Health economic evaluation of a web-based intervention for depression: The EVIDENT-trial, a randomized controlled study

Gräfe, Viola et al. Article Health economic evaluation of a web-based intervention for depression: The EVIDENT-trial, a randomized controlled study Health Economics Review Provided in Cooperation with: Springer Nature Suggested Citation: Gräfe, Viola et al. (2019) : Health economic evaluation of a web-based intervention for depression: The EVIDENT-trial, a randomized controlled study, Health Economics Review, ISSN 2191-1991, Springer, Heidelberg, Vol. 9, Iss. 16, pp. 1-13, https://doi.org/10.1186/s13561-019-0233-y This Version is available at: https://hdl.handle.net/10419/285129 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ RESEARCH Open Access Health economic evaluation of a webbased intervention for depression: the EVIDENT-trial, a randomized controlled study Viola Gräfe 1* , Thomas Berger 2 , Martin Hautzinger 3 , Fritz Hohagen 4 , Wolfgang Lutz 5 , Björn Meyer 6 , Steffen Moritz 7 , Matthias Rose 8 , Johanna Schröder 7,9 , Christina Späth 4 , Jan Philipp Klein 4† and Wolfgang Greiner 1† Abstract Background: Depression often remains undiagnosed or treated inadequately. Web-based interventions for depression may improve accessibility of treatment and reduce disease-related costs. This study aimed to examine the potential of the web-based cognitive behavioral intervention “deprexis”in reducing disease-related costs. Methods: Participants with mild to moderate depressive symptoms were recruited and randomized to either a 12- week web-based intervention (deprexis) in addition to care as usual (intervention group) or care as usual (control group). Outcome measures were health-related resource use, use of medication and incapacity to work as well as relating direct health care costs. Outcomes were assessed on patients’self-report at baseline, three months and six months. Results: A total of 1013 participants were randomized. In both groups total direct health care costs decreased during the study period, but changes from baseline did not significantlydifferbetweenstudygroups.Numericdifferencesbetween study groups existed in outpatient treatment costs. They could be attributed to differences in changes of costs for psychotherapeutic treatment from baseline. Whereas costs for psychotherapeutic treatment decreased in the intervention group, costs increased in the control group (−16.8% (€80) vs. + 14.7% (€60)) (t df = 685 =2.57;p=0.008). Conclusion: The study indicates the health economic potential of innovative e-mental-health programs. There is evidence to suggest that the use of deprexis over a period of 12 weeks leads to a decrease in outpatient treatment cost, especially in those related to different types of psychotherapeutic treatment. Keywords: Economic issues, Outcome studies, Health economic evaluation, E-mental-health, Deprexis, Depression, Randomized controlled trial Background Major depression is a worldwide health problem, which lowers quality of life for the individual and generates huge costs for society. The lifetime prevalence of a diagnosed depression is estimated at 11.6% to 13.0% in German adults, with women having a nearly twice as high risk of disease as compared to men [1–5]. From a societal perspective, depressive disorders are associated with a substantial loss of resources. The diagnosis of depression has become the second most important reason for an incapacity for work [6–8]. In comparison to people without depression, patients with depressive disorder report twice as many days of incapacity for work [9]. Therefore, employees had an average absence of 51.8 days due to depressive episodes in 2014 [10]. In addition to indirect costs due to disease related productivity losses, depressive disorders are associated with high health care costs. Thus, the estimated annual direct treatment costs for Germany range between €686 and €3849 per patient within different studies. The total © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. * Correspondence: [email protected] † Jan Philipp Klein and Wolfgang Greiner contributed equally to this work and share the senior authorship 1 Department of Health Economics and Health Care Management, School of Public Health, Bielefeld University, Universitätsstraße 25, 33615 Bielefeld, Germany Full list of author information is available at the end of the article Gräfe et al. Health Economics Review (2019) 9:16 https://doi.org/10.1186/s13561-019-0233-y direct costs of depression in Germany were estimated at 5,2 billion Euro for the whole population [5,11]. A depressive episode needs to be treated if symptoms exceed a certain period, persistence and strength [12]. But despite differentiated guidelines and a welldeveloped health care system, depressive episodes are rarely identified in time and treated adequately. Thus, many individuals with depression remain untreated, even in countries with well-developed health care systems [13]. This globally documented treatment gap in the management of mental illnesses may be counteracted by internet based self-help interventions. This form of intervention is particularly relevant as a treatment of mild to moderate depression [14,15]. Employed in a stepped-care model, low intensity online-based interventions may bridge the treatment gap as an appropriate first option for patients with mild to moderate depressive symptoms. Advantages are low threshold, local and temporal independence, reductions in waiting time for face-to-face treatment, empowerment and anonymity [15,16]. During the recent years different studies along randomized controlled trials as well as some meta-analyses provided evidence for the clinical effectiveness of emental health interventions (especially in the treatment of mild to moderate depressive symptoms). For example, a meta-analysis by Cujipers and colleagues stated that that self-guided psychological treatment has a small but statistically significant effect on participants with elevated levels of depressive symptomatology [14]. Moreover, another meta-analysis that was conducted in this textual context underlined the effectiveness of webbased interventions in the treatment of depression. The meta-analysis by Karyotaki et al. demonstrated self-guided internet-based behavioral therapy to be significantly more effective on depressive symptoms severity and treatment response in comparison to control conditions [17]. While there is strong evidence for the effectiveness of web-based treatments for depression, effects on overall health care costs have been less well researched. In this context, only a few health economic evaluations exist up to now, most of them evaluating guided, less commonly unguided or minimally-guided internet interventions. Whereas most studies indicated that guided web-based interventions have the potential to be cost-effective [18], health economic evaluations of self-guided treatment programs tend to classify those interventions as not cost-effective according to direct costs of health services or productivity losses [19–21]. Against this background, the present study was designed to examine, whether the use of the minimallyguided cognitive behavioral self-help program deprexis over a period of 12 weeks in addition to care as usual leads to a significant reduction in direct health care costs within six months of observation. The main results of this study, the EVIDENT-trial, has been published elsewhere [22]. Methods Study design The EVIDENT-Trial is as a prospective, parallel-group, multicenter, randomized, controlled and assessorblinded study which was conducted between August 2013 and December 2014. The study was approved by the Ethics Committee of the German Psychological Association (reference-number SM 04_2012) and registered at ClinicalTrials.gov (identifier: NCT01636752). A study protocol with a detailed description of the trial design has been published [23]. Using an a-priori generated allocation schedule with random numbers, all participants were randomized into either an intervention group or a control group. Participants of the intervention group gained access to the online based self-help program “deprexis”for a period of 12 weeks in addition to care as usual. The program aims to promote self-management skills as well as to empower people with depressive symptoms to learn new and healthier behaviors. It consists of ten different modules, covering a broad range of elements of cognitive behavioral therapy such as behavioral activation and cognitive modification, psychoeducation, mindfulness and acceptance or interpersonal skills. For a detailed description of the program and all of its modules see Meyer et al. [24]. The control group received care as usual and was permitted to use any kind of therapy or treatment offered in standard care under the statutory health insurance scheme (e.g. outpatient medical care, inpatient hospital care or pharmaceutical care as well as psychiatric or psychotherapeutic treatment). After taking part in the last follow-up assessment, the participants of the control group were also invited to use the internet based program for a 12-week period. Participants Participants were recruited from various settings, including inpatient and outpatient medical and psychological clinics, health insurance companies, online forums for depression as well as different media (e. g. newspaper and radio) between August 2012 and December 2013. Inclusion criteria were the presence of mild to moderate depressive symptoms, defined by scores between 5 and 14 on the Patient Health Questionnaire-9 (PHQ-9), age between 18 and 65 years, an adequate command of the German language, the availability of internet access and electronically written informed consent, which was obtained online prior to baseline assessment. People with moderately severe to severe depressive symptoms (PHQ-9 score > 14), an acute suicidal tendency Gräfe et al. Health Economics Review (2019) 9:16 Page 2 of 13 (> 0, PHQ-9 Item 9), a diagnosis of bipolar disorder or lifetime schizophrenia (both determined by a diagnostic telephone interview) or other serious mental or physical illnesses that required acute treatment were excluded from the study. Resource use and costing The health economic evaluation of the EVIDENT-trial focused on healthcare utilization, medication use and incapacity for work due to illness, as well as on resulting direct costs. Estimates of direct costs were derived from the payer-perspective. Therefore, patients’time costs, traveling costs as well as indirect costs due to absenteeism or presentism were not included in the analysis. Fees for the use of the web based program were excluded from this analysis, as these are negotiated individually with clients such as health insurance companies and vary depending on usage circumstances [25]. Information on the amount of the fee for the online intervention is kept secret for competitive reasons and therefore not available for the German health care market. All assessed data were based on participants’retrospective self-reports, collected via an online survey platform at baseline, after three months (post-assessment) and after six months (follow-up assessment). The recall periods ranged from six months at baseline to three months during the post- and the follow-up assessment. For data collection on healthcare utilization, we used a modified version of the FIMA [26], a standardized questionnaire which originally was designed for the assessment of health-related resource use within the older population groups in cross sectional and longitudinal surveys. For the purpose of this study, we adapted the recall-periods of FIMA and extended the list of assessed medical services by specific psychiatric and psychotherapeutic treatments. The complete list of assessed medical services is presented in Table 1. In order to monetarily value the assessed resource use, the quantitative data on utilization of services were priced, using standardized unit costs from Bock et al. [27]. Costs were calculated by multiplying the units of resource utilization with corresponding unit cost prices and are expressed in euro. For further analyses the single Table 1 Assessed medical services by health care sector and corresponding valuation rates adapted from Bock et al. [28], indexed for 2014 a Health care sector Unit of measure Service/service provider Valuation rate Outpatient medical care Number of contacts General practitioner €20.20 Psychiatrist/psychologist €45.03 Psychotherapist/psychotherapy €78.63 Neurologist €45.03 Internist €65.90 Urologist €24.87 Gynaecologist €30.34 Surgeon €43.69 Orthopaedist €25.60 Dermatologist €19.02 Ophtalmologist €35.02 Dentist €56.26 Outpatient paramedical services Number of contacts Physiotherapy €16.53 Logopaedics €38.86 Medical pedicure €27.70 Homeopathic practitioner/osteopath €20.12 Inpatient hospital services Number of days Inpatient hospital treatment €579.93 Inpatient hospital treatment - intensive care unit €1347.08 Psychiatric inpatient treatment €342.09 Rehabilitation Number of days Inpatient rehabilitation €122.70 Outpatient rehabilitation €47.01 Sickness benefit Number of days Incapacity to work €45.01 ab a Bock et al. (2015) do not present any unit cost prices for calculating the amount of sickness benefit. The valuation for sickness benefit derives from a large routine-data analysis of more than 3.000 patients with depressive disorders of a major German sickness fund, conducted by the University of Bielefeld (paper under revision) b long-term disability more than 60 days Gräfe et al. Health Economics Review (2019) 9:16 Page 3 of 13 health care costs were summarized to sector related health care costs as presented in Table 1. Following international standards of health economic evaluation [28], the unit costs from Bock et al. (calculated for the year 2011) were adjusted for inflation. Therefore, all cost-rates were adjusted to inflation for the reference year 2014, based on the German consumer price sub-index for health care [29]. To calculate the medication costs, we used a large database (“Stammdatei Plus” ) with information on all pharmaceuticals listed in Germany, corresponding active ingredient groups, defined daily doses, pharmacies’retail prices and more. Since the primary data on medication use were collected between 2012 and 2014, we used the database version 46 with the latest update in December 2014 [30] for our analyses. On basis of the Stammdatei Plus , we calculated drugspecific unit costs per pill, injection, suppository etc. first. Therefore, the pharmacy retail price was used. In a second step, the number of drug units per recall period was calculated for each participant, using the assessed self-reports on dose rate and period of application. Finally, the calculated units of drug use per recall period were combined with the drug-specific unit costs to estimate medication costs. Statistical analysis Before starting with the data analysis, the whole dataset was checked for validity. Whenever reported numbers of resource use (e.g. number of contacts, number of therapy sessions, number of nights spent in hospital) exceeded the maximum number of days of the corresponding recall period, these single implausible data were deleted from the dataset and coded as missing. The basic data analysis focused on descriptive parameters. We used measures of central tendency and measures of variability to describe differences concerning the sociodemographic variables or healthcare utilization between study groups. To determine the precision of mean values, 95%-confidence intervals were calculated. Additionally, chi-square tests were utilized for further examination of observed group differences. To describe the assessed outcomes in variation of time and to check the observed values for regularities, time series analyses were conducted. In cases of normally distributed data, a paired t-test was carried out. If this condition was not fulfilled, a Wilcoxon signed-rank test was used. Comparative subgroup-analysis (especially between IG and CAU) were applied using t-test for independent samples. In order to check whether the intervention also has an influence on the costs independently from baseline costs, we conducted a difference in differences analysis. Therefore, the difference in costs between baseline and the study period was calculated. The changes in mean costs were then examined for differences between study groups, using paired t-tests for independent samples. The corresponding h 0 -hypothesis to be tested was: there are no significant differences in changes of mean costs between interventions and controls. Our statistical analyses are based on all available data (pairwise deletion), as this method has the advantage of using all observed data of each subject and leads to unbiased estimations. We did therefore not impute missing values as the used statistical methods are robust and valid for missing at random data. Besides, complete case analysis is the most common way of handling missing data in the analysis of clinical RCTs [31]. To enable a direct comparison of health care expenditures and sickness benefits in variation of time, all costs were calculated for a six-month period. Therefore, the health-care expenditures during the post-assessment period weresummedupwiththoseatfollow-upassessment.Thus, the presented cost-analyses refer to the time-periods “six months pre enrollment”(baseline) versus “six months post enrolment”(post-assessment and follow-up assessment). All statistical analyses were performed using IBM SPSS Statistics for windows version 22.0 and R version 3.3.2. The calculation of medication costs was conducted with Microsoft Excel 2016. The final cost variables were reimported to IBM SPSS-statistics for further analyses. Results Participant flow Participants were enrolled in the study between August 2012 and December 2013. Of the 2020 screened subjects, 1007 (49.9%) did not meet the inclusion criteria. Most of them were excluded because they reached a score on PHQ-9 of 14 points or more (n= 748; 74.3%). Finally, 1013 participants were randomized into the study groups: 509 to intervention and 504 to care as usual group. The post-assessment-questionnaire was completed by 781 participants (77.1%), 692 (68.3%) completed the 3-months follow-up questionnaire. There were no significant differences in rates of attrition at post treatment or 3-months follow-up between groups. Further, a logistic regression analysis concluded, that neither randomization group nor age, sex, family status, educational status, baseline PHQ- score, baseline diagnosis of depression or panic disorder were significantly associated with dropout status [22]. To obtain full information on participant flow, see the CONSORT flow chart (Fig. 1). Participant characteristics Detailed descriptive statistics for socio-demographic characteristics are presented in Table 2. About two-thirds (68.6%) of the 1013 participants who completed baseline questionnaire were female. The mean age of the sample was 44 years at baseline, ranging from 18 to 65 years. Gräfe et al. Health Economics Review (2019) 9:16 Page 4 of 13 Nearly 60% reported to be married or to have a steady relationship. Approximately the same number of participants stated to work on a regular contract (44.0% fulltime, 23.4% part-time). No differences between study groups were found for any of the sociodemographic variables (gender: χ 2 = 0.011, p= 0.915; age: t df = 1011 = 0.15, p= 0.883; material status: χ 2 = 6.753; p= 0.240; highest academic qualification: χ 2 = 5.647, p= 0.447, employment status: χ 2 = 2.793, p= 0.940). This indicates that randomization had been successful. Health-related resource use At baseline-assessment, there was no significant difference in health-related resource use between participants of the intervention group and those receiving care as usual, indicating that randomization was well balanced for resource use as well. Listed medical services or treatments. Briefly, about 80% of the participants in both groups reported that they took at least one medication during the past six months, 85% consulted a general practitioner, about 35% received psychotherapy and nearly 6% had an inpatient hospital stay. Table 3presents the percentage of subjects who reported to have used the listed medical services or treatments. Briefly, about 80% of the participants in both groups reported that they took at least one medication during the past six months, 85% consulted a general practitioner, about 35% received psychotherapy and nearly 6% had an inpatient hospital stay. Compared to baseline, the mean percentage of participants reporting to have received the assessed medical services six months post enrollment did not differ significantly, neither in variation of time nor between study groups. An exception is treatment by a surgeon or an orthopedist. Whereas the percentage of participants who consulted a surgeon or an orthopedist increased by 4.4%age points from 18.9% at baseline to 23.3% six months post enrollment in the intervention group, it decreased by 4.0 percentage points within the care as usual group (20.6% to 16.6%; between-group difference postenrollment: χ 2 = 0.011, p= 0.915). Incapacity for work In both study groups, the duration of incapacity for work decreased significantly during the period of six months post enrollment compared to six months pre enrollment (see Fig. 2). The average number of days of incapacity to work decreased by 4 days in both groups, whereas participants of the control-group reported a significant higher mean duration of incapacity for work during the six weeks pre enrollment to the study as well as during the six months post enrollment (IG pre-post : 19.1 to 15.1 days, −20.9%, p= 0.001; CAU pre-post :24.0to 20.3 days, −15.4%; p= 0.026). The changes in duration of incapacity for work did not differ significantly between groups (t df = 640 = 0.74; p= 0.462). Costs There were no significant differences in total direct health care costs between the study conditions at baseline (see Table 4). Six months pre enrolment mean total direct costs per participant were €2346 in the intervention group and €2475 in the care as usual group, showing only a small difference of €129 (t df = 980 = 0.35; p= 0.730). During the six months after enrollment in the trial, mean total costs decreased significantly in both study groups. In the intervention group, total costs Fig. 1 CONSORT participant flow chart Gräfe et al. Health Economics Review (2019) 9:16 Page 5 of 13 decreased by €631 (t df = 412 = 1.70; p= 0.002), in the care as usual group average costs decreased by €625 (t df = 420 = 4.004; p= 0.000) (see Fig. 3). The average reduction in total costs did not significantly differ between study conditions (t df = 579 =−1.77; p= 0.139). Besides, on closer examination of sector-specific health care costs we found some important differences between the study groups: Whereas the mean direct costs of outpatient treatment slightly decreased in the intervention group, the average per participant costs increased in the care as usual group (see Table 4). Thus, average outpatient health care costs decreased by 4.4% (€33) from €750 to €717 in the intervention group (t df = 344 = 1.47; p= 0.144), while costs increased about 11.5% (€78) in the control group from €684 during the period of six months before enrollment to €762 six months after enrollment to the trial (t df = 340 =−1.65; p= 0.099). These changes in average outpatient costs did significantly differ between groups (t df = 684 = 2.16; p= 0.036). The increase in outpatient health care costs in the control group could mainly be traced back to a rise in costs of psychotherapeutic treatment: the six months pre enrollment costs for utilization of psychotherapy increased by €60 on average during the six months post enrollment. By contrast, mean costs for psychotherapeutic treatment decreased by €80 in the intervention group. The described contrary trend in costs for utilization of psychotherapeutic treatment lead to a significant difference in the development of mean changes between intervention group and care as usual (t df = 685 = 2.57; p= 0.008). Table 2 Sociodemographic characteristics at baseline Intervention (n= 509) CAU (n= 504) Total (n= 1.013) n% n%n% Gender Male 159 31.2% 159 31.5% 318 31.4% Female 350 68.8% 345 68.5% 695 68.6% Age Mean (SD) 42 (11.1), 42 (10.9) 42 (11.0) Marital status Married 203 39.9% 222 44.0% 425 42.0% Married, but living separated 12 2.4% 16 3.2% 28 2.8% Single 118 23.2% 129 25.6% 247 24.4% In relationship 106 20.8% 83 16.5% 189 18.7% Divorced 65 12.8% 50 9.9% 115 11.4% Widowed 5 1.0% 4 0.8% 9 0.9% Highest academic qualification a Not yet graduated 2 0.4% 0 0.0% 2 0.2% No graduation 1 0.2% 0 0.0% 1 0.1% Lower secondary 29 5.7% 24 4.8% 53 5.2% Middle secondary 131 25.7% 112 22.2% 243 24.0% Higher secondary 87 17.1% 85 16.9% 172 17.0% Highest secondary 249 48.9% 271 53.8% 520 51.3% Other 10 2.0% 12 2.4% 22 2.2% Employment status b Full time 220 44.4% 214 43.5% 434 44.0% Regular part-time 117 23.6% 114 23.2% 231 23.4% Mini-Job 0 0.0% 2 0.4% 2 0.2% Temporary employed 20 4.0% 25 5.1% 45 4.6% Retraining 7 1.4% 2 0.4% 9 0.9% Maternity leave/ parental leave/ other absence 14 2.8% 7 1.4% 21 2.1% Not working 117 23.6% 128 26.0% 245 24.8% a Highest academic qualification according to the German classification: “Hauptschule”(“lower”, 9 years, until age 15/16), “Realschule”(“middle”, 10 years, until age 16/17), “Fachhochschulreife”(“higher”, 12 years, until age 17/18), “Abitur”(“highest”, 12 or 13 years, until age 17–19) b Multiple answers possible Gräfe et al. Health Economics Review (2019) 9:16 Page 6 of 13 Table 3 Resource use by health care sector and study condition, six months pre-enrollment versus six months post-enrolment 6 months pre-enrollment 6 months post-enrollment Intervention (n= 509) CAU (n= 504) Betweengroup differences Intervention CAU Betweengroup differences %n%nχ 2 p%n(n total )% n(n total )χ 2 p Medication 80.7 411 81.3 410 0.060 0.807 81.4 285 (350) 78.1 271 (347) 1.198 0.274 Outpatient medical care General practitioner 85.9 437 85.9 433 0.001 0.979 79.3 279 (352) 82.8 289 (349) 1.434 0.231 Psychiatrist/psychologist 30.1 153 31.5 159 0.263 0.608 34.7 122 (352) 32.7 114 (349) 0.312 0.576 Psychotherapist 36.1 184 33.5 169 0.764 0.382 38.9 137 (352) 42.4 148 (349) 0.883 0.347 Neurologist 14.3 73 17.1 86 1.418 0.234 16.2 57 (352) 18.6 65 (349) 0.721 0.396 Psychiatric day care unit 3.7 19 4.8 24 0.660 0.417 4.3 15 (352) 4.6 16 (349) 0.430 0.835 Internist 17.1 87 15.7 79 0.371 0.542 21.9 77 (352) 16.0 56 (349) 3.873 0.490 Gynecologist/urologist 40.5 206 39.3 198 0.149 0.700 33.8 119 (352) 37.2 130 (349) 0.907 0.341 Surgeon and/or orthopedist 18.9 96 20.6 104 0.503 0.478 23.3 82 (352) 16.6 58 (349) 4.888 0.027 Dermatologist 19.3 98 17.7 89 0.428 0.513 19.3 68 (352) 17.8 62 (349) 0.280 0.597 Ophthalmologist 14.7 75 16.1 81 0.347 0.556 19.3 68 (352) 16.6 58 (349) 0.866 0.352 Dentist 62.1 316 59.5 300 0.696 0.404 54.5 192 (352) 59.3 207 (349) 1.624 0.203 Outpatient hospital treatment 12.6 64 12.9 65 0.240 0.877 8.5 30 (352) 11.7 41 (349) 2.003 0.157 Outpatient paramedical services Physiotherapy 31.2 159 29.6 149 0.335 0.562 36.5 126 (345) 27.8 95 (342) 6.018 0.014 Logopedics 0.8 4 0.6 3 1.000 a 0.9 3 (345) 0.9 3 (342) 1.000 a Medical pedicure 5.5 28 5.8 29 0.310 0.861 6.1 21 (345) 7.6 26 (342) 0.619 0.431 Homeopathic practitioner/ osteopath 11.8 60 11.3 57 0.570 0.812 14.5 50 (345) 12.9 44 (342) 0.385 0.535 Inpatient hospital treatment Inpatient hospital treatment 7.3 37 10.7 54 3.676 0.055 6.7 23 (345) 6.7 23 (342) 0.001 0.976 Psychiatric inpatient treatment 6.9 35 7.7 39 0.278 0.598 2.0 7 (345) 4.7 16 (342) 3.726 0.540 Rehab Outpatient 0.6 3 1.6 8 2.463 0.292 1.4 5 (345) 1.2 4 (342) 2.154 0.341 Inpatient 6.3 32 6.7 34 5.8 20 (345) 3.5 12 (342) Sickness benefit 9.5 48 12.2 61 1.947 0.163 7.0 24 (352) 9.5 32 (349) 1.324 0.250 a Fisher’s exact test Fig. 2 Duration of incapacity for work six months pre enrollment compared to six months post enrollment Gräfe et al. Health Economics Review (2019) 9:16 Page 7 of 13 In comparison to the described development of outpatient health care cost, an opposing trend could be identified when analyzing changes in cost for outpatient paramedical services. Whereas the mean direct costs for outpatient paramedical services slightly decreased in the care as usual group by 6.6% (€6) on average, costs for utilization of outpatient paramedical services increased in the intervention group by 31.0% (€24). Even if the change in costs between baseline and post enrollment was significant only for the intervention group (see Table 4), the difference in development of costs between the study groups was statistically significant (t df = 684 =2.16;p=0.031).The significant increase in costs for outpatient paramedical services in the intervention group was mainly caused by Table 4 Health care expenditures (in €) by sector and study condition, six months pre-enrollment versus six months post-enrollment Intervention CAU p-value betweengroup differences # Mean 5% trimmed mean 95% - CI of the mean p-value within-group differences # Mean 5% trimmed mean 95% - CI of the mean p-value within-group differences Total amount 6 months pre enrollment 2345.91 1506.42 1842.50 –2784.98 0.002* 2474.88 1705.61 2032.64 –2820.61 0.000* 0.730 6 months post enrolment 1714.91 1681.41 1772.44 –2525.51 1849.70 1021.75 1231.78 –1973.36 0.139 Medication costs 6 months pre enrollment 277.50 33.17 34.25 –589.26 0.007* 276,96. 45.83 90.82 –463.09 0.634 0.998 6 months post enrolment 108.41 26.09 20.09 –196.74 154.91 19.45 −43.55 –353.36 Outpatient medical care 6 months pre enrollment 749.70 585.73 647.40 –852.0 0.144 683.85 581.25 611.54 –756.16 0.099 0.302 …thereof psychotherapy 477.32 313.54 382.85 –571.8 0.031 405.93 304.76 342.60 –469.25 0.164 0.218 6 months post enrolment 716.69 634.68 637.16 –796.2 762.41 670.20 673.70 –851.12 0.036* …thereof psychotherapy 397.24 325.58 333.67 –460.8 465.78 370.32 387.18 –544.39 0.008* Outpatient paramedical services 6 months pre enrollment 76.24 53.42 62.10 –90.37 0.036* 85.66 58.31 69.89 –101.43 0.411 0.382 6 months post enrolment 99.90 75.82 81.99 –117.81 79.99 48.14 59.05 –100.94 0.031* Inpatient hospital treatment 6 months pre enrollment 646.97 61.35 374.47 –919.48 0.484 680.32 135.49 421.59 –939.04 0.075 0.862 6 months post enrolment 374.25 15.63 170.19 –578.32 468.91 30.01 207.61 –730.21 0.427 Rehabilitation 6 months pre enrollment 285.82 39.4 181.9 –389.8 0.820 297.20 65.61 199.61 –394.79 0.161 0.875 6 months post enrolment 275.96 36.3 145.8 –406.1 188.64 0.00 84.10 –293.18 0.484 Sickness benefit 6 months pre enrollment 309.68 78.27 212.65 –406.70 0.005* 450.89 197.72 328.94 –572.85 0.000* 0.075 6 months post enrolment 139.70 24.89 81.30 –198.10 194.84 65.89 126.21 –263.47 0.111 #t-test for independent samples; pre enrollment: comparison of mean values, post enrollment: comparison of differences in mean costs between pre and post enrollment *p≤0.05 Gräfe et al. Health Economics Review (2019) 9:16 Page 8 of 13