Jobs for People With Mental Health Conditions: Trends and Prospects
Abstract
Objective:
For individuals with serious mental illness, work can play an important role in improving quality of life and community integration. Since the 1960s, demand has shifted away from routine cognitive (e.g., clerical work) and manual skills (warehouse picking and packing) toward nonroutine analytical (computer coding), interpersonal (nursing), and manual skills (home health attendant). This study aimed to determine whether individuals with serious mental illness are likely to hold the types of jobs that are in decline and to assess their ability to compete for the types of jobs that have been in increased demand.
Methods:
Using data from the National Health Interview Survey and the Occupational Information Network database on occupational skills (N=387,240 person-year responses), this study explored changes in patterns of employment from 1997 to 2017 for people with mental illnesses.
Results:
Individuals with any mental health condition experienced a 10.9 percentage point decline in employment in jobs requiring routine cognitive or any manual skills. Much of this decline was offset by an increase in employment in jobs involving nonroutine cognitive skills. However, individuals with serious psychological distress experienced a 7.9 percentage point decline in employment in jobs requiring routine cognitive or any manual skills, and about 75% of this decline coincided with reduced levels of employment rather than a shift toward employment in nonroutine cognitive jobs. These patterns were more striking among men.
Conclusions:
Likely directions for interventions include renewed efforts at workplace accommodations, greater investment in evidence-based return-to-work programs, and efforts to popularize early intervention programs.
HIGHLIGHTS
•
Work has long been viewed as valuable for promoting well-being and social integration for people with mental illnesses.
•
People with mental illnesses work types of jobs different from those of otherwise similar people without such conditions.
•
Our results show that over the past 20 years, people with serious mental health conditions have been less likely to participate in the labor force in part because the types of jobs they have traditionally held have been disappearing.
Work has been shown to promote well-being, including among people with chronic illnesses and disabilities (1). For people with mental illnesses, especially severe mental illnesses, work can play an important role in improving quality of life and community integration (2). Individuals with severe mental illnesses are often anxious to work and to seek help in obtaining and maintaining employment. All Organization for Economic Cooperation and Development (OECD) countries have policies designed to help people with illnesses and disabilities stay at work or return to work after an acute episode of illness (3). As a result, interest in programs that encourage and support competitive employment for people with mental illnesses have gained traction in most OECD countries.
While these programmatic developments have been occurring, labor markets have been evolving due to automation technologies such as robotics, computerization, and artificial intelligence (AI) (4). The impact of changing technologies has been documented in a variety of recent reports and academic papers (5, 6). These changes have raised an important question: Will the changes in employment opportunities disadvantage people with mental illness in obtaining competitive employment? The U.S. Social Security Administration recognizes the importance of being able to match education and skills to existing jobs when assessing disability status for Social Security Disability Insurance (SSDI). More practically, the ability of people with mental illnesses to compete successfully for jobs requires enough demand for their work capabilities in the form of appropriate jobs.
Background
The employment-to-population ratio in the United States has declined notably since 2000. After peaking at 64.7% in 2000, labor force participation has declined, reaching a nadir of 60.4% in 2018 (7). The types of job skills demanded in the labor market have also consistently changed, in part because of a rise in automation (8, 9). The use of robots and AI has been increasing rapidly (10). Overall, demand has shifted away from occupations requiring routine cognitive skills (e.g., typists, filing clerks, bookkeepers) and manual skills (warehouse picking and packing) toward those requiring nonroutine analytical skills (computer coding, engineering), interpersonal skills (nursing), and manual skills (home health attendant, certified nursing assistant). Recent estimates indicate that shifts in labor markets are likely to continue, suggesting that roughly a quarter of U.S. jobs will undergo high levels of automation, affecting and potentially displacing 36 million people (11). These changes might disproportionately affect Americans with disabilities and medical conditions such as mental illnesses. Prior research shows that such impacts may have already been felt, noting that nearly 53% of people with disabilities who cannot work cite the availability of appropriate jobs as a key factor in their lack of employment (12).
The skill impairments associated with severe mental illnesses have long posed a challenge to integrating people with these conditions into the workforce. Even less severe forms of illness have been shown to disrupt work activity (13). Mental illnesses have been posited to affect the ability to engage in work and the ability to meet the task and skill requirements of the types of available jobs. Research has mapped specific illnesses to cognitive dysfunctions that are associated with common workplace skill demands (14). This research is consistent with evidence showing how neurocognitive impairments stemming from schizophrenia, major depression, and bipolar disorder interfere with social and occupational functioning (15–17). In addition, persistent functional impairments have been observed among people with anxiety disorders (18). The analyses below provide more rigorous estimates of these relationships, which will help to inform understanding of future employment prospects for people with mental illnesses.
In this study, we explored recent changes in the U.S. labor market and patterns of employment for people with mental illnesses. We explored two related issues using data that track the economic statistics that inform federal and state policy. First, we examined recent trends in the employment-to-population ratio among people with mental health conditions. Second, we determined the characteristics of jobs held by people with mental health conditions who are employed. We were interested in learning whether those jobs require skills that are likely to exhibit growth or to decline in future demand, because this information will offer insight into employment prospects for people with mental illnesses.
Methods
Data
We relied on two sets of data: The National Health Interview Survey (NHIS) and the Occupational Information Network (O*NET) database on occupational skills. The NHIS is a large national household survey that collects information on approximately 87,000 people who are members of roughly 35,000 households. The NHIS collects detailed data on household demographics, income, employment, occupations, and indicators of mental health status. The two main outcomes we examined were the percentage of the population that is employed (employment-to-population ratio) and, among those working, the proportion of individuals (stratified by mental health condition) in each type of occupation (characterized by dominant skill requirements).
Measures
To measure employment, the NHIS uses the Bureau of Labor Statistics’ (BLS) employment-to-population ratio statistic. The measure is based on questions that identify participants as individuals who are working either for pay or not for pay at a family-owned business.
The NHIS collects information on symptoms of mental illnesses, focusing on psychological distress, using the Kessler Screen Scale of Psychological Distress (K6) (19). Although the scale does not yield specific diagnoses, it has been found to be a reliable measure of the overall population prevalence of mental illnesses and serious mental illnesses (e.g., bipolar disorder, schizophrenia, and major depression). In particular, the K6 produces consistent estimates across surveys and populations and has shown little bias in estimated rates of illness by education and gender compared with structured clinician interviews. The K6 estimates of psychological distress and serious psychological distress are available in the NHIS for 1997–2017. We used the K6 scores to define four levels of mental illness severity: none, mild, moderate, and serious. The cutoff scores used to classify people into severity groups were based on a 2008 study that compared the K6 score with clinician diagnoses based on a structured diagnostic interview. The K6 scores were used as explanatory variables in a model predicting the presence of a diagnosis based on the structured interview. The parameters of that model were then used to create the severity groupings (20).
In the analyses below, we focused only on the experiences of people with K6 scores indicating the presence of a mental illness. Survey respondents, both those currently working and those who had worked in the past, were asked about their current or most recent occupation. The occupations were classified according to modified census codes, which were expanded in 2004 to include 94 discrete occupations. These occupations were aggregated into 23 broader occupational categories. However, because the coding changed in 2004, we used census data regarding apportionment of the older occupation codes into the new codes in order to ensure consistent occupation classification throughout the study’s time frame.
We used the data on respondents’ occupations along with the O*NET data to classify respondents according to the skill requirements of their jobs. The O*NET data are compiled by the BLS. The database measures 400 variables describing job skill, ability, and knowledge requirements by industry and occupation. These variables were assigned to job classifications according to BLS 2010 Standard Occupational Classification (SOC) and were further expanded into additional O*NET-SOC occupations. These eight-digit O*NET-SOC codes were collapsed into six-digit SOC codes and aligned with the 2010 census occupation codes used in the NHIS data. The O*NET values by occupational codes were merged onto the census occupational codes for everyone in the NHIS survey with occupational information. We then followed the procedures set out by Acemoglu and Autor (4) to produce composite scores for each respondent that summarize the skill requirements for each occupation described above. We used an indicator for whether an individual was working as well as five job skill groups: nonroutine analytical, nonroutine interpersonal, nonroutine manual/physical, routine cognitive, and routine manual. To create these mutually exclusive skill type categories, we assigned individuals to groups on the basis of the skill type that was most prevalent compared with other skills in their given job.
Analyses
Our research examined trends in employment among individuals with indication of having a mental illness and in the skill requirements of the occupations they held. Specifically, among participants with a K6 score indicating serious psychological distress, we examined these trends with sex pooled. We then examined these trends stratified by sex. We focused on participants with serious illness because they receive services from supported employment programs and because their employment patterns are most disrupted by mental illnesses. To ensure that we had adequate sample for each level of mental health status, we pooled data into 3-year intervals: 1997–1999, 2006–2008, and 2015–2017. Estimates of prevalence were based on percentage with respect to the entire sample; they were adjusted for sample design and restricted to those with only mental health conditions or serious psychological distress.
Results
The NHIS yielded 945,815 person-year (ages 18–65) survey responses during 1997–2017. There were 393,012 person-year observations across the three time intervals: 268,409 in 1997–1999; 55,775 in 2006–2008; and 68,828 in 2015–2017. A negligible number (N=5,772, 1.5%) had missing responses on the K6 measure of mental health and were therefore excluded, leaving a final sample of 387,240. Across the three time intervals, the average rates of having any mental health condition and of having serious psychological distress were 19.7% and 3.4%, respectively.
Table 1 provides an overview of the employment-to-population ratio among people with any type of mental health condition, by job skill class. The percentage of people with a mental health condition who were not working grew from 37.3% to 39.1% between 1997–1999 and 2015–2017, an increase of 4.9%. That increase stemmed almost entirely from declines in the number of people employed in both routine and nonroutine manual/physical jobs and in jobs requiring routine cognitive skills (e.g., clerical work). The employment of people with mental health conditions in jobs requiring nonroutine manual/physical skills fell by about 4.7 percentage points, or nearly 33%. Employment in jobs requiring routine cognitive skills fell by 3.7 percentage points, or 19%. Employment in jobs requiring routine manual skills fell by 2.5 percentage points, or 20%. These declines were largely offset by increases in employment in jobs that required nonroutine analytical skills (by 2.8 percentage points) and jobs that required interpersonal skills (by 6.2 percentage points); about 83% of jobs lost were replaced by jobs gained in these skill groups.
| Job skill class | 1997–1999 | 2006–2008 | 2015–2017 | Percentage point change |
|---|---|---|---|---|
| Nonroutine analytical | 7.1 | 9.2 | 9.9 | 2.8 |
| Nonroutine interpersonal | 10.2 | 12.4 | 16.4 | 6.2 |
| Nonroutine manual | 14.0 | 11.8 | 9.3 | –4.7 |
| Routine cognitive | 19.0 | 14.8 | 15.3 | –3.7 |
| Routine manual | 12.5 | 10.1 | 10.0 | –2.5 |
| Not working | 37.3 | 41.8 | 39.1 | 1.8 |
a
Values are expressed as percentages. Source: authors’ analyses of National Health Interview Survey and Occupational Information Network data.
Table 2 focuses on people with serious psychological distress. This group was much less likely to work than people with any mental health condition, with 62.3% not working during 2015–2017. The fraction of nonworkers increased by 5.9 percentage points, or 10.4%, over the 20-year period. Most of the decline in the employment-to-population ratio was coincident with Great Recession, which took place from December 2007 through June 2009 (21). As in the case of participants with any mental health condition, those with serious psychological distress experienced substantial declines in employment in jobs involving nonroutine manual/physical (3.4 percentage points), routine cognitive (1.7 percentage points) and routine manual/physical (2.8 percentage points) skills. Unlike for those with any mental health condition, only about 25% of the declines in employment for those with serious conditions was offset by increases in employment in jobs requiring routine cognitive skills; a reduction in the employment-to-population ratio accounted for the other 75%.
| Job skill class | 1997–1999 | 2006–2008 | 2015–2017 | Percentage point change |
|---|---|---|---|---|
| Nonroutine analytical | 4.0 | 3.9 | 4.0 | .0 |
| Nonroutine interpersonal | 6.2 | 6.1 | 8.1 | 1.9 |
| Nonroutine manual | 10.1 | 7.2 | 6.7 | –3.4 |
| Routine cognitive | 13.8 | 11.8 | 12.1 | –1.7 |
| Routine manual | 9.7 | 7.5 | 6.9 | –2.8 |
| Not working | 56.4 | 63.5 | 62.3 | 5.9 |
a
Values are expressed as percentages. Source: authors’ analyses of National Health Interview Survey and Occupational Information Network data.
We also considered trends in employment among people with serious psychological distress by sex. Table 3 reports the employment patterns for men with serious psychological distress. The employment trends for these men showed several differences compared with trends among both sexes. The employment-to-population ratio for men with serious conditions diminished more than for the overall population. The proportion of men with serious conditions and not working rose by 13%, or 7.2 percentage points. Over the 20-year period, employment among these men increased only in jobs requiring nonroutine interpersonal skills and jobs requiring routine cognitive skills. Collectively, the two categories with increases accounted for a 2.5 percentage point increase in employment, with nearly all gains stemming from increased employment in the category of jobs requiring nonroutine interpersonal skills.
| Job skill class | 1997–1999 | 2006–2008 | 2015–2017 | Percentage point change |
|---|---|---|---|---|
| Nonroutine analytical | 4.5 | 3.8 | 3.9 | −.6 |
| Nonroutine interpersonal | 4.2 | 6.6 | 6.5 | 2.3 |
| Nonroutine manual | 16.6 | 13.9 | 12.2 | –4.4 |
| Routine cognitive | 7.1 | 5.5 | 7.3 | .2 |
| Routine manual | 12.4 | 8.5 | 7.7 | –4.7 |
| Not working | 55.2 | 61.7 | 62.4 | 7.2 |
a
Values are expressed as percentages. Source: authors’ analyses of National Health Interview Survey and Occupational Information Network data.
Table 4 reports the corresponding results for women with serious psychological distress. Women experienced a smaller decrease in employment-to-population ratio than did men: a 5.1 percentage point change, or 9%. Employment among women with serious conditions increased in the job category requiring nonroutine analytical skills and the category requiring nonroutine interpersonal skills. Furthermore, these increases offset the declines in employment in jobs requiring nonroutine manual/physical skills and jobs requiring cognitive skills more for women (about 31% of jobs replaced) than for men (19% of jobs replaced). Nevertheless, the declines in employment share in jobs requiring routine and nonroutine manual/physical skills and jobs requiring routine cognitive skills were considerably greater than increases seen in the two categories with growth in employment.
| 1997–1999 | 2006–2008 | 2015–2017 | Percentage point change | |
|---|---|---|---|---|
| Nonroutine analytical | 3.6 | 3.9 | 4.0 | .4 |
| Nonroutine interpersonal | 7.3 | 5.9 | 9.1 | 1.8 |
| Nonroutine manual | 6.4 | 2.9 | 3.2 | –3.2 |
| Routine cognitive | 17.5 | 15.8 | 15.1 | –2.4 |
| Routine manual | 8.1 | 6.9 | 6.4 | –1.7 |
| Not working | 57.1 | 64.6 | 62.2 | 5.1 |
a
Values are expressed as percentages. Source: authors’ analyses of National Health Interview Survey and Occupational Information Network data.
Discussion and Conclusions
Recent labor market trends and most projections for the next two decades suggest that demand for nonroutine analytical and interpersonal job skills will grow more rapidly than for other job skill categories. In contrast, jobs that require routine cognitive and manual skills are most prone to automation; these jobs have been in decline and are projected to decline further. Our results suggest that this decline is likely to be disadvantageous for people with mental health conditions, especially those with serious psychological distress. As our results highlight, a large segment of people with mental health conditions has been and continues to be employed in jobs that have been vanishing and are projected to continue to disappear. Our results also suggest that a relatively small share of people with serious mental health conditions is employed in the job classes that are growing. In fact, this group has shifted toward not working all (i.e., a decline in the employment-to-population ratio) in response to declines in routine cognitive and nonroutine manual jobs, rather than shifting toward nonroutine cognitive jobs (which have experienced increased demand overall).
Notably, mental health conditions appear to be less disruptive of work activities for women than men, with women more likely to shift toward jobs in greater demand rather than to exit employment. This trend may be due to other factors related to productivity, such as educational attainment and training. These differences are likely to be important in the future as women continue to comprise a growing segment of the workforce and of those with college and advanced degrees.
Limitations
Several limitations to our study are worth noting. We used the widely cited K6 measure for psychological distress, which does not capture specific diagnoses and relies on self-reported symptoms. However, at the cutoff level of scores ≥13, the K6 measure has high specificity in detecting serious mental illness, suggesting that our category of serious psychological distress is likely conservative. Although the category is unlikely to include false positives, it might not capture all persons with serious mental illness. These false negatives (i.e., individuals with serious mental illness with K6 scores <13) might have led us to overstate trends, because these individuals might function at higher levels and find it easier to hold different types of jobs, such as those involving nonroutine analytical and interpersonal skills. The direction of this bias is likely counterbalanced by the exclusion of institutionalized individuals, who are unlikely to be working, from the study’s sampling frame. In addition, we were unable to observe the mechanisms underpinning the associations identified here. Although job automation is a likely mechanism, given prior research in the field, we did not measure it directly in this study.
Policy Implications
Researchers and advocates have had a long-standing interest in efforts to support people with severe mental illnesses in the workplace. Our results suggest that existing supported employment programs will need to take steps to adapt to the changing labor market. Establishing durable attachments to competitive employment for people with severe mental illnesses depends on the availability of jobs that require the skills that match those of that population. Our results and the data from the larger literature on the evolving labor market suggest that those matches are becoming harder to make.
One step toward promoting continued employment of people with mental illnesses would entail renewed efforts at workplace accommodations under the Americans With Disabilities Act. Given recent initiatives that provide paid leave, a second approach would be to complement paid medical leave supports with expanded investments in evidence-based return-to-work programs. Finally, a third approach would be to establish early intervention programs as a mainstream component of public health and occupational safety so that interventions are applied before people require public programs (i.e., Medicaid, SSDI) for support.
References
1.
Waddell GB, Burton AK: Is Work Good for Your Health and Well-Being? Norwich, England, Stationery Office, 2006
2.
Luciano A, Bond GR, Drake RE: Does employment alter the course and outcome of schizophrenia and other severe mental illnesses? A systematic review of longitudinal research. Schizophr Res 2014; 159:312–321
3.
Sick on the Job? Myths and Realities About Mental Health and Work. Paris, OECD Publishing, 2012
4.
Acemoglu D, Autor D: Skills, tasks and technologies: implications for employment and earnings; in Handbook of Labor Economics, vol 4. Edited by Ashenfelter O, Card D. Amsterdam, Elsevier, 2011
5.
Abeliansky A, Algur E, Bloom DE, et al: The Future of Work: Challenges for Job Creation Due to Global Demographic Change and Automation. IZA Discussion Paper 12962. Bonn, Germany, Institute of Labor Economics, 2020
6.
Autor DH, Levy F, Murnane RJ: The skill content of recent technological change: an empirical exploration. Q J Econ 2003; 118:1279–1333
7.
Employment-Population Ratio. Washington, DC, US Bureau of Labor Statistics, 2020. https://www.bls.gov/charts/employment-situation/employment-population-ratio.htm. Accessed Feb 20, 2020
8.
David H, Dorn D: The growth of low-skill service jobs and the polarization of the US labor market. Am Econ Rev 2013; 103:1553–1597
9.
Atalay E, Phongthiengtham P, Sotelo S, et al: The evolving US occupational structure. Am Econ J Appl Econ 2017 12:1–34
10.
Graetz G, Michaels G: Robots at work. Rev Econ Stat 2018; 100:753–768
11.
Maxim R, Muro M: Automation and AI will disrupt the American labor force: here’s how we can protect workers. Washington, DC, Brookings Institution, 2019. https://www.brookings.edu/blog/the-avenue/2019/02/25/automation-and-ai-will-disrupt-the-american-labor-force-heres-how-we-can-protect-workers
12.
Loprest P, Maag E: Barriers to and Supports for Work Among Adults With Disabilities: Results From the NHIS-D. Washington, DC, Urban Institute, 2001
13.
Stewart WF, Ricci JA, Chee E, et al: Lost productive work time costs from health conditions in the United States: results from the American Productivity Audit. J Occup Environ Med 2003; 45:1234–1246
14.
Millan MJ, Agid Y, Brüne M, et al: Cognitive dysfunction in psychiatric disorders: characteristics, causes and the quest for improved therapy. Nat Rev Drug Discov 2012; 11:141–168
15.
Withall A, Harris LM, Cumming SR: The relationship between cognitive function and clinical and functional outcomes in major depressive disorder. Psychol Med 2009; 39:393–402
16.
Duarte W, Becerra R, Cruise K: The relationship between neurocognitive functioning and occupational functioning in bipolar disorder: a literature review. Eur J Psychol 2016; 12:659–678
17.
Green MF, Kern RS, Heaton RK: Longitudinal studies of cognition and functional outcome in schizophrenia: implications for MATRICS. Schizophr Res 2004; 72:41–51
18.
Iancu SC, Batelaan NM, Zweekhorst MB, et al: Trajectories of functioning after remission from anxiety disorders: 2-year course and outcome predictors. Psychol Med 2014; 44:593–605
19.
Kessler RC, Barker PR, Colpe LJ, et al: Screening for serious mental illness in the general population. Arch Gen Psychiatry 2003; 60:184–189
20.
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. Int J Methods Psychiatr Res 2010; 19(suppl 1):36–48
21.
Rich R: The Great Recession of 2007–2009. New York, Federal Reserve, 2013
Information & Authors
Information
Published In
History
Received: 13 April 2020
Revision received: 21 May 2020
Accepted: 25 June 2020
Published online: 23 September 2020
Published in print: February 01, 2021
Keywords
Authors
Competing Interests
Dr. Frank has consulting relationships with Greylock-McKinnon Associates, Arnold Ventures, and West Health Foundation. Dr. Glied is a member of the board of directors of NeuroRx. Dr. Shields reports no financial relationships with commercial interests.
Funding Information
This research was supported by the Robert Wood Johnson Foundation.
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References
1.
Waddell GB, Burton AK: Is Work Good for Your Health and Well-Being? Norwich, England, Stationery Office, 2006
2.
Luciano A, Bond GR, Drake RE: Does employment alter the course and outcome of schizophrenia and other severe mental illnesses? A systematic review of longitudinal research. Schizophr Res 2014; 159:312–321
3.
Sick on the Job? Myths and Realities About Mental Health and Work. Paris, OECD Publishing, 2012
4.
Acemoglu D, Autor D: Skills, tasks and technologies: implications for employment and earnings; in Handbook of Labor Economics, vol 4. Edited by Ashenfelter O, Card D. Amsterdam, Elsevier, 2011
5.
Abeliansky A, Algur E, Bloom DE, et al: The Future of Work: Challenges for Job Creation Due to Global Demographic Change and Automation. IZA Discussion Paper 12962. Bonn, Germany, Institute of Labor Economics, 2020
6.
Autor DH, Levy F, Murnane RJ: The skill content of recent technological change: an empirical exploration. Q J Econ 2003; 118:1279–1333
7.
Employment-Population Ratio. Washington, DC, US Bureau of Labor Statistics, 2020. https://www.bls.gov/charts/employment-situation/employment-population-ratio.htm. Accessed Feb 20, 2020
8.
David H, Dorn D: The growth of low-skill service jobs and the polarization of the US labor market. Am Econ Rev 2013; 103:1553–1597
9.
Atalay E, Phongthiengtham P, Sotelo S, et al: The evolving US occupational structure. Am Econ J Appl Econ 2017 12:1–34
10.
Graetz G, Michaels G: Robots at work. Rev Econ Stat 2018; 100:753–768
11.
Maxim R, Muro M: Automation and AI will disrupt the American labor force: here’s how we can protect workers. Washington, DC, Brookings Institution, 2019. https://www.brookings.edu/blog/the-avenue/2019/02/25/automation-and-ai-will-disrupt-the-american-labor-force-heres-how-we-can-protect-workers
12.
Loprest P, Maag E: Barriers to and Supports for Work Among Adults With Disabilities: Results From the NHIS-D. Washington, DC, Urban Institute, 2001
13.
Stewart WF, Ricci JA, Chee E, et al: Lost productive work time costs from health conditions in the United States: results from the American Productivity Audit. J Occup Environ Med 2003; 45:1234–1246
14.
Millan MJ, Agid Y, Brüne M, et al: Cognitive dysfunction in psychiatric disorders: characteristics, causes and the quest for improved therapy. Nat Rev Drug Discov 2012; 11:141–168
15.
Withall A, Harris LM, Cumming SR: The relationship between cognitive function and clinical and functional outcomes in major depressive disorder. Psychol Med 2009; 39:393–402
16.
Duarte W, Becerra R, Cruise K: The relationship between neurocognitive functioning and occupational functioning in bipolar disorder: a literature review. Eur J Psychol 2016; 12:659–678
17.
Green MF, Kern RS, Heaton RK: Longitudinal studies of cognition and functional outcome in schizophrenia: implications for MATRICS. Schizophr Res 2004; 72:41–51
18.
Iancu SC, Batelaan NM, Zweekhorst MB, et al: Trajectories of functioning after remission from anxiety disorders: 2-year course and outcome predictors. Psychol Med 2014; 44:593–605
19.
Kessler RC, Barker PR, Colpe LJ, et al: Screening for serious mental illness in the general population. Arch Gen Psychiatry 2003; 60:184–189
20.
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. Int J Methods Psychiatr Res 2010; 19(suppl 1):36–48
21.
Rich R: The Great Recession of 2007–2009. New York, Federal Reserve, 2013