Journal of Affective Disorders 165 (2014) 87–94

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Research report

The costs of social anxiety disorder: The role of symptom severity and comorbidities Nina Stuhldreher a,n, Eric Leibing b, Falk Leichsenring c, Manfred E. Beutel d, Stephan Herpertz e, Juergen Hoyer f, Alexander Konnopka a, Simone Salzer b, Bernhard Strauss g, Joerg Wiltink d, Hans-Helmut König a a Department of Health Economics and Health Services Research, Hamburg Center for Health Economics (HCHE), University Medical Center HamburgEppendorf, Germany b Department of Psychosomatic Medicine and Psychotherapy, University Medicine, Georg-August-University Goettingen, Germany c Clinic of Psychosomatics and Psychotherapy, Justus-Liebig-University Giessen, Germany d Department of Psychosomatic Medicine and Psychotherapy, University Medical Center of the Johannes Gutenberg-University Mainz, Germany e Department of Psychosomatic Medicine and Psychotherapy, LWL-University Clinic Bochum, Ruhr-University Bochum, Germany f Clinical Psychology and Psychotherapy and Clinic for Psychotherapy and Psychosomatic Medicine, Technische Universitaet Dresden, Germany g Institute of Psychosocial Medicine and Psychotherapy, University Hospital, Jena, Germany

art ic l e i nf o

a b s t r a c t

Article history: Received 28 October 2013 Received in revised form 8 April 2014 Accepted 16 April 2014 Available online 24 April 2014

Background: Social anxiety disorder (SAD) is associated with low direct costs compared to other anxiety disorders while indirect costs tend to be high. Mental comorbidities have been identified to increase costs, but the role of symptom severity is still vague. The objective of this study was to determine the costs of SAD, and to explore the impact of symptoms and comorbidities on direct and indirect costs. Methods: Baseline data, collected within the SOPHO-NET multi-centre treatment study (N ¼495), were used. Costs were calculated based on health care utilization and lost productivity. Symptom severity was measured with the Liebowitz-Social-Anxiety-Scale; comorbidities were included as covariates. Results: Total 6-month costs were accrued to €4802; 23% being direct costs. While there was no significant association with SAD symptom severity for direct costs, costs of absenteeism increased with symptom severity in those with costs 40; comorbid affective disorders and eating disorders had an additional effect. Self-rated productivity was lower with more pronounced symptoms even after controlling for comorbidities. Limitations: As the study was based on a clinical sample total costs were considered, rather than net costs of SAD and no population costs could be calculated. Discussion: The burden associated with lost productivity was considerable while costs of healthcare utilization were rather low as most patients had not sought for treatment before. Efforts to identify patients with SAD earlier and to provide adequate treatment should be further increased. Mental comorbidities should be addressed as well, since they account for a large part of indirect costs associated with SAD. & 2014 Elsevier B.V. All rights reserved.

Keywords: Social anxiety disorder Cost Mental comorbidity Economic

1. Introduction Social anxiety disorder (SAD) is one of the most prevalent mental disorders imposing a great individual burden on the affected patient (Stein and Stein, 2008; Kessler et al., 2012). It is characterized by fear and avoidance of social situations and often lead to social impairment with severe consequences for the patients' education and occupation (Lipsitz and Schneier, 2000; n Correspondence to: Department of Health Economics and Health Services Research, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany. Tel.: þ49 40 7410 54180; fax: þ 49 40 7410 54934. E-mail address: [email protected] (N. Stuhldreher).

http://dx.doi.org/10.1016/j.jad.2014.04.039 0165-0327/& 2014 Elsevier B.V. All rights reserved.

Tolman et al., 2009; Steinert et al., 2013). In addition, the majority of patients suffer from comorbid mental disorders, in particular depression, other anxiety disorders, substance abuse, and avoidant personality disorder (Lipsitz and Schneier, 2000; Fehm et al., 2005). Some of these comorbidities are assumed to develop as consequence of SAD (Fehm et al., 2005), and can aggravate the long-term course (Steinert et al., 2013). Moreover, it has been observed that SAD patients often seem to be unaware of their condition and do not seek treatment unless they need medical help for any of the comorbidities (Lipsitz and Schneier, 2000; Fehm et al., 2005). Besides the individual burden, SAD is also associated with an economic burden for the society in terms of direct and indirect

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costs: direct costs encompass health care costs resulting from hospitalizations, outpatient treatment and pharmaceuticals and from non-medical costs including travel expenses, formal and informal care and utilization of social services. Indirect costs monetarily value lost productivity due to sick leave, presenteeism, i.e. reduced productivity at the workplace, and disability pension. The literature regarding the economic burden of SAD is limited but corresponds to the findings from epidemiological studies. Compared to other anxiety disorders the direct costs of SAD seem to be rather low while the indirect costs are elevated likewise (Patel et al., 2002; Smit et al., 2006; Acarturk et al., 2009; Konnopka et al., 2009; Olesen et al., 2012). However, for decision-makers as well as clinicians it is not only interesting to know the cost-of-illness but also which modifiable factors drive these costs in order to be able to develop effective interventions and to adapt the service provision according to the patients' needs. To our knowledge the determinants of health care utilization and direct costs in SAD have not been researched comprehensively: three studies analyzed the impact of comorbidities and identified different conditions to explain the largest part of direct costs and to even produce negative cost estimates for SAD (Patel et al., 2002; Smit et al., 2006; Acarturk et al., 2009). One of these studies, moreover, found that direct costs increased with the number of fears and also found that they were associated with specific fears such as speaking in front of a small group (Acarturk et al., 2009). The evidence focussing on determinants of productivity loss in SAD is more comprehensive but heterogeneous regarding the studied populations, applied instruments and outcomes. Comorbid conditions were also found to play a role regarding indirect costs due to absenteeism (Patel et al., 2002; Smit et al., 2006; Acarturk et al., 2009). In addition, more severe symptoms were associated with decreased probability of college graduation and reduced wages (Lipsitz and Schneier, 2000), and the specific fear of using public toilettes yielded higher indirect costs due to absenteeism (Acarturk et al., 2009). Moreover, factors predicting sick leave, return to work after sickness absence, and the development of long-term disability in people with mental disorders in general have been summarized in three recent reviews (Blank et al., 2008; Cornelius et al., 2011; Henderson et al., 2011). On the one hand, depression, anxiety, and the severity of symptoms were determined as specific medical conditions that impact on productivity. On the other hand, different work-related factors, health risk behaviors, social and demographic factors could be identified but their respective effects were not always consistent. However, although there is some evidence from epidemiologic studies and clinical knowledge that severity of symptoms and avoidance behavior probably affect health care utilization and productivity this has not been analyzed and quantified systematically, in particular not in combination with known predictors and comorbidities. Therefore, the objective of our study was to estimate the 6-month cost-of-illness in SAD patients and to identify determinants of direct and indirect costs. We hypothesized direct costs to be lower and indirect costs to be higher with increased symptom severity. As a secondary analysis, we investigated the particular impact of avoidance behaviors.

main study of the SOPHO-NET was a large multi-centre randomized controlled trial evaluating the efficacy of manualized cognitivebehavioural psychotherapy and psychodynamic psychotherapy compared to waiting list. The results of the efficacy trial have been published elsewhere (Leichsenring et al., 2013). For this analysis we used baseline data collected prior to the intervention. Participants were recruited between April 2007 and April 2009 by the outpatient clinics of the universities of Bochum, Dresden, Goettingen, Jena, and Mainz using mass media. Patients then directly contacted the outpatient clinics or were referred to them by psychotherapists or physicians in private practice. Included were adults from 18 to 70 years with a current diagnosis of SAD according to the Structured Clinical Interview (SCID I, II) for DSM-IV (Wittchen et al., 1997) and Liebowitz Social Anxiety Scale (LSAS) Z30 (Mennin et al., 2002) as well as a primary diagnosis of social anxiety disorder according to the rating of the AnxietyDisorders-Interview-Schedule scale (Brown et al., 1994) and an informed consent. In order to derive a clinically representative sample, all co-morbid mental disorders less severe than social anxiety disorder (according to the Anxiety-Disorders-InterviewSchedule scale rating) were permitted except for the conditions listed among the following exclusion criteria: psychotic and acute substance-related disorders; clusters A and B personality disorders; prominent risk of self-harm; organic mental disorders; severe medical conditions; concurrent psychotherapeutic or psychopharmacological treatments. The final sample consisted of N ¼495 participants. The study protocol was approved by the Ethics Committee of the Medical Faculty of the University of Goettingen. 2.2. Measures SAD symptoms were rated by a clinician on the LSAS, which consist of four subscales measuring fear of social interactions, fear of performance, avoidance of social interaction and avoidance of performance (Mennin et al., 2002). Mental and somatic comorbidities were diagnosed with the SCID I, II (Wittchen et al., 1997) by independent raters. The severity of co-morbid mental disorders was each rated on a scale from 0 to 8 with 0 indicating “not present”. Diagnoses of somatic comorbidities were used to derive a combined comorbidity score as suggested by Gagne et al. (2011). Moreover, sociodemographic characteristics as well as information on previous outpatient and inpatient treatment for SAD were collected. Data on resource use were collected 6 months retrospectively. The questionnaire was developed at our institution and has been applied in previous studies (Heinrich et al., 2008; Luppa et al., 2008a, 2008b; Leicht et al., 2011). Frequency, type, duration of, and reason for health care utilization were recorded, including inpatient care, treatment at day clinics and rehabilitation, outpatient treatment by physicians and other therapists, pharmaceuticals as well as formal nursing care and informal care. Moreover, costs of transportation were collected. Productivity losses were measured using the number of sick leave days and hours with health service use. Presenteeism was assessed for the remaining work days by means of a visual analog scale from 0, indicating ‘completely impaired’ to 10 ‘no reduction in productivity’. For participants who received disability pension days of lost work were calculated.

2. Methods 2.3. Unit costs 2.1. Study design and participants This study is part of the Social Phobia Psychotherapy Network (SOPHO-NET) which was established to comprehensively investigate the assessment and treatment of SAD (Leichsenring et al., 2009). The

Healthcare utilization was valued according to unit costs reflecting average prices within the German healthcare system (Verband der Ersatzkassen, 2001, 2002; Krauth et al., 2005; Rote Liste Service GmbH, 2008; Statistisches Bundesamt, 2009c, 2009d, 2010a).

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The calculation of costs per diem of inpatient treatment at medical wards was based on the annual DRG statistic (Bundesverband, 2008; InEK, 2008a, 2008b). We used the information on total costs per major diagnostic category and the total number of hospital days in each category to calculate the average costs per major diagnostic category and diem. For inpatient treatment at psychiatric or psychosomatic wards specific unit costs were employed (Krankenhausgesellschaft, 2009; Statistisches Bundesamt, 2009a, 2009b). Treatment at day clinics was assumed to incur two-thirds of the respective costs per diem of inpatient treatment. The usage of public transport was valued using the actual reported costs and travel distances by car were valued with €0.30/km. The calculation of indirect costs was based on the human capital method including all productivity loss that occurred during the observation period of six months. Costs of absenteeism (i.e., costs of sick leave and costs of productivity loss due to health service use) were valued using the average gross wage including non-wage labor costs in Germany (Statistisches Bundesamt, 2009d). To monetize the lost productivity for people receiving disability pension we assumed that they were on sick leave for the whole observation period and calculated the costs similarly to the costs of absenteeism using the average gross wage including non-wage labor costs in Germany (Statistisches Bundesamt, 2009d). The monetary valuation of presenteeism is complex and not without restrictions (Brooks et al., 2010). To consider, nonetheless, the entity of indirect costs and to provide an informative basis for the impact of presenteeism compared to the other costs the item was translated into days of lost productivity by multiplying the days at work by the proportion of impairment according to Kessler et al. (2004) (e.g. if productivity was reduced by 50% and the participant had 10 working days, this resulted in 5 additional days of lost productivity). All costs were calculated in € for the year 2008. If unit costs were not available for 2008, previous data were inflated to 2008 price levels (Statistisches Bundesamt, 2010b).

2.4. Statistical analysis Complete baseline data were available for 194 patients; the remaining cases had one or more missings on a variable of resource use. The percentage of missings per variable ranged between 8% and 16%. Since it is not clear whether complete cases are representative for the whole sample, and moreover, to avoid a loss of statistical power multiple imputation was employed. To this end, prior to the analyses missing information was imputed using multiple imputation by chained equations (MICE) including all available data from baseline and post-measurement (Schafer and Olsen, 1998; Azur et al., 2011; van Buuren and GroothuisOudshoorn, 2011), and 50 imputations were created (Graham et al., 2007). This approach allows incorporating some of the uncertainty resulting from missing values. In contrast to other procedures it is not based on the assumption of multivariate normality, which often does not hold for resource use and cost data. Commonly, only a few people incur high costs whereas the majority incurs low or even no costs and as such this kind of data often is skewed. After imputation the average costs per sector (outpatient, inpatient including treatment at day clinics and rehabilitation, pharmaceuticals, travel, nursing care, costs of sick leave, presenteeism, disability pension) were determined across the whole sample. Then for each sector the average costs were calculated for those who used the respective service, and regarding indirect costs, for those with respective productivity loss. Furthermore, we

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analyzed different kinds of productivity losses descriptively for gainfully employed. To explore determinants of direct costs a generalized linear mixed model (GLMM) with gamma distribution and log link function was fitted, which is appropriate to model skewed data (Kilian et al., 2002; Barber and Thompson, 2004; Manning et al., 2005). A random intercept was included to allow for the multicentre design of the study. The analysis of indirect costs was restricted to those gainfully employed; participants receiving disability pension were excluded as well. However, about 40% of the employed had no costs due to absenteeism. Therefore, a two-part model was applied to appropriately account for the large proportion of zeros. In a first step a logistic regression was done modeling the probability of having incurred zero costs compared to any costs. In a second step the actual costs of absenteeism were analyzed in those with costs 40 using a GLMM similar to the analysis of direct costs. Factors influencing presenteeism were explored using a generalized linear mixed model with binomial distribution and logit link function based on the self-reported percentage of productivity instead of the estimated costs in order to avoid the introduction of any bias due to imprecise costing. Several sociodemographic, clinical and psychometric variables were analyzed regarding their respective impact on direct costs and the different aspects of productivity loss. The LSAS total score was employed as a measure of symptom severity, alternatively the LSAS subscales were used to determine the effect of specific symptoms, in particular avoidance behaviors. Additionally, we investigated the impact of age, gender, level of education, marital status, living situation, parenthood, type of health insurance (private vs. statutory), number of previous treatments for SAD, and comorbidities together with the LSAS total score/LSAS subscales. These variables remained in the final models if they either had a significant effect or altered the effect of the LSAS score or the subscales. Moreover, we checked for probable interactions between comorbidities and symptom severity or specific symptoms but they did not remain in the final models since the interaction effects were not significant. In the final models, LSAS total score was used as main independent variable while age and gender were included as covariates. Alternatively, the four LSAS subscales were employed (Model 1). In a second step, we additionally included co-morbid mental disorders using the severity scales as well as a score for somatic comorbidities (Model 2). The presented effects are the exponentiated coefficients which can be interpreted as the percentage change of the dependent variable when the independent variable changes by one unit. The level of significance was set at 0.05 using two-tailed tests. Imputation and Variance Estimation Software (IVEware), a SASbased application developed by Raghunathan et al. (2001) was used to perform MICE, and the statistical analyses were done with SAS software (Version 9.2 of the SAS System for Windows. Copyright© 2002–2008 SAS Institute Inc., Cary, NC, USA).

3. Results 3.1. Sample characteristics The total study sample consisted of 495 adults with a mean age of 35.2 years. The sample characteristics are presented in Table 1. 54.6% of the participants were female and about 40% were gainfully employed or received disability pension. The mean LSAS total score was 72.8 and 69.9% of the sample were diagnosed with GSAD. About 59% of the participants were diagnosed with at least one co-morbid mental disorder; affective disorders were the most

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Six participants received disability pensions during the complete observation period.

Table 1 Sample characteristics (n ¼495). Female Age Living in a partnership Number of children Z 1

n (%) Mean (SD) n (%) n (%)

270 (54.6) 35.2 (12.2) 313 (63.2) 149 (30.1)

Secondary school degree or higher Employed Disability pension

n (%) n (%) n (%)

456 (92.1) 191 (38.6) 6 (1.2)

Health insurance statutory private

n (%)

Quality of life (Index UK) SPAI total LSAS total score Number of previous outpatient psychotherapies Z 1 Number of previous inpatient psychotherapies Z 1

Mean (SD) Mean (SD) Mean (SD) n (%)

0.75 (0.45) 4.1 (1.1) 72.8 (22.0) 194 (39.2)

n (%)

183 (37.0)

Psychiatric comorbidities

n (%)(mean severity)

Substance abuse Affective disorder Anxiety disorder Somatization disorder Eating disorder Personality disorder

417 (84.2) 74 (15.0)

3 (0.6) 2.25 165 (33.3) 3.42 77 (15.6) 2.84 16 (3.2) 3.13 13 (2.6) 3.85 131 (26.5) 4.16

3.3. Determinants of direct costs When analyzing the impact of SAD symptom severity adjusted for age and gender the LSAS total score had a positive but nonsignificant effect on direct costs (Table 3). Regarding the different subscales, these showed a diametrical association: while costs increased with additional points on the subscales “avoidance of social interaction” and “fear of performance”, costs decreased with higher values on the subscales “avoidance of performance” and “fear of social interaction”. However, none of the subscales had a significant effect. Incorporating the severity of psychiatric comorbidities and the score for somatic comorbidities (Model 2) reduced the effect of the LSAS score by 0.2% from 1.006 to 1.004 but none of the variables became significant. The effects of the respective LSAS subscales only changed marginally when controlled for comorbidities and none of these were significant. Irrespective of the SAD measure, direct costs were found to decrease with more severe comorbid substance abuse as well as a more severe personality disorder. All other comorbidities were associated with higher costs. 3.4. Determinants of productivity losses

prevalent (33.6%) and personality disorders (mostly anxious avoidant personality disorder) had the highest mean severity (4.16). 3.2. Direct cost, productivity losses and indirect costs The mean total costs for six months accrued to €4802 with direct costs accounting for 22.6% of the total sum. Table 2 provides an overview on the average costs per sector for the whole sample and the respective users. The largest share of mean direct costs resulted from hospitalizations (€727; 15.1% of the total costs) with more than the half being attributable to care at psychiatric or psychosomatic wards (€413; 8.6% of the total costs). Focussing on the subgroup of users it was apparent that while only 5.6% of the study population was treated at psychiatric or psychosomatic wards these participants incurred most of the costs. Outpatient care accounted for only 4.8% of the mean total costs (€232). However, over 90% of the study participants used outpatient services and the mean costs for users were nearly similar to the estimate across all patients. 55.3% of the sample regularly used pharmaceuticals and this made up for 1.1% of the mean total costs (€54). No formal nursing care was utilized but 2% of the patients relied on informal care that is help from family or friends and this incurred 0.9% of the mean total costs (€45). Indirect costs accounted for €3718 (77.4% of the total sum) but absenteeism only played a minor role with €378 (7.9% of the total costs). By far the largest share of total costs resulted from presenteeism (€3039; 63.3%). Disability pensions made up for 6.3% of the total mean costs (300€). Focussing on the subgroup of employed, only 61.5% incurred any costs due to sick leave or productivity loss due to health service use (absenteeism). The mean number of sick leave days was 4.4 (median 1 day) and only three participants were on sick leave for more than 30 (working) days which equal the official period of continued payment in Germany. The mean self-rated productivity for the remaining time at work was 6.83 (median 7) and 20% of the employed were not impaired at all.

The results on determinants of productivity can be derived from Table 4. The self-rated productivity was found to be significantly associated with SAD symptom severity. The productivity decreased by 1.9% per additional point on the LSAS total score. Regarding the subscales only “fear of performance” showed a significant effect, patients with higher scores felt less productive by 9.4% per additional point on the subscale. The impact of SAD on productivity was not affected by the inclusion of comorbidities. However, apart from the somatic comorbidity score and personality disorders all comorbidities had an independent effect on productivity, and involved a significant reduction. In contrast to this, patients with a more severe comorbid personality disorder experienced higher productivity. In the model with the LSAS total score this effect even was significant, and the productivity increased by 7.1% per severity grade. The results on absenteeism have to be considered separately for both parts of the analysis. The first part, the logistic regression on having no costs versus any costs due to absenteeism, did not yield any significant association – neither regarding SAD nor in relation to any comorbidity. Solely, age showed a significant effect; with higher age the probability of having costs due to absenteeism diminished (data not shown here). In the second part, where a GLMM was employed to analyze the costs of absenteeism in those with costs 40 symptom severity of SAD as well as the comorbidities emerged as relevant and significant (Table 5). The effect of the LSAS total score remained quite similar regardless whether it was adjusted for comorbidities or not. Costs of absenteeism increased with higher values by about 1% per additional point. Again, only the subscale “fear of performance” significantly affected the costs, and costs rose by about 9% with each additional point. Moreover, higher values on the subscales “avoidance of social interaction” were associated with higher costs while both other subscales involved lowered costs. Furthermore, comorbid affective disorders were significantly associated with costs of absenteeism: costs were 15.5% higher when the LSAS total score was employed instead of the subscales. None of the other comorbidities were found to have a significant effect. However, anxiety disorders and personality disorders rather seemed to be associated with reduced costs while the other co-morbid mental disorders as well as the

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Table 2 Mean 6-months costs. Cost sector

Inpatient care (including day clinics and rehabilitations) Psychiatric or psychosomatic wards Outpatient care Psychiatrist and psychologist GP and other specialists Other outpatient services

Total sample (N ¼495)

Participants with respective service use or productivity loss

Mean SE

Mean

Median % of total costs

SE

Median Number of users

% of the total sample with utilization

727

548

0

15.1

5142 3811

2949

70

14.1

413 232 41 171 20

535 19 5 17 3

0 786 0 112 506

8.6 4.9 0.9 3.6 0.4

10,566 5397 251 20 133 11 190 17 168 18

2443 184 77 138 121

23 457 154 444 59

4.6 92.3 31.2 89.7 11.9

Pharmaceuticals Psychotropics

54 2

23 1

4 0

1.1 o 0.1

98 22

41 6

26 15

274 47

55.3 9.5

Transport

26

8

2

0.5

47

14

10

270

54.5

Nursing care Professional care Informal care

45 0 45

35 0 35

0 0 0

0.9 0.0 0.9

2031 1537 0 0 2031 1537

360 0 360

11 0 11

2.0 0 2.0

1084 378 3039 300

553 5 254 124

943 0 0 0

22.6 7.9 63.3 6.3

1139 580 245 1595 221 1037 9768 490 7963 24,773 1769 26,542

471 117 154 6

95.2 23.7 31.1 1.2

Indirect costs

3718

302

0

77.4

10,516

566

8574

175

35.4

Total costs

4802

623 1754

100.0

5019

649

705

474

95.7

Direct costs Absenteeism Presenteeism Disability pension

comorbidity score for somatic disorders showed higher costs with higher severity.

4. Discussion This cost-of-illness study was based on primary data from a clinical representative sample of adults with SAD and investigated 6-month direct and indirect costs as well as the association between these costs, symptom severity, and comorbidities. The total costs for 6 months accrued to €4802 with direct costs accounting for only about 23% of the total sum. In line with previous studies we found rather low direct costs for SAD patients in relation to the high costs resulting from productivity losses due to sick leave, and reduced productivity (Smit et al., 2006; Acarturk et al., 2009; Olesen et al., 2012). Comparing our estimates of direct costs to the mean annual direct costs in the German general population aged 30–45 years, the costs incurred by SAD patients were about 470€ higher per year (2168€ versus 1700€). This difference is clearly lower than our (extrapolated) annual mental health care costs of 904€ and as such might indicate an avoidance of health care utilization in general. However, in the multivariate analysis neither symptom severity nor social avoidance predicted lower direct costs as it was suggested by Konnopka et al. (2009). The additional inclusion of interaction terms between comorbidities and symptom severity did not alter these results. Instead, the analysis revealed lower costs for participants with more severe substance abuse or more severe (avoidant) personality disorder. However, whether these findings really indicate a decreased resource use, warrants further research since both effects lacked significance. The effect of increased direct costs due to somatic disorders can be ascribed to a small number of patients who suffered from severe chronic conditions that are unrelated to SAD but involve costly treatment, e.g. rheumatoid arthritis or HIV infection. To estimate the indirect costs we applied the human capital method which considers all productivity losses that result from an individual's illness across the whole life span, although we restricted the valuation of productivity losses, in accordance to

our observation period, to six months. Another approach to value productivity losses from a societal perspective is the friction cost method. In contrast to the human capital method it is based on the assumption that an employee would be replaced after a defined friction period such that from a societal perspective no productivity losses would occur beyond the friction period. Since no recommendations exist regarding the length of this friction period in Germany we alternatively employed the statutory period of continued payment, i.e. six weeks or 30 working days to derive a very conservative estimate of indirect costs. In this analysis the mean costs of absenteeism were reduced by €38 and people receiving disability pension were excluded leading to indirect costs of €3380. The determinants of costs of absenteeism were the same. Thus, for these patients and the observation period of six months the costing methodology has little impact, in particular since the majority of costs probably resulted from presenteeism. However, the monetary valuation of presenteeism is also prone to different kinds of errors (Brooks et al., 2010): first, regarding the conversion of reduced productivity into lost time and second, regarding the translation of lost time into costs. Moreover, it can be questioned whether reduced productivity can be reliably recalled over six months. Nonetheless, reduced productivity is regularly stated to cause a large part of the costs resulting from SAD although these costs have not been quantified yet (Pilling et al., 2013). Our aim, therefore, was to provide at least an approximation of the impact of presenteeism compared to other costs. For our further analysis, we employed the self-rated productivity instead which should not be biased by the chosen time frame or the costing method. The more detailed analysis of productivity losses revealed clear associations with co-morbid mental disorders, and symptom severity. Similar to Acarturk et al. (2009), we identified comorbid affective disorders to increase the costs of absenteeism. Our results, in addition, show that co-morbid affective disorders, substance abuse, anxiety disorders, eating disorders, somatization disorders severely limit the patients' self-rated productivity. Furthermore, the reverse effects of (avoidant) personality disorder and anxiety disorders might be of interest although results were not significant across the analyses: both conditions

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Table 3 Determinants of direct costs (n¼ 495): results from generalized linear mixed models with gamma distribution and log link function. Parameter

Model 1a (adjusted for age and gender)

Model 2a (additionally adjusted for comorbidities)

LSAS total score exp (coeff.)

LSAS subscales exp (coeff.)

LSAS total score exp (coeff.)

LSAS subscales exp (coeff.)

Age Gender (ref¼male)

1.016 1.006

1.017 0.998

1.013 0.936

1.014 0.926

LSAS total score

1.006

LSAS subscales: “Avoidance of social interaction” Avoidance of performance” “Fear of social interaction” “Fear of performance”

1.004 0.995

0.993

1.034

1.035

1.026

1.026

0.965

0.960

Elixhauser–Charlson comorbidity score Severity of psychiatric comorbidities (range 0–8): Affective disorder Substance abuse Anxiety disorder Personality disorder Somatization disorder Eating disorder Intercept

361.727

431.912

2.305

2.179

1.021 0.624 1.08 0.948

1.01 0.629 1.094 0.939

1.229

1.233

1.219

1.229

442.552

512.696

LSAS – Liebowitz Social Anxiety Scale. a Presented are the exponentiated coefficients, which can be interpreted as the percentage change of costs when the dependent variable changes by one unit.

were associated with decreased costs of absenteeism, and (avoidant) personality disorder was additionally associated with increased productivity. One possible explanation could be that patients unduly fear negative consequences from ‘inadequate’ work performance and therefore, try to reduce their sickness absence, and strive for enhanced productivity. However, this kind of avoidance seems to be different from the avoidance behaviors covered by the LSAS subscales because only ‘fear of performance’ was associated with considerably higher costs of absenteeism, as well as reduced productivity in all analyses. This finding requires further research including information on the patients' jobs to investigate whether specific situations on the job induce fear, or whether situations outside the job cause fear and impact on the patient's ability to work. In contrast to other studies (Cornelius et al., 2011) we found that the probability of having costs due to absenteeism decreased with older age. Two different factors might contribute to this finding: one the one hand we only considered short-term absence up to six months while older age was repeatedly found to be associated with long-term disability beyond six months. On the other hand we included absenteeism due to all causes; not only for mental health reasons, but no information on the respective diagnosis was available. Apart from age, none of the other sociodemographic factors showed a significant effect. However, our sample was relatively small and rather homogeneous and these variables were primarily incorporated as covariates. Thus, these results should not be considered conclusive. Furthermore, we could not control other health behaviors like weight or smoking which have been reported to impact on productivity loss (Blank

Table 4 Determinants of productivity in employed (n ¼191): results from generalized linear mixed models with binomial distribution and logit link function. Parameter

Model 1a (adjusted for age and gender)

Model 2a (additionally adjusted for comorbidities)

LSAS total score exp. (coeff.)

LSAS subscales exp. (coeff.)

LSAS total LSAS score exp. subscales exp. (coeff.) (coeff.)

Age Gender (ref¼ male)

1.002 1.118

1.003 1.090

1.000 1.184

LSAS total score

0.981n

LSAS subscales: “Avoidance of social interaction” “Avoidance of performance” “Fear of social interaction” “Fear of performance”

1.000 1.151

0.981n

0.971

0.969

1.023

1.035

1.020

1.011

0.906n

0.905n

Elixhauser–Charlson comorbidity score

0.834

0.833

Severity of psychiatric comorbidities (range 0–8): Affective disorder Substance abuse Anxiety disorder Personality disorder Somatization disorder Eating disorder

0.888n 0.635n 0.897n 1.071n 0.731n 0.731n

0.890n 0.613n 0.895n 1.060 0.725n 0.744n

Intercept

10.127

16.080

7.741

11.401

LSAS – Liebowitz Social Anxiety Scale. a Presented are the exponentiated coefficients, which can be interpreted as the percentage change of productivity when the dependent variable changes by one unit. n p o 0.05.

et al., 2008; Cornelius et al., 2011). Hence, more comprehensive research investigating the complex relationships between SAD, educational attainment, job success and productivity is needed. 4.1. Strengths and limitations Our study has several strengths and limitations. The study population consisted of SAD patients who were willing to participate in a clinical trial and thus to be treated for their SAD. For this reason not only cost data were available but a large number of clinical parameters were collected which enabled us to investigate their respective impact on direct and indirect costs. However, compared to previous studies which used claims data or population-based samples our sample was relatively small. Despite the broad inclusion criteria, it also has to be assumed that patients with extreme fears and avoidance behaviors did not participate in the SOPHO-NET study. As such, the sample was clinically representative for psychotherapy patients but not necessarily representative for SAD patients in general. Moreover, no healthy controls were included, thus no excess costs could be calculated. The validity of our results might also be limited due to the chosen recall period of six months but this period represents a compromise between three months and twelve months: while resource use can be recalled reliably over three months (Johnston et al., 1999), this time frame is too short to record more infrequent events like inpatient treatment. This seemed of particular importance in this population who was expected to rarely utilize health care services. However, it is unclear whether this rather leads to an over- or an underestimation of costs. Nonetheless, as a

N. Stuhldreher et al. / Journal of Affective Disorders 165 (2014) 87–94

93

Table 5 Determinants of costs of absenteeism in employed with costs 40 (n¼117): results from generalized linear mixed models with gamma distribution and log link function. Model 1a (adjusted for age and gender)

Parameter

Model 2a (additionally adjusted for comorbidities)

LSAS total score exp (coeff.)

LSAS subscales exp (coeff.)

LSAS total score exp. (coeff.)

LSAS subscales exp. (coeff.)

Age Gender (ref¼male)

1.003

1.001

0.797

0.836

1.005 0.852

1.004 0.839

LSAS total score

1.012n

LSAS subscales: “Avoidance of social interaction” “Avoidance of performance” “Fear of social interaction” “Fear of performance”

1.01n 1.056 0.969 0.946 1.087n

1.036 0.971 0.972 1.087n

Elixhauser–Charlson comorbidity score

0.991

1.159

Severity of psychiatric comorbidities (range 0–8): Affective disorder Substance abuse Anxiety disorder Personality disorder Somatization disorder Eating disorder

1.155n 1.517 0.952 0.936 1.005 1.311

1.135 1.433 0.974 0.928 1.046 1.311

529.984

464.966

Intercept

612.769

543.497

LSAS – Liebowitz Social Anxiety Scale. a n

Presented are the exponentiated coefficients, which can be interpreted of the percentage change of costs when the dependent variable changes by one unit. po 0.05.

consequence we decided not to extrapolate our costs to annual total population costs for SAD. An important strength of our study is that we used primary data: we not only had access to clinical information but also collected comprehensive data on health care utilization and productivity losses and were able to investigate probable effects in comprehensive multivariate analyses. Furthermore, we employed MICE to appropriately incorporate the uncertainty resulting from missing information within selfreports. Nonetheless, data were chiefly collected to evaluate two different psychotherapy approaches, and thus only baseline data reflected the regular utilization while the naturalistic development of costs over the course of time could not be examined.

4.2. Conclusions The total amount of costs per patient should be interpreted carefully since the proportion of costs attributable to SAD was equivocal for some cost components. However, relevant conclusions can be derived regarding the relative share of direct and indirect costs and the impact of comorbidities. While costs incurred by healthcare utilization were rather low, the societal burden resulting from lost productivity was considerable. This might indicate that most patients had not sought for sufficient treatment yet. As such the efforts to identify and treat patients with SAD adequately should be further increased, and the impact of SAD on the job should be considered as well. Moreover, patients diagnosed with SAD should be screened for further co-morbid mental disorders and these should be addressed appropriately in the following treatment. In addition, it would be interesting to compare the different patterns of healthcare utilization depending on the patients' specific avoidance behaviors in order to develop better tailored interventions. The effects of manualized cognitive-behavioural psychotherapy and psychodynamic psychotherapy on direct and indirect costs will be investigated in future research.

Role of funding source This study was funded by a grant from the German Ministry of Research and Education (Grant no. 01GV1002).

Conflict of interest All authors declare that there is no conflict of interest.

Acknowledgment None.

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