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Mental health and academic performance: a study on selection and causation effects from childhood to early adulthood

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Mental health and academic performance: a study on selection and causation effects from childhood to early adulthood

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When it comes to internalizing and externalizing problems -

"In practice, it means that these kinds of problems need to be noticed at preschool age and educational practices adjusted and adequate treatment given to promote transition to the school environment and completion of compulsory school."

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Mental health and academic performance: a study on selection and causation effects from childhood to early adulthood

Cite this article

A Correction to this article was published on 16 September 2023

Abstract

Purpose

An inverse relationship between mental health problems and academic achievement is a well-known phenomenon in the scientific literature. However, how and when this association develops is not fully understood and there is a lack of longitudinal, population-based studies on young children. Early intervention is important if associations are to be found already during childhood. The aim of the present study was to investigate the development of the association between mental health and academic performance during different developmental periods of childhood and adolescence.

Methods

Data from a longitudinal birth cohort study of 1700 children were used. Child mental health was assessed through mother's reports at age 3, and self-reports at age 12 and 20. Academic performance was assessed through teacher reports on educational results at age 12 and final grades from compulsory school (age 15-16) and upper secondary school (age 18-19). The association between mental health and academic performance was assessed through regression models.

Results

The results indicate that social selection mechanisms are present in all three periods studied. Behavioral and emotional problems at age 3 were associated with performing below grade at age 12. Similarly, mental health problems at age 12 were associated with lack of complete final grades from compulsory school and non-eligibility to higher education. Academic performance at ages 15 and 19 did not increase the risk for mental health problems at age 20.

Conclusion

Mental health problems in early childhood and adolescence increase the risk for poor academic performance, indicating the need for awareness and treatment to provide fair opportunities to education.

Introduction

An inverse relationship between mental health and educational attainment is a well-known phenomenon in the scientific literature of sociology, epidemiology and social psychiatry [ 1, 2, 3]. Despite nearly a century of research into the matter, no consensus has been reached about how the association develops and persists. Several studies have demonstrated the effect of educational attainment on mental health (social causation) [ 1, 3, 4] and likewise, there is support for the influence of mental health problems on educational attainment (social selection) [ 1, 2, 5]. Naturally, this knowledge comes mostly from research on the adult population as the level of SES and educational attainment are established over the years. However, there are several reasons to investigate the association between mental health and academic performance already during childhood. Social causation processes during childhood are dependent on the level of SES and educational tradition of the family of origin. Equally important, schooling is a central part of all of childhood, with the educational path starting at an early age. If an association between mental health and academic performance can be found already during childhood and adolescence, early recognition and interventions are warranted. However, there are few longitudinal, population-based studies on children and adolescents. As academic performance is associated with future educational attainment [ 6], and mental health problems during childhood increases the risk for subsequent mental health problems [ 7], early intervention is highly valuable. Given the multifactorial etiology of mental illness, and the number of factors predicting educational attainment, there is reason to assume that the relation between the two is complex [ 8].

Studies on the impact of educational achievement and academic performance on mental health in the younger population mainly include adolescents. In a meta-analysis of 17 original works, early school drop-out was found to be associated with substance abuse, depression and externalizing problems [ 4]. Moreover, academic performance in adolescence has been associated with suicide in men but not in women [ 9]. In a recent Swedish study, a low Grade Point Average (GPA) at age 16 was found to be associated with depression in early adulthood, and this association was attenuated by externalizing comorbidity [ 10]. In a large population-based cohort, Jonsson et al. [ 11] found that a low GPA was associated with hospitalization due to depression in adolescence. However, academic performance was assessed at age 16 and depression between ages 12 and 17 making a definite conclusion about the direction of the association precarious. With regard to younger children, Deighton et al. [ 12] found support for the effect of poor academic performance on subsequent internalizing problems in middle childhood. In sum, a couple of studies investigated the effect of academic performance on mental health in adolescence and early adulthood, although most studies focused mainly on depression.

Several population-based studies investigated the effect of mental health on academic performance in children and adolescents. In a longitudinal study, Fletcher showed that adolescent depression was linked to years of schooling, controlling for psychiatric comorbidity and sociodemographic factors [ 5]. In a study on 800 children followed from age 6 to 18, externalizing but not internalizing problems predicted poor academic performance [ 13]. Breslau and colleagues found that attention problems at age 6 predicted math and reading achievement at age 17, while no effect was seen for externalizing and internalizing problems [ 14]. In a cohort study of 400 children, McLeod and Kaiser found that internalizing and externalizing problems at age 6-8 strongly diminished the chance of accomplishing a high-school degree [ 15]. Deighton et al. [ 12] investigated the association between internalizing and externalizing problems and academic performance during middle childhood and early adolescence, confirming the effect of externalizing problems on later academic achievement. In a longitudinal population-based study, Miech et al. [ 8] found that externalizing problems had direct negative effects on adolescent school performance, while neither causation nor selection processes applied for depression and SES. The authors conclude that disorder-specific models are required. In sum, previous research shows that mental health problems predict academic performance from middle childhood and up to adulthood. Results seem, however, to vary by the type of mental health problems and assessments of whether mental health problems as early as pre-school age predict educational outcomes have not yet been done.

The present study uses a sample from a longitudinal cohort study to investigate whether mental health predicts academic performance and, vice versa, whether academic performance predicts mental health during different developmental periods in childhood and adolescence. The study offers the opportunity to control for a variety of variables potentially impacting mental health and academic performance, including maternal mental health and parental education level. Given the evidence presented above, it is likely that there are mechanisms in both directions but that these vary between populations, national/cultural contexts, age groups, types of mental illness, different aspects of social class/educational achievement, etc. Further studies are, therefore, warranted.

Aim

The aim of the present study was to investigate the development of the association between mental health and academic performance during different developmental periods in childhood and adolescence. The following five hypotheses are used to test the associations empirically:

1. 1.
Internalizing and/or externalizing problems at age 3 increase the risk for poor academic performance at age 12.

2. 2.
Internalizing and/or externalizing problems at age 12 increase the risk for incomplete final grades from compulsory school (age 15-16).

3. 3.
Internalizing and/or externalizing problems at age 12 increase the risk for non-eligibility to higher education (age 18-19).

4. 4.
Incomplete final grades from compulsory school (age 15-16) increase the risk for internalizing and/or externalizing problems at age 20.

5. 5.
Non-eligibility to higher education increases the risk for internalizing and/or externalizing problems at age 20.

Methods

Subjects and procedures

Data from a longitudinal birth cohort study, the SESBiC study, were used [ 16]. All women who gave birth to children during 20 consecutive months 1995-1996 in five geographically adjacent municipalities in southern Sweden were asked to take part in the overarching study. Of those, 88% ( n = 1723) accepted participation. For an overview of the study population and the waves of data collection, see Fig. 1. Among participating children, 52.8% were boys, and there were 27 pairs of twins. The baseline study and the 3-year follow-up were conducted at Child Welfare Centers (CWC's), in connection with the routine age-based examination. At baseline, the mothers were interviewed by a psychologist, and at the 3-year follow-up, they filled out questionnaires on mental health and well-being for themselves as well as for their children.

Model of the study outline and participants

The 12-year follow-up, was carried out at school as most of the children still lived in the catchment area. Research assistants supervised the children as they filled out questionnaires on mental health and well-being and helped with questions. A package of questionnaires including standardized instruments regarding mental health and well-being was sent to parents by ground mail. Teachers rated academic performance for reading, mathematics and English language respectively at the 12-year follow-up. At the 20-year follow-up, the now young adults answered standardized instruments on mental health and behavior. Parents had to give written consent for the child to be enrolled in the baseline, 3-year and 12-year follow-ups. Written consent was obtained from the young adults themselves at the 20-year follow-up.

Instruments

The Edinburgh Postnatal Depression Scale (EPDS) [ 17] is a self-report scale designed to screen for postnatal depression in community samples. It holds 10 items ranged 0-3, with a total score of 30, a high score indicating postnatal depression. The EPDS refers to symptoms of depression and anxiety perceived during the recent week and was filled out by the mothers at baseline. A cut-off of 10 was set for the EPDS, which has been done previously for screening purposes [ 18]. In the analysis, the variable was, thus, dichotomized and women with a score of ≥ 10 were compared to women with a score of 9 or lower (reference).

The Child Behavior Checklist/2-3 (CBCL) [ 19] is a well-known form assessing child behavior into two main domains of internalizing and externalizing problems. The form holds 100 items, each scored 0-2 from "not a problem" to "often a problem". The CBCL has been used previously in Scandinavian population-based studies and has shown good precision when screening for child psychiatric disorders [ 20]. The mothers filled out the Swedish version of the CBCL 2/3 at the 3-year follow-up. In logistic regression, the 90th percentile was used as cut-off. The variable was, thus, dichotomized, and children with a score within the 90th percentile were compared to children with a lower score (reference).

The Strengths and Difficulties Questionnaire (SDQ) is a screening instrument [ 21] consisting of 25 items divided between four problem subscales (emotional-, conduct-, hyperactivity- and peer problems) and one strength subscale (prosocial behavior). The self-report version was filled out by the children at the 12-year follow-up. In logistic regression, the 90th percentile was used as cut-off. The variable was, thus, dichotomized, and children with a score within the 90th percentile were compared to children with a lower score (reference).

The Symptom Checklist (SCL-25) was completed by the mothers at the 12-year follow-up [ 22]. The form consists of 25 items scoring on a scale of 1-4, from "not at all" to "extremely" and is designed to measure anxiety and depression during the most recent 14 days. When used in logistic regression, a cut-off was set to mean value 1.75 which has been used previously [ 23].

The teacher's report form (TRF) [ 24] is a screening instrument for child behavior problems including information on academic performance. The TRF 5-18 was answered by the teachers at the 12-year follow-up and in this study, only information on child performance in reading, mathematics and English language was used. English is the secondary language for the study participants, taught from age 10 at the latest. The teachers rated child performance on a 5-point scale as "far below grade", "somewhat below grade", "at grade level", "somewhat above grade" and "far above grade". In models using logistic regression, the variable was dichotomized and "far below grade" and "somewhat below grade" was compared to "at grade level", "somewhat above grade" and "far above grade" (reference).

The Adult Self Report [ 25] is a 126-item form assessing mental health divided into the two main domains of internalizing and externalizing problems. Each item is rated on a 3-point scale from "not true" to "somewhat or sometimes true" and "very true or often true". The ASR has shown good validity and has been used previously in population-based studies [ 26]. The ASR was answered by the young adults at the 20-year follow-up.

Register data

The Swedish school system is based on 10 years of compulsory education, followed by 3-year optional upper secondary education. Individuals without complete grades from upper secondary education may attend municipal adult education, with the possibility to achieve the eligibility requirements for university studies. In 2019, 84.3% of students received complete final grades from compulsory school [ 27], and approximately 80% completed upper secondary education. Final grades from compulsory and upper secondary school were obtained from the national statistics office. Lack of complete grades (and thereby inability to continue upper secondary school) was compared to complete final grades from compulsory school (reference). Non-eligibility to university studies/higher education was compared to eligibility to higher education (reference).

Socio-demographic factors

Parental immigration status was noted at the baseline study, and children of one or both parents born abroad were compared to children of parents born in Sweden (reference). Information on school drop-out of the mothers was obtained at the baseline survey. Mothers who did not complete compulsory school or upper secondary school were compared to mothers who did (reference). Information on parental education was obtained at the 12-year follow-up. Mothers and fathers reported their highest level of education, and compulsory/upper secondary school (≤ 12 years of schooling) was compared to post-secondary education (> 12 years of schooling, reference level), based on the parent with the highest level of education.

Data analysis

For frequencies of included variables, that is, mental health parameters (CBCL, SDQ, ASR), academic performance (performance in reading, mathematics and English language at age 12, final grades from compulsory school and upper secondary school) and control variables (maternal school drop-out, maternal mental health, parental education level, gender and parental immigrations status), see Table 1. Missing data ranged from 0% (gender) to 58.4% (ASR externalizing). To test the hypotheses of an association between mental health and education, bivariate linear regression or logistic regression was performed in five separate models. Linear regression was used for continuous dependent variables, and logistic regression was used for binary outcome variables. Then, for each model, multivariate linear or logistic regression was carried out controlling for maternal school drop-out, maternal mental health, parental education level, gender and parental immigration status, and when possible also for mental health at a previous data collection point. Stepwise regression was performed, excluding the control variable with the highest p value until all remaining control variables showed statistical significance. Gender was, however, included in all final models. In the linear regression tables the beta coefficients ( B) and the 95% confidence intervals (CI) are presented, and in the logistic regression tables the odds ratios (OR) and their respective CI are presented. Statistical significance was defined as (two-sided) p ≤ 0.05. All statistical analyses were performed using IBM SPSS version 24. For an overview of the study outline, see Fig. 1.

Table 1 Frequency characteristics of the study population

Drop-out rate analysis

At the 3-year follow-up, the retention rate was 84.2% ( n = 1452). At the 12-year follow-up, the corresponding number was 68.7% ( n = 1183). At the 20-year follow-up, two individuals had died, 10 had moved out of the country, 25 had incorrect or missing postal addresses, 19 individuals declined participation due to learning disabilities or autism resulting in 1667 eligible participants out of whom 41.7% ( n  = 695) accepted participation. For detailed information on the drop-out rate analysis, see Supplementary Material.

Results

Model 1, social selection

In bivariate analysis, externalizing problems at age 3 increased the risk for performing below grade in English and mathematics, but not reading, at age 12 (Table 2). Internalizing problems increased the risk for low performance in reading, English and mathematics (Table 2).

In multivariate analysis, externalizing problems at age 3 were still associated with English language performance at age 12, after controlling for parental education level and gender (Table 2). When including concurrent conduct problems in the model, the association remained. No associations were seen for externalizing problems at age 3 on mathematics and reading performance at age 12 (Table 2). Internalizing problems at age 3 were shown to predict performing below grade in English language and mathematics after controlling for parental education level and gender (Table 2). The associations remained even when including concurrent emotional problems in the models.

Model 2, social selection

In bivariate analysis, conduct problems at age 12 was associated with lack of final grades from compulsory school (Table 2). No association between emotional problems at age 12 and incomplete grades from compulsory school was found (Table 2).

In multivariate analysis, conduct problems at age 12 predicted incomplete grades from compulsory school after controlling for gender and parental education level at the child's age 12 (Table 2). Likewise, an association was found between internalizing problems at age 12 and incomplete grades from compulsory school after controlling for the above-mentioned variables (Table 2).

Model 3, social selection

In bivariate analysis, both conduct and emotional problems at age 12 were associated with non-eligibility to higher education (Table 2).

In multivariate analysis, conduct problems at age 12 increased the risk for non-eligibility to higher education after controlling for gender and parental education level at the child's age 12 (Table 2). The same applied for emotional problems.

Model 4, social causation

In bivariate analysis, incomplete grades from compulsory school were associated with externalizing problems at age 20, while no association was found for internalizing problems (Table 3).

In multivariate analysis, no association was found between incomplete grades from compulsory school and externalizing problems at age 20 after controlling for gender, maternal symptoms of postpartum depression, conduct problems at age 12 and emotional problems at age 12 (Table 3). However, when controlling only for gender and maternal symptoms of postpartum depression, incomplete grades from compulsory school increased the risk for externalizing problems at age 20 (OR 2.77, CI 0.32-5.22). No association was found between incomplete grades from compulsory school and internalizing problems at age 20 after controlling for gender, maternal symptoms of postpartum depression and emotional problems at age 12 (Table 3).

Model 5, social causation

Non-eligibility to higher education was associated with externalizing problems at age 20 in bivariate analysis (Table 3). No association was found for internalizing problems.

In multivariate analysis, no association was found between non-eligibility to higher education and externalizing problems at age 20, controlling for gender, maternal symptoms of postpartum depression, conduct problems at age 12 and emotional problems at age 12 (Table 3). Likewise, no association was found between non-eligibility to higher education and internalizing problems at age 20 controlling for above mentioned factors (Table 3).

To further investigate the support for the social causation theory using data from the SESBiC study, whether academic performance at age 12 predicted internalizing and externalizing problems at age 20 was investigated; however, no associations were found. Likewise, parental education level at the child's age 12 did not predict internalizing or externalizing problems at age 20.

Discussion

The aim of the present study was to investigate whether mental health predicts academic performance and, vice versa, whether academic performance predicts mental health during different developmental periods in childhood and adolescence. The main findings are discussed below.

Externalizing problems at age 3 predicted academic performance (English language) at age 12, after controlling for a number of relevant factors. Conduct problems at age 12 were also found to increase the risk for incomplete grades from compulsory school and non-eligibility for higher education. Additionally, externalizing problems at age 3 increased the risk for incomplete final grades from compulsory school (data not shown). These findings are in line with previous studies investigating the association between externalizing problems during middle childhood and adolescence and subsequent academic achievement [ 8, 14, 15]. Externalizing problems include Conduct Disorder and Oppositional Defiant Disorder which implicates behavioral disturbances that could affect the adjustability in the classroom, and thereby the performance. Early behavioral problems might also trigger child-teacher conflicts and social exclusion leading to negative experiences of the school environment.

In the present study, selection effects were also found for internalizing problems on academic achievement. Mental health at pre-school age was associated with academic performance (English and mathematics) at age 12. Similarly, internalizing problems at age 12 increased the risk for incomplete grades at ages 15 and 19. Previous research shows, however, mixed results. A recent study found strong selection effects for internalizing problems such as anxiety and depression in an adult twin-population [ 2]. Internalizing problems in early school age has been shown to diminish the chances of completing a high-school degree [ 15]. In contrast, other studies found no effects of internalizing problems on academic performance from childhood to adolescence [ 13], or from adolescence to early adulthood [ 8]. With the background of previous conflicting results, the present study adds to the literature confirming social selection processes for the association between internalizing problems and academic performance.

Interestingly, boys were more likely to perform below grade in reading at age 12 and to be non-eligible for higher education compared to girls. No gender differences were noted for lack of compulsory school grades. In fact, the impact of gender on eligibility to higher education in the present study was considerable; 61% of non-eligible individuals were boys. Gender differences in academic performance with an advantage for girls are a well-known phenomenon [ 28].

Incomplete grades from compulsory school (age 15-16) were not associated with mental health at age 20. When controlling only for gender and maternal symptoms of postpartum depression, incomplete grades from compulsory school predicted externalizing problems at age 20. When controlling for mental health problems at age 12, however, the association diminishes, indicating that the association is a result of social selection rather than social causation mechanisms. Regarding the effect of academic performance on internalizing problems, no associations were found neither in bivariate nor in multivariate analysis.

These findings stand in contrast to the previous findings on the adult population including a meta-analysis, where the risk of reporting depression was almost doubled in low SES groups [ 29]. Similarly, Miech et al. found effects of adolescent school performance on anxiety conditions [ 8], while Sörberg Wallin et al. demonstrated an increased risk for depressive symptoms in early adulthood following poor academic performance in adolescence [ 10]. The social causation models in the present study were limited by a large drop-out rate, and a higher retention rate might have rendered other results. Also, at the age of 20, many individuals have not established a stable level of SES; university students not yet even graduated. For a child, poor academic performance does not have the same direct consequences as low educational attainment might in adulthood (possibly lower income, lesser ability to compete on the labor market). Moreover, the Swedish society offers additional possibilities to complete upper secondary education, which could influence the effect of poor academic performance on mental health during adolescence. Academic performance in childhood is, however, related to later educational attainment [ 6], which in turn has a strong effect on mental health in adulthood. It is, therefore, still, from a mental health perspective, of utterly importance that effort is being made to support children who perform "below grade level" to prevent health risks associated with low SES later in life.

Girls had a fivefold increased risk for internalizing problems at age 20 compared to boys; however, no gender differences were seen for externalizing problems. Previous results support gender differences in mental health with a pattern of higher frequency of internalizing problems in girls and higher prevalence of externalizing problems in boys [ 30]. In the present study, girls participated in the 20-year follow-up to a greater extent than boys (51.4% compared to 30.5%, p  < 0.001), possibly impacting the results.

Limitations

While the present study is strengthened by the large number of participants and the longitudinal design, a few limitations need to be considered when interpreting the results. First, using the social selection and social causation approach in a child population means that possible associations need to be interpreted carefully. While the social causation hypothesis generally implies that lower education level brings lower income, unhealthier life style and more life stressors, this does not readily apply during childhood and adolescence. Rather, children are under influence of the environmental circumstances impacting their parents and thus, a number of possibly confounding factors need to be considered. We have included several such factors in the models but cannot be certain that some important variable has not been left out. Second, a long period of time elapsed between the measures of mental health problems and academic performance, possibly diluting the effects. Narrower timespans between the follow-ups, or additional follow-ups would have been of great value. Moreover, while the influence of mental health on academic performance was modelled with three separate outcomes of academic performance during adolescence, the influence of academic performance on mental health was based on one single measurement of mental health at age 20. The effect of academic performance on mental health might have been stronger if measured for example within the year after receiving incomplete grades from compulsory school. Third, there was a considerable drop-out (58.3%) at the 20-year follow-up. In general, differences between participants and non-participants were noted on educational variables rather than mental health parameters. Moreover, fewer 20-year olds whose parents were born abroad and fewer men than women chose to take part in the 20-year follow-up. Since immigration status often is associated with lower SES [ 31], the skewed drop-out could have an impact on the results. However, parental immigration status was not found to be strongly associated with the outcomes measured in the present study. The skewness regarding men and women might have contributed to a lower degree of externalizing problems, as this is more common in men than in women. While no difference between participants and non-participants regarding mental health at age 3 or 12 was noted, we cannot completely rule out the possibility that individuals who developed mental health problems after 12 years of age were less likely to participate in the follow-up. If so, that would diminish the chance of detecting an association between academic performance in adolescence and mental health problems in early adulthood in line with the social causation hypothesis.

Conclusions

The study adds to the existing literature by the use of a large, two-generational cohort, and longitudinal prospective design with multiple data collection points, investigating the association between mental health and academic performance during different developmental periods from age 12 to 20. The results emphasize the necessity to detect externalizing and internalizing problems at a young age and continuously throughout the school years. In practice, it means that these kinds of problems need to be noticed at preschool age and educational practices adjusted and adequate treatment given to promote transition to the school environment and completion of compulsory school. Knowledge and recognition of the potential effects of internalizing problems on academic performance might be especially important as these problems tend to be less explicit to others compared to externalizing problems.

No support was found for the association between academic performance during adolescence and mental health status in early adulthood in this Swedish context. However, this result needs to be interpreted in the light of a considerable drop-out rate and a long time span between measures of impact and outcome.

Change history

16 September 2023

A Correction to this paper has been published: https://doi.org/10.1007/s00127-023-02560-7

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Acknowledgements

We would like to thank Professor emerita Marianne Cederblad, and Professor emeritus Carl Göran Svedin for their work in the SESBiC study.

Funding

Funding was provided by Skandia research, Swedish Research Council for Health Working Life and Welfare and Professor Bror Gadelius memorial foundation. Open access funding provided by Linköping University.

Author information

Authors and Affiliations

1. Division of Children's and Women's Health, Department of Biomedical and Clinical Sciences, Linköping University, 581 85, Linköping, Sweden

Sara Agnafors & Gunilla Sydsjö

2. Department of Sociology, Lund University, 221 00, Lund, Sweden

Mimmi Barmark

Corresponding author

Correspondence to Sara Agnafors.

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Conflicts of interest

The authors declare that they have no conflicts of interest.

Ethical considerations

The study was approved by the Ethics committee at the University of Lund in 1994 and 1998, and by the Ethical review board in Linköping in 2007 and 2015.

Additional information

The original online version of this article was revised: In this article added the "problem" word under the abstract.

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Agnafors, S., Barmark, M. & Sydsjö, G. Mental health and academic performance: a study on selection and causation effects from childhood to early adulthood. Soc Psychiatry Psychiatr Epidemiol 56, 857-866 (2021). https://doi.org/10.1007/s00127-020-01934-5
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Keywords

# data[].attributes.attachment_link.title
Mental health and academic performance: a study on selection and causation effects from childhood to early adulthood - Social Psychiatry and Psychiatric Epidemiology

# data[].attributes.attachment_link.description
Purpose An inverse relationship between mental health problems and academic achievement is a well-known phenomenon in the scientific literature. However, how and when this association develops is not fully understood and there is a lack of longitudinal, population-based studies on young children. Early intervention is important if associations are to be found already during childhood.

# data[].attributes.body
Students all around the world as having issues balancing school, and their mental health issues. This is continuing to put a strain on academic performance and lowering chances of going for higher education.

“Mental health problems in early childhood and adolescence increase the risk for poor academic performance, indicating the need for awareness and treatment to provide fair opportunities to education”

These issues need to be brought more to light and dealt with in many different communities. Not enough studies have been done on the subject to find out how to really deal with these issues.

"how and when this association develops is not fully understood and there is a lack of longitudinal, population-based studies on young children. Early intervention is important if associations are to be found already during childhood. The aim of the present study was to investigate the development of the association between mental health and academic performance during different developmental periods of childhood and adolescence."

# data[].attributes.headline
What could be the long term affects on employment for students with mental health disorders?

# data[].attributes.subject
What could be the long term affects on employment for students with mental health disorders?

# data[].attributes.headline
Long-Term Effects of Mental disorders on Employment In the National Comorbidity Survey Ten-Year Follow-up

# data[].attributes.subject
Long-Term Effects of Mental disorders on Employment In the National Comorbidity Survey Ten-Year Follow-up

# data[].attributes.attachment_link.readable_text
Soc Psychiatry Psychiatr Epidemiol. Author manuscript; available in PMC 2016 Nov 1.

Published in final edited form as:

Abstract

Purpose

Although significant negative associations of mental disorders with employment have been documented in epidemiological research, much of this research was based on cross-sectional samples and focused only on severe and persistent mental disorders. The present study examined the longitudinal associations of more common mental disorders with employment.

Methods

Data on the associations of common mental disorders with employment are presented here from 4,501 respondents in the National Comorbidity Survey panel study, a two-wave community epidemiological survey of respondents aged 15-54 at baseline (1990-1992) who were re-interviewed in 2001-2003 and were employed, unemployed in the labor force or student at baseline. Lifetime mental disorders at baseline and disorders with onset after baseline were assessed with the Composite International Diagnostic Interview, a fully-structured interview that assessed lifetime prevalence of internalizing fear disorders (panic, phobias), anxiety/misery disorders (major depression, generalized anxiety disorder, post-traumatic stress disorder), externalizing disorders (conduct disorder, alcohol and illicit drug abuse-dependence), and bipolar disorder.

Results

Both baseline lifetime disorders and disorders with onsets after baseline were associated with significantly reduced odds of subsequent employment among respondents who were either employed or students at baseline. Population projections based on the assumption that these associations represented causal effects suggest that the mental disorders considered here were associated with 1.7-3.2 million adults being unemployed in the US population at follow-up.

Conclusions

Expanded access to treatment among current employees and students with mental disorders might lead to improved employment outcomes in these segments of the population.

INTRODUCTION

While the associations of psychiatric disorders with impaired role functioning has received significant research attention [ 1- 3], much of this research has focused on severe psychotic illnesses known to be associated with substantial disability [ 4- 6]. It is only in more recent years that research has broadened to include social and economic burdens of unemployment and underemployment among individuals with less severe but more common mental disorders [ 7- 13]. Recent comparisons indicate that the societal burden of these common disorders with regard to impaired role functioning may be greater than that associated with most common physical disorders [ 14, 15, 7]. However, gaps of two sorts exist in this research. First, much of this research focused on depression [ 10, 16- 24] and little is known about the associations of other common mental disorders with employment outcomes. Second, this research has been mainly based on cross-sectional rather than longitudinal studies. Prospective data are important for examining the associations of mental disorders with employment outcomes because of possible reciprocal associations (i.e., unemployment leading to mental disorder) [ 25, 26].

The current study addresses the above limitations by examining prospective associations of common mental disorders with employment status in a nationally representative two-wave panel survey of the US household population. The first wave of the panel was the 1990-1992 National Comorbidity Survey (NCS) [ 27]. The second wave was the 2001-2003 NCS follow-up survey (NCS-2) [ 28]. We also examine the association of mental disorders having first onsets after the baseline assessment with employment status at follow-up. The NCS panel data provide a rare opportunity to examine the long-term associations of a broad range of mental disorders with unemployment prospectively in a relatively large national sample.

METHODS

Sample

A total of 5,001 respondents completed interviews in both the 1990-1992 NCS and the 2001-2003 NCS-2. The NCS was a nationally representative survey of the US household population ages 15-54 that focused on estimating the prevalence and correlates of DSM-III-R mental and substance disorders [ 27]. The NCS was administered to 8,098 respondents. Informed consent was obtained before administering interviews. These recruitment and consent procedures were approved by the Human Subjects Committee of the Institute for Social Research at the University of Michigan. The response rate was 82.4%. Interviews were conducted by professional interviewers and administered in two parts. Part I, which included the core diagnostic interview, was administered to all respondents. Part II, which included assessments of additional disorders and risk factors, was administered to a probability subsample of 5,877 respondents including all those in the age range 15-24 years, all others with any lifetime DSM-III-R disorder assessed in Part I, and a random sub-sample of other Part I respondents. The Part II sample was weighted to adjust for differential probabilities of selection and for non-response bias. Further details about the NCS design and weighting are reported elsewhere [ 27].

The NCS-2 sought to trace and re-interview all 5,877 of the original Part II NCS respondents. Recruitment and consent procedures were identical to those in the baseline survey and were, as in the baseline survey, approved by the Human Subjects Committee of the Institute for Social Research at the University of Michigan. The data collection team successfully resolved 5,463 of the original 5,877 respondents (166 deceased, 5,001 re-interviewed), for a conditional response rate of 87.6%. Thus, the total response rate of the surveys was 72.2% (.876 × .824). NCS-2 respondents were administered an expanded version of the baseline interview assessing onset and course of disorders between the two surveys. Relative to other baseline NCS respondents, those who were re-interviewed in NCS-2 were somewhat more likely to be female, well-educated, and residents of rural areas. A non-response adjustment weight corrected for these differences [ 29]. Analyses are based on these weighted data. Analyses were further limited to 4,501 participants with employment status data at T1 and T2 who were employed, unemployed in the labor force or student at T1 as described below.

Assessments

Diagnostic assessment

The baseline NCS assessed lifetime DSM-III-R disorders using a modification of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI) Version 1.1, a fully-structured, lay-administered diagnostic interview [ 30]. Lifetime DSM-IV disorders with first onsets between the two interviews were assessed in the NCS-2 using CIDI Version 3.0 [ 31]. The disorders assessed were divided into four broad categories based on the results of previous studies of the latent structure underlying common mental disorders [ 32, 33]. These categories included internalizing fear disorders (simple phobia, social phobia, panic disorder with or without agoraphobia, agoraphobia without panic disorder), internalizing anxiety/misery disorders (major depressive disorder, generalized anxiety disorder, post-traumatic stress disorder), externalizing disorders (conduct disorder, alcohol and drug abuse or dependence), and broadly-defined bipolar disorder (bipolar I and II disorders and sub-thresholds bipolar disorder). We focused on these categories rather than on individual disorders because the number of respondents with specific disorders was small and comorbidity was common, leading to the associations of individual disorders with employment status being unstable in multivariable models. Blinded clinical reappraisal interviews administered to a probability sub-sample of NCS respondents using the Structured Clinical Interview for DSM-III-R [ 34] documented generally good concordance between CIDI diagnoses and clinical diagnoses. Because employment status in the NCS-2 could have been influenced by mental disorders with first onsets in the decade after the NCS, we used retrospective reports in the NCS-2 to code disorders with onsets after the NCS into the same four categories as those used in the NCS except that the latter were based on DSM-IV criteria rather than DSM-III-R criteria. Blinded clinical reappraisal interviews administered to a probability sub-sample of NCS-2 respondents using the Structured Clinical Interview for DSM-IV [ 35] documented generally good concordance between CIDI diagnoses and independent blinded clinical reappraisal interviews [ 36, 37].

Employment status

Employment status was assessed in both surveys using an 11-category scheme that included response categories of employed, self-employed, temporarily laid off, on sick leave, on maternity leave, unemployed and looking for work, student, homemaker, disabled, retired, and an open-ended category of "other" that, if chosen, was followed by a probe asking for a description of this employment status. Respondents could select multiple categories. The vast majority of the open-ended responses were subsequently recoded into one or more of the other 10 categories based on the fact that they typically represented multiples of those categories (e.g., a student with a part-time job). For purposes of our analysis, baseline values on this 11-category coding scheme were collapsed into 4 categories. The first category, which we refer to as broadly-defined employed, includes respondents who reported that they were either currently employed, self-employed, temporarily laid off, on sick leave, or on maternity leave. The latter 4 groups were included in the definition of broadly-defined employed because these statuses imply the existence of a permanent job. The second category, unemployed in the labor force, consists of respondents not coded in the broadly-defined employed category who reported being unemployed and looking for work. The third category, student, consisted of respondents not coded in either the broadly-defined employed or unemployed in the labor force who reported being students at the time of interview. All remaining baseline respondents (i.e., homemakers, retired, disabled, others) were categorized as not in the labor force. The coding scheme used to define employment status outcomes in the NCS-2 was somewhat different. The main outcome was whether or not the respondent was in the broadly-defined employed category at the time of the NCS-2 interview. Additional outcomes were whether or not the respondent was disabled or was unemployed in the labor force.

Baseline controls

We included a number of baseline socio-demographic variables as controls. These included age, sex, race/ethnicity, marital status, number of years of education, and number of years of education of the head of household of the respondent's family of origin.

Analytic approach

Analyses examined the associations of lifetime mental disorders in the NCS (T1) with current employment status in the NCS-2 (T2). Parallel analyses were carried out for the associations of lifetime mental disorders having onsets after T1 with T2 employment status. Caution is needed in interpreting results involving significant associations of disorders having onsets after T1 because temporal priority between these disorders and T2 employment is uncertain as timing of change in employment status at T2 was not recorded. Logistic regression was used to estimate these associations. Three T2 dichotomous employment status outcomes were considered separately: employed, disabled, and unemployed in the labor force. Models were estimated separately among respondents who at T1 were either employed, unemployed in the labor force, or students. Controls were included in each model for baseline socio-demographic characteristics. We also carried out preliminary analyses of respondents who were classified at T1 as homemakers or retired, but the numbers employed at T2 among these groups were too small for reliable estimation of associations with mental disorders.

Analyses were conducted in three stages. First, associations were examined between each individual lifetime disorder group (any vs. none) and outcomes controlling for T1 socio-demographic characteristics. Second, a summary disorder count variable was added to each of these models for the number of disorders in the same category (i.e., fear, anxiety/misery, externalizing, bipolar) and the same time frame of onset (i.e., either prior to or subsequent to T1). This was done in order to determine if the existence of any disorder had a significant association with the outcome not captured by the summary disorder count variable. Based on the finding that the summary disorder count variables captured the associations of the individual disorders with the outcomes, the third stage of analysis examined multivariable associations of the 8 count variables (4 categories of disorder having onsets either before or subsequent to T1) with the outcomes. We tested whether the coefficients of the within-category count variable for disorders having onsets prior to T1 were significantly different from those of the count variable for disorders having onsets subsequent to T1. In cases where the two coefficients were not significantly different, they were combined in a final model.

Simulations based on the parameter estimates in the final models were used to calculate population attributable risk proportions (PARP) of not being employed, broadly-defined, at T2 associated with mental disorders. PARP can be interpreted as the percent of observed adverse outcomes that would have been avoided if the causal effects of mental disorders could have been prevented based on the assumption that the regression coefficients accurately represent causal effects [ 38]. Population projections for PARP were made based on US Census data for 2010 [ 39] showing that 161,902,094 Americans were in the age range 15-54 (the age range of the NCS), assuming that 67.9% of these individuals were employed and 11.2% were students at T1 (based on NCS proportions), and assuming that the proportions of these people who were employed, disabled, or unemployed in the labor force at T2 were the same as those in the NCS-2.

Standard errors and significance tests were estimated using the Taylor series method [ 40] implemented in the SUDAAN software system [ 41] to adjust for the geographic clustering and weighting of the sample. Multivariable significance was evaluated using Wald χ ² tests based on design-corrected coefficient variance-covariance matrices. Statistical significance was evaluated consistently using two-tailed .05-level tests. All reported coefficients (percentages and regression coefficients) are based on weighted data.

RESULTS

Baseline employment status and employment outcomes

The breakdown of the sample according to the cross-classification of T1 and T2 employment statuses is presented in Table 1 . A total of 3,583 respondents met our broad definition of being employed at T1. The vast majority of these broadly-defined T1 employed respondents (84.5% of the T1 employed; and 74.1% of those who at T1 were either temporarily laid off, on sick leave, or on maternity leave) continued to be employed (broadly-defined) at T2. An additional 303 respondents were unemployed in the labor force at T1, 66.7% of whom had returned to being employed (broadly-defined) at T2. An additional 615 respondents were students at T1, 82.4% of whom were employed (broadly-defined) at T2. Finally, an additional 399 respondents were homemakers at T1, 52.4% of whom were employed (broadly-defined) at T2. The remaining respondent groups had low base rates at T1 (n=74 disabled, n=13 retired, n=14 "other"). We excluded T1 homemakers from the analyses because their T2 broadly-defined employment rate (52.4%) was lower than that of the other T1 categories considered here (66.7-84.5%).

Table 1

Employed

Unemployed,
looking for
work

Temporarily
laid off, on
sick leave,
or
maternity
leave

Disabled

Retired

Homemaker

Student

Other

Missing

Total

N
Row %

N
Row %

N
Row %

N
Row %

N
Row %

N
Row %

N
Row %

N
Row %

N
Row %

N
Row %

195
64.4%

35
11.6%

7
2.3%

19
6.3%

4
1.3%

15
5.0%

1
0.3%

11
3.6%

16
5.3%

303
100%

Temporarily laid
off, sick leave, or
maternity leave

42
72.4%

4
6.9%

1
1.7%

5
8.6%

0
0%

2
3.5%

0
0%

3
5.2%

1
1.7%

58
100%

Lifetime prevalence of mental disorders

Prevalence of mental disorder groups are reported in Table 2 . Both lifetime and disorders with first onset after T1 were more common among individuals who were unemployed at T1, with the single exception of first onset externalizing disorders after T1.

Table 2

Mental disorder groups

Employed
at T1a
n=3,583

Unemployed in the
labor force at T1
n=303

Students at T1
n=615

N

Column %b

N

Column % b

N

Column % b

Associations of mental disorders with employment outcomes

The results of multivariable analyses of the association of individual groups of mental disorders with employment outcomes are presented in Table 3 . Among those who were employed at T1, lifetime disorders at T1 as a whole were significantly associated with disability at T2; whereas, disorders with first onset after T1 were associated with remaining employed and with being temporarily unemployed at T2.

Table 3

Mental disorders

Employed at T2a

Disabled at T2

Temporarily laid off and
looking for work at T2

AOR

95% CI

p

AOR

95% CI

p

AOR

95% CI

p

Employed at T1 (n=3,583)a

Unemployed in the labor force at T1 (N=303)

Student at T1 (n=615)

Lifetime disorders at T1

Bipolar disorder

>999

>999->999

<.001

-- e

<.01

<.01-<.01

<.001

Overall test

-- e

χ ² df=4=131.70, p<.001

Disorders with first onset after T1

Bipolar disorder

.48

.11-2.03

.320

-- e

1.66

.30-9.15

.560

Overall test

-- e

Looking at specific disorder groups among those employed at T1, respondents with lifetime fear disorders at T1 had more than two times the odds of being disabled at T2 (adjusted odds ratio [AOR]=2.08, 95% CI=1.07-4.06, p=.032); whereas, respondents with first onset of bipolar disorder after T1 had less than half the odds of remaining employed (AOR=.46, 95% CI=.25-.85, p=.014) and more than two times the odds of being temporarily laid off at T2 (AOR=2.35, 95% CI=1.00-5.49, p=.049). Respondents with first onset anxiety/misery disorders after T1 also had more than two times the odds to be laid off at T2 (AOR=2.32, 95% CI=1.31-4.10, p=.004).

Among those who were unemployed in the labor force, only lifetime T1 disorders were significantly associated with being temporally laid off and looking for work at T2, mainly due to the strong negative association of bipolar disorder with the this outcome at T2 ( Table 3 ). However, only 11 respondents among the T1 unemployed in the labor force met the lifetime criteria for bipolar disorder ( Table 2 ). In contrast, bipolar disorder with first onset after T1 was associated with more than 6 times the odds of being temporally laid off and looking for work at T2 in this subsample of respondents (AOR=6.54, 95% CI=1.25-34.33, p=.026).

Very few T1 students were disabled at T2. Therefore, this outcome was not included in the analyses for these respondents ( Table 3 ). Lifetime mental disorders at T1 were associated with both current employment and being temporarily laid off and looking for work at T2. In analyses of individual diagnostic groups, T1 lifetime bipolar disorder was strongly associated with the greater odds of being employed at T2 and with lower odds of being unemployed in the labor force. The odds ratios for these associations were very large due to the small number of individuals with T1 lifetime bipolar disorder among T1 students (n=5; Table 2 ).

In the next stage of the analyses, the associations of summary scores of T1 lifetime disorders and disorders with first onset after T1 with employment outcomes were assessed ( Table 4 ). Among the T1 employed, the summary score for T1 lifetime disorders was associated with 22% lower odds of employment at T2 in the model that included only this measure of mental disorders (AOR=.78, 95% CI=.63-.96, p=.016). Thus, every 1 additional lifetime disorder at T1 decreased the odds of being employed by 22%. This summary score was also significantly associated with a 44% higher odds of being unemployed in the labor force at T2 (AOR=1.44, 95% CI=1.12-1.85, p=.004).

Table 4

Mental disorders

Employed at T2a

Disabled at T2

Temporarily laid off and
looking for work at T2

AOR

95% CI

p

AOR

95% CI

p

AOR

95% CI

p

Employed at T1 (n=3,583)

Model for T1 lifetime disorders only

Overall test

χ ² df=2=2.73, p<.001

Test for comparing T1 lifetime disorders and
disorders with onset after T1

Unemployed in the labor force at T1 (N=303)

Model for T1 lifetime disorders only

Student at T1 (n=615)

Model for T1 lifetime disorders only

Lifetime disorders at T1 b

.75

.49-1.14

.177

1.85

1.00-3.41

.049

Lifetime disorders at T1 b

.80

.50-1.29

.361

1.78

.91-3.48

.092

Disorders with onset after T1 c

.65

.42-1.00

.048

1.33

.69-2.56

.392

Overall test

When summary scores for disorders with first onset after T1 were added to these models, the odds ratios for T1 lifetime disorders were somewhat attenuated but remained statistically significant for being temporally laid off and looking for work at T2. In addition, the summary score for disorders with first onset after T1 was statistically significant in these models with 32% lower odds of remaining employed (AOR=.68, 95%CI-.56-.83, p<.001) and 59% higher odds of being temporarily laid off and looking for work (AOR=1.59, 95% CI=1.14-2.21, p=.006) associated with each 1 additional disorder. The overall tests of these summary scores were also statistically significant in models predicting employment at T2 and being temporarily unemployed. None of the tests comparing the summary scores for T1 lifetime disorders and disorders with onset after T1 were statistically significant ( Table 4 ). Therefore these summary scores were combined. The combined summary score was significantly associated with lower odds of being employed at T2 (AOR=.75, 95% CI=.66-.86, p<.001) and higher odds of being temporarily laid off and looking for work (AOR=1.46, 95% CI=1.19-1.79, p<.001). In addition, this combined summary score was significantly associated with higher odds of disability at T2 (AOR=1.39, 95% CI=1.04-1.86, p=.026). These results suggest that presence of every 1 additional mental disorder is associated with 25% lower odds of remaining employed, 46% higher odds of becoming temporally laid off and looking for work and 39% higher odds of becoming disabled.

Among respondents who were temporarily unemployed in the labor force at T1, only the summary score for disorders with onset after T1 in the model predicting being employed at T2 was statistically significant (AOR=.70, 95% CI=.51-.97, p=.031), suggesting that presence of every 1 additional disorder is associated with 30% lower odds of remaining employed. The overall test for the summary scores was also statistically significant in this model, as was the test for comparing the two summary scores, indicating that the association of T1 lifetime disorders with being employed at T2 is different than the association of disorders with onset after T1 ( Table 4 ). Therefore, the combined summary score for all mental disorder groups was not computed.

Among T1 students, the summary score for T1 lifetime disorders was significantly associated with higher odds of being temporarily laid off and looking for work at T2 (AOR=1.85, 95% CI=1.00-3.41, p=.049) ( Table 4 ). In addition the summary score for disorders with first onset after T1 was associated with lower odds of being employed at T2 (AOR=.65, 95% CI=.42-1.00, p=.048) and the overall test in this model was statistically significant. These results suggest that presence of every 1 additional lifetime disorder is associated with 85% higher odds being temporally laid off and looking for work at T2 and every 1 additional disorder with onset after T1 with 35% lower odds of being employed. As the test for comparison of summary scores of T1 lifetime disorders and disorders with onset after T1 was not statistically significant, these summary scores were combined and a combined summary score for all disorders was computed. This combined summary score was significantly associated with lower odds of being employed (AOR=.71, 95% CI=.55-.92, p=.010) and higher odds of being temporarily laid off and looking for work at T2 (AOR=1.51, 95% CI=1.07-2.13, p=.020). These data suggest that presence of every 1 additional disorder was associated with 29% lower odds of being employed and 51% higher odds of being temporally laid off and looking for work at T2.

Population attributable risk proportions

The PARPs were computed for summary scores of T1 lifetime disorders and the combined T1 lifetime disorders and disorders with onset after T1 that were associated with the outcomes at a statistically significant level ( Table 5 ). A PARP estimate of −1.8% obtained from the model predicting T2 employment among T1 employed respondents suggests that 1.8% more of currently employed individuals would remain employed 10 years later if the effect of T1 lifetime mental disorders on employment outcomes could be fully prevented and assuming that the model is correctly specified. This PARP value translates into 1,777,000 more employed individuals. Similarly, a PARP estimate of 26.7% suggests 26.7% fewer temporarily unemployed individuals 10 years later if the effect of T1 lifetime disorders on employment can be fully prevented. Both the PARP estimates and the projected numbers were larger when the combined associations with both T1 lifetime disorders and disorders with onset after T1 were considered compared to T1 lifetime disorders only.

Table 5

Model

Employed at T2a

Disabled at T2

Temporarily laid off and
looking for work at T2

PARP

Projected number of
individualsa

PARP

Projected number of
individualsa

PARP

Projected number of
individualsa

Employed at T1

Model for T1 lifetime disorders only c

−1.8

1,777,000

-- b

-- b

26.7

704,000

Model including both T1 lifetime
disorders and disorders with onset
after T1 d

−3.2

3,159,000

36.6

925,000

44.1

1,164,000

Student at T1

Model for T1 lifetime disorders only c

-- b

-- b

31.8

294,000

Model including both T1 lifetime
disorders and disorders with onset
after T1 d

−3.6

594,000

44.7

413,000

DISCUSSION

The results of this study are consistent with past research identifying significant associations between mental disorders and employment outcomes [ 7- 13]. In addition, the nationally representative sampling and longitudinal design of the NCS panel allowed us to assess the contribution of mental disorders to the overall burden of unemployment and disability at the population level. While the associations of individual disorder groups with unemployment were for the most part statistically non-significant, tests involving summary scores showed significant associations. Furthermore, many of the associations were in the expected direction for both T1 lifetime disorders and disorders with onset after T1. The lack of statistical significance, therefore, is likely attributable to the small sizes of the samples and the relatively low prevalence of change in employment status between T1 and T2. It is also difficult to compare coefficients for specific disorder groups due to lack of precision in these coefficients as a result of the small sample sizes. Past research has found some differences in the association of the mental disorders with work disability according to the type and severity of disorders [ 42- 44]. For example, in a clinical study, bipolar I disorder was associated with greater work impairment than major depressive disorder [ 43]. Future research with larger sample sizes may be needed to more accurately assess variations in employment outcomes across diagnostic groups.

The population impact of mental disorders is especially pronounced among individuals who are currently employed or student at baseline. Over 1.7 million more currently employed working age adults would remain employed 10 years later if the effect of lifetime mental disorders on occupational functioning can be prevented. This number will be much larger if the effect of emergent mental disorders on occupational functioning is also considered. The currently employed people comprise the bulk of young and middle-aged adults in the community. Thus, even small attributable risk in this population group translates into large numbers of unemployed or disabled people years later. The effects among students were comparable. However, fewer associations were statistically significant in the student sample because of the smaller size of the sample. This finding highlights the importance of prevention and early intervention efforts to reduce the burden of mental illness and associated disability in working adults and students [ 20, 45, 46]. Individuals suffering from mental disorders, their families, employers and the society at large would potentially benefit from these efforts.

Over the years a number of programs to enhance access to mental health services among employees and students have been implemented [ 45, 20, 47, 48]. While the long-term effects of these programs on employment outcomes have rarely been examined, many have been shown to be effective in short-term with regard to symptoms and associated disability. The benefits of these programs are not limited to improved retention in workforce but also improved performance of current workers [ 17].

We found some differences in associations with employment outcomes among mental disorders with first onset after T1 compared to T1 lifetime disorders. These differences may simply reflect recall bias as T1 lifetime disorders could have had onsets many years before T1, whereas disorders with first onset after T1 were, by definition, more recent. It is also possible that the impact of recent onset disorders on employment outcomes may be different than the impact of disorders with onset in the remote past. Many lifetime fear disorders in NCS had very early ages of onset [ 49]; whereas, first onset disorders after T1, by definition started after age of 15.

The limitations of this study and of the NCS data should be considered in interpreting the results. First, despite the large sample size, the number of respondents with individual mental disorders was too small to provide reliable estimates, requiring us to combine individual disorders into groups. Second, the list of mental disorders assessed in the NCS was incomplete and did not include psychotic disorders, attention deficit hyperactivity disorder, or personality disorders, which have been shown in past research to impact employment outcomes. Third, although analyses adjusted for socio-demographic characteristics, the possibility of residual confounding by unmeasured variables limits causal inference. For example, the educational level of the head of the household may not fully capture the social class of origin of the respondents and the residual confounding may bias the observed association of mental disorders and employment outcomes. In addition, a number of other social and contextual factors that can impact both the risk of mental disorders and employment opportunities (e.g., early upbringing, early socialization, and neighborhood factors) were not captured in the surveys. Fourth, we did not assess the impact of mental health treatment. However, treatments in the community often fall short of minimally adequate quality indicators [ 50]. Fifth, we did not assess the impact of the types of jobs on the association of mental disorders with employment outcomes. Mental disorders may have a different impact on blue-collar than white-collar employment. Sixth, the temporal order between mental disorders with onset after T1 and employment at T2 could not be established based on the NCS-2 data. Thus, it is possible that loss of employment or disability occurred before the onset of mental disorder. This is especially a concern with regard to the younger respondents at T1 and for mental disorders with typically later ages of onset (e.g., major depression). Unemployment is a known risk factor for common mental disorders and psychological distress [ 51]. Therefore, the results of these analyses should be interpreted with caution. Seventh, the NCS and NCS-2 assessed employment in a period of relatively low unemployment rates in the US. The mid-year (June) unemployment rates varied between 5.2-7.8% in 1990-1992 when NCS interviews were conducted and 4.5-6.3% in 2001-2003 when NCS-2 interviews were fielded [ 52]. The association of mental disorders with unemployment may be different in periods of high unemployment because individuals with mental disorders face greater challenges when competing for scarce employment opportunities [ 53]. There is also evidence for variation in employment across states and between rural and urban counties [ 54]. We could not assess these variations in NCS because the survey is designed to provide representative national data and is not representative at state or county level. Finally, loss of employment is only one work-related adverse outcome of mental disorders. Many individuals with mental disorders who continue to work experience work-related impairment and there is evidence that mental disorders are associated with lower income among those who remain employed [ 55, 56]. These adverse effects of mental illness need to be investigated in future research using NCS panel data.

In the context of these limitations, the findings highlight potentially significant prospective associations between mental disorders and employment outcomes. Research on treatment and prevention programs in workplaces, schools and colleges has produced promising results [ 45, 20, 47, 48]. However, significant barriers to broader implementation of these programs remain [ 57- 60, 21]. Improvements in insurance coverage in the Affordable Care Act, most notably the age extension for coverage under parents' insurance for the youth, mandating insurance coverage for workers, and strengthening of mental health parity may enhance mental health treatment seeking in the coming years. The potential effects of these efforts to improve access to care on employment and other social outcomes needs to be assessed in future research.

​

Original collection of NCS and NCS-2 data has been approved by the Institutional Review Board of Harvard University and performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. The analyses reported here have been approved by the Institutional Review Board of Johns Hopkins University, Bloomberg School of Public Health. All persons interviewed for NCS and NCS-2 surveys gave their informed consent prior to their inclusion in the study.

Acknowledgments

FINANCIAL SUPPORT

The secondary analysis of the NCS/NCS-2 data reported here was supported by National Institute of Mental Health (NIMH) (RM PI; grant number R01MH096826). The NCS data collection was also supported by NIMH (RCK PI; grant number R01MH46376), while NCS-2 data collection was supported by the National Institute on Drug Abuse (RCK PI; grant number R01DA012058). The views and opinions expressed in this report are those of the authors and should not be construed to represent the views of any of the sponsoring organizations, agencies, or U.S. Government. A complete list of NCS and NCS-2 publications can be found at http://www.hcp.med.harvard.edu/ncs. The NCS-2 is carried out in conjunction with the World Health Organization World Mental Health (WMH) Survey Initiative. We thank the staff of the WMH Data Collection and Data Analysis Coordination Centres for assistance with instrumentation, fieldwork, and consultation on data analysis. These activities were supported by the NIMH (R.C.K., grant number R01MH070884), the John D. and Catherine T. MacArthur Foundation, the Pfizer Foundation, the US Public Health Service (R.C.K., grant numbers R13MH066849, R01MH069864, and R01DA016558), the Fogarty International Center (R.C.K., R03TW006481), the Pan American Health Organization, Eli Lilly and Company, Ortho-McNeil Pharmaceutical, Inc., GlaxoSmithKline, and Bristol-Myers Squibb. A complete list of WMH publications can be found at http://www.hcp.med.harvard.edu/wmh/.

Footnotes

CONFLICT OF INTEREST

Dr. Mojtabai has received research funding from Bristol Myers-Squibb and Lundbeck pharmaceuticals. In the past 12 months, Dr. Kessler has served as a consultant for Hoffmann-La Roche, Inc. and the Johnson & Johnson Wellness and Prevention. Dr. Kessler has served on advisory boards for Mensante Corporation, Johnson & Johnson Services Inc. Lake Nona Life Project, and U.S. Preventive Medicine. Dr. Kessler owns 25% share in DataStat, Inc. Other authors declare no potential conflict of interest.

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Long-Term Effects of Mental disorders on Employment In the National Comorbidity Survey Ten-Year Follow-up

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Although significant negative associations of mental disorders with employment have been documented in epidemiological research, much of this research was based on cross-sectional samples and focused only on severe and persistent mental disorders. The ...

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How do we support students going through this rough time?

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How do we support students going through this rough time?

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Mental Health and Your Child or Teen: What to Watch for and How to Help

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Mental Health and Your Child or Teen: What to Watch for and How to Help

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"Check in with your child or teen. Ask how they’re feeling and how things are going in their life. Listen to their words, and also pay attention to any changes in their behavior......

Ask your child to come up with ideas for how to cope when they start to feel scared, mad, sad or otherwise upset. Talk with your child about their concerns. Let them take the lead, as they learn coping skills that work for them."

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You can download this article as a PDF ( English) ( Spanish).

What is mental health?

Your child's overall health includes both their physical and their mental health. Physical health includes things like how well a child grows, how strong they are and how well they can recover from an infection. Mental health includes how well a child thinks, copes with feelings and behaves. A mental health disorder in childhood involves delays or disruptions in thinking, behaviors, social skills, or regulation of emotions for a child of that age. These delays or disruptions cause distress to children or interfere with how well they function at home, school or in social situations.

Just like your child's physical health, your child's mental health changes over time, getting better or worse. We can help our children build habits that help them be mentally healthy, in the same way that we teach them habits such as brushing their teeth, eating nutritious food and getting proper exercise to help them be physically healthy.

Mental health problems are common in children and teens. It is best to notice a problem early on and get the right treatment to support your child, just as you would for a broken bone. The tricky thing is that kids and teens are growing and changing all of the time. It can be hard to tell the difference between mood and behavior changes that commonly happen as children age and ones that are a sign of anxiety, depression or other mental health concerns.

Ways to support your child or teen's mental wellness

Parents can do a lot to help kids be mentally healthy and well:

Check in with your child or teen. Ask how they're feeling and how things are going in their life. Listen to their words, and also pay attention to any changes in their behavior.

Listen more than you talk, and truly hear what your child is saying. Be aware that your child may not want to problem-solve, they may just want to be heard and accepted. Be curious.

Encourage your child to keep talking. Validate their feelings by saying things like: "Tell me more." "I hear you." "I understand you feel left out."

Encourage your child to learn about and label their emotions, and to accept all emotions.

Ask your child to come up with ideas for how to cope when they start to feel scared, mad, sad or otherwise upset. Talk with your child about their concerns. Let them take the lead, as they learn coping skills that work for them.

Your child is likely to pick up on your healthy and unhealthy habits, so try to start adding more healthy habits to your family's daily routine. These include getting enough sleep and exercise, choosing healthy foods and drinks, and reducing and managing stress. These habits are important for physical and mental health. Ask your doctor for support if you need help creating healthier habits and enlist your whole family to make small changes together.

Here are some examples of other things that might boost your family's mental wellness:

Build a strong and positive parent-child relationship. Respond to your child's emotional and physical needs.

Establish a weekly family time to share an activity, like game night or cooking a meal together.

Make time for child-directed play, one-to-one play in which your child leads.

Decrease stress and conflict while still supporting normal activities.

Establish daily routines that support healthy habits to help children and teens know what to expect and depend on. Include regular sleep and wake times, family meals, and clear responsibilities and consequences as part of your family's way of life.

Work on your own coping and wellbeing so that you are a powerful role model for your child; you can even talk through how you handle big emotions in healthy ways as they happen.

Limit time with screens.

Get outside; spend time in nature.

Get creative with art time (music, drawing, writing in a journal).

Reflect on what you're thankful for.

Learn a new hobby or build more mastery in a hobby you already have.

Build friendships, connections and community supports for your child and your family.

Signs of a mental health problem

Mental health problems affect thinking, emotions and behavior. Parents, teachers, coaches and others can help identify early signs of mental health issues. Notice if a child:

Is feeling very sad or withdrawn for 2 or more weeks.

Has severe mood swings.

Shows big changes in behavior.

Often has problems in friendships and other relationships.

Has a sudden overwhelming fear or worry that does not match the situation.

Seems unusually irritable.

Displays explosive anger.

Has trouble sleeping.

Changes their eating habits.

Loses weight.

Spends so much time alone that it gets in the way of doing other activities, or starts hanging out with peers who are an unhealthy influence.

Is taking new risks, like using drugs or alcohol.

Has lots of stomachaches or headaches.

Avoids school or stops doing as well as they used to in school.

If a child is showing one or more of these signs, talk with their parent right away. If it's your child, contact their doctor, and be specific about which behaviors you, teachers and others have noticed. Getting the right treatment will help your child improve their mental health. In the meantime, talk with your child about a plan for coping and managing strong emotions to prevent a crisis.

Signs of a possible mental health crisis or suicide

Some things a child says or does can be a sign that they are in immediate danger. Watch for a child or teen who:

Talks or "jokes" about death.

Begins giving away items that they love.

Says goodbye to friends, including on social media.

Talks about hurting themselves or wanting to die.

Says they feel like they're a burden and it would be better if they were gone.

Stops wanting to do things they used to enjoy.

Engages in self-harm behaviors.

What to do if a child or teen shows signs of being in a mental health crisis

If a child or teen shows these signs, ask them directly if they are thinking about suicide. Asking does not increase the risk of suicide. Stay calm if they say they are thinking about suicide and:

Call the National Suicide Prevention Lifeline for help: 1-800-273-8255.

Reduce the risk of suicide by removing pills and firearms from your home. If you can't remove them, place them in a safe, lockbox or other secure place.

If they are in immediate danger of harming themselves, don't leave them alone. Take your child to the closest Emergency Department (ED). If you cannot safely transport your child, please call 911. Tell them you have a mental health emergency and need your child taken to the ED. You can also use your county crisis line for help with problem-solving.

Give your teen tips to notice and help peers

Teens spend a lot of in-person and online time with peers. They can notice friends and peers who may be dealing with mental health challenges before adults notice.

Teach your teen about mental health warning signs and urge them to tell an adult if they think they know someone who may have a mental health problem.

Tell them to never keep it a secret if someone they know is thinking about suicide or harming others.

Have your teen put the Crisis Text Line in their phone contacts: 741-741. Anyone can text the word "Home" to that number to be connected live with a trained crisis counselor. This service is free.

Avoid blame and shame

Childhood and adolescence are times of big changes, and it's normal for all kids to experience emotional ups and downs. But at least 1 in 5 kids will have a diagnosable mental health problem that needs treatment. If your child develops a mental health problem, know it is not you or your child's fault. Please get help just as you would for a physical health problem. Don't let blame or shame keep you from getting help for a child. Effective help is available and can make an important difference in helping your child get back on track with healthy development and life.

To Learn More:

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Mental Health and Your Child or Teen: What to Watch for, How to Help

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We can help our children build habits that help them be mentally healthy.

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Back to School and Mental Health: Supporting Our Children for a Successful Year Ahead

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Back to School and Mental Health: Supporting Our Children for a Successful Year Ahead

# data[].attributes.body
"Often unseen and untreated, child and adolescent mental health challenges can significantly impact the ability to learn and develop. One in five children has a diagnosable mental, emotional, or behavioral disorder, yet many of them do not receive the help they need."

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Sunny Patel, M.D., M.P.H., Center for Mental Health Services

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Back to School and Mental Health: Supporting Our Children for a Successful Year Ahead

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As the heat of the summer reaches its peak, and (hopefully soon) gives way to the cooler autumn breezes, it’s time for children and youth to return to school. This transition — filled with excitement, anticipation, and some natural apprehension — is an annual ritual for millions of students across our nation. As we prepare for the new academic year, it’s crucial to remember that school readiness isn’t just about having the right supplies, it’s also about mental health.

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Is there a lack of professional help in schools?

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Is there a lack of professional help in schools?

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Effects of Covid -

"Further, 40% of public schools indicated it was very difficult or

difficult to fill “mental health professional” roles."

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Comprehensive School-Based Mental and Behavioral Health Services and School Psychologists

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Comprehensive School-Based Mental and Behavioral Health Services and School Psychologists

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"Increased access to mental health services in schools is vital to improving the physical and psychological safety of our students and schools, as well as academic performance and problem-solving skills."

"Good mental health is critical to children’s success in school and life. Research demonstrates that students who receive social–emotional and mental and behavioral health support achieve better academically."

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National Association of School Psychologists (NASP)

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Mentally healthy children are more successful in school and life. Good mental health is critical to children's success in school and life. Research demonstrates that students who receive social-emotional and mental and behavioral health support achieve better academically. School climate, classroom behavior, engagement in learning, and students' sense of connectedness and well-being all improve as well. Mental health is not simply the absence of mental illness but also encompasses wellness promotion; social, emotional, and behavioral health; and the ability to cope with life's challenges. Left unmet, mental health problems are linked to costly negative outcomes such as academic and behavior problems, dropping out, and delinquency. Mental and behavioral health problems not only affect students' short-term classroom engagement, but also interfere with long-term development of positive relationships and work-related skills.

There is a growing and unmet need for mental and behavioral health services for children and youth. According to the U.S. Department of Health and Human Services, one in five children and adolescents experience a mental health problem during their school years. Examples include stress, anxiety, bullying, family problems, depression, learning disability, and alcohol and substance abuse. Serious mental health problems, such as self-injurious behaviors and suicide, are on the rise, particularly among youth. Unfortunately, many children and youth do not receive the help they need, and disparities in access to care exist among low-income communities and minoritized youth. Among the 3.8 million adolescents ages 12-17 who reported a major depressive episode in the past year, nearly 60% did not receive any treatment, according to a 2019 report by the Substance Abuse and Mental Health Services Administration. Of the adolescents who do get help, nearly two thirds do so only in school.

Schools are an ideal place to provide mental and behavioral health services to children and youth. Schools provide an ideal context for prevention, intervention, positive development, and regular communication between school and families. Schools also offer caring relationships and regular, consistent connections between students and staff both in person and virtually. School-employed mental health professionals (i.e., school psychologists, school counselors, school social workers, and school nurses) know the students, parents, and other staff, which contributes to accessibility of services. Research shows that students are more likely to seek counseling when services are available in schools. In some cases, such as rural areas, schools provide the only mental health services in the community. Comprehensive, culturally responsive school mental health services can help address inequities in access and help reduce the stigma associated with receiving mental health services by making it part of the fabric of the school system.

Comprehensive school mental and behavioral health services support the mission and purpose of schools: learning. All services provided in schools should be appropriate to the learning environment; those that are not risk being ineffective or even counterproductive. Just as children are not simply small adults, schools are not merely private clinics with white boards. Being trained to work within the school culture is essential to being effective (and cost-effective). School psychologists have specialized training in child development, mental health, learning, diversity, culturally responsive services, and school systems and law. Their unique expertise lies in how these elements interact to shape children's behavior, learning, and overall adjustment.

Comprehensive school mental health services are essential to creating and sustaining safe schools. Increased access to mental health services in schools is vital to improving the physical and psychological safety of our students and schools, as well as academic performance and problem-solving skills. Culturally responsive school mental health supports that encompass social-emotional learning, mental wellness, behavioral health, resilience, and positive connections between students and adults are essential to creating a school culture in which students feel safe and empowered to report safety concerns, which is proven to be among the most effective school safety strategies. Additionally, in the aftermath of a crisis, school-employed mental health professionals provide supports that facilitate a return to normalcy, are sustainable, and can help to identify and work with students with more intense or ongoing needs.

Providing a continuum of school mental and behavioral health services is critical to effectively addressing the breadth of students' needs. Comprehensive mental health services are most effective when provided through a multitiered system of supports (MTSS). MTSS encompasses the continuum of need, enabling schools to promote mental wellness for all students, identify and address problems before they escalate or become chronic, and provide increasingly intensive, data-driven services for individual students as needed. Access to adequate staffing of school-employed mental health professionals, like school psychologists, is essential to the quality and effectiveness of these services.

School psychologists provide a continuum of services that connect mental health, behavior and learning, school and home, and school and community services. Meeting the full continuum of student needs requires collaboration and coordination. Typically, community providers offer supplementary or intensive services that go beyond school capacities. An MTSS approach facilitates collaboration while ensuring that services provided in school are appropriate to the learning context and those that are provided after school hours are appropriately linked to and supported in the school setting. Partnerships are most effective when they are coordinated by school-employed mental health professionals, are defined by clear memoranda of understanding, and reinforce an appreciation for the unique contribution each group makes to creating more seamless and comprehensive service delivery. This not only reduces gaps, redundancy, and conflict, it also reduces stress on families and supports their roles as primary caregivers and decision makers regarding their child's development.

School psychologists are part of a team of school-employed mental health professionals who provide these services. Improving staffing ratios for these professionals is critical to adequately supporting students' mental and behavioral health. For more information on school-based mental health services and the role of school psychologists, visit www.nasponline.org.

Please cite this document as:

National Association of School Psychologists. (2021). Comprehensive School-Based Mental and Behavioral Health Services and School Psychologists [handout]. Author.

© 2021, National Association of School Psychologists, 4340 East West Highway, Suite 402, Bethesda, MD 20814, 301-657-0270, www.nasponline.org

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Comprehensive School-Based Mental and Behavioral Health Services and School Psychologists

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Share this resource with key stakeholders to convey the breadth of your skills at all tiers and the importance of improving staffing ratios.

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These articles show and agree that helping your child is simple if you follow a few different steps. Communication with your child is essential to helping them through rough times.

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These two articles talk about the lack of professional mental health care services in schools and how they are affecting the academic integrity of children