Employment Status of People With Mental Illness: National Survey Data From 2009 and 2010
Abstract
Objective
The aim of this study was to describe employment according to mental illness severity in the United States during 2009 and 2010.
Methods
The sample included all working-age participants (ages 18–64) from the 2009 and 2010 National Survey on Drug Use and Health (N=77,326). Two well-established scales of mental health distinguished participants with none, mild, moderate, and serious mental illness. Analyses compared employment rate and income by mental illness severity. Employment status was estimated with logistic regression models that controlled for demographic characteristics and substance use disorders. In secondary analyses the relationship between mental illness and employment was assessed for variation by age and education status.
Results
Employment rates decreased with increasing mental illness severity (no mental illness, 75.9% employment; mild, 68.8%; moderate, 62.7%; and serious, 54.5%, p<.001). Over a third of people with serious mental illness, 38.5%, had incomes <$10,000 (compared with 23.1% of people with no mental illness, p<.001). The gap in adjusted employment rates comparing persons with serious versus no mental illness was 1% among people 18–25 years old versus 21% among people 50–64 (p<.001).
Conclusions
More severe mental illness was associated with lower employment rates in 2009 and 2010. People with serious mental illness are less likely than people with no, mild, or moderate mental illness to be employed after age 49.
Mental disorders are associated with diminished labor market activity: people with mental illness are less likely to work than the general population (1–10), and those who work earn less than workers without mental illness (1,9). In studies of the general population, work has been associated with improvements in health and socioeconomic domains (11–14). Among people with mental illness, work has a positive association with economic (15), psychosocial (16–20), and clinical (21,22) improvements. In many studies, employment also correlates with short-term reductions in mental health costs (23–29). Monitoring disparities in employment by mental health status is thus a public health priority.
Three recent national phenomena are likely to have influenced labor participation in the United States: the large influx of people with mental illness enrolling for Social Security disability benefits (30), high unemployment rates associated with the recent recession (31), and evidence-based psychosocial services that support the employment goals of people with more severe mental illness (including schizophrenia) (31–33).
Disability enrollment
Economists estimate that $276 billion federal and state dollars were spent on working-age people with disabilities in 2002 (34). According to a Continuing Disability Review from the Social Security Administration, mental illness is now the primary diagnosis for one in three persons under the age of 50 who receive disabled worker benefits (unpublished raw data, Barrett CL, 2007). Beneficiaries with psychiatric impairments are often younger than other Social Security disability beneficiaries and therefore incur costs over a longer period (35,36). As the number of disability beneficiaries with mental illness grows steadily, policy makers have an increased interest in monitoring employment rates by mental health status.
Economic recession
The 2007–2009 recession in the United States was a period of substantially reduced economic activity. Unemployment changed dramatically, from an historic low of 4.4% before the recession in 2006 to a peak of 9.5% in 2009, with a slow recovery (31). Unemployment rates in 2010 remained well above 9%, even though the recession ended officially in June 2009 (31,37). The youth labor force (16- to 24-year-olds) and racial-ethnic minority groups were particularly vulnerable to unemployment during this period (38). Previous epidemiological studies describing associations between mental health and labor market outcomes may not generalize to the current period of high unemployment.
Evidence-based interventions
Employment rates among people with severe mental disorders, such as schizophrenia, major depressive disorder, or bipolar disorder, more than double when individuals receive evidence-based supported employment services (specifically, individual placement and support) (33). Evidence-based supported employment increases labor force participation among people with severe psychiatric illnesses through individualized services that focus on integrating vocational specialists into the mental health team and rapid job placement (39,40). This model represents a paradigmatic shift from previous employment interventions (such as day treatment) that offered sheltered experiences in preparation for work; these segregating models of care are slowly being defunded in the United States (41). Compared with previous rehabilitation models, services that support integrated jobs may make employment more likely for people with severe mental illness.
With data from the 2009 and 2010 National Survey on Drug Use and Health (NSDUH), this article provides a comprehensive overview of the current employment situation of people in the United States by mental health status.
Methods
Data source and study population
To study the link between employment and mental illness severity since the 2007–2009 recession, we analyzed survey responses of all 77,326 working-age adults (18–64 years old) from the 2009 and 2010 NSDUH public use files (www.icpsr.umich.edu/icpsrweb/SAMHDA/browse). The NSDUH is an annual survey of the civilian, noninstitutionalized U.S. population age 12 and older and is based on an independent, multistage area probability sample. The weighted response rate for all ages was 75.7% in 2009 and 74.7% in 2010 (42).
Measures
Employment status and related outcomes.
Employment served as the primary outcome variable. Respondents were asked whether they worked in the week before the interview and, among those who worked, whether they usually worked 35 or more hours per week. Following the practice used by the U.S. Bureau of Labor Statistics, we use “full-time” to refer to respondents who usually worked 35 or more hours per week and “part-time” to refer to other working respondents. “Unemployed” respondents did not have a job, were looking for a job, or were laid off. “Out of labor force” respondents were not in the labor force, which included students, persons caring for children full time, retired or disabled persons, and others not in the labor force.
In addition, the NSDUH collected information on each respondent’s total income in increments of $10,000, absenteeism (which we defined as missed or skipped at least one day of work in the past week), occupation categories (using 2003 U.S. Census codes), and benefits status (family member received Social Security or U.S. Railroad payments in the past year and family member received Supplemental Security Income payment in the past year). Less than .3% of Social Security payments are U.S. Railroad payments (43). Hereafter we describe them as just “Social Security” payments, which, in this sample of adults age 18–64, describe the population receiving disability payments.
Past-year mental illness severity.
The study focused on four categories of mental illness severity—no mental illness, mild mental illness, moderate mental illness, and serious mental illness—based on two assessments available in the NSDUH. The Substance Abuse and Mental Health Services Administration developed models to predict mental illness severity based on responses to two short self-assessments, the K6 assessment of nonspecific psychological distress (44,45) and a shortened, eight-item version of the World Health Organization Disability Assessment Schedule (WHODAS) for functional impairment (46,47). In 2008, a total of 1,506 adults were administered the Structured Clinical Interview for DSM-IV (SCID) via telephone by mental health clinicians. In years since, NSDUH reported four categories of mental illness severity based on parameter estimates from a model of scores on the clinician-administered SCID as a function of the K6 and WHODAS scores (48,49).
Selection of adjustment factors.
We selected potential adjustment factors based on past labor supply studies. A meta-analysis of 62 studies of employment among people with schizophrenia found that cognitive functioning, education, negative symptoms, social support and skills, age, work history, and rehabilitation services predicted better employment outcomes, whereas positive symptoms, substance abuse, gender, and hospitalization history did not; marital status was marginally significant (50). Relevant covariates among people with none or mild to moderate mental disorders were determined by referring to a review of studies conducted in industrialized nations (1) and census data. Among people with mild mental illness, the following characteristics were associated with work status: gender (10,51), age (10,51,52), education (10,51,52), marital status (53), race-ethnicity (10,52), substance use (54), general health (10), children in household (51), criminal justice involvement (55), and a measure of the local community context (51) (urbanicity).
Past-year substance use disorder.
The NSDUH provides measures of substance abuse or dependence based on DSM-IV criteria. Alcohol, marijuana, hallucinogens, inhalants, tranquilizers, cocaine, heroin, pain relievers, stimulants (including methamphetamine), and sedatives were all directly covered by questions in the survey. Participants were categorized as having no substance use disorder, alcohol abuse only, alcohol dependence only, drug abuse only, drug dependence only, or abuse or dependence of both alcohol and drugs.
Health status.
Self-reported general health was captured by asking, “Would you say your health in general is excellent, very good, good, fair, or poor?” Because of the low frequency of responses indicating poor health, “fair” and “poor” categories were collapsed.
Sociodemographic characteristics.
This study also included the following sociodemographic variables: age categories (18–25, 26–34, 35–49, and 50–64), gender (male or female), race-ethnicity (white, black, Hispanic, or other), education attainment (less than high school, high school graduate, some college, and college graduate or higher), marital status (never married or ever married), number of children under age 18 in the household (zero, one, two, or three or more), number of times arrested and booked in the past year (zero, one, two, or three or more times), and county type of residence (large metropolitan area, small metropolitan area, or nonmetropolitan area).
Analytic strategy
Descriptive analyses were conducted to compute employment rates, sociodemographic characteristics, and the remaining employment outcomes across mental illness severity categories. Multivariate logistic regression was used to identify factors associated with any employment stratified by mental illness severity. We ran all models twice: using the validated mental illness severity for the NSDUH based on WHODAS, K6, and a clinically validated subsample and again using just the K6 symptom score based on approximate mental illness percentile cutoffs (none versus mild illness at the 80th percentile, mild versus moderate at the 90th, and moderate versus serious at the 95th). The models based on only the K6 measure tested the sensitivity of our results to items in the WHODAS that may be too close to our outcome variables describing employment.
Given differences in the association between mental illness severity and education and between mental illness and age, we tested interactions of age and education × mental illness status in the final multivariate logistic regression model. All proportions and other estimates were computed with sample weights to reflect the target population of the study, working-age adults in the United States. In addition, variance estimates using standard approaches (specifically, Taylor series approximations) accounted for the complex stratified sampling design in the NSDUH. We used Stata SE, version 12, to conduct all analyses. The Dartmouth College Committee for the Protection of Human Subjects deemed these analyses, using publicly available, deidentified secondary data, exempt from review.
Results
Demographic characteristics
Table 1 displays demographic information for 77,326 working-age adults by mental illness severity. The age distribution of respondents was similar across categories, with most of the population falling between ages 26 and 49. In contrast, more educated respondents were concentrated in the group without mental illness (30.7% and 20.6% graduated from college in the no mental illness and serious mental illness categories, respectively). The share of individuals without a substance use disorder was highest among respondents without mental illness (92.8%) compared with the serious mental illness group (75.6%). Self-reported fair or poor general health was also much more common in the group with serious mental illness (27.8%) relative to the group without mental illness (8.7%). Approximately 8% of the sample with serious mental illness reported an arrest in the past year, compared with only 2.6% in the group without mental illness. All differences shown in Table 1 across mental illness severity groups were statistically significant (p<.001).
| Past-year mental illness | ||||||||
|---|---|---|---|---|---|---|---|---|
| None (N=57,283) | Mild (N=10,643) | Moderate (N=4,170) | Serious (N=5,230) | |||||
| Characteristic | N | % | N | % | N | % | N | % |
| Female | 26,647 | 48.1 | 6,069 | 57.0 | 2,524 | 58.9 | 3,589 | 66.7 |
| Age | ||||||||
| 18–25 | 26,604 | 15.9 | 6,229 | 25.0 | 2,474 | 24.4 | 3,013 | 23.8 |
| 26–34 | 8,506 | 18.5 | 1,587 | 21.4 | 634 | 22.0 | 807 | 21.3 |
| 35–49 | 12,655 | 33.8 | 1,847 | 32.1 | 718 | 29.5 | 1,019 | 32.6 |
| 50–64 | 5,749 | 31.7 | 642 | 21.4 | 262 | 24.1 | 331 | 22.4 |
| Education | ||||||||
| Less than high school | 8,384 | 13.3 | 1,745 | 14.1 | 775 | 17.1 | 909 | 15.5 |
| High school graduate | 17,496 | 30.0 | 3,308 | 29.6 | 1,358 | 28.7 | 1,758 | 33.0 |
| Some college | 15,508 | 26.0 | 3,166 | 28.2 | 1,262 | 29.4 | 1,709 | 30.9 |
| College graduate or higher | 12,126 | 30.7 | 2,086 | 28.1 | 713 | 24.8 | 794 | 20.6 |
| Ever married | 24,960 | 28.8 | 3,669 | 40.7 | 1,453 | 41.8 | 2,031 | 38.5 |
| Race-ethnicity | ||||||||
| White | 33,120 | 64.8 | 6,629 | 68.7 | 2,652 | 68.3 | 3,532 | 73.0 |
| Black | 6,882 | 12.5 | 1,270 | 12.0 | 468 | 11.3 | 470 | 9.5 |
| Hispanic | 8,961 | 15.9 | 1,433 | 12.4 | 593 | 14.4 | 684 | 12.1 |
| Other | 4,551 | 6.8 | 973 | 6.9 | 375 | 6.1 | 484 | 5.5 |
| Substance use | ||||||||
| No substance use disorder | 47,851 | 92.8 | 7,880 | 82.5 | 2,942 | 78.4 | 3,487 | 75.6 |
| Alcohol abuse only | 2,727 | 3.7 | 771 | 6.0 | 312 | 5.2 | 343 | 4.9 |
| Alcohol dependence only | 1,374 | 2.2 | 748 | 6.3 | 340 | 8.1 | 525 | 9.8 |
| Drug abuse only | 304 | .3 | 121 | .9 | 66 | 1.6 | 72 | 1.1 |
| Drug dependence only | 624 | .7 | 368 | 2.4 | 172 | 4.0 | 316 | 5.3 |
| Abuse of or dependence on alcohol and drugs | 357 | .3 | 250 | 1.8 | 149 | 2.7 | 277 | 3.4 |
| General health | ||||||||
| Excellent | 15,952 | 27.7 | 2,188 | 19.3 | 699 | 14.0 | 741 | 11.6 |
| Very good | 21,365 | 38.5 | 4,037 | 35.8 | 1,521 | 32.7 | 1,759 | 30.1 |
| Good | 12,717 | 25.1 | 2,905 | 29.3 | 1,269 | 32.0 | 1,614 | 30.5 |
| Fair or poor | 3,474 | 8.7 | 1,175 | 15.6 | 598 | 21.3 | 1,056 | 27.8 |
| Children <18 years old in household | ||||||||
| 0 | 35,028 | 62.0 | 7,210 | 63.8 | 2,868 | 68.0 | 3,590 | 66.9 |
| 1 | 8,272 | 15.9 | 1,445 | 15.0 | 611 | 14.8 | 753 | 14.6 |
| 2 | 6,477 | 14.0 | 1,041 | 13.5 | 382 | 10.9 | 500 | 11.2 |
| ≥3 | 3,680 | 8.1 | 603 | 7.7 | 224 | 6.3 | 325 | 7.3 |
| Arrests and bookings in past year | ||||||||
| 0 | 50,594 | 97.4 | 9,470 | 95.1 | 3,688 | 94.5 | 4,626 | 91.9 |
| 1 | 1,712 | 2.0 | 523 | 3.7 | 223 | 3.7 | 338 | 5.8 |
| 2 | 337 | .4 | 108 | .7 | 74 | 1.1 | 88 | 1.4 |
| ≥3 | 192 | .2 | 68 | .4 | 37 | .8 | 60 | .9 |
| County type | ||||||||
| Large metropolitan area | 23,860 | 54.7 | 4,557 | 53.2 | 1,759 | 53.1 | 2,096 | 48.9 |
| Small metropolitan area | 18,526 | 29.9 | 3,651 | 31.3 | 1,479 | 32.5 | 1,922 | 31.9 |
| Nonmetropolitan | 11,128 | 15.4 | 2,097 | 15.5 | 850 | 14.3 | 1,152 | 19.2 |
a
Source: National Survey on Drug Use and Health, 2009 and 2010. Values are expressed as crude Ns and adjusted percentages. Proportions are weighted to be nationally representative. All p values for chi square test of differences across mental illness severity groups were statistically significant (p<.001).
Employment rates
Table 2 presents (and Figure 1 highlights) nationally representative employment rates among working-age adults by mental health status. Employment fell sharply as mental illness severity increased. Full-time employment in 2009–2010 was 61.7% among people with no mental illness versus 38.1% among people with serious mental illness. Rates of part-time employment and unemployment showed similar patterns across severity categories. Rates of being out of the labor force were twice as high for adults with serious mental illness (35.1%) compared with adults without mental illness (17.1%). Differences in employment across mental illness severity groups were statistically significant (p<.001).
| Past-year mental illness | ||||||||
|---|---|---|---|---|---|---|---|---|
| None (N=57,283) | Mild (N=10,643) | Moderate (N=4,170) | Serious (N=5,230) | |||||
| Observation | N | % | N | % | N | % | N | % |
| Employment | ||||||||
| Full-time | 28,100 | 61.7 | 4,394 | 50.9 | 1,576 | 46.6 | 1,777 | 38.1 |
| Part-time | 10,300 | 14.2 | 2,428 | 17.9 | 944 | 16.1 | 1,149 | 16.4 |
| Unemployed | 5,149 | 7.0 | 1,211 | 9.4 | 548 | 10.2 | 660 | 10.5 |
| Out of labor force | 9,965 | 17.1 | 2,272 | 21.8 | 1,020 | 27.1 | 1,584 | 35.1 |
| Respondent’s total income | ||||||||
| <$10,000 (including loss) | 19,812 | 23.1 | 4,778 | 32.0 | 2,014 | 35.5 | 2,596 | 38.5 |
| $10,000–$19,999 | 10,625 | 16.3 | 2,207 | 18.9 | 913 | 21.1 | 1,230 | 23.2 |
| $20,000–$29,999 | 6,912 | 13.6 | 1,223 | 13.4 | 451 | 12.4 | 546 | 12.1 |
| $30,000–$39,999 | 5,013 | 11.9 | 761 | 10.8 | 298 | 10.1 | 299 | 8.2 |
| $40,000–$49,999 | 3,489 | 9.2 | 456 | 6.7 | 157 | 7.6 | 194 | 5.8 |
| $50,000–$74,999 | 4,295 | 13.2 | 554 | 10.3 | 151 | 6.9 | 190 | 7.5 |
| ≥$75,000 | 3,368 | 12.8 | 326 | 7.8 | 104 | 6.3 | 115 | 4.8 |
| Past-year benefits to family | ||||||||
| Social Security | 5,050 | 12.8 | 1,186 | 14.7 | 531 | 18.2 | 786 | 20.8 |
| Supplemental Security Income | 2,925 | 5.8 | 818 | 8.6 | 380 | 11.5 | 567 | 13.2 |
| Employed respondent’s total incomeb | ||||||||
| <$10,000 (including loss) | 9,130 | 12.3 | 2,241 | 19.1 | 874 | 20.1 | 1,017 | 21.4 |
| $10,000–$19,999 | 8,296 | 15.7 | 1,655 | 18.7 | 654 | 20.2 | 816 | 23.0 |
| $20,000–$29,999 | 5,989 | 15.0 | 1,035 | 15.6 | 358 | 15.5 | 429 | 16.3 |
| $30,000–$39,999 | 4,488 | 13.8 | 666 | 13.5 | 256 | 14.0 | 242 | 11.8 |
| $40,000–$49,999 | 3,222 | 11.0 | 419 | 8.9 | 142 | 10.7 | 166 | 8.3 |
| $50,000–$74,999 | 4,062 | 16.2 | 503 | 13.9 | 138 | 9.9 | 153 | 11.1 |
| ≥$75,000 | 3,213 | 16.0 | 303 | 10.4 | 98 | 9.6 | 103 | 8.1 |
| Missed or skipped work ≥1 day in past weekb | 9,559 | 21.5 | 2,304 | 30.5 | 997 | 37.9 | 1,239 | 40.7 |
| Occupation categoryb | ||||||||
| Executive, administrative, managerial, or financial | 4,053 | 14.5 | 578 | 13.6 | 199 | 12 | 5,062 | 11.4 |
| Professional (not education, entertainment, or media) | 3,696 | 12.8 | 586 | 11.4 | 194 | 11.9 | 4,688 | 10.3 |
| Education and related occupations | 2,166 | 6.2 | 401 | 7.1 | 154 | 6.5 | 2,886 | 8.1 |
| Entertainers, sports, media, and communications | 805 | 2.2 | 196 | 3.1 | 60 | 2.2 | 1,141 | 3.6 |
| Technicians and related support occupations | 2,216 | 5.2 | 457 | 5.9 | 173 | 5.3 | 3,075 | 6.9 |
| Sales occupations | 4,574 | 9.9 | 953 | 11.6 | 392 | 14.3 | 6,352 | 11.4 |
| Office and administrative support workers | 4,937 | 12.4 | 973 | 14.2 | 369 | 14.3 | 6,722 | 14.5 |
| Protective service occupations | 930 | 2.5 | 125 | 1.9 | 43 | 2.5 | 1,143 | 2.2 |
| Service occupations, except protective | 6,648 | 11.7 | 1,485 | 15.5 | 547 | 14.3 | 9,368 | 18.1 |
| Farming, fishing, and forestry occupations | 375 | .7 | 45 | .3 | 15 | .2 | 447 | .3 |
| Installation, maintenance, and repair workers | 1,386 | 4.0 | 154 | 2.5 | 53 | 2.5 | 1,653 | 1.5 |
| Construction trades and extraction workers | 2,426 | 5.9 | 319 | 4.4 | 101 | 4.7 | 2,925 | 2.5 |
| Production, machinery setters, operators, and tenders | 2,199 | 5.9 | 286 | 4.0 | 117 | 5.4 | 2,734 | 4.9 |
| Transportation and material moving workers | 2,283 | 6.0 | 334 | 4.7 | 131 | 3.9 | 2,883 | 4.2 |
a
Source: National Survey on Drug Use and Health, 2009 and 2010. Values are expressed as crude Ns and adjusted percentages. Proportions are weighted to be nationally representative. All p values for chi square test of differences across mental illness severity groups were statistically significant (p<.001).
b
Among persons employed full- or part-time in the past year
Figure 1 Employment rates among adults 18–64, by mental health status, 2009–2010a
a Source: National Survey on Drug Use and Health, 2009 and 2010. Percentages are weighted to be nationally representative.
Other employment outcomes
Table 2 also provides detail about occupation, income, and absenteeism among workers by mental illness severity. Employment rates by occupation were largely consistent across mental illness severity categories, although individuals with mental illness were slightly more likely to be in sales or service occupations. In spite of these similarities, employed persons with a serious mental illness earned far less than employed persons without a serious mental illness. For example, 21.4% of employed individuals with serious mental illness earned under $10,000, compared with only 12.3% of employed persons without mental illness. Among families of respondents with serious mental illness, 20.8% received Social Security payments, and 13.2% received Supplemental Security Income in the past year. People with serious mental illness were more likely to miss or skip a day of work (40.7%) compared with people with no mental illness (21.5%), mild mental illness (30.5%), or moderate mental illness (37.9%). All differences shown in Table 2 across mental illness severity groups were statistically significant (p<.001).
Associations with full- or part-time employment
Table 3 provides estimates from logistic regression analyses that identified variables associated with employment status. The likelihood of employment generally increased from young adulthood (18–25) to adulthood (26–34), except among individuals with serious mental illness. After reaching age 50, people with moderate and serious mental illness were far less likely to work than those with mild or no mental illness (p<.001 for a test of joint significance of age × mental illness severity) (Figure 2). Education status was strongly associated with employment, within all categories of mental illness severity. [A figure showing employment rate among adults by education level is shown in the online data supplement to this article.]
| Model 1: No mental illness | Model 2: Mild mental illness | Model 3: Moderate mental illness | Model 4: Serious mental illness | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Observation | % | OR | 95% CI | % | OR | 95% CI | % | OR | 95% CI | % | OR | 95% CI |
| Age | ||||||||||||
| 18–25 (reference) | 65 | — | — | 66 | — | — | 65 | — | — | 64 | — | — |
| 26–34 | 77 | 1.92 | 1.75–2.12 | 70 | 1.23 | .99–1.52 | 72 | 1.41 | 1.04–1.92 | 61 | .90 | .69–1.16 |
| 35–49 | 80 | 2.25 | 2.04–2.48 | 74 | 1.47 | 1.15–1.87 | 71 | 1.36 | .95–1.94 | 64 | 1.00 | .76–1.33 |
| 50–64 | 69 | 1.22 | 1.08–1.38 | 65 | .93 | .69–1.27 | 52 | .56 | .36–.86 | 48 | .50 | .34–.72 |
| Race-ethnicity | ||||||||||||
| White (reference) | 71 | — | — | 70 | — | — | 70 | — | — | 62 | — | — |
| Black | 69 | .87 | .78–.97 | 61 | .66 | .51–.85 | 57 | .59 | .43–.81 | 58 | .83 | .56–1.22 |
| Hispanic | 71 | 1.00 | .87–1.14 | 70 | 1.02 | .81–1.29 | 71 | 1.16 | .83–1.62 | 63 | 1.04 | .75–1.43 |
| Other | 68 | .84 | .71–.98 | 64 | .76 | .56–1.04 | 61 | .71 | .42–1.20 | 66 | 1.22 | .62–2.41 |
| Education | ||||||||||||
| Less than high school (reference) | 58 | — | — | 54 | — | — | 46 | — | — | 46 | — | — |
| High school graduate | 69 | 1.64 | 1.46–1.83 | 65 | 1.61 | 1.29–2.00 | 64 | 2.17 | 1.58–3.00 | 59 | 1.67 | 1.20–2.33 |
| Some college | 75 | 2.26 | 2.02–2.52 | 73 | 2.48 | 1.94–3.18 | 70 | 2.88 | 2.00–4.15 | 65 | 2.26 | 1.69–3.03 |
| College graduate or higher | 77 | 2.60 | 2.25–3.01 | 78 | 3.17 | 2.45–4.09 | 79 | 4.69 | 3.02–7.28 | 74 | 3.44 | 2.28–5.18 |
| Gender | ||||||||||||
| Male (reference) | 76 | — | — | 72 | — | — | 76 | — | — | 76 | — | — |
| Female | 65 | .55 | .52–.59 | 65 | .73 | .61–.87 | 65 | .97 | .77–1.22 | 65 | .82 | .64–1.05 |
| Ever married | ||||||||||||
| No (reference) | 69 | — | — | 66 | — | — | 65 | — | — | 58 | — | — |
| Yes | 73 | 1.22 | 1.10–1.34 | 71 | 1.31 | 1.05–1.63 | 67 | 1.09 | .78–1.52 | 67 | 1.52 | 1.10–2.09 |
| General health | ||||||||||||
| Excellent (reference) | 73 | — | — | 71 | — | — | 71 | — | — | 68 | — | — |
| Very good | 74 | 1.07 | .97–1.18 | 72 | 1.03 | .87–1.22 | 67 | .82 | .57–1.17 | 65 | .90 | .67–1.20 |
| Good | 69 | .82 | .74–.91 | 67 | .83 | .70–.99 | 67 | .82 | .54–1.24 | 59 | .67 | .51–.88 |
| Fair or poor | 51 | .34 | .30–.39 | 48 | .35 | .26–.45 | 45 | .30 | .19–.46 | 36 | .25 | .18–.34 |
| Children <18 years old in household | ||||||||||||
| 0 (reference) | 71 | — | — | 69 | — | — | 65 | — | — | 62 | — | — |
| 1 | 74 | 1.20 | 1.10–1.31 | 69 | 1.02 | .81–1.27 | 70 | 1.28 | .87–1.88 | 59 | .85 | .63–1.16 |
| 2 | 71 | 1.03 | .94–1.13 | 69 | 1.04 | .81–1.34 | 72 | 1.43 | .95–2.16 | 62 | 1.00 | .71–1.42 |
| ≥3 | 63 | .67 | .60–.76 | 62 | .73 | .55–.98 | 60 | .80 | .46–1.37 | 61 | .92 | .61–1.39 |
| Arrests and bookings in past year | ||||||||||||
| 0 (reference) | 71 | — | — | 69 | — | — | 67 | — | — | 62 | — | — |
| 1 | 62 | .65 | .52–.80 | 60 | .67 | .50–.89 | 62 | .78 | .51–1.21 | 51 | .59 | .38–.92 |
| 2 | 55 | .46 | .29–.72 | 46 | .34 | .20–.60 | 56 | .59 | .31–1.12 | 59 | .87 | .42–1.82 |
| ≥3 | 59 | .55 | .31–.95 | 55 | .52 | .24–1.14 | 50 | .45 | .14–1.44 | 53 | .66 | .27–1.65 |
| County type | ||||||||||||
| Large metropolitan area (reference) | 71 | — | — | 68 | — | — | 68 | — | — | 63 | — | — |
| Small metropolitan area | 70 | .98 | .91–1.06 | 69 | 1.03 | .88–1.21 | 64 | .79 | .62–1.02 | 62 | .94 | .77–1.14 |
| Nonmetropolitan | 70 | .97 | .87–1.08 | 68 | 1.02 | .83–1.25 | 66 | .92 | .68–1.23 | 59 | .84 | .61–1.15 |
| Substance use | ||||||||||||
| No substance use disorder (reference) | 70 | — | — | 68 | — | — | 66 | — | — | 61 | — | — |
| Alcohol abuse only | 74 | 1.23 | 1.01–1.49 | 72 | 1.20 | .87–1.65 | 69 | 1.17 | .73–1.88 | 65 | 1.19 | .77–1.82 |
| Alcohol dependence only | 71 | 1.01 | .83–1.24 | 69 | 1.04 | .72–1.49 | 72 | 1.33 | .93–1.90 | 68 | 1.37 | 1.01–1.86 |
| Drug abuse only | 72 | 1.07 | .75–1.51 | 61 | .70 | .41–1.20 | 63 | .86 | .41–1.80 | 59 | .92 | .34–2.45 |
| Drug dependence only | 71 | 1.04 | .75–1.44 | 59 | .63 | .44–.89 | 52 | .52 | .31–.87 | 57 | .83 | .57–1.21 |
| Abuse of or dependence on alcohol and drugs | 64 | .74 | .45–1.21 | 60 | .67 | .42–1.06 | 60 | .74 | .44–1.23 | 68 | 1.40 | .92–2.19 |
a
Source: National Survey on Drug Use and Health, 2009–2010. Percentages are adjusted predicted probabilities based on logistic regression models stratified by mental illness severity groups. Odds ratios and confidence intervals for the adjusted relationship between mental illness severity and employment status are reported in the online data supplement to this article.
Figure 2 Employment rates among adults 18–64, by age within mental health status groups, 2009–2010a
a Source: National Survey on Drug Use and Health, 2009 and 2010. Percentages are adjusted predicted probabilities based on logistic regression models stratified by mental health status, with adjustment for age, gender, education, marital status, race-ethnicity, substance use disorders, self-reported general health, number of children in household, arrests in past year, and county type. Full model results are available in Table 3.
Overall models where mental illness severity was defined with the validated NSDUH model versus the symptom-only classification (K6) showed strikingly similar patterns [details are provided in the online data supplement].
Discussion
In a nationally representative sample of working-age adults in 2009–2010, people with moderate or serious mental illness were employed less often than adults with no reported mental illness. As with national data from the 1990s, we found that people with mental illness were represented in all occupation categories (10), yet income disparities remained. Nearly 40% of people with serious mental illness had income under $10,000 per year—well below substantial gainful activity thresholds that determine eligibility for federal disability payments. Mental illness had a much weaker relationship to employment among people under age 50 than those 50 and older.
People with more serious mental illness were less likely to report full-time employment than people without serious mental illness, although this estimate is nearly double the full-time employment rates reported in an earlier study (38% in this study versus 24% in a previous study) (10). The previous study analyzed data from the 1994–1995 National Health Interview Survey on Disability, which used a more stringent definition of serious mental illness that excluded undiagnosed individuals (self-reported diagnosis of schizophrenia, paranoid states, mood disorders, other nonorganic psychoses, or psychosis with origins specific to childhood in the past 12 months). One possible explanation is that undiagnosed individuals may not access services that would result in diagnostic assessment because they have fewer functional limitations.
Compared with the large differences in full-time work by mental illness severity, differences in unemployment and part-time employment were much more subtle. Rather than working part-time or seeking work, people with mental illness who are not working full-time appear to be displaced from the labor force entirely (out of the labor force). Most people with mental illness, even the most severely disabled, are capable of part-time work when provided appropriate supports (56). There are several explanations for why so many individuals with mental illness are out of the labor force entirely. People with more serious mental health issues have fewer incentives to seek work because disability policies often restrict eligibility to those not working in any significant capacity (57), employers are reluctant to hire individuals with psychiatric disabilities (58), and people with serious mental illness may be unaware of or unable to access job supports (33).
Variation in the age × employment relationship across mental illness severity groups was substantial. Among older adults, half with moderate or serious mental illness worked part-time or full-time, substantially less than their peers with mild or no mental illness, replicating an earlier study (10). Many older nonworking adults with moderate or serious mental illness were out of the labor force, rather than unemployed, a comparison not examined in prior research. Adults over age 50 with moderate or serious mental illness may be more likely to drop out of the workforce because of social acceptability (supply), but discrimination against older workers with mental illness (demand) is a more likely explanation because many older people with serious mental illness want to work (59). In contrast, younger workers living with mental illness did not experience the same decrement to labor force participation, suggesting opportunities to prevent exits from the labor force in younger populations.
Education status, known to facilitate employment opportunities (60), was the strongest predictor of employment even among people with serious mental illness. This finding is consistent with previous research in clinical and community samples (10,61,62) and suggests that facilitating educational achievement may facilitate job placement. Longitudinal research is needed to test alternative explanations: educational achievement may be a proxy for later illness onset, less serious illness, or more intensive service use.
Several limitations warrant consideration. This cross-sectional, descriptive study does not permit causal interpretation of any association between mental illness and employment outcomes. Even without the ability to draw causal inference from the results, these descriptive data fill a gap in evidence. Most psychiatric epidemiological studies of workforce participation focus on a single diagnostic group, use simplistic vocational outcomes (such as employment versus no employment), or fail to compare samples with mental illness with mentally well control samples. Mechanic and colleagues (10) provided a richer overview, describing employment rates by work intensity and occupational category among people with none, any, or serious mental illness, although the study presented data from the 1990s when the economic circumstances differed considerably from those since the most recent recession (2007–2009).
In addition, this study sample did not include people in institutional settings (prisons, hospitals, or treatment centers), where individuals with the greatest illness burden are likely to reside, although institutionalized individuals are not generally participating in the labor force. Third, short-form diagnostic surveys commonly used in the NSDUH are limited in their ability to distinguish between individuals with moderate affective illness and individuals with serious mental illness (typically defined as psychotic disorders with at least two years of illness burden). Although steps were taken to validate these self-reported measures of illness (48,49), self-report bias may have over- or underestimated the prevalence of mild, moderate, or serious mental illness. Lack of information on date of illness onset significantly limited possible inferences (1). Finally, participation in the national survey was high but incomplete, which may have resulted in an under- or overestimation of mental illness.
Conclusions
Employment rates varied substantially by mental illness severity in 2009–2010. Even during times of high unemployment, college graduates with serious mental illness had relatively strong employment outcomes. Unemployment rates spiked among people with serious mental illness over age 50, even compared with age-matched peers.
Acknowledgments and disclosures
This work was supported by grant DA030391 from the National Institute on Drug Abuse and grant HS22191 from the Agency for Healthcare Research and Quality. The authors thank Robert E. Drake, M.D., Ph.D., for insightful comments on drafts of the manuscript.
The authors report no competing interests.
Supplementary Material
Supplementary Material (1201_ds001.pdf)
- View/Download
- 40.03 KB
References
1.
Frank RG, Koss C: Mental Health and Labor Markets Productivity Loss and Restoration. Working Paper No 38. Geneva, World Health Organization, 2005. Available at www.dcp2.org/file/50/wp38.pdf
2.
Wu EQ, Birnbaum HG, Shi L, et al.: The economic burden of schizophrenia in the United States in 2002. Journal of Clinical Psychiatry 66:1122–1129, 2005
3.
Baldwin ML, Marcus SC: Labor market outcomes of persons with mental disorders. Industrial Relations 46:481–510, 2007
4.
Kessler RC, Heeringa S, Lakoma MD, et al.: Individual and societal effects of mental disorders on earnings in the United States: results from the National Comorbidity Survey Replication. American Journal of Psychiatry 165:703–711, 2008
5.
Blyler CR: Employment and Major Depressive Episode. Rockville, Md, Substance Abuse and Mental Health Services Administration, 2009
6.
Cowell AJ, Luo Z, Masuda YJ: Psychiatric disorders and the labor market: an analysis by disorder profiles. Journal of Mental Health Policy and Economics 12:3–17, 2009
7.
Burnett-Zeigler I, Ilgen MA, Bohnert K, et al.: The impact of psychiatric disorders on employment: results from a national survey (NESARC). Community Mental Health Journal 49:303–310, 2013
8.
Pratt LA: Characteristics of adults with serious mental illness in the United States household population in 2007. Psychiatric Services 63:1042–1046, 2012
9.
Levinson D, Lakoma MD, Petukhova M, et al.: Associations of serious mental illness with earnings: results from the WHO World Mental Health surveys. British Journal of Psychiatry 197:114–121, 2010
10.
Mechanic D, Blider S, McAlpine DD: Employing persons with serious mental illness. Health Affairs 21(5):242–253, 2002
11.
Warr P: Work, Unemployment, and Mental Health. Oxford, Oxford University Press, 1987
12.
Blustein DL: The role of work in psychological health and well-being: a conceptual, historical, and public policy perspective. American Psychologist 63:228–240, 2008
13.
Fogg NP, Harrington PE, McMahon BT: The impact of the Great Recession upon the unemployment of Americans with disabilities. Journal of Vocational Rehabilitation 33:193–202, 2010
14.
Barr B, Taylor-Robinson D, Scott-Samuel A, et al.: Suicides associated with the 2008–10 economic recession in England: time trend analysis. BMJ 345:e5142, 2012
15.
Cook JA, Blyler CR, Leff HS, et al.: The employment intervention demonstration program: major findings and policy implications. Psychiatric Rehabilitation Journal 31:291–295, 2008
16.
Fabian ES: Work and the quality of life. Psychosocial Rehabilitation Journal 12:39–49, 1989
17.
Fabian ES: Supported employment and the quality of life: does a job make a difference? Rehabilitation Counseling Bulletin 36:84–97, 1992
18.
Arns PG, Linney JA: Work, self, and life satisfaction for persons with severe and persistent mental disorders. Psychosocial Rehabilitation Journal 17:63–79, 1993
19.
Arns PG, Linney JA: The relationship of service individualization to client functioning in programs for severely mentally ill persons. Community Mental Health Journal 31:127–137, 1995
20.
Van Dongen CJ: Self-esteem among persons with severe mental illness. Issues in Mental Health Nursing 19:29–40, 1998
21.
Cook JA, Razzano L: Vocational rehabilitation for persons with schizophrenia: recent research and implications for practice. Schizophrenia Bulletin 26:87–103, 2000
22.
Gaebel W, Pietzcker A: Prospective study of course of illness in schizophrenia: part II. prediction of outcome. Schizophrenia Bulletin 13:299–306, 1987
23.
Bond GR, Dietzen LL, Vogler KM, et al.: Toward a framework for evaluating costs and benefits of psychiatric rehabilitation: three case examples. Journal of Vocational Rehabilitation 5:75–88, 1995
24.
Burns T, Catty J, White S, et al.: The impact of supported employment and working on clinical and social functioning: results of an international study of individual placement and support. Schizophrenia Bulletin 35:949–958, 2009
25.
Clark RE: Supported employment and managed care: can they coexist? Psychiatric Rehabilitation Journal 22:62–68, 1998
26.
Henry AD, Lucca AM, Banks SM, et al.: Inpatient hospitalizations and emergency service visits among participants in an Individual Placement and Support (IPS) model program. Mental Health Services Research 6:227–237, 2004
27.
Latimer E: Economic impacts of supported employment for the severely mentally ill. Canadian Journal of Psychiatry 46:496–505, 2001
28.
Perkins DV, Born DL, Raines JA, et al.: Program evaluation from an ecological perspective: supported employment services for persons with serious psychiatric disabilities. Psychiatric Rehabilitation Journal 28:217–224, 2005
29.
Schneider J, Boyce M, Johnson R, et al.: Impact of supported employment on service costs and income of people with mental health needs. Journal of Mental Health 18:533–542, 2009
30.
Burkhauser RV, Daly M: The Declining Work and Welfare of People With Disabilities. Washington, DC, American Enterprise Institute for Public Policy Research, 2011
31.
Labor Force Statistics From the Current Population Survey. Washington, DC, US Department of Labor, Bureau of Labor Statistics, 2013. Available from data.bls.gov/timeseries/LNS14000000
32.
Autor DH, Duggan MG: The growth in the Social Security Disability rolls: a fiscal crisis unfolding. Journal of Economic Perspectives 20:71–96, 2006
33.
Drake RE, Bond GR, Becker DR: Individual Placement and Support: An Evidence-Based Approach to Supported Employment. New York, Oxford University Press, 2012
34.
Goodman N, Stapleton D: Federal program expenditures for working-age people with disabilities. Journal of Disability Policy Studies 18(2):66–78, 2007
35.
McAlpine DD, Warner L: Barriers to Employment Among Persons With Mental Illness: A Review of the Literature. New Brunswick, NJ, Rutgers University, Institute for Health, Health Care Policy and Aging Research, Center for Research on the Organization and Financing of Care for the Severely Mentally Ill, 2002
36.
Hennessey JC, Dykacz JM: Projected outcomes and length of time in the Disability Insurance program. Social Security Bulletin 52:2–41, 1989
37.
The Business Cycle Dating Committee Meeting Minutes. Cambridge, Mass, National Bureau of Economic Research, 2010. Available at www.nber.org/cycles/sept2010.html
38.
Employment Status of the Civilian Noninstitutional Population 16 to 24 Years of Age by Sex, Race, and Hispanic or Latino Ethnicity, July 2010–2013. Washington, DC, US Department of Labor, Bureau of Labor Statistics, 2013
39.
Becker DR, Swanson SJ, Bond GR, et al: Evidence-Based Supported Employment Fidelity Review Manual. Hanover, NH, Dartmouth Psychiatric Research Center, 2011. Available at delawareebse.pbworks.com/f/EB%20Supported%20Employment%20Fidelity%20Review%20Manual.pdf
40.
Luciano A, Drake RE, Bond GR, et al.: Evidence-based supported employment for people with severe mental illness: past, current, and future research. Journal of Vocational Rehabilitation 40(1):1–13, 2014
41.
Cimera RE: The cost-effectiveness of supported employment and sheltered workshops in Wisconsin (FY 2002–FY 2005). Journal of Vocational Rehabilitation 26:153–158, 2007
42.
Results from the 2010 National Survey on Drug Use and Health: Summary of National Findings. HHS pub no SMA 11-4658. Rockville, Md, Substance Abuse and Mental Health Services Administration, 2011
43.
Number of Social Security Beneficiaries. Washington, DC, Social Security Administration, 2012. Available at www.ssa.gov/oact/progdata/icpGraph.html
44.
Furukawa TA, Kessler RC, Slade T, et al.: The performance of the K6 and K10 screening scales for psychological distress in the Australian National Survey of Mental Health and Well-Being. Psychological Medicine 33:357–362, 2003
45.
Kessler RC, Barker PR, Colpe LJ, et al.: Screening for serious mental illness in the general population. Archives of General Psychiatry 60:184–189, 2003
46.
Rehm J, Üstün TB, Saxena S, et al.: On the development and psychometric testing of the WHO screening instrument to assess disablement in the general population. International Journal of Methods in Psychiatric Research 8:110–122, 1999
47.
Novak SP, Colpe LJ, Barker PR, et al.: Development of a brief mental health impairment scale using a nationally representative sample in the USA. International Journal of Methods in Psychiatric Research 19(suppl 1):49–60, 2010
48.
Colpe LJ, Barker PR, Karg RS, et al.: The National Survey on Drug Use and Health Mental Health Surveillance Study: calibration study design and field procedures. International Journal of Methods in Psychiatric Research 19(suppl 1):36–48, 2010
49.
Aldworth J, Colpe LJ, Gfroerer JC, et al.: The National Survey on Drug Use and Health Mental Health Surveillance Study: calibration analysis. International Journal of Methods in Psychiatric Research 19(suppl 1):61–87, 2010
50.
Tsang HW, Leung AY, Chung RC, et al.: Review on vocational predictors: a systematic review of predictors of vocational outcomes among individuals with schizophrenia: an update since 1998. Australian and New Zealand Journal of Psychiatry 44:495–504, 2010
51.
Hamilton VH, Merrigan P, Dufresne É: Down and out: estimating the relationship between mental health and unemployment. Econometrics and Health Economics 6:397–406, 1997
52.
Frank R, Gertler P: An assessment of measurement error bias for estimating the effect of mental distress on income. Journal of Human Resources 26:154–164, 1991
53.
Labor Force Participation Rates by Marital Status, Sex, and Age: 1970 to 2010. Washington, DC, US Department of Labor, Bureau of Labor Statistics, 2011
54.
Alexandre PK, French MT: Labor supply of poor residents in metropolitan Miami, Florida: the role of depression and the co-morbid effects of substance use. Journal of Mental Health Policy and Economics 4:161–173, 2001
55.
Apel R, Sweeten G: The impact of incarceration on employment during the transition to adulthood. Social Problems 57:448–479, 2010
56.
Bond GR, Drake RE, Becker DR: Generalizability of the Individual Placement and Support (IPS) model of supported employment outside the US. World Psychiatry 11:32–39, 2012
57.
A History of the Social Security Disability Programs. Washington, DC, Social Security Administration, 1986. Available at www.ssa.gov/history/1986dibhistory.html. Accessed May 22, 2014
58.
Kinn LG, Holgersen H, Aas RW, et al.: ‘‘Balancing on skates on the icy surface of work’’: a metasynthesis of work participation for persons with psychiatric disabilities. Journal of Occupational Rehabilitation 24:125–138, 2014
59.
Auslander LA, Jeste DV: Perceptions of problems and needs for service among middle-aged and elderly outpatients with schizophrenia and related psychotic disorders. Community Mental Health Journal 38:391–402, 2002
60.
Eide ER, Showalter MH: Human capital; in International Encyclopedia of Education, 3rd ed. Edited by, Peterson P, Baker E, McGaw B. Oxford, England, Elsevier, 2010
61.
Michon HW, van Weeghel J, Kroon H, et al.: Person-related predictors of employment outcomes after participation in psychiatric vocational rehabilitation programmes—a systematic review. Social Psychiatry and Psychiatric Epidemiology 40:408–416, 2005
62.
Bond GR, Drake RE: Predictors of competitive employment among patients with schizophrenia. Current Opinion in Psychiatry 21:362–369, 2008
Information & Authors
Information
Published In
Cover: Shamrock Ranch, by Peter Hurd, 1962. Watercolor, 12 × 16 inches. New Mexico Museum of Art, Santa Fe. Gift of the family of Edythe C. Mattone, 2005.
History
Published online: 1 October 2014
Published in print: October 2014
Authors
Metrics & Citations
Metrics
Citations
Export Citations
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
For more information or tips please see 'Downloading to a citation manager' in the Help menu.
View Options
View options
PDF/EPUB
View PDF/EPUBLogin options
Already a subscriber? Access your subscription through your login credentials or your institution for full access to this article.
Personal login Institutional Login Open Athens loginNot a subscriber?
PsychiatryOnline subscription options offer access to the DSM-5-TR® library, books, journals, CME, and patient resources. This all-in-one virtual library provides psychiatrists and mental health professionals with key resources for diagnosis, treatment, research, and professional development.
Need more help? PsychiatryOnline Customer Service may be reached by emailing [email protected] or by calling 800-368-5777 (in the U.S.) or 703-907-7322 (outside the U.S.).