Correlates of Mental Health Service Use Among Young Adults With Mental Illness: Results From the National Survey on Drug Use and Health
Abstract
Objective:
This study examined correlates of use of outpatient and inpatient mental health services and psychotropic medication in a large, nationally representative sample of young adults ages 18–26 with mental illness (N=22,600).
Methods:
Data were from the 2008–2012 National Survey on Drug Use and Health, an annual nationally representative survey of the civilian, noninstitutionalized U.S. population. Separate logistic regression models examined past-year use of three mental health service types (outpatient services, inpatient services, and psychotropic medication). Correlates included demographic characteristics, factors developmentally relevant to young adults, and general medical and mental health status.
Results:
Within this sample of young adults with mental illness, 20.4% used outpatient services, 3.6% used inpatient services, and 25.4% used psychotropic medication. Variables associated with use of one or more types of mental health services included being female (outpatient and medication), one to two moves in the past year (medication), having health insurance (all types), past-year criminal justice involvement (all types), poor health (inpatient and medication), substance use disorders (inpatient and medication), and mental illness with severe impairment (all types). Non-Hispanic blacks, Asians, and Hispanics were less likely than non-Hispanic whites to receive outpatient mental health services or psychotropic medications. Surprisingly, young adults employed full-time were less likely than those who were unemployed to receive services, and living with a partner (versus living alone) was not associated with a likelihood of using outpatient services.
Conclusions:
Results support the unique nature of young adulthood and the need to tailor mental health services to close gaps in service use during this developmental period.
Mental illness is a leading cause of disability in the United States (1), and initial episodes often occur in young adulthood. Schizophrenia commonly has its onset during this developmental period (2), and one in three individuals with major depressive disorder first receive the diagnosis as young adults (3). Furthermore, serious mental illness exists among 2.4 million young adults (4). Synchronous with this heightened vulnerability to mental illness are challenges with transitioning into adulthood (5). Developmental tasks associated with young adulthood include establishing romantic relationships, securing stable housing, and attaining educational and employment goals (6).
The confluence of mental illness onset and challenging developmental tasks underscores the need to identify factors related to mental health service use among young adults, especially since mental illness can contribute to negative economic and educational outcomes (7). Consequently, young adults might benefit from developmentally appropriate services to facilitate the transition to adulthood.
Despite having high levels of need, young adults are less likely than members of other age groups to use mental health services, according to national data (8,9). Colleges face increasing numbers of students with mental health problems (10). Despite having access to campus services, only half of college students with mental health problems use supports (11,12). The largest study of mental health service use among young adults compared admissions data from the 1997 Client/Patient Sample Survey (13) for three age groups: six to 15 (pretransition), 16–25 (transition), and 26–35 (posttransition). Outpatient care was significantly more prevalent in the pretransition group compared with the transition or posttransition groups. However, use of inpatient services was lower in the pretransition group compared with the other two groups.
Studies with adults have identified factors associated with service use. Demographic correlates included female gender, age younger than 60, being married, non-Hispanic white race-ethnicity, and residence in a nonrural area (14). Studies show that African Americans (15,16) and Latinos (17,18) are less likely than whites to use mental health services. Other factors related to greater service use include having insurance (19) and employment (20). Less is known about correlates of mental health service use among young adults. As adolescents age into young adulthood, African Americans report worsening mental health problems (21). African-American young adults are also less likely to receive mental health services compared with white young adults (22).
The purpose of this study was to examine rates of service use among young adults with mental illness and correlates of use of mental health services, including outpatient and inpatient services and psychotropic medication. Correlates included demographic and developmental characteristics, level of impairment related to mental illness, and other factors related to service use. Given high levels of unmet need among young adults, we focused on young adults with some indicator of mental health service need rather than a general young adult sample. Attentive to the unique developmental transition among young adults, we focused on correlates with particular salience for this period.
Methods
Sample
This study analyzed data from 22,600 young adults, ages 18–26, who were identified as having any mental illness and who participated in the 2008–2012 National Survey on Drug Use and Health (NSDUH), a nationally representative survey of the civilian, noninstitutionalized U.S. population (23). NSDUH is conducted annually by the Substance Abuse and Mental Health Services Administration. Face-to-face interviews are conducted with approximately 70,000 randomly selected individuals age 12 or older. Respondents are selected via a multistage area probability sample within each state and the District of Columbia. They provide consent after hearing a detailed study description and are paid $30 upon completion. Trained interviewers visit households and administer the survey by using computer-assisted interviewing. Procedures were approved by the contracting organization’s institutional review board.
Measures
Demographic, developmental, and other correlates.
Demographic variables included age, gender, race-ethnicity, and county type (metropolitan and nonmetropolitan). We collected data in four domains that are relevant to service use during young adulthood: current school enrollment (middle or high school student, high school dropout, high school graduate, college student, and college graduate), past-year employment (full-time, part-time, full-time student, disabled, not in work force, and unemployed), past-year residential stability (zero, one, two, and three or more moves), and current living situation.
The determination of mental illness and impairment level was based on analyses conducted on a subsample of NSDUH’s adult respondents who were administered a clinical follow-up study (24,25). This subsample was administered a clinical psychiatric diagnostic interview (Structured Clinical Interview–DSM-IV-TR [SCID] Axis I) (26) in addition to the NSDUH survey. Respondents who received at least one diagnosis of a past-year mental disorder in the clinical interview were considered as having any mental illness. The Global Assessment of Functioning (GAF) score was used to determine functional impairment level among the clinical subsample; adults with any mental illness and a GAF score under 50 were categorized as having serious mental illness. NSDUH data collected for each member of the subsample were then used to construct a prediction model that best fit the SCID data and GAF scores collected from the clinical subsample. The model included the following five variables from the NSDUH survey: the K6 scale of psychological distress (27), the World Health Organization Disability Assessment Schedule for measuring functional impairment (28), past-year major depressive episode, past-year serious thoughts of suicide, and age. Cutpoints were derived to indicate any mental illness and various levels of mental illness severity from the model developed in the clinical follow-up study. This model and cutpoints then were applied to the entire NSDUH sample to classify adults with any mental illness in the past year and, among those with any mental illness, a mental illness with mild, moderate, or serious impairment (serious mental illness) (29,30).
We also examined the presence or absence of co-occurring substance use disorders in the past year (dependence on or abuse of alcohol or illicit drugs) on the basis of criteria in DSM-IV (31). Other correlates included past-year criminal justice involvement, health (excellent, very good, good or fair, and poor), and current health insurance provider (private only, Medicaid only, CHAMPUS/CHAMPVA, military insurance only, more than one, other, and uninsured).
Mental health service outcomes.
Respondents are asked questions about past-year use of outpatient services (treatment from a therapist, psychologist, psychiatrist, social worker, and counselor and partial-day treatment), inpatient services (overnight stays or longer), and psychotropic medication to treat emotions, nerves, or mental health problems, excluding treatment for substance use disorders.
Data Analysis
Logistic regressions evaluated associations between demographic, developmental, mental illness, and related correlates and three mental health service use outcomes (outpatient services, inpatient services, and psychotropic medication). Correlates were entered in the model simultaneously, and PROC Logistic in SUDAAN, version 11.0, was used to account for the complex sample design. Sampling weights were used to yield population estimates. Because we combined five years of survey data, sampling weights were divided by the number of survey years.
Collinearity and Missing Data
Collinearity was investigated by using the variance inflation factor, condition index, variance decomposition proportion, eigenvalues (32–34), and full- versus reduced-model approaches which account for the complex survey design (35). Correlates did not exhibit major collinearity. We also conducted analyses to examine model-level missingness; levels were low (<1.8%). A Bonferroni-Holm adjustment was made across all tests to adjust for type I error associated with correlates (36,37).
Results
Sample Characteristics
Within the full NSDUH sample of young adults (ages 18–26), 18.8% (N=22,600) were estimated to have a mental illness. This subpopulation represents six million young adults with a mental illness in the general population. Within this subpopulation, it is estimated that 79.3% had a mild or moderate mental illness and 20.7% had a serious mental illness. The sample was 38.6% male, 63.3% Caucasian, 17.0% Hispanic, and 11.6% African American. The remainder was non-Hispanic and was from more than one race or Asian, Native American/Alaska Native, or Native Hawaiian or Other Pacific Islander. Age was grouped into three categories: 18–20 (32.8%), 21–23 (32.5%), and 24–26 (34.7%).
Mental health service use rates were 20.4%, 3.6%, and 25.4% for outpatient services, inpatient services, and medication, respectively. Table 1 shows percentages of service use by correlate. Of particular interest are rates of service use among young adults with serious mental illness. Specifically, 63.4% did not receive outpatient mental health services in the past year, and 57.3% did not receive psychotropic medication.
| Correlate | N | Weighted N (thousands) | Outpatient services | Inpatient services | Psychotropic medication | |||
|---|---|---|---|---|---|---|---|---|
| % | SE | % | SE | % | SE | |||
| Age | ||||||||
| 18–20 | 8,000 | 2,337 | 19.6 | .6 | 4.5 | .3 | 23.1 | .6 |
| 21–23 | 8,000 | 2,322 | 20.8 | .6 | 3.6 | .3 | 25.7 | .7 |
| 24–26 | 6,600 | 2,477 | 20.8 | .8 | 2.7 | .3 | 27.3 | .9 |
| Gender | ||||||||
| Male | 8,200 | 2,753 | 15.1 | .5 | 3.8 | .3 | 19.5 | .6 |
| Female | 14,400 | 4,382 | 23.7 | .5 | 3.4 | .2 | 29.1 | .5 |
| Race-ethnicity | ||||||||
| Non-Hispanic white | 14,600 | 4,516 | 24.1 | .5 | 3.3 | .2 | 31.8 | .5 |
| Non-Hispanic black or African American | 2,400 | 826 | 13.4 | 1.0 | 5.1 | .6 | 12.4 | .9 |
| Non-Hispanic Native American/Alaska Native | 300 | 48 | 11.1 | 2.1 | 5.1 | 2.2 | 23.6 | 4.0 |
| Non-Hispanic Native Hawaiian or Other Pacific Islander | 100 | 34 | 8.8 | 4.0 | 4.1 | 2.1 | 9.3 | 3.5 |
| Non-Hispanic Asian | 800 | 340 | 13.3 | 1.8 | 2.6 | .7 | 9.8 | 1.4 |
| Non-Hispanic more than one race | 900 | 158 | 21.4 | 2.3 | 4.4 | 1.0 | 27.4 | 2.9 |
| Hispanic | 3,400 | 1,214 | 14.1 | .9 | 3.3 | .4 | 15.3 | 1.0 |
| County type | ||||||||
| Metropolitan area (large or small) | 18,100 | 6,135 | 20.8 | .4 | 3.5 | .2 | 24.7 | .5 |
| Nonmetropolitan area | 4,500 | 1,001 | 18.2 | .9 | 3.9 | .4 | 29.6 | 1.1 |
| School enrollment | ||||||||
| Middle or high school student | 1,400 | 406 | 19.8 | 1.5 | 6.8 | 1.0 | 22.5 | 1.5 |
| College or other postsecondary school student | 8,800 | 2,842 | 23.7 | .6 | 2.5 | .2 | 25.6 | .6 |
| High school dropout | 1,700 | 525 | 16.4 | 1.3 | 6.4 | .9 | 26.9 | 1.45 |
| High school graduate | 8,600 | 2,575 | 16.8 | .6 | 4.4 | .3 | 25.2 | .7 |
| College graduate | 1,900 | 738 | 24.2 | 1.6 | .7 | .2 | 26.7 | 1.8 |
| Employment | ||||||||
| Full-time | 8,400 | 2,670 | 16.0 | .6 | 2.1 | .2 | 22.7 | .7 |
| Part-time | 6,200 | 1,941 | 22.7 | .8 | 2.9 | .3 | 24.5 | .8 |
| Unemployed | 3,000 | 951 | 18.7 | 1.0 | 5.5 | .6 | 24.1 | 1.1 |
| Disabled, not in the workforce | 600 | 171 | 50.1 | 3.1 | 17.8 | 2.4 | 63.3 | 3.0 |
| Full-time student, not in the workforce | 2,300 | 768 | 26.1 | 1.3 | 3.3 | .5 | 26.9 | 1.3 |
| Other, not in the workforce (including homemaker and retired) | 2,100 | 634 | 19.5 | 1.2 | 5.4 | .7 | 29.7 | 1.3 |
| Past-year residential stability | ||||||||
| No moves | 10,100 | 3,319 | 19.6 | .6 | 3.2 | .2 | 25.9 | .7 |
| 1–2 moves | 9,500 | 2,960 | 20.7 | .6 | 3.3 | .3 | 24.4 | .6 |
| ≥3 moves | 2,900 | 854 | 22.3 | 1.2 | 6.0 | .6 | 26.8 | 1.2 |
| Living situation | ||||||||
| With parent or grandparent | 10,700 | 3,501 | 19.1 | .5 | 4.2 | .3 | 23.8 | .6 |
| With spouse, significant other, or partnerb | 5,100 | 1,528 | 20.7 | .8 | 2.6 | .4 | 29.9 | 1.0 |
| With roommate or others | 3,300 | 1,075 | 24.9 | 1.2 | 2.4 | .4 | 24.3 | 1.2 |
| Alone, with child | 1,200 | 392 | 13.2 | 1.4 | 4.5 | .9 | 18.1 | 1.5 |
| Alone, without another adult | 2,400 | 639 | 23.8 | 1.3 | 3.9 | .5 | 29.7 | 1.5 |
| Past-year criminal justice involvement | ||||||||
| Yes | 2,600 | 805 | 21.5 | 1.1 | 8.3 | .8 | 29.1 | 1.3 |
| No | 19,900 | 6,311 | 20.3 | .4 | 2.9 | .2 | 24.9 | .4 |
| Mental illness | ||||||||
| Serious impairment | 4,700 | 1,469 | 36.6 | 1.1 | 8.3 | .6 | 42.7 | 1.1 |
| Mild or moderate impairment | 17,900 | 5,666 | 16.2 | .4 | 2.3 | .2 | 20.9 | .4 |
| Overall health | ||||||||
| Excellent, very good, or good | 20,200 | 6,406 | 20.0 | .4 | 3.0 | .2 | 24.3 | .4 |
| Fair or poor | 2,400 | 729 | 24.2 | 1.2 | 8.1 | .9 | 34.9 | 1.4 |
| Substance use disorder | ||||||||
| Yes | 7,600 | 2,412 | 21.7 | .7 | 5.3 | .4 | 27.9 | .8 |
| No | 15,000 | 4,723 | 19.8 | .5 | 2.7 | .2 | 24.2 | .5 |
| Health insurance | ||||||||
| Private only | 11,100 | 3,555 | 23.7 | .6 | 2.6 | .2 | 28.4 | .6 |
| Medicaid only | 3,400 | 975 | 21.5 | 1.0 | 7.1 | .7 | 30.5 | 1.1 |
| CHAMPUS, CHAMPVA, or military insurance only | 600 | 177 | 25.9 | 2.6 | 5.0 | 1.2 | 31.1 | 2.7 |
| >1 type | 700 | 212 | 34.7 | 2.8 | 8.8 | 1.4 | 40.1 | 2.8 |
| Other | 900 | 258 | 15.9 | 1.9 | 2.9 | .7 | 21.5 | 1.9 |
| Uninsured | 5,900 | 1,959 | 12.4 | .6 | 3.0 | .3 | 15.8 | .6 |
a
Data are from the 2008–2012 National Survey on Drug Use and Health.
b
Living with spouse (44.5%±1.1%) and living with significant other (55.5%±1.1%)
Correlates of Mental Health Service Use
Table 2 shows the multiple logistic regression results. Given NSDUH’s large size, statistical power to detect change was very high. Thus many variables were significant, including some with confidence intervals close to 1.0. We focus on results that were significant on the basis of the Bonferroni adjustment and do not report results in which confidence intervals were close to 1.0. The table portrays the adjusted odds ratios (AORs) for the association between the correlates and each of the three types of mental health services.
| Outpatient services | Inpatient services | Psychotropic medication | ||||
|---|---|---|---|---|---|---|
| Correlate | AORa | 95% CI | AORa | 95% CI | AORa | 95% CI |
| Age (reference: 18–20) | ||||||
| 21–23 | 1.1 | 1.01–1.29 | .81 | .63–1.03 | 1.18 | 1.05–1.32 |
| 24–26 | 1.3* | 1.1–1.44 | .66 | .50–.88 | 1.31* | 1.15–1.49 |
| Female (reference: male) | 1.6* | 1.47–1.83 | .89 | .72–1.10 | 1.67* | 1.50–1.85 |
| Race-ethnicity (reference: non-Hispanic white) | ||||||
| Non-Hispanic black or African American | .5* | .39–.55 | 1.33 | .98–1.80 | .27* | .22–.31 |
| Non-Hispanic Native American/Alaska Native | .5* | .27–.72 | 1.01 | .38–2.72 | .59 | .35–1.00 |
| Non-Hispanic Native Hawaiian or Other Pacific Islander | .3 | .11–.73 | 1.15 | .38–3.51 | .19* | .08–.48 |
| Non-Hispanic Asian | .4* | .32–.60 | 1.28 | .70–2.33 | .24* | .17–.33 |
| Non-Hispanic more than one race | .8 | .59–1.09 | 1.04 | .61–1.66 | .78 | .58–1.05 |
| Hispanic | .6* | .49–.69 | .99 | .74–1.32 | .41* | .34–.49 |
| County located in metropolitan area (large or small) (reference: nonmetropolitan area) | 1.2* | 1.08–1.41 | 1.04 | .79–1.36 | .91 | .81–1.02 |
| School enrollment (reference: high school dropout) | ||||||
| Middle or high school student | 1.3 | .94–1.69 | 1.19 | .76–1.86 | .84 | .63–1.09 |
| College or other postsecondary school student | 1.5* | 1.16–1.86 | .60 | .40–.91 | .99 | .80–1.22 |
| High school graduate | 1.1 | .85–1.32 | .97 | .68–1.37 | .92 | .76–1.12 |
| College graduate | 1.7* | 1.3–2.3 | .3* | .15–.63 | 1.02 | .77–1.36 |
| Employment (reference: unemployed) | ||||||
| Full-time | .6* | .5–.8 | .5* | .4–.8 | .7* | .6–.9 |
| Part-time | 1.0 | .8–1.2 | .7 | .5–1.0 | .8 | .7–.9 |
| Full-time student, not in the workforce | 1.2 | .9–1.4 | .8 | .6–1.2 | 1.0 | .9–1.3 |
| Disabled, not in the workforce | 3.3* | 2.4–4.5 | 2.6* | 1.7–4.0 | 3.6* | 2.6–5.0 |
| Other, not in the workforce (including homemaker and retired) | .9 | .8–1.1 | 1.1 | .8–1.6 | 1.0 | .9–1.3 |
| Past-year residential stability (reference: 0 moves) | ||||||
| 1–2 moves | 1.0 | .9–1.2 | 1.1 | .9–1.4 | .9* | .8–.9 |
| ≥3 moves | 1.2 | 1.0–1.4 | 1.5 | 1.2–2.0 | .9 | .8–1.0 |
| Living situation (reference: alone, without another adult) | ||||||
| With parent or grandparent | .8 | .7–1.0 | .8 | .6–1.2 | .8 | .7–1.0 |
| With spouse, significant other, or partner | .9 | .7–1.1 | .6 | .4–1.0 | 1.0 | .8–1.2 |
| With roommate or others | .9 | .8–1.2 | .7 | .4–1.0 | .8 | .6–1.0 |
| Alone, with child | 1.0 | .7–1.5 | .6 | .3–1.1 | 1.0 | .7–1.4 |
| Past-year criminal justice involvement (reference: no) | 1.4* | 1.2–1.6 | 2.0* | 1.5–2.5 | 1.5* | 1.3–1.7 |
| Mental illness, serious impairment (reference: mild or moderate impairment) | 2.9* | 2.6–3.2 | 3.4* | 2.8–4.1 | 2.8* | 2.5–3.1 |
| Overall health fair or poor (reference: excellent, very good, or good) | 1.1 | 1.0–1.3 | 1.6* | 1.2–2.1 | 1.5* | 1.3–1.7 |
| Substance use disorder (reference: no) | 1.1 | 1.0–1.2 | 1.7* | 1.4–2.1 | 1.2* | 1.1–1.3 |
| Health insurance (reference: uninsured) | ||||||
| Private only | 2.0* | 1.8–2.3 | 1.3 | 1.0–1.8 | 2.2* | 2.0–2.5 |
| Medicaid only | 1.7* | 1.5–2.0 | 2.0* | 1.4–2.7 | 2.2* | 1.9–2.5 |
| CHAMPUS, CHAMPVA, or military insurance only | 2.0* | 1.5–2.7 | 1.8 | 1.0–3.3 | 2.2* | 1.6–2.8 |
| >1 type | 3.3* | 2.5–4.4 | 2.5* | 1.6–4.0 | 3.4* | 2.6–4.4 |
| Other | 1.2 | .9–1.7 | 1.1 | .6–1.9 | 1.5 | 1.1–1.9 |
a
Adjusted odds ratio
*
p<.001, after Bonferroni-Holm adjustment
Outpatient mental health services.
The likelihood of outpatient service use was significantly greater for 24- to 26-year-olds versus 18- to 20-year-olds (20.8% versus 19.6%, AOR=1.3), females versus males (23.7% versus 15.1%, AOR=1.6), and residents of metropolitan versus nonmetropolitan areas (20.8% versus 18.2%, AOR=1.2). With the exception of young adults reporting more than one race-ethnicity (21.4%) and non-Hispanic Native Hawaiian or Other Pacific Islanders (8.8%), all groups (11.1%−14.1%) were less likely to receive services compared with non-Hispanic whites (24.1%).
As for developmental correlates, college students (23.7%) and college graduates (24.2%) were more likely than high school dropouts (16.4%) to receive outpatient services (AOR=1.5 and 1.7, respectively). Full-time employees were less likely to receive services compared with those who were unemployed (16.0% versus 18.7%, AOR=.6). Disabled young adults who were not in the workforce were three times more likely to receive outpatient services compared with young adults who were unemployed (50.1% vs. 18.7%, AOR=3.3). One’s living situation and past-year substance use disorders were not significantly associated with the use of outpatient services.
Young adults with serious mental illness were more likely to receive outpatient mental health services compared with young adults with mild or moderate mental illness (36.6% versus 16.2%, AOR=2.9). Having health insurance increased the likelihood of outpatient service use, particularly among young adults with more than one type of insurance (34.7%) or CHAMPUS/CHAMPVA or other military insurance (25.9%). Past-year criminal justice involvement (versus none) increased the likelihood of receiving outpatient services (21.5% vs. 20.3%, AOR=1.4)
Inpatient mental health services.
Unlike the findings for outpatient services, no demographic variables (gender, race-ethnicity, and county type) were associated with inpatient service use. College graduates were less likely than high school dropouts to use inpatient services (.7% versus 6.4%, AOR=.3). Disabled young adults who were not in the workforce were more likely than unemployed young adults to use inpatient services (17.8% versus 5.5%, AOR=2.6). Compared with young adults with mild or moderate mental illness, young adults with serious mental illness had greater likelihood of using inpatient services (2.3% versus 8.3%, AOR=3.4). Other factors that increased the likelihood of inpatient service use were poor (versus good) health (8.1% versus 3.0%, AOR=1.6), past-year criminal justice involvement versus none (8.3% versus 2.9%, AOR=2.0), past-year substance use disorders versus none (5.3% versus 2.7%, AOR=1.7), and having Medicaid (7.1%) or more than one type of insurance (8.8%) versus being uninsured (3.0%) (AOR=2.0 and 2.5, respectively).
Psychotropic medication.
Findings for use of psychotropic medication were similar to results for outpatient service use. Full-time employment versus unemployment was associated with decreased likelihood of using psychotropic medication (22.7% versus 24.1%, AOR=.7). Use of psychotropic medication was more likely among disabled young adults who were not in the workforce versus unemployed young adults (63.3% versus 24.1%, AOR=3.6). Serious mental illness, criminal justice involvement, poor health, substance use disorders, and being insured were related to psychotropic medication use. Unlike results for outpatient and inpatient service use, school enrollment status did not predict medication use.
Discussion
Results highlight low rates of service use among young adults with mental illness across all levels of severity. In particular, more than six in ten (63.4%) young adults with serious mental illness did not receive outpatient mental health services in the past year, and more than half (57.3%) did not receive psychotropic medication. Future studies should focus on reasons that respondents with mental illness do not access services, particularly those with a seriously impairing mental illness. Although results may be due to respondents not perceiving treatment need, that may be less likely for those with serious mental illness. Furthermore, mental health service use in this study was based on a minimal definition—receipt of only one past-year service; therefore, it is particularly concerning that a substantial proportion of young adults with serious mental illness did not report service receipt.
Findings revealed that about three in four young adults with a mental illness did not receive outpatient services, and more than half did not receive psychotropic medication. Results call attention to factors unique to young adulthood that differ from the variables addressed in the literature on adults, such as school enrollment. Compared with young adults with mental illness who had dropped out of school, young adults with mental illness who were enrolled in or had graduated from college were more likely to receive outpatient services. Nonetheless, they still experienced challenges, including social isolation and fear of stigma (38). Policies such as withdrawing financial aid if a student is temporarily out of school might interfere with a student returning to college (39). Innovative models to support college students with mental illness to achieve their education goals are needed (38,40).
Our findings highlight correlates of mental health service use that are particularly relevant in young adulthood. The findings related to living situation, which includes living with a spouse or significant other, were unexpected. Studies with older samples indicate that marriage is associated with greater service use (14), although we did not find significant associations between living with a spouse or partner and use of outpatient, inpatient, or psychotropic medication services. This divergence may reflect unique aspects of young adulthood that are intertwined with romantic or marital partners. A recent report indicated that the median age of marriage was 27 for females and 29 for males, the oldest in modern history (41). Marriage or cohabitation does not appear to have the same influence on facilitating service use among young adults as it does in later adulthood. Because a growing number of young adults live with their parents (42), the living situation variable in this study also included living with parents. During childhood, parents are critical for children’s services access; however, in this study, young adults who lived with parents were not more likely to use services. Research is needed to understand how parents and romantic partners (including distinctions between marriage and cohabitation) encourage young adults’ service receipt.
Another finding that diverged from the literature on mental health service use among adults involved employment. In the adult literature, full-time employment is positively associated with service use (22). However, in this study, individuals who were employed full-time were less likely to use services compared with those who were unemployed. One explanation is that full-time employment among young adults with mental illness is a sign of positive functioning, which may decrease service need. Young adults with mental illness express interest in having careers and being self-sufficient (38,43). Jobs also signify social acceptance, which can promote self-esteem and quality of life (44). Research is needed to understand factors that promote successful employment and to evaluate programs supporting employment and career development for young adults with mental illness (38).
Some findings were similar to those found in the adult literature, suggesting the generalizability of these correlates across age. Demographic findings were similar to those in the adult literature: females made greater use of outpatient and medication services compared with males, residents of metropolitan counties were more likely to use outpatient services compared with residents of nonmetropolitan counties (14), and African Americans (15,16) and Latinos (17,18) were less likely to use outpatient and medication services compared with non-Hispanic whites. Our results suggest that these factors are overarching correlates of mental health service use across adulthood.
Insurance is an important correlate of mental health service use and is particularly critical for young adults. In this study, having any insurance was associated with outpatient and medication services; having Medicaid and more than one type of insurance was associated with inpatient service use. Compared with adults of other ages, young adults are the most likely to be uninsured (45). Starting at age 18, insurance eligibility varies by multiple factors. Prior to Affordable Care Act (ACA) reforms, only young adults who were students were eligible for parental insurance, and Medicaid eligibility typically ended at age 18 for most young adults. We need to understand the impact of insurance instability discrepancies on young adults’ mental health service use, particularly how the changing health care coverage scene, for example changes resulting from implementation of the ACA and mental health parity laws, affects service utilization.
Findings underscore how other service sectors are related to mental health service use. Young adults with criminal justice involvement, poor health, or disabilities were more likely to receive mental health services. More than half of inmates have some type of mental health problem (46); 14.5% of male inmates, and 31.0% of female inmates have a serious mental illness (47). Also, general medical and mental health problems often co-occur (48,49).
Findings revealed that substance use disorders were significantly associated with higher use of inpatient and psychotropic medication services. These results highlight the influence of co-occurring disorders on the likelihood of using inpatient and psychotropic medication services for mental illnesses. However, co-occurring substance use disorders were unrelated to the likelihood of outpatient service use. Seventy-eight percent of respondents with co-occurring disorders did not use outpatient services in the past year. Mental illness and substance use disorders frequently co-occur: more than 65% of participants with lifetime substance use disorders have at least one lifetime mental disorder (50). Findings underscore the importance of integrated treatment models that address comorbid substance use disorders and mental illness (51).
This study had some limitations. Because NSDUH collects income data at the household level, we could not include individual-level income status. We did not examine differences across age groups, nor did we focus on perceived need for mental health services as a determinant of access. Despite these limitations, the large, population-based sample of young adults with mental illness in NSDUH offers a widely generalizable survey to study service use.
Conclusions
Mental illness often emerges in young adulthood, making the young adult years an important period to implement mental health treatment. Service use may be impeded by difficulties navigating complex systems and transitioning from child to adult services (38,52–54). Even when mental health services are received, they may not address developmentally relevant needs (55). Promising service models for young adults emphasize specialized transition services that include vocational, independent living, and housing support (56). These results highlight how correlates of mental health service use—education, employment, and living situation—differ among young adults compared with results from the adult literature. Findings reinforce the need to examine correlates that may be unique to young adults. Results support the uniqueness of young adulthood and the need to tailor mental health services to close service use gaps during this period.
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Information & Authors
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Published In
Cover: Small chest, attributed to Johannes Spitler, ca. 1800. Paint on yellow pine, poplar, and white pine with metal and wrought iron hardware. Collection American Folk Art Museum, New York City. Gift of Ralph Esmerian, 2005.8.28.
History
Received: 22 October 2014
Revision received: 9 September 2015
Accepted: 26 October 2015
Published online: 15 April 2016
Published in print: June 01, 2016
Authors
Competing Interests
The authors report no financial relationships with commercial interests.
Funding Information
National Institute of Mental Healthhttp://dx.doi.org/10.13039/100000025
This study was funded under contract 284-2010-0003C (project 0212800.002), which was supported by funding from the National Institute of Mental Health.Metrics & Citations
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