Next, in Table 3 we move on to identifying the symptoms of the disorders that are most important for labor market outcomes—these correspond to equations (6–13) in our model. We excluded a few work-related symptoms to minimize the possibility of reverse causality, that is, adverse work outcomes can cause psychiatric symptoms and thus prevent us from making claims about a causal relationship between poor mental health and work outcomes. For each of the 4 disorders, the loading on one symptom is normalized to 1. Thus, the importance of all the other symptoms were estimated with respect to that normalized symptom. The normalized symptoms were listed first for each disorder; these symptoms were: depressed mood for MDE and excess anxiety for GAD.
Our results suggest that insomnia/hypersomnia, indecisiveness, and severe emotional distress for both men and women, and fatigue for women were the most crucial indicators of MDE, which are debilitating for work outcomes. The length of a GAD episode is most detrimental aspect of GAD for labor market outcomes, followed by symptoms relating to difficulty controlling worry and symptoms of worry/anxiety/nervousness causing significant emotional distress for both men and women. As we did not find significant adverse impact of either panic attack or social phobia on any work outcome, we shall not discuss the results for indicators of these disorders.
Next, we performed a concordance analysis to determine whether there was a significant advantage to our latent variable approach over the standard approach of using a binary indicator of mental illness in estimating effects of psychiatric disorder on labor market outcomes. To this effect, we dichotomized the estimated latent scale over the relevant range for different alternative values for the cutoff points (τ). Thus, individuals with a predicted score for the latent mental disorder variable to the left of τ were characterized as not having a disorder and those with a score ≥τ were classified as having the disorder. The predicted values were obtained from Eqs. (6 and 7) for MDE; Eqs. (8 and 9) for panic attack; Eqs. (10 and 11) for social phobia; and Eqs. (12 and 13) for GAD.
Given a cutoff point (τ), we defined the hit rate (H) (also called sensitivity26) as the proportion of correct diagnosis (based on our measure) when an individual meets diagnostic criteria for a disorder and the false alarm rate (F) [(1−F) is called specificity26] as the proportion of incorrect diagnosis when an individual does not meet diagnostic criteria.27 In Table 4, we show the contingency table for diagnosis based on clinical measures and that based on the model, given τ. In terms of Table 4, H=a/(a+c) and F=b/(b+d).
We used 2 measures to evaluate the performance of our latent indices vis-à-vis the standard binary variables used: (a) the Peirce skill score (PS)27 and (b) the odds ratio (OR), which are better discriminatory measures when the outcome of interest is relatively uncommon. PS is the difference between the hit rate and the false alarm rate (PS=H−F) and the OR is defined as the ratio of the odds of making a correct prediction and the odds of an incorrect prediction (OR=[H/(1−H)]/[F/(1−F)]). A value of 0 for the PS or, alternatively, a value of 1 for the odds ratio indicates a perfect mismatch between our prediction based on the MIMIC model and a clinical diagnosis of the disorder. In Table 5, we report these statistics for given values of
for each psychiatric disorder used in our study. Our preferred choice of cutoff value τ was one which maximized PS and/or OR. Thus, we choose τ=0.1 for MDE, τ=0.1 for panic attack, τ=0.7 for social phobia, and τ=0.4 for GAD. Following the study by Van Doorslaer and Jones,28 we normalized the predicted values of the latent mental disorder variables such that they lie in the [0, 1] interval.
In Table 6, we present contingency tables for clinical diagnosis and diagnosis of a mental disorder based on the optimal cutoff values chosen above for each psychiatric disorder. In the case of MDE, social phobia, and GAD, we identified a large number of individuals who did not meet diagnostic criteria but would be classified as having the disorder based on our chosen cutoff value (176, 262, and 367 individuals, respectively). Further, the distribution of the latent indices for mental disorders for this set of individuals closely resembles those who meet diagnostic criteria for the disorder, thus indicating similarly poor mental health. In an analysis of the labor market effects of mental illness using a binary indicator for meeting diagnostic criteria for a disorder, one would misclassify these groups of individuals as being healthy, thus potentially generating a misleading estimate of the impact of mental illness on work outcomes. Note that the number of false negatives, denoted by c in Table 4, is very small for each disorder (Table 6).
In this study, we have proposed an alternative methodological approach using latent indices for disorders to examine the effect of mental disorders on labor market outcomes of individuals. Our findings identified specific symptoms of disorders that were particularly harmful for labor market outcomes. In this way, we go beyond the main thrust of previous research, which merely indicates particular categories of mental disorders, such as depression, that were associated with worse labor market outcomes, but it is not clear which symptoms were relatively more detrimental.11,14,15 Our focus on symptoms, rather than on binary indicators for diagnostic categories, is also consistent with the decreasing emphasis on such categories by clinicians, researchers, and policymakers.29,30 For example, using a multinomial probit model, Slade & Salkever31 estimated the effects of specific symptoms of schizophrenia that are most important in the choice of not working for pay, employment in a nonsupported job, and employment in supported/sheltered jobs.
Our results suggest that insomnia/hypersomnia, indecisiveness, and severe emotional distress for both men and women, and symptom of fatigue for women were relatively more important indicators of depression in explaining work-related outcomes. This result suggests that medications or interventions that target these symptoms (eg, medications to improve sleep) may be especially helpful for improving work functioning. In an earlier study, Bombardier and Buchwald32 had found chronic fatigue, along with chronic fatigue syndrome and fibromyalgia, to be associated with work disability and low rates of employment. Our findings were consistent with these results. In the case of GAD, the length of a GAD episode, followed by symptoms relating to difficulty controlling worry and symptoms of worry/anxiety/nervousness causing significant emotional distress are crucial indicators of the disorder with respect to the labor market.
Our analysis also identified individual psychiatric disorders, which were detrimental for work. MDE had the greatest impact and detracts from employment and labor force participation of individuals, which was consistent with prior research. For example, Chang and Yen33 found that a higher score based on depressive symptoms significantly detracts from employment of the elderly. Using binary indicators for mental disorders, Ettner et al11 found major depression to significantly lower the likelihood of employment by about 7 percentage points for both men and women.
One limitation of our study was that we have not accounted for the endogeneity of mental disorders in our analysis. Omitted variables bias, measurement error, and/or simultaneity between work outcomes and mental health might lead to biased and inconsistent estimates, preventing us from making any claims about the causal impact of psychiatric disorders on labor market outcomes. In other work, we examined the causal effect of mental illness on labor market outcomes, addressing the endogeneity of mental illness using instrumental variables.34 The estimated effect of mental ill health on the outcomes was typically larger by a small margin after accounting for endogeneity. In the context of this study, therefore, we believe that our estimates, if anything, understate the true causal impact of psychiatric disorders on work outcomes.
We also emphasize that there exists substantial correlation among pairs of symptoms, which may make it difficult to tease out their independent effects on labor market outcomes. Therefore, targeting particular symptoms in isolation may not be restorative and a more holistic approach that directs treatment towards multiple symptoms would most likely be effective. The estimated factor loadings associated with different psychiatric disorders of our MIMIC model were consistent with this approach.
On the basis of the concordance analysis, the results of this paper show that significant numbers of individuals do not meet diagnostic criteria for psychiatric disorder but actually have similarly poor mental health as diagnosed individuals. This finding has 2 important implications. First, in studies estimating the labor market consequences of mental illness, coding sub-threshold individuals as “healthy” may lead to misleading estimates. Second, from a policy perspective, interventions targeting workplace consequences of mental illness may benefit not only those who meet diagnostic criteria for mental illness but also many of those with subclinical levels of symptoms. Besides the afflicted individuals, employers also would potentially stand to gain from improved work functioning of those individuals.
We thank Margarita Alegria for information regarding diagnostic algorithms, Partha Deb and also the conference participants at the New York Camp Econometrics, April 5 to 7, 2013 and Ifo/CESifo and University of Munich and MEA Conference on Empirical Health Economics, February 15 to 16, 2013 and 2013 Annual Health Econometrics Workshop, October 3 to 5, 2013 for comments and suggestions.
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mental disorders; depression; labor market; absenteeism; unemployment
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