9–24.0) 0.88 (0.77–1.00)
Annual Family Income
 <$20,000 22.4 (21.5–23.2) 78.2 (76.8–79.5) 21.8 (20.5–23.2) 1.00
 $20,000-$49,999 31.7 (31.0–32.5) 79.0 (77.8–80.2) 21.0 (19.8–22.2) 1.02 (0.94–1.10)
 $50,000-$74,999 15.3 (14.7–15.9) 78.6 (76.9–80.3) 21.4 (19.7–23.1) 1.02 (0.93–1.11)
 >$75,000 30.6 (29.8–31.5) 80.4 (79.0–81.7) 19.6 (18.3–21.0) 0.96 (0.88–1.05)
Education c
 Less than high school 11.8 (11.3–12.4) 83.7 (82.1–85.3) 16.3 (14.7–17.9) 1.00
 High school diploma 23.4 (22.7–24.1) 82.5 (81.3–83.6) 17.5 (16.4–18.7) 1.01 (0.89–1.15)
 Some college 35.5 (34.8–36.2) 76.9 (75.9–78.0) 23.1 (22.0–24.1) 1.23 (1.12–1.34) c
 College degree 29.3 (28.4–30.1) 77.4 (75.9–78.9) 22.6 (21.1–24.1) 1.51 (1.35–1.68) c
Married c
 No 58.8 (57.9–59.7) 76.7 (75.9–77.5) 23.3 (22.5–24.1) 1.00
 Yes 41.2 (40.3–42.1) 82.7 (81.6–83.8) 17.3 (16.2–18.4) 0.94 (0.87–1.01)
Sexual Identity
 Heterosexual 90.0 (89.5–90.5) 80.9 (80.1–81.6) 19.1 (18.4–19.9) 1.00
 Gay/Lesbian 3.1 (2.8–3.4) 69.2 (65.4–72.7) 30.8 (27.3–34.6) 1.30 (1.13–1.49) b
 Bisexual 6.9 (6.6–7.3) 62.2 (60.1–64.2) 37.8 (35.8–39.9) 1.24 (1.16–1.32) c
Government Assistance
 No 74.6 (73.7–75.5) 79.5 (78.7–80.4) 20.5 (19.6–21.3) 1.00
 Yes 25.4 (24.5–26.3) 78.2 (76.9–79.5) 21.8 (20.5–23.1) 0.95 (0.88–1.03)
Insurance a
 No 89.7 (89.2–90.2) 79.5 (78.7–80.3) 20.5 (19.7–21.3) 1.00
 Yes 10.3 (9.82–10.8) 76.7 (74.6–78.6) 23.3 (21.4–25.4) 1.06 (0.97–1.16)
Perceived Overall Health
 Excellent 12.4 (11.9–12.9) 81.5 (80.1–82.8) 18.5 (17.2–19.9) 1.00
 Very good 31.6 (30.8–32.3) 78.8 (77.7–79.9) 21.2 (20.1–22.3) 1.13 (1.04–1.22)
 Good 31.7 (31.0–32.5) 79.0 (77.7–80.3) 21.0 (19.7–22.3) 1.17 (1.07–1.28) b
 Fair 17.6 (17.0–18.3) 79.2 (77.3–81.0) 20.8 (19.0–22.7) 1.26 (1.12–1.40) c
 Poor 6.7 (6.2–7.3) 77.7 (74.4–80.6) 22.3 (19.4–25.6) 1.43 (1.23–1.66) c
Mental Illness in Past Year c
 Mild mental illness 51.6 (50.8–52.5) 88.5 (87.9–89.1) 11.5 (10.9–12.1) 1.00
 Moderate mental illness 26.2 (25.4–27.1) 77.2 (75.7–78.5) 22.8 (21.5–24.3) 1.86 (1.73–1.99) c
 Serious mental illness 22.1 (21.5–22.8) 59.9 (58.5–61.2) 40.1 (38.8–41.5) 3.06 (2.87–3.26) c
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Note. Significant bivariable differences are noted with superscript letters next to variable names. The multivariable model controlled for all variables including year of survey administration. aPR=adjusted prevalence ratio; CI= confidence interval.

a

p < .05

b

p < .01

c

p <.001

Table 2 presents bivariable and multivariable models examining past-month drug use in relation to unmet need and reasons for unmet need. Bivariable test results suggest that compared to those not reporting unmet need, those reporting unmet need were more likely to report marijuana use (29.7% vs. 19.5%, p < .001). With respect to reasons for unmet need, marijuana use was more likely in those reporting stigma (9.5% vs. 6.1%, p < .001), cost (15.8% vs. 9.3%, p < .001), structural barriers (12.5% vs. 8.2%, p < .001), minimization (8.5% vs. 5.9%, p < .001), and low perception of effectiveness (3.4% vs. 2.3%, p = .003). In the multivariable models, unmet need remained significant, with those reporting unmet need at higher odds of marijuana use after controlling for all covariates (aOR = 1.37, 95% CI: 1.24–1.51). With respect to reasons for unmet need, in the multivariable model, only cost remained significant, with those reporting cost as a reason being at higher odds for marijuana use (aOR = 1.37, 95% CI: 1.19–1.58). In our sensitivity tests considering only marijuana use not recommended by a doctor in the past year (81.9% of those reporting past-month use), results were nearly identical (Table 3).

Table 2 –

Bivariable and multivariable models examining unmet need and reasons for unmet need in relation to past-month drug use.

Marijuana Use Cocaine Use
No Weighted % Yes Weighted % aOR (95% CI) No Weighted % Yes Weighted % aOR (95% CI)
Unmet Need
 No 80.5 (79.7–81.3) 70.3 (68.7–71.9) c 1.00 79.2 (78.5–79.9) 75.2 (68.2–81.1) 1.00
 Yes 19.5 (18.7–20.3) 29.7 (28.1–31.3) 1.37 (1.24–1.51) c 20.8 (20.1–21.5) 24.8 (18.9–31.8) 0.94 (0.64–1.39)
Forms of Unmet Need
 Stigma 6.1 (5.8–6.5) 9.6 (8.4–32.6) c 0.97 (0.78–1.21) 6.5 (6.2–6.9) 6.4 (3.8–10.6) 0.67 (0.37–1.24)
 Cost 9.3 (8.8–9.8) 15.8 (14.5–17.1) c 1.37 (1.19–1.58) c 10.1 (9.6–10.5) 13.6 (8.8–20.5) 1.14 (0.59–2.18)
 Structural Barriers 8.2 (7.6–8.7) 12.5 (11.4–13.7) c 1.10 (0.92–1.33) 8.7 (8.3–9.2) 9.1 (5.2–15.7) 0.88 (0.41–1.89)
 Minimization 5.9 (5.5–6.4) 8.5 (7.6–9.5) c 1.11 (0.90–1.37) 6.2 (5.8–6.7) 6.8 (3.5–13.1) 1.10 (0.48–2.50)
 Low Perceived Effectiveness 2.3 (2.0–2.5) 3.4 (2.7–4.2) b 0.90 (0.63–1.28) 2.4 (2.2–2.6) 2.3 (1.0–4.9) 0.73 (0.23–2.33)
Methamphetamine Use Prescription Opioid Misuse
No Weighted % Yes Weighted % aOR (95% CI) No Weighted % Yes Weighted % aOR (95% CI)
Unmet Need
 No 79.2 (78.5–79.9) 72.6 (57.7–83.8) 1.00 79.5 (78.7–80.2) 64.3 (59.5–68.8) c 1.00
 Yes 20.8 (20.1–21.5) 27.4 (16.2–42.3) 1.35 (0.71–2.56) 20.5 (19.8–21.3) 35.7 (31.2–40.5) 1.65 (1.29–2.13) c
Forms of Unmet Need
 Stigma 6.5 (6.2–6.9) 11.1 (2.6–36.5) 2.13 (0.39–11.67) 6.4 (6.1–6.8) 12.7 (9.6–16.7) c 1.48 (0.97–2.24)
 Cost 10.1 (9.6–10.6) 8.1 (2.9–20.7) 0.54 (0.19–1.55) 9.9 (9.4–10.3) 23.1 (19.2–27.5) c 1.95 (1.44–2.64) c
 Structural Barriers 8.7 (8.2–9.2) 12.1 (5.2–25.7) 1.69 (0.60–4.74) 8.7 (8.2–9.1) 11.5 (9.1–14.5) a 0.79 (0.55–1.16)
 Minimization 6.2 (5.8–6.7) 6.8 (1.9–22.1) 1.29 (0.26–6.45) 6.2 (5.8–6.7) 7.0 (5.1–9.6) 0.71 (0.39–1.29)
 Low Perceived Effectiveness 2.4 (2.2–2.7) 0.5 (0.1–2.9) 0.10 (0.01–1.05) 2.4 (2.2–2.6) 4.1 (2.3–7.2) a 1.47 (0.63–3.42)
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Note. aOR=adjusted odds ratio; CI= confidence interval.

a

p < .05

b

p < .01

c

p < .001

Table 3 –

Bivariable and multivariable models examining unmet need and reasons for unmet need in relation to past-month marijuana use among those not advised to use by a doctor in the past year.

Nonmedical Marijuana Use
No Weighted % Yes Weighted % aOR (95% CI)
Unmet Need
 No 80.2 (79.5–81.0) 70.4 (68.4–72.3) c 1.00
 Yes 19.8 (19.0–20.5) 29.6 (27.7–31.6) 1.36 (1.21–1.53) c
Forms of Unmet Need
 Stigma 6.2 (5.9–6.6) 9.4 (8.0–10.9) c 0.96 (0.75–1.22)
 Cost 9.5 (9.0–10.0) 15.3 (13.9–16.8) c 1.30 (1.10–1.53) b
 Structural Barriers 8.3 (7.8–8.8) 12.5 (11.4–13.6) c 1.12 (0.93–1.34)
 Minimization 6.0 (5.5–6.4) 8.6 (7.5–9.8) c 1.14 (0.91–1.41)
 Low Perceived Effectiveness 2.3 (2.0–2.6) 3.3 (2.8–4.0) b 0.86 (0.63–1.17)
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aOR = adjusted odds ratio; CI = confidence interval.

b

p < .01

c

p < .001

Neither unmet need nor specific reasons for unmet need were related to cocaine or methamphetamine use, but results from our bivariable test suggest that those reporting unmet need were more likely to report opioid misuse (35.7% vs 20.5%, p < .001). With regard to reasons for unmet need, opioid misuse was more likely in those reporting stigma (12.7% vs. 6.4%, p < .001), cost (23.1% vs. 9.9%, p<.001), structural barriers (11.5 vs. 8.7, p = .019), and low perception of effectiveness (4.1% vs. 2.4%, p = .049) as reasons for their unmet need. In the multivariable models, those reporting unmet need were at higher odds of opioid misuse compared to those not reporting unmet need (aOR = 1.65, 95% CI: 1.29–2.13). With respect to reasons for unmet need, only cost remained significant, with those reporting cost as a reason being at higher odds for opioid misuse (aOR = 1.95, 95% CI: 1.44–2.64).

Discussion

Using recent data from a nationally representative survey of adults in the US, this study investigated the association between past-year unmet need for mental healthcare and current or recent substance use among those diagnosed with a mental illness. We estimate that approximately a fifth of those who have mental illness without a co-occurring SUD report recent unmet need for mental healthcare. The widespread prevalence of unmet need in this population highlights the need for research investigating this trend, and policies aimed at improving mental healthcare access in this population.

Our findings regarding the demographic correlates of unmet need were consistent with the published literature (Ojeda & Bergstresser 2008; Smith et al. 2017; Yang et al. 2019). Previous research has suggested that the lower unmet need observed among racial minority individuals may reflect increased stigma and mistrust associated with the mental healthcare system in these communities, rather than differences in access (Smith et al. 2017). Indeed, studies have shown that despite reporting lower prevalence of unmet need, racial minority groups also report lower prevalence of mental healthcare utilization, and are more likely to experience stigma or structural barriers to care than white individuals (Alang 2015; Ojeda & Bergstresser 2008; Smith et al. 2017). Our results also corroborate a recent study demonstrating that sexual minority individuals are at higher risk for reporting such unmet need (Haney 2021). Haney’s study suggests this may be due, in part, to such individuals either not knowing where to find appropriate treatment or where to find the type of treatment desired, but more research is needed. Previous research has shown that men report lower prevalence of both unmet need and mental healthcare utilization than women and more often report avoidance of stigma as a reason for not obtaining care (Ojeda & Bergstresser 2008; Smith et al. 2017). Thus, the lower risk of reporting unmet need among males may be due to the heightened stigma associated with men seeking out mental healthcare compared to women. Consistent with previous research (Ojeda & Bergstresser 2008; Smith et al. 2017), risk of reporting unmet need was higher among younger individuals, those with a college degree, and in those with more severe mental illness.

After controlling for demographic and survey characteristics, those reporting unmet need for mental healthcare were at greater odds of using marijuana and misusing prescription opioids in the past month. No associations were detected with regard to recent use of methamphetamine or cocaine. Further, reporting cost as a barrier to obtaining mental healthcare was associated with increased odds of marijuana use and opioid misuse. One prominent theory that may explain these results is the theory of self-medication, whereby individuals turn to substances in order to relieve painful emotional states that otherwise feel unmanageable (Khantzian 1985). The substitutability of mental health treatment and psychoactive substances is implicit in the self-medication hypothesis, such that an individual with unmet mental healthcare needs would be more likely to self-medicate using psychoactive substances (Harris & Edlund 2005; Smith et al. 2017). This is consistent with our finding that unmet need was associated with recent marijuana use and opioid misuse. Furthermore, according to this theory, the choice to either self-medicate or seek treatment is influenced by the relative accessibility of each option (Harris & Edlund 2005). It is therefore plausible that with the decriminalization, legalization, medicalization, and heightened social acceptance and availability of marijuana, self-medicating behavior using this drug has become more prevalent (National Academies of Sciences 2017).

Previous research has also suggested that marijuana use increases risk for the development or exacerbation of certain psychiatric disorders (Hindley et al. 2020; Volkow et al. 2014). For example, marijuana use has been shown to increase risk for the development of schizophrenia and other psychotic-spectrum disorders in those with a genetic predisposition, as well as worsening the course of illness in this patient population (Hall & Degenhardt 2008; Volkow et al. 2014). Marijuana use is also associated with the development of marijuana use disorder, as well as SUDs involving nicotine and alcohol (Blanco et al. 2016; Sarvet et al. 2018). Indeed, studies have indicated that long-term marijuana use can lead to addiction, with approximately 9% of people who use marijuana eventually developing marijuana use disorder (Volkow et al. 2014).

Unlike marijuana, both availability and nonmedical use of opioids has decreased during the opioid crisis (Agarwal et al. 2020; Center for Behavioral Health Statistics and Quality 2019; Johnston LD et al. 2020; Schieber et al. 2019). Still, self-medication may be a common motivation for opioid misuse. One study found that among people who use opioids without a prescription, approximately 8% reported using opioids to relieve anxiety, as well as 8% using opioids to help them sleep (McCabe, West & Boyd 2013). Among those who were using their own prescriptions in ways not intended by the prescribing physician, approximately 10.5% of respondents in one study used to relieve anxiety, and 12% used to improve sleep (McCabe, West & Boyd 2013).

To improve mental healthcare access among those with mental illness, it is necessary to understand the barriers they face in obtaining this care. The literature has generally found that cost is the most common barrier to mental healthcare in those with mental illness (Choi, DiNitto & Marti 2014; Mason et al. 2013), as well as in those with co-occurring SUD (Han et al. 2017; Novak et al. 2019). Our study elucidates a potential consequence of this trend, as experiencing cost as a barrier to care was associated with increased odds of recent marijuana use and prescription opioid misuse. Contextualized within the existing literature, our findings suggest that policies aiming to reduce the cost of care may be particularly effective in reducing substance use in those with mental illness.

Based on our findings, improving access to mental healthcare may reduce substance use in those with mental illness, and in doing so, potentially curtail the development of co-occurring SUD in a subset of cases (Harris & Edlund 2005). However, in addition to serving a preventative role, improving mental healthcare access would likely also improve outcomes for those who still go on to develop co-occurring SUD. For individuals with co-occurrence, current treatment guidelines recommend participation in both mental health treatment and substance abuse treatment (Han et al. 2017). Despite these recommendations, this population utilizes mental health treatment at more than three times the rate that they engage in substance abuse treatment (Ali, Teich & Mutter 2015; Han et al. 2017; Novak et al. 2019). As such, mental healthcare may serve as an essential entry point into treatment for those with co-occurring SUD. It is therefore important to identify and reduce barriers to access not only to prevent co-occurring SUD, but to increase treatment participation and improve outcomes for those who develop it.

This study has several strengths. First, while many existing papers explore the association between unmet need and substance use, a number of them have overcome relatively small sample sizes by grouping together various drugs (Chen et al. 2013; Choi, DiNitto & Marti 2014; Harris & Edlund 2005; Smith et al. 2017; Wang et al. 2019). The large sample size of our pooled dataset enables us to distinguish between discrete drugs and drug classes. Second, the majority of previous analyses investigate similar questions in a population of those who have already developed co-occurring SUD (Chen et al. 2013; Choi, DiNitto & Marti 2014; Novak et al. 2019). This study focused on those with mental illness who have not developed a co-occurring SUD, and in doing so, explores whether unmet need for mental healthcare is a risk factor for substance use, and therefore the potential development of co-occurring SUD in this high-risk group.

Data from this study do not include data from individuals who are homeless (who do not use shelters), incarcerated, or individuals who reside in institutional group quarters. Thus, some groups at relatively high risk for mental illness and/or substance use are underrepresented in this national study. Data on mental illness provided by NSDUH are not accompanied with any diagnosis or proxy diagnosis of specific mental illnesses. It is likely that some mental illnesses place individuals at higher or lower risk for use of different drugs than others. For example, individuals with anxiety disorders may be more drawn to drugs like marijuana. In addition, only adults were asked about unmet need so we could not examine such associations among adolescents. Finally, given that data are cross-sectional, we could not deduce temporality of detected associations.

We found a positive association between past-year unmet need for mental healthcare and current or recent use of marijuana and misuse of prescription opioids. Experiencing cost as a barrier to mental healthcare access was significantly associated with use of these drugs. Policies aimed at mitigating cost-related barriers to mental healthcare may be particularly effective at reducing substance use in this population, potentially curtailing the development of co-occurring SUD in a subset of cases.

Footnotes

Declaration of Interest: The authors declare no conflict of interest.

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