ORIGINAL ARTICLE
Health-Related Quality of Life and Productivity Losses in Patients With Depression and Anxiety Disorders C. A. M. Bouwmans, MSc, P. Vemer, MSc, A. van Straten, PhD, S. S. Tan, PhD, and L. Hakkaart-van Roijen, PhD
Objectives: To assess the explanatory power of disease severity and healthrelated quality of life (HRQOL) on absenteeism and presenteeism in a working population suffering from depression and/or anxiety disorders. Methods: We used data of a large, multicenter, randomized trial (n = 644). Pearson chisquared tests, analysis of variance, and multinomial logistic regression analyses were performed to explore associations of the type of the disorder and HRQOL with different types of productivity losses. Multivariate regression analyses were performed to assess associations with the duration of absenteeism. Results: The type of the disorder, disease severity, and HRQOL were associated with different types of productivity losses. Health-related quality of life and age were significantly associated with the duration of absenteeism. Conclusions: Our findings indicate that HRQOL may significantly explain the type of productivity loss as well as the duration of absenteeism.
E
conomic evaluations are increasingly used to assist health decision makers in selecting which established and new medical technologies should be included in their national health care benefit package.1,2 In an economic evaluation, an intervention is compared with at least one alternative in terms of costs and (health) effects. Which costs (and effects) are to be considered depends on the perspective from which the economic evaluation is performed. If a societal perspective is adopted, all costs, including productivity losses, and health effects should be incorporated, regardless of who bears the costs and who experiences the health effects.3 Nevertheless, productivity losses are frequently neglected in economic evaluations, even when the societal perspective is taken.4 Neglecting productivity losses in economic evaluations may have several explanations.5,6 First, there may be concerns regarding the equity implications of including productivity losses. The patients receiving the intervention may differ importantly from the patients receiving the alternative in characteristics relevant for productivity losses (eg, age, severity of illness, and other prognostic factors). Second, the time and effort needed to collect the data required to calculate productivity losses may be limiting factors. Collecting the appropriate data involves asking patients a broad range of questions regarding their employment status and it is necessary to repeatedly ask patients about their absence from work (absenteeism) and inefficient work time due to disease or treatment (presenteeism). Besides, it may not always be possible to collect necessary data regarding patients’ productivity. For instance, productivity data are commonly unavailable when working with retrospective data or when data collection is restricted to information available in medical files. Third, at this moment, there is no scientific consensus on how to identify From the Institute for Medical Technology Assessment (Drs Bouwmans, Vemer, Tan, and Hakkaart-van Roijen), Erasmus University, Rotterdam; and Department of Clinical Psychology (Dr van Straten), VU University Amsterdam, the Netherlands. iMTA received an unrestricted grant from Lundbeck SAS, France for this study. For the remaining authors no conflict of interest was declared. Address correspondence to C. Bouwmans, MSc, Institute for Medical Technology Assessment, Erasmus University Rotterdam, PO Box 1738, 3000 DR Rotterdam, the Netherlands ([email protected]). C 2014 by American College of Occupational and Environmental Copyright Medicine DOI: 10.1097/JOM.0000000000000112
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measure and value productivity losses and gains, which may make researchers reluctant to collect data.5,7 Depression and anxiety disorders may have a major impact on health-related quality of life (HRQOL) and interfere with work performance in terms of absenteeism and presenteeism.8 Several studies showed the dominance of health-related absenteeism in depression and anxiety disorders.9–12 In a working population, prevalence rates of 6.1% for depression and 9.9% for anxiety disorders were found.13 A review of Krol and coauthors4 showed that on average 60% of the total costs of interventions targeted at depression were due to absenteeism. In addition, depression and anxiety disorders significantly affect presenteeism.8,14–17 Presenteeism has been estimated to account for a majority of the economic costs of productivity losses from depression.18 A review of Schultz and coauthors19 showed that the percentage of total costs of depression attributed to presenteeism ranged from 27% to 81%. A recent systematic review by Lagerveld and coauthors18 identified 30 studies exploring factors associated with absenteeism and presenteeism among depressed workers and found that the majority of studies report on the relationship with disease severity. More severe depressive symptoms were associated with more work limitations, while less clinical improvement was related to a reduction in work productivity.18 Plaisier and coauthors16 showed that anxiety disorders also have a significantly negative impact on absenteeism and presenteeism, although to a lesser extent than depression. Previous research showed that productivity losses are significantly correlated with levels of HRQOL in persons suffering from low back pain.20 These studies show that lower levels of impairment and therefore lower levels of HRQOL lead to a different level of observed productivity. Next to severity, quality of life has become an important outcome measure in mental health patients. Health-related quality of life can be seen as an indicator of severity of illness and impairment.21 Although there is overlap between the two concepts, there is also an important difference between the two variables. A severity index considers the severity of mental disorders, while a quality-of-life measure regards a broader concept, namely the influence of other diseases than mental disorders, on HRQOL. A recent study of Vemer and coauthors22 showed that HRQOL emerged as a predictor of return to work (RTW) whereas severity of depression did not. We, therefore, propose the hypothesis that HRQOL has an added explanatory value compared with other factors in predicting productivity losses. Additional information on the relationship between HRQOL and productivity losses is useful in several ways. This information could help predict whether productivity costs are likely to occur in groups of patients with mental disorders. Further understanding of the relationship between HRQOL and productivity could also help in determining what type of productivity loss should be measured (eg, absence from work and/or presenteeism at work) taking a societal perspective. Furthermore, the empirical assessment of the relation between productivity and quality of life may also prove useful in modeling productivity costs with the use of information on quality of life in absence of specific data on productivity.21,23 Thus far, no study investigated the relative importance of HRQOL compared with other factors for absenteeism and presenteeism in patients suffering from depression and anxiety disorders. Therefore, the aim of this study is to assess the added explanatory power of HRQOL compared JOEM r Volume 56, Number 4, April 2014
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with other factors on absenteeism and presenteeism in a working population suffering from depression and/or anxiety disorders.
METHODS A secondary analysis was performed on the data of a large, multicenter, randomized trial evaluating the cost and effects of alternative psychological interventions in patients suffering from depression and/or anxiety disorders.24 Patients with the following Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, diagnoses were included: major depressive disorder (single episode or recurrent), dysthymic disorder, panic disorder (with or without agoraphobia), social phobia, or generalized anxiety disorder. Comorbidity associated with other psychiatric diagnoses (except psychotic or bipolar disorder) was allowed, including personality disorders, alcohol abuse, or dependence and somatic disorders. The current assessment was based on baseline data. Productivity losses were measured using the Short-Form Health and Labour Questionnaire (SF-HLQ).25 The SF-HLQ is a self-reported questionnaire covering work status (ie, employment), type of employment (ie, full-time/part-time), disability for work, and type of productivity loss (ie, absenteeism and presenteeism). Absenteeism was divided into short-term absence (the number of days absent from work during the preceding 2 weeks) and long-term absence (more than 2 weeks absent from work). Presenteeism included the number of days at work while impeded during the preceding 2 weeks and an estimation of work efficiency during these days rated on a visual analogue scale ranging from 1 (extremely impeded) to 10 (no efficiency loss). Type of mental disorder (ie, depression and/or anxiety disorder) was determined using the Composite International Diagnostic Interview. Presence of physical comorbidity was measured using a list of the most prevalent health disorders that was derived from POLS, a permanently performed national survey on health and welfare (www.cbs.nl). In addition, socioeconomic data were collected, including age, gender, and education. Disease severity was measured with the total score of the 90-item Symptoms Check List (SCL90), which is a validated instrument measuring severity of neurotic illness.26 It consists of 90 psychological symptoms rated on a fivepoint scale, ranging from 1 (no distress) to 5 (extremely distressed by the symptom). By summing up the item scores, a total score can be obtained that ranges from 90 to 450, with 118 representing the mean score for the Dutch population. Based on SCL-90 scores, respondents were categorized into three equal groups according to severity of psychopathology. The cutoff points for mild, moderate, and severe psychopathology were respective scores of less than 187, 187 to 240, and more than 240. Health-related quality of life was measured using the EuroQol-5D (EQ-5D), which is a validated instrument for measuring general HRQOL covering five dimensions of health (ie, mobility, self-care, usual activities, pain/discomfort, and anxiety/depression). Each dimension is rated on a three-point scale with categories “no problems,” “some problems,” or “extreme problems,” resulting in a descriptive health profile. Respondents’ health profiles were valued using valuations derived from the general population in the Netherlands.27
Associations of Type of Mental Disorder, Disease Severity, and HRQOL With Productivity Losses Pearson chi-squared tests and analysis of variance were used to assess associations of type of mental disorder, disease severity, and HRQOL with type of productivity loss (ie, short-term absenteeism, long-term absenteeism, presenteeism, and “no productivity losses”). Post hoc analyses were performed to assess significant differences.
Health-Related Quality of Life and Productivity Losses
Exploring the Explanatory Power of HRQOL on Productivity Losses Multinomial logistic regression analyses were performed to explore the explanatory power of type of mental disorder, physical comorbidity, age, gender, disease severity, and HRQOL on type of productivity loss. We applied backward elimination of covariates with the highest P value to construct an optimized model. Odds ratios and 95% confidence intervals were calculated for the type of productivity loss, taking “no productivity losses” as the reference group. Although including both disease severity and HRQOL may bias the results of the regression analyses due to multicollinearity, there is also an important difference between the two variables. The SCL-90 solely considers the severity of mental disorders, whereas the EQ-5D additionally regards the influence of other diseases than mental disorders on HRQOL. It may, therefore, be beneficial to use both variables in a model. On the basis of the correlation between the two variables, we will show either a model using disease severity and HRQOL or a model with disease severity and a modified HRQOL variable that is centered on the QOL average within severity levels. This centralization would be performed as follows. After calculating the HRQOL average for each severity level, the “EQ-5D difference” (ie, the HRQOL after controlling for disease severity or, alternatively, the difference between the QOL average and the individual HRQOL) would be taken as a covariate. For example, when two patients have an individual HRQOL of 0.7 but the first patient’s severity level HRQOL average is 0.5 and second patient’s severity level HRQOL average is 0.8, the “EQ-5D difference” for the first patient is +0.2 and for the second patient –0.1. This means that, after controlling for disease severity, the first patient would be appointed a relatively high HRQOL and the second patient a relatively low HRQOL.
Duration of Long-Term Absenteeism Multivariate regression analyses were performed to assess the explanatory power of type of mental disorder, physical comorbidity, age, gender, disease severity, and HRQOL on the duration of longterm absenteeism. We tested the model both including and excluding disease severity. Descriptive statistics were used to describe the study population. All analyses were performed in SPSS for Windows, version 17.0. A two-sided significance level of 0.050 was adopted (P < 0.050).
RESULTS In total, 702 patients gave their informed consent and were included in the study. Complete data were available for 644 respondents, of which 66% (n = 425) had paid work. Table 1 presents the characteristics of respondents with paid work at baseline. Respondents were on average 35 years of age. More than half of the respondents were diagnosed with depression. Thirteen percent of the respondents reported short-term absenteeism during the preceding 2 weeks. The number of days of short-term absence was on average 4 (SD = 3). More than half of the respondents reported long-term absenteeism. Episodes of long-term absenteeism were, on average, 109 calendar days (3.6 months) ranging from 16 to 665 days. Thirty percent of the respondents reported presenteeism during the preceding 2 weeks. Among these patients, the mean number of days suffering from presenteeism was 6.2 (SD = 3), with an average productivity loss of 60% per day.
Associations of Type of Mental Disorder, Disease Severity, and HRQOL With Productivity Losses Table 2 presents the distribution of productivity losses in respondents with depression and/or anxiety disorders. Significant differences in productivity losses were found between respondents with
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different types of mental disorders (χ 2 = 31.5; P < 0.001). No productivity losses were reported by significantly more respondents with anxiety disorders and fewer respondents with both depression and anxiety disorders. In addition, long-term absenteeism was reported by significantly fewer respondents with anxiety disorders. Overall, disease severity was significantly associated with the type of productivity loss (χ 2 = 17.4; P = 0.008). No productivity losses were more frequently reported by respondents with mild psychopathology and less frequently by respondents with severe psychopathology. Analysis-of-variance (ANOVA) tests revealed significant differences between the HRQOL of respondents with productivity
TABLE 1. Characteristics of Respondents With Paid Work at Baseline (n = 425) Age, mean (SD), yr Female, % Education, % Primary Secondary High Type of mental disorder, % Depression Anxiety Depression and anxiety Physical comorbidity Disease severity, mean (SD) Mild (n = 140) Moderate (n = 139) Severe (n = 145) Health-related quality-of-life Productivity losses,* % (n) Short-term absenteeism (2 wks absent from work) Presenteeism No productivity-losses Full-time employment,† %
35.2 (9.4) 58.4 30.4 39.6 30.0
losses in comparison with that of respondents without productivity losses (P < 0.050).
Exploring the Explanatory Power of HRQOL on Productivity Losses A model including type of mental disorder, physical comorbidity, age, gender, disease severity, and HRQOL showed that the type of mental disorder and HRQOL were significantly associated with productivity losses, but disease severity was not significantly associated (P = 0.580). Health-related quality of life was significantly correlated with disease severity (rho = 0.470). Therefore, HRQOL was replaced by the “EQ-5D difference.” This model resulted in significant associations of type of mental disorder, disease severity, and “EQ-5D difference” with productivity losses. None of the other covariates were significantly associated with productivity losses. Table 3 presents the explanatory power of disease severity and HRQOL on productivity losses. Adjusted for the other variables included in the model severe psychopathology was a significant risk factor for long-term absenteeism. In addition, significant associations were found between depression and all types of productivity losses. Depression, combined with anxiety disorders, was a risk factor for long-term absenteeism. Finally, the “EQ-5D difference” was significantly associated with all types of productivity losses.
Duration of Long-Term Absenteeism
51.5 11.3 37.2 68.2
Complete information on the duration of long-term absenteeism was available for 195 respondents (on average 106 calendar days; SD = 90). The model including disease severity showed no significant associations of type of mental disorder and disease severity with the duration of long-term absenteeism. Age was significantly associated with the duration of absenteeism (P = 0.017); that is, the duration of long-term absence increased with age. The model excluding disease severity showed significant associations of HRQOL and age with duration of long-term absenteeism (P = 0.013 and 0.067, respectively).
154.8 (21.2) 211.4 (15.3) 281.2 (34.1) 0.52 (0.29) 13.1 (55) 55.5 (233) 30.5 (130) 12.4 (52) 60.1
DISCUSSION Employees suffering from depression and anxiety disorders are at risk for productivity losses. Contrary to findings from other studies, gender was significantly associated neither with productivity losses nor with the duration of long-term absenteeism.28–30 Our results indicate that HRQOL has a significant explanatory power on the type of productivity loss and the duration of longterm absenteeism. Disease severity was significantly associated with the type of productivity loss. Nevertheless, type of mental
*The total percentage is more than 100% because respondents may have reported both short-term absenteeism and presenteeism during the preceding 2 weeks. †Full-time employment was defined as at least 34 working hours per week.
TABLE 2. Distribution of Productivity Losses in Respondents With Depression and/or Anxiety Disorders
Type of mental disorder Depression (n = 214) Anxiety disorders (n = 48) Depression and anxiety disorders (n = 157) Disease severity Mild (n = 140) Moderate (n = 139) Severe (n = 145) Health-related quality of life
Short-Term Absenteeism
Long-Term Absenteeism
Presenteeism*
No Productivity Losses
% 13.0 12.5 14.0
% 55.6 29.2 63.1
% 17.6 27.1 18.5
% 13.9 31.3 4.5
15.8 12.6 11.3 0.50†
45.3 54.1 65.5 0.47†
19.4 20.7 17.6 0.57†
19.4 12.6 5.6 0.70
*Without absenteeism. †Significantly different in comparison to respondents without productivity losses.
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Health-Related Quality of Life and Productivity Losses
TABLE 3. Explanatory Power of Disease Severity and HRQOL on Productivity Losses*
Mild psychopathology (reference) Moderate psychopathology Severe psychopathology EQ-5D difference Anxiety (reference) Depression Depression and anxiety
Short-Term Absenteeism
Long-Term Absenteeism
Presenteeism /No Absence
OR (95% CI)
P
OR (95% CI)
P
OR (95% CI)
P
1.12 (0.45–2.81) 2.20 (0.72–6.72) 0.04 (0.01- 0.28)
0.808 0.168 0.001
1.78 (0.85–3.73) 4.44 (1.72–11.47) 0.05 (0.01–0.28)
0.129 0.002 0.001
1.54 (0.67–3.54) 2.79 (0.99–7.91) 0.14 (0.02–0.87)
0.307 0.053 0.035
6.19 (1.65–23.20) 2.05 (0.68–6.17)
0.007 0.203
9.83 (3.26–29.66) 3.87 (1.62–9.23)
0.000 0.002
3.61 (1.15–11.35) 1.36 (0.55–3.34)
0.028 0.503
*Reference category is “no productivity-losses.”
disorder and disease severity could not significantly explain the duration of long-term absenteeism. The latter may suggest that other factors may play an important role, for example, work-related factors and financial incentives. In addition, the longer the absence more barriers may exist for RTW. Thus, more research is recommended for identifying factors that contribute to RTW. In economic evaluations of health care programs, both HRQOL and productivity losses of patients are aspects to be studied. Previous research has shown that productivity losses are significantly correlated with levels of HRQOL.20 In addition, treatments for depression are associated with significant improvement of HRQOL.31 Health-related quality of life can be seen as an indication of disease severity. We therefore applied the “EQ-5D difference,” assessing the exploratory value of HRQOL next to disease severity. A limited number of European studies report figures on the duration of long-term absenteeism ranging from 73 to 213 days.28,32 Several studies report increased risk of long-term absenteeism and disability for work in mentally ill patients.33,34 Our finding of the increased risk of long-term absenteeism in respondents with severe depression was in line with the study of B¨ultmann and coauthors.11 Nevertheless, the comparison of our results with these studies is hampered because of methodological differences, the application of different diagnostic criteria, and differences between the patient populations included. In addition, differences of social insurance systems between countries complicate a comparison. Presenteeism was generally ignored in these studies. The results of the regression analyses must be interpreted cautiously as only 3.4% of the variation in the duration of long-term absenteeism was explained. In addition, the duration of absenteeism varied considerably. More research is recommended to assess other relevant variables for explaining the variation in the duration of sick leave. Although education may be associated with productivity losses, information was not available for almost one-third of the respondents. Several domains may influence absenteeism, that is, individual characteristics, work environment, socioeconomic aspects, the health care system, and sociopolitical aspects. Further research on the impact of all these domains on productivity losses is needed. Measuring presenteeism is complex.35 There is no consensus on the most valid self-report measure to use.36,37 In the current study, presenteeism was measured by multiplying the number of days one went to work while suffering from health problems and the efficiency on these days (module 2 of the SF-HLQ). A difficulty with this method is that efficiency indication may not necessarily be interpreted as the amount of work respondent did compared with normal, but rather what they could do compared with normal, although it is not clear how subtle this difference is. Furthermore, this method ignores the possibility of making up for lost work during normal hours and may yield an overestimation in terms of time and costs.38
A growing body of literature is emerging demonstrating that early detection of depression and adequate treatment are associated with decreased productivity losses.12,39,40 Consequently, costeffective treatments could reduce productivity losses due to absenteeism and presenteeism and increase HRQOL in patients with depression and anxiety disorders. The study has a cross-sectional design using baseline data that were derived from a clinical study. All respondents included were scheduled for treatment. Consequently, employees with less severe disorders may have been excluded from the study. Furthermore, data on productivity losses were derived from self-reported data. Generally, the number of self-reported days of absenteeism is higher than that of absenteeism in registration data. Nevertheless, registration data may be less valid than assumed, especially for shorter episodes of sick leave.41 The quality and consistency of registration data may vary depending on the business type and scale. Since all individuals have some level of mental disorder, this study cannot measure the impact of depression and/or anxiety disorders on productivity losses. The study did not include a reference population for comparing productivity losses with the general working population. This study provides descriptive statistics of the productivity losses among workers with anxiety and depression. This study provides comprehensive insights in productivity losses in workers with anxiety and depression. We have examined the predictive power of HRQOL and severity next to general demographic characteristics, age and gender, on absenteeism and presenteeism in a working population suffering from depression and/or anxiety. This study showed that severity and HRQOL were associated with different types of productivity loss, for example, presenteeism, short-term absence from work, and long-term absence from work in depressed and anxiety patients. In line with a recent study of Vemer and coauthors,22 we also found that next to age, HRQOL was significantly associated with the duration of absenteeism. Herewith, this study supports the predictive added value of HRQOL next to severity for productivity losses in this patient group. This is especially of interest as productivity losses are likely to occur in mental health patients. Since HRQOL is becoming a common outcome measure in studies on mental illness, this information can be used to indicate the type of absence from work in mental patients, respectively presenteeism, absence from work, or long-term absence, in designing a cost-effectiveness study, Furthermore, the results of this study could help facilitate the inclusion of productivity losses in economic evaluations taking a societal perspective. It may not always be possible to collect necessary data regarding patients’ productivity. For instance, when working with retrospective data, or when data collection is bounded to information available in medical files, productivity data are commonly unavailable. Under these circumstances it might be helpful to predict productivity costs based on patient characteristics
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such as age, sex, clinical data, and HRQOL. Replication studies are needed to assess the generalizability of the results to other populations of mental health patients.
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Health-Related Quality of Life and Productivity Losses in Patients With Depression and Anxiety Disorders C. A. M. Bouwmans, MSc, P. Vemer, MSc, A. van Straten, PhD, S. S. Tan, PhD, and L. Hakkaart-van Roijen, PhD
Objectives: To assess the explanatory power of disease severity and healthrelated quality of life (HRQOL) on absenteeism and presenteeism in a working population suffering from depression and/or anxiety disorders. Methods: We used data of a large, multicenter, randomized trial (n = 644). Pearson chisquared tests, analysis of variance, and multinomial logistic regression analyses were performed to explore associations of the type of the disorder and HRQOL with different types of productivity losses. Multivariate regression analyses were performed to assess associations with the duration of absenteeism. Results: The type of the disorder, disease severity, and HRQOL were associated with different types of productivity losses. Health-related quality of life and age were significantly associated with the duration of absenteeism. Conclusions: Our findings indicate that HRQOL may significantly explain the type of productivity loss as well as the duration of absenteeism.
E
conomic evaluations are increasingly used to assist health decision makers in selecting which established and new medical technologies should be included in their national health care benefit package.1,2 In an economic evaluation, an intervention is compared with at least one alternative in terms of costs and (health) effects. Which costs (and effects) are to be considered depends on the perspective from which the economic evaluation is performed. If a societal perspective is adopted, all costs, including productivity losses, and health effects should be incorporated, regardless of who bears the costs and who experiences the health effects.3 Nevertheless, productivity losses are frequently neglected in economic evaluations, even when the societal perspective is taken.4 Neglecting productivity losses in economic evaluations may have several explanations.5,6 First, there may be concerns regarding the equity implications of including productivity losses. The patients receiving the intervention may differ importantly from the patients receiving the alternative in characteristics relevant for productivity losses (eg, age, severity of illness, and other prognostic factors). Second, the time and effort needed to collect the data required to calculate productivity losses may be limiting factors. Collecting the appropriate data involves asking patients a broad range of questions regarding their employment status and it is necessary to repeatedly ask patients about their absence from work (absenteeism) and inefficient work time due to disease or treatment (presenteeism). Besides, it may not always be possible to collect necessary data regarding patients’ productivity. For instance, productivity data are commonly unavailable when working with retrospective data or when data collection is restricted to information available in medical files. Third, at this moment, there is no scientific consensus on how to identify From the Institute for Medical Technology Assessment (Drs Bouwmans, Vemer, Tan, and Hakkaart-van Roijen), Erasmus University, Rotterdam; and Department of Clinical Psychology (Dr van Straten), VU University Amsterdam, the Netherlands. iMTA received an unrestricted grant from Lundbeck SAS, France for this study. For the remaining authors no conflict of interest was declared. Address correspondence to C. Bouwmans, MSc, Institute for Medical Technology Assessment, Erasmus University Rotterdam, PO Box 1738, 3000 DR Rotterdam, the Netherlands ([email protected]). C 2014 by American College of Occupational and Environmental Copyright Medicine DOI: 10.1097/JOM.0000000000000112
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measure and value productivity losses and gains, which may make researchers reluctant to collect data.5,7 Depression and anxiety disorders may have a major impact on health-related quality of life (HRQOL) and interfere with work performance in terms of absenteeism and presenteeism.8 Several studies showed the dominance of health-related absenteeism in depression and anxiety disorders.9–12 In a working population, prevalence rates of 6.1% for depression and 9.9% for anxiety disorders were found.13 A review of Krol and coauthors4 showed that on average 60% of the total costs of interventions targeted at depression were due to absenteeism. In addition, depression and anxiety disorders significantly affect presenteeism.8,14–17 Presenteeism has been estimated to account for a majority of the economic costs of productivity losses from depression.18 A review of Schultz and coauthors19 showed that the percentage of total costs of depression attributed to presenteeism ranged from 27% to 81%. A recent systematic review by Lagerveld and coauthors18 identified 30 studies exploring factors associated with absenteeism and presenteeism among depressed workers and found that the majority of studies report on the relationship with disease severity. More severe depressive symptoms were associated with more work limitations, while less clinical improvement was related to a reduction in work productivity.18 Plaisier and coauthors16 showed that anxiety disorders also have a significantly negative impact on absenteeism and presenteeism, although to a lesser extent than depression. Previous research showed that productivity losses are significantly correlated with levels of HRQOL in persons suffering from low back pain.20 These studies show that lower levels of impairment and therefore lower levels of HRQOL lead to a different level of observed productivity. Next to severity, quality of life has become an important outcome measure in mental health patients. Health-related quality of life can be seen as an indicator of severity of illness and impairment.21 Although there is overlap between the two concepts, there is also an important difference between the two variables. A severity index considers the severity of mental disorders, while a quality-of-life measure regards a broader concept, namely the influence of other diseases than mental disorders, on HRQOL. A recent study of Vemer and coauthors22 showed that HRQOL emerged as a predictor of return to work (RTW) whereas severity of depression did not. We, therefore, propose the hypothesis that HRQOL has an added explanatory value compared with other factors in predicting productivity losses. Additional information on the relationship between HRQOL and productivity losses is useful in several ways. This information could help predict whether productivity costs are likely to occur in groups of patients with mental disorders. Further understanding of the relationship between HRQOL and productivity could also help in determining what type of productivity loss should be measured (eg, absence from work and/or presenteeism at work) taking a societal perspective. Furthermore, the empirical assessment of the relation between productivity and quality of life may also prove useful in modeling productivity costs with the use of information on quality of life in absence of specific data on productivity.21,23 Thus far, no study investigated the relative importance of HRQOL compared with other factors for absenteeism and presenteeism in patients suffering from depression and anxiety disorders. Therefore, the aim of this study is to assess the added explanatory power of HRQOL compared JOEM r Volume 56, Number 4, April 2014
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with other factors on absenteeism and presenteeism in a working population suffering from depression and/or anxiety disorders.
METHODS A secondary analysis was performed on the data of a large, multicenter, randomized trial evaluating the cost and effects of alternative psychological interventions in patients suffering from depression and/or anxiety disorders.24 Patients with the following Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, diagnoses were included: major depressive disorder (single episode or recurrent), dysthymic disorder, panic disorder (with or without agoraphobia), social phobia, or generalized anxiety disorder. Comorbidity associated with other psychiatric diagnoses (except psychotic or bipolar disorder) was allowed, including personality disorders, alcohol abuse, or dependence and somatic disorders. The current assessment was based on baseline data. Productivity losses were measured using the Short-Form Health and Labour Questionnaire (SF-HLQ).25 The SF-HLQ is a self-reported questionnaire covering work status (ie, employment), type of employment (ie, full-time/part-time), disability for work, and type of productivity loss (ie, absenteeism and presenteeism). Absenteeism was divided into short-term absence (the number of days absent from work during the preceding 2 weeks) and long-term absence (more than 2 weeks absent from work). Presenteeism included the number of days at work while impeded during the preceding 2 weeks and an estimation of work efficiency during these days rated on a visual analogue scale ranging from 1 (extremely impeded) to 10 (no efficiency loss). Type of mental disorder (ie, depression and/or anxiety disorder) was determined using the Composite International Diagnostic Interview. Presence of physical comorbidity was measured using a list of the most prevalent health disorders that was derived from POLS, a permanently performed national survey on health and welfare (www.cbs.nl). In addition, socioeconomic data were collected, including age, gender, and education. Disease severity was measured with the total score of the 90-item Symptoms Check List (SCL90), which is a validated instrument measuring severity of neurotic illness.26 It consists of 90 psychological symptoms rated on a fivepoint scale, ranging from 1 (no distress) to 5 (extremely distressed by the symptom). By summing up the item scores, a total score can be obtained that ranges from 90 to 450, with 118 representing the mean score for the Dutch population. Based on SCL-90 scores, respondents were categorized into three equal groups according to severity of psychopathology. The cutoff points for mild, moderate, and severe psychopathology were respective scores of less than 187, 187 to 240, and more than 240. Health-related quality of life was measured using the EuroQol-5D (EQ-5D), which is a validated instrument for measuring general HRQOL covering five dimensions of health (ie, mobility, self-care, usual activities, pain/discomfort, and anxiety/depression). Each dimension is rated on a three-point scale with categories “no problems,” “some problems,” or “extreme problems,” resulting in a descriptive health profile. Respondents’ health profiles were valued using valuations derived from the general population in the Netherlands.27
Associations of Type of Mental Disorder, Disease Severity, and HRQOL With Productivity Losses Pearson chi-squared tests and analysis of variance were used to assess associations of type of mental disorder, disease severity, and HRQOL with type of productivity loss (ie, short-term absenteeism, long-term absenteeism, presenteeism, and “no productivity losses”). Post hoc analyses were performed to assess significant differences.
Health-Related Quality of Life and Productivity Losses
Exploring the Explanatory Power of HRQOL on Productivity Losses Multinomial logistic regression analyses were performed to explore the explanatory power of type of mental disorder, physical comorbidity, age, gender, disease severity, and HRQOL on type of productivity loss. We applied backward elimination of covariates with the highest P value to construct an optimized model. Odds ratios and 95% confidence intervals were calculated for the type of productivity loss, taking “no productivity losses” as the reference group. Although including both disease severity and HRQOL may bias the results of the regression analyses due to multicollinearity, there is also an important difference between the two variables. The SCL-90 solely considers the severity of mental disorders, whereas the EQ-5D additionally regards the influence of other diseases than mental disorders on HRQOL. It may, therefore, be beneficial to use both variables in a model. On the basis of the correlation between the two variables, we will show either a model using disease severity and HRQOL or a model with disease severity and a modified HRQOL variable that is centered on the QOL average within severity levels. This centralization would be performed as follows. After calculating the HRQOL average for each severity level, the “EQ-5D difference” (ie, the HRQOL after controlling for disease severity or, alternatively, the difference between the QOL average and the individual HRQOL) would be taken as a covariate. For example, when two patients have an individual HRQOL of 0.7 but the first patient’s severity level HRQOL average is 0.5 and second patient’s severity level HRQOL average is 0.8, the “EQ-5D difference” for the first patient is +0.2 and for the second patient –0.1. This means that, after controlling for disease severity, the first patient would be appointed a relatively high HRQOL and the second patient a relatively low HRQOL.
Duration of Long-Term Absenteeism Multivariate regression analyses were performed to assess the explanatory power of type of mental disorder, physical comorbidity, age, gender, disease severity, and HRQOL on the duration of longterm absenteeism. We tested the model both including and excluding disease severity. Descriptive statistics were used to describe the study population. All analyses were performed in SPSS for Windows, version 17.0. A two-sided significance level of 0.050 was adopted (P < 0.050).
RESULTS In total, 702 patients gave their informed consent and were included in the study. Complete data were available for 644 respondents, of which 66% (n = 425) had paid work. Table 1 presents the characteristics of respondents with paid work at baseline. Respondents were on average 35 years of age. More than half of the respondents were diagnosed with depression. Thirteen percent of the respondents reported short-term absenteeism during the preceding 2 weeks. The number of days of short-term absence was on average 4 (SD = 3). More than half of the respondents reported long-term absenteeism. Episodes of long-term absenteeism were, on average, 109 calendar days (3.6 months) ranging from 16 to 665 days. Thirty percent of the respondents reported presenteeism during the preceding 2 weeks. Among these patients, the mean number of days suffering from presenteeism was 6.2 (SD = 3), with an average productivity loss of 60% per day.
Associations of Type of Mental Disorder, Disease Severity, and HRQOL With Productivity Losses Table 2 presents the distribution of productivity losses in respondents with depression and/or anxiety disorders. Significant differences in productivity losses were found between respondents with
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different types of mental disorders (χ 2 = 31.5; P < 0.001). No productivity losses were reported by significantly more respondents with anxiety disorders and fewer respondents with both depression and anxiety disorders. In addition, long-term absenteeism was reported by significantly fewer respondents with anxiety disorders. Overall, disease severity was significantly associated with the type of productivity loss (χ 2 = 17.4; P = 0.008). No productivity losses were more frequently reported by respondents with mild psychopathology and less frequently by respondents with severe psychopathology. Analysis-of-variance (ANOVA) tests revealed significant differences between the HRQOL of respondents with productivity
TABLE 1. Characteristics of Respondents With Paid Work at Baseline (n = 425) Age, mean (SD), yr Female, % Education, % Primary Secondary High Type of mental disorder, % Depression Anxiety Depression and anxiety Physical comorbidity Disease severity, mean (SD) Mild (n = 140) Moderate (n = 139) Severe (n = 145) Health-related quality-of-life Productivity losses,* % (n) Short-term absenteeism (2 wks absent from work) Presenteeism No productivity-losses Full-time employment,† %
35.2 (9.4) 58.4 30.4 39.6 30.0
losses in comparison with that of respondents without productivity losses (P < 0.050).
Exploring the Explanatory Power of HRQOL on Productivity Losses A model including type of mental disorder, physical comorbidity, age, gender, disease severity, and HRQOL showed that the type of mental disorder and HRQOL were significantly associated with productivity losses, but disease severity was not significantly associated (P = 0.580). Health-related quality of life was significantly correlated with disease severity (rho = 0.470). Therefore, HRQOL was replaced by the “EQ-5D difference.” This model resulted in significant associations of type of mental disorder, disease severity, and “EQ-5D difference” with productivity losses. None of the other covariates were significantly associated with productivity losses. Table 3 presents the explanatory power of disease severity and HRQOL on productivity losses. Adjusted for the other variables included in the model severe psychopathology was a significant risk factor for long-term absenteeism. In addition, significant associations were found between depression and all types of productivity losses. Depression, combined with anxiety disorders, was a risk factor for long-term absenteeism. Finally, the “EQ-5D difference” was significantly associated with all types of productivity losses.
Duration of Long-Term Absenteeism
51.5 11.3 37.2 68.2
Complete information on the duration of long-term absenteeism was available for 195 respondents (on average 106 calendar days; SD = 90). The model including disease severity showed no significant associations of type of mental disorder and disease severity with the duration of long-term absenteeism. Age was significantly associated with the duration of absenteeism (P = 0.017); that is, the duration of long-term absence increased with age. The model excluding disease severity showed significant associations of HRQOL and age with duration of long-term absenteeism (P = 0.013 and 0.067, respectively).
154.8 (21.2) 211.4 (15.3) 281.2 (34.1) 0.52 (0.29) 13.1 (55) 55.5 (233) 30.5 (130) 12.4 (52) 60.1
DISCUSSION Employees suffering from depression and anxiety disorders are at risk for productivity losses. Contrary to findings from other studies, gender was significantly associated neither with productivity losses nor with the duration of long-term absenteeism.28–30 Our results indicate that HRQOL has a significant explanatory power on the type of productivity loss and the duration of longterm absenteeism. Disease severity was significantly associated with the type of productivity loss. Nevertheless, type of mental
*The total percentage is more than 100% because respondents may have reported both short-term absenteeism and presenteeism during the preceding 2 weeks. †Full-time employment was defined as at least 34 working hours per week.
TABLE 2. Distribution of Productivity Losses in Respondents With Depression and/or Anxiety Disorders
Type of mental disorder Depression (n = 214) Anxiety disorders (n = 48) Depression and anxiety disorders (n = 157) Disease severity Mild (n = 140) Moderate (n = 139) Severe (n = 145) Health-related quality of life
Short-Term Absenteeism
Long-Term Absenteeism
Presenteeism*
No Productivity Losses
% 13.0 12.5 14.0
% 55.6 29.2 63.1
% 17.6 27.1 18.5
% 13.9 31.3 4.5
15.8 12.6 11.3 0.50†
45.3 54.1 65.5 0.47†
19.4 20.7 17.6 0.57†
19.4 12.6 5.6 0.70
*Without absenteeism. †Significantly different in comparison to respondents without productivity losses.
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Health-Related Quality of Life and Productivity Losses
TABLE 3. Explanatory Power of Disease Severity and HRQOL on Productivity Losses*
Mild psychopathology (reference) Moderate psychopathology Severe psychopathology EQ-5D difference Anxiety (reference) Depression Depression and anxiety
Short-Term Absenteeism
Long-Term Absenteeism
Presenteeism /No Absence
OR (95% CI)
P
OR (95% CI)
P
OR (95% CI)
P
1.12 (0.45–2.81) 2.20 (0.72–6.72) 0.04 (0.01- 0.28)
0.808 0.168 0.001
1.78 (0.85–3.73) 4.44 (1.72–11.47) 0.05 (0.01–0.28)
0.129 0.002 0.001
1.54 (0.67–3.54) 2.79 (0.99–7.91) 0.14 (0.02–0.87)
0.307 0.053 0.035
6.19 (1.65–23.20) 2.05 (0.68–6.17)
0.007 0.203
9.83 (3.26–29.66) 3.87 (1.62–9.23)
0.000 0.002
3.61 (1.15–11.35) 1.36 (0.55–3.34)
0.028 0.503
*Reference category is “no productivity-losses.”
disorder and disease severity could not significantly explain the duration of long-term absenteeism. The latter may suggest that other factors may play an important role, for example, work-related factors and financial incentives. In addition, the longer the absence more barriers may exist for RTW. Thus, more research is recommended for identifying factors that contribute to RTW. In economic evaluations of health care programs, both HRQOL and productivity losses of patients are aspects to be studied. Previous research has shown that productivity losses are significantly correlated with levels of HRQOL.20 In addition, treatments for depression are associated with significant improvement of HRQOL.31 Health-related quality of life can be seen as an indication of disease severity. We therefore applied the “EQ-5D difference,” assessing the exploratory value of HRQOL next to disease severity. A limited number of European studies report figures on the duration of long-term absenteeism ranging from 73 to 213 days.28,32 Several studies report increased risk of long-term absenteeism and disability for work in mentally ill patients.33,34 Our finding of the increased risk of long-term absenteeism in respondents with severe depression was in line with the study of B¨ultmann and coauthors.11 Nevertheless, the comparison of our results with these studies is hampered because of methodological differences, the application of different diagnostic criteria, and differences between the patient populations included. In addition, differences of social insurance systems between countries complicate a comparison. Presenteeism was generally ignored in these studies. The results of the regression analyses must be interpreted cautiously as only 3.4% of the variation in the duration of long-term absenteeism was explained. In addition, the duration of absenteeism varied considerably. More research is recommended to assess other relevant variables for explaining the variation in the duration of sick leave. Although education may be associated with productivity losses, information was not available for almost one-third of the respondents. Several domains may influence absenteeism, that is, individual characteristics, work environment, socioeconomic aspects, the health care system, and sociopolitical aspects. Further research on the impact of all these domains on productivity losses is needed. Measuring presenteeism is complex.35 There is no consensus on the most valid self-report measure to use.36,37 In the current study, presenteeism was measured by multiplying the number of days one went to work while suffering from health problems and the efficiency on these days (module 2 of the SF-HLQ). A difficulty with this method is that efficiency indication may not necessarily be interpreted as the amount of work respondent did compared with normal, but rather what they could do compared with normal, although it is not clear how subtle this difference is. Furthermore, this method ignores the possibility of making up for lost work during normal hours and may yield an overestimation in terms of time and costs.38
A growing body of literature is emerging demonstrating that early detection of depression and adequate treatment are associated with decreased productivity losses.12,39,40 Consequently, costeffective treatments could reduce productivity losses due to absenteeism and presenteeism and increase HRQOL in patients with depression and anxiety disorders. The study has a cross-sectional design using baseline data that were derived from a clinical study. All respondents included were scheduled for treatment. Consequently, employees with less severe disorders may have been excluded from the study. Furthermore, data on productivity losses were derived from self-reported data. Generally, the number of self-reported days of absenteeism is higher than that of absenteeism in registration data. Nevertheless, registration data may be less valid than assumed, especially for shorter episodes of sick leave.41 The quality and consistency of registration data may vary depending on the business type and scale. Since all individuals have some level of mental disorder, this study cannot measure the impact of depression and/or anxiety disorders on productivity losses. The study did not include a reference population for comparing productivity losses with the general working population. This study provides descriptive statistics of the productivity losses among workers with anxiety and depression. This study provides comprehensive insights in productivity losses in workers with anxiety and depression. We have examined the predictive power of HRQOL and severity next to general demographic characteristics, age and gender, on absenteeism and presenteeism in a working population suffering from depression and/or anxiety. This study showed that severity and HRQOL were associated with different types of productivity loss, for example, presenteeism, short-term absence from work, and long-term absence from work in depressed and anxiety patients. In line with a recent study of Vemer and coauthors,22 we also found that next to age, HRQOL was significantly associated with the duration of absenteeism. Herewith, this study supports the predictive added value of HRQOL next to severity for productivity losses in this patient group. This is especially of interest as productivity losses are likely to occur in mental health patients. Since HRQOL is becoming a common outcome measure in studies on mental illness, this information can be used to indicate the type of absence from work in mental patients, respectively presenteeism, absence from work, or long-term absence, in designing a cost-effectiveness study, Furthermore, the results of this study could help facilitate the inclusion of productivity losses in economic evaluations taking a societal perspective. It may not always be possible to collect necessary data regarding patients’ productivity. For instance, when working with retrospective data, or when data collection is bounded to information available in medical files, productivity data are commonly unavailable. Under these circumstances it might be helpful to predict productivity costs based on patient characteristics
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such as age, sex, clinical data, and HRQOL. Replication studies are needed to assess the generalizability of the results to other populations of mental health patients.
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