Eur J Health Econ DOI 10.1007/s10198-015-0707-8

ORIGINAL PAPER

Long-term sick leave and the impact of a graded return-to-work program: evidence from Germany Udo Schneider1 • Roland Linder1 • Frank Verheyen1

Received: 10 April 2014 / Accepted: 19 June 2015  Springer-Verlag Berlin Heidelberg 2015

Abstract The implementation of a graded return-to-work (RTW) program to reintegrate the long-term sick started in Germany in 1971 and has been manifested in the Social Code Book V since 1989. Based on a return plan by the physician and the insured, participants increase their working hours slowly over a specified period of time. As participants are still classified as incapable of working they still receive sick leave benefits. Using claims data from the Techniker Krankenkasse, the largest German sickness fund, the study aims at identifying participants and analyzing the full return-to-work and the impact of the RTW program. Thereby, we account for socio-economic factors, insurance-based characteristics, and medical and healthrelated information. We consider a possible selection bias by using individual weights to analyze determinants of length of the sickness absence by applying models for survival analysis (Cox proportional hazard model). As a main result — depending on the central assumption of unconfoundedness — sickness absence is positively related to participation in the RTW program for those with sickness absence longer than 120 days. For mental disorders, our results indicate an even stronger effect. The study results emphasize the need further promotion of this instrument among those insured, physicians and employers, as occupational health management is one key for a successful return-to-work.

& Udo Schneider [email protected] 1

WINEG | Scientific Institute of TK for Benefit and Efficiency in Health Care, Bramfelder Straße 140, 22035 Hamburg, Germany

Keywords Return to work  Long-term sickness  Health insurance  Survival analysis

Introduction Direct and indirect costs of sickness absence are immensely high in industrialized countries such as Germany. Direct costs encompass expenditure for medical treatment; indirect costs cover reductions in productivity [1]. These productivity losses are related to sickness absence, disability or death. The working years lost for the year 1990 were estimated at 1.6 million and indirect cost at € 25.3 billion [1]. In 2011, loss in working years was about 1.3 million and loss in production summed up to € 46 billion, about 1.8 % of gross national income [2]. In a projection based on data from 2004, indirect cost of illness will rise from € 116.45 billion in 2007 to over € 160 billion in 2037 with an unchanged health status and up to € 144.66 billion for an improved health status [3]. With an increasing life expectancy, the rise in indirect costs would be slowed down further (€ 137.31 billion). Other authors investigated costs of sickness absence from an international perspective [4]. In a study of 20 industrial countries, health related expenditure in Germany amounted to a share of 14.7 % of the gross domestic product (GDP) (rank four in the comparison). This share consists of 4.2 % related to sickness absence and of 10.5 % related to health care expenditure. The above patterns substantiate the relevance of sickness absence from an economic perspective. Return-to-work (RTW) programs that give participants the opportunity to gradually increase working hours and hence work load might reduce especially indirect costs. By doing so, the workload can be steadily increased and participants are able to successively get used to their work.

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In Germany, graded return-to-work has been implemented within the Social Code Book (SGB V) for the Statutory Health Insurance (SHI) since the late 1980s; the Statutory Pension Insurance is responsible for the RTW program after a rehabilitation treatment. The first predecessors of the program were established by sickness funds more than 40 years ago. All employees are eligible for participation, i.e. also those working only part-time, trainees or freelancers. The physician prescribes RTW participation after clearance with the patient and employer. The employee himself has to decide whether to take part in the program or not and employers have the right to refuse without justification. The program aims at gradually enhancing the workload of those being on sick leave without exposing participants to the risk of recurring health problems. During program participation, individuals are still classified as sick and receive sickness benefits.1 Before participation, the physician and the insured agree upon program duration, daily working time and its progression as well as the definition of reasonable work patterns in a socalled reintegration scheme. This plan needs a final approval by the employer and the sickness fund. Overall, the general framework of the RTW program is the same for all sickness funds but individual return-to-work plans differ in a multitude of issues, e.g. age, branch, duration, starting point. Return to work is effective if the employee is again fully able to work or if the sickness episode is reduced. If the graded RTW is abandoned, other medical and occupational rehabilitation activities or disability pension are possible. Generally, there exist several possible effects of taking part in the program [5]. Firstly, there is a positive effect on occupation, loss in human capital is prevented or slowed down. By this, on the one hand, participants have the chance to get to know their occupational capacity and gain self-assurance whereas on the other hand, anxiety regarding excessive demand or relapses may be reduced. Secondly, it is an open question whether participation goes along with health effects. On the one hand, participants may experience a better health status because of the slow and graded increase in working time. On the other hand, if RTW is performed too quickly, it may result in a higher risk of longer sickness absence. Thirdly, an employer may have incentives to grant the RTW participation of an employee because the person will get a part-time worker at no additional costs, as the employee will be receiving sickness benefits.

1

If the participants receive additional benefits from the employer, sickness payments are reduced accordingly. After a rehabilitation treatment, program costs are born by the Statutory Pension Insurance (SPI).

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From the perspective of an employee on sick leave, participation in the RTW program does not change the financial situation, as the person will go on receiving sickness benefits. In case of payments by the employer, sickness benefits are reduced accordingly. Furthermore, and important not only from an economic perspective, by starting part-time working, employees retain their social contacts at work and their knowledge. Alternatively, if a return to work is not feasible, disability or unemployment could be possible consequences. In both cases, payments from the disability or unemployment insurance are lower than full-time earnings. Another option would be to reduce working hours and work part time instead of full time. Again, this would result in a loss in working income. The aim of the analysis at hand is to give a detailed view of graded return-to-work in the German Statutory Health Insurance. Based on claims data from a large sickness fund, participants are identified, duration in sickness and the impact of the return-to-work program is analyzed. As information on other choices like disability, unemployment, or part-time is not entirely available for those on sick leave, we concentrate on the RTW program. The central research question is whether it is possible to reintegrate workers on long-term sick leave more quickly into their familiar working environment. Thus, to our best knowledge we are the first to analyze program effects for the field of health insurance in Germany, and thereby are able to contribute to the international research on RTW programs. Moreover, compared to the research on this topic, our comprehensive data should permit us to evaluate the RTW program and its outcome based on a large sample for those on sick leave. The paper is organized as follows: Section two gives a brief overview about literature on RTW programs. In Section three, data and methods are described. Results are presented and discussed in Sect. 4 and the main findings are summarized in Sect. 5.

Literature review Literature on sickness absence from work is a huge field in labor and health economics. Important topics cover aspects like financial incentives, short- versus long-term absence, disability or rehabilitation. More general studies focus on aspects like determinants of return to work [6, 7] or absence due to mental disorders [8]. Concerning principal aspects of sickness and absenteeism, job absenteeism goes along with a large degree of heterogeneity, mainly driven by employees [9]. Absenteeism and presenteeism are analyzed in various studies. Absences from work are shorter when taking part in a graded absence program [10]. In a study based on Finish data, presenteeism is more sensitive to working-time arrangements than absenteeism and depends positively on

Long-term sick leave and the impact of a graded…

permanent fulltime work or regular overtime [11]. Various authors analyze financial aspects (e.g. [9, 12–14]). They find that an expansion or cut of benefits influences short-term absenteeism but no effect on long-term sickness periods was present. For disability insurance, the level of the temporary disability insurance in Norway affects transition into permanent disability or unemployment [15]. Moreover, the estimated elasticity of employment hazard with respect to the benefit is small (-0.3). Analyzing the impact of job loss on disability insurance, one finding is that job loss doubles the risk of permanent disability for men in Norway and contributes to about one quarter of new claims in disability insurance [16]. The effects of rehabilitation programs are analyzed in the context of sick leave episodes. Workplace training is superior to other forms of rehabilitation programs but there exist no positive effects in comparison to the case of non-participation [17]. Regarding the privatization of vocational rehabilitation, no differences in employment rates after rehabilitation in a private or public program for Sweden are found [18]. For Germany, the rise in co-payments in the late 1990s showed low price elasticity for rehabilitation programs aiming at preventing work incapacity; instead, demand for treatment at health spas is priceelastic [19]. In a 4-year follow-up in Sweden, gender, type of diagnosis, type of work and previous sick leave episodes are found to influence the probability of working, sickness benefits and the transition between the two states after workrelated rehabilitation [20]. A study on unemployment and the effect of activation policies on sick leave comes to the conclusion that the transition into short-term sick leave increases, which can be classified as a moral hazard effect [21]. Besides general aspects of sick leave, there exist a reasonable number of studies on return-to-work programs. For Germany, only studies on RTW programs for the Statutory Pension Insurance (SPI) are available in which a rehabilitation treatment is analyzed using claims data in combination with survey data [22]. Here, participation strongly depends on the rehabilitation hospital and on the assistance of the employer. In 84 % of all cases participants returned directly to work after completion of the RTW program. In a second study, a matched pairs sample is used to analyze program effects in detail [23]. Especially for patients with psychiatric illnesses and those with long sick leave durations, program effects are positive. In the Netherlands, a return-to-work plan between employer and employee can be established after 6 weeks of sick leave, depending on the report of an occupational physician on health status and limitations as well as a projection of further employment [24]. Using a Cox proportional hazard model, the impact of several return-towork activities by the employer is analyzed. The authors find that a planned return to work has a positive significant

effect with respect to the duration of sickness episodes [hazard ratio (HR) = 1.23]. In a couple of studies, the Danish ‘‘graded return-to-work program’’ is analyzed. In a first analysis possible selection effects are controlled for by using a multivariate mixedproportional-hazard model with random effects that allows simultaneously estimating program participation and returning to regular work [5]. The authors find that there is a systematic selection into the RTW program and that participation into the program goes along with a positive impact of returning to work for those being on sick leave longer than 12 weeks. With a focus on mental disorders, the Danish program on part-time sick leave shows no effect when controlling for unobserved heterogeneity, only program effects for physical illness are present [25]. For three Danish municipalities, the effects of the RTW program are studied [26]. The authors find strong differences between the three municipalities and conclude contextual factors to be important for program success. For musculoskeletal disorders, health effects of early part-time sick leave in Finland are analyzed in two studies [27, 28]. In a randomized controlled trial of six occupational units of medium and large enterprises, working time was reduced to 50 % for the treatment group whereas the control group was on full-time sick leave [27]. Results show that for a sickness episode longer than 4 weeks, members of the treatment group show a faster return to work, on average. The total absence from work due to sickness was 20 % lower compared to the control group. In the second study, based on the same randomized controlled trial, the authors find no difference between intervention and control group with respect to health outcomes like pain intensity or pain interference with work and sleep. At least, the intervention group reported better self-assessed health and quality of life [28]. The effects of part-time sick leave on the probability of returning to regular work for Sweden are analyzed in several papers [29–32]. The calculated average treatment effects on the treated for sickness spells longer than 150 days show that the return to work probability is about 10 % higher for those on part-time sick leave. For mental disorders, participation in the RTW program has positive effects after 60 days of full-time sick leave, whereas immediate participation only shows a low treatment effect [32]. For musculoskeletal disorders, employees on parttime sick leave do recover with a higher probability than those on full-time sick leave [33].

Data and methods The study at hand uses claims data from the Techniker Krankenkasse (TK), currently the largest sickness fund in

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Germany with more than 9 million insured. This data is primarily collected and used for accounting purposes. In addition, it serves as a valuable database for identifying potential regarding optimization of health care services. It encompasses data on patient utilization for ambulatory and hospital services, pharmaceuticals, remedies and therapeutic appliances and sick leave, among others. Data on the return-to-work participation has been systematically included since October 2010. In detail, it contains information on participation and planned data for the beginning and end of the RTW program. For the analysis, we focus on members with sickness episodes longer than 42 days — which is the endpoint of the statutorily continued remuneration — starting from 1st October 2010 to 31st January 2011. For all individuals, the follow-up period, including the first 42 days, was 517 days and the resulting maximum follow-up date was 30th June 2012.2 Hence, it is possible to investigate whether a member participated in the RTW program, whether this participation was effective in the sense that participants fully returned to work and at which point in time he returned to work. If the sickness period is longer than 517 days or no end date is provided, we treat the observation as right censored. Moreover, we restrict the analysis to those sickness fund members who are continuously insured from 1 year before the sickness episode until 30th June 2012. In addition, we exclude members with sickness benefit entitlement different to the statutorily defined entitlement that takes effect after 6 weeks. As we suspend unemployed, self-employed and those with optional sickness benefit we end up only with blue- and white-collar workers. Moreover, some persons show planned RTW dates with sickness periods ending before starting the RTW program and for others, the planned end of the program is more than 30 days after returning to work. To avoid a bias from these planned days, we restrict the analysis to those with planned start and end days within the sickness episode. In total, our sample encompasses 28,856 individuals with a sickness spell longer than 42 days. About a quarter (7,101) participated in the RTW program. Sick days are distributed unequally between participants and non-participants. Whereas the mean (median) duration for non-participants is 151 (83) days, for RTW participants, it is 198 (153 days). A share of 72 % of non-participants but only about 48 % of the participants showed a sickness spell up to 150 days. It took 142 (113) days on average from the beginning of the sickness episode until the RTW program

2

The maximum sickness benefits duration is 78 weeks including continued remuneration by the employer: hence 546 days. Our 517-day period results from the difference between latest start into sickness (31st January 2011) and maximum observation date available (30th June 2012).

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started. For those with up to 150 sickness days, it lasted 77 (77) days whereas for individuals with spells longer than 150 days the time difference was 210 (182) days, on average. Average program duration in our sample was 34 days: 25 days for sickness spells shorter than 150 days and 39 for longer episodes. Explanatory factors for the analysis are socio-demographic information, ICD-10 chapters, sickness benefits per month, education based on ISCED, socio-economic status (ISEI [34]) as well as diagnoses, medical demand and job related information (see Table 1). Regarding ICD diagnoses related to sickness spells, only one main diagnosis should substantiate the benefits paid by the sickness fund. In some cases, we observe two or more diagnoses. In such a case, we take the diagnosis with longest duration as valid. In case of two diagnoses with the same duration, we concentrate on the most up-to-date one. The key analysis of the impact of a graded return-towork program on the duration of sick leave is based on a Cox proportional hazard model. In the estimation process, the so-called treatment effects are of particular interest for the analysis. The first is the population average treatment effect (ATE) that indicates the average effect of the RTW program on the duration of the sickness spell for a randomly assigned individual [35]. Let Y(0) be the result without RTW, Y(1) the result for the treatment and T the treatment indicator. The gain from treatment is then Y(0) - Y(1). The ATE is defined as: sATE ¼ E½Yð1Þ  Yð0Þ The second effect is the average treatment effect on the treated (ATT) which is the average effect of the RTW program on the sickness duration for a participant: sATT ¼ E½Yð1ÞjT ¼ 1  E½Yð0ÞjT ¼ 1

ð1Þ

A central question regarding reliability of the estimated treatment effects covers possible selection into the program. The second part of Eq. (1) is the so-called counterfactual mean for those being treated that is not observable. Hence, choosing the mean outcome of untreated individuals E½Yð0ÞjT ¼ 0 to be a substitute for the counterfactual mean is questionable in non-experimental studies because non-observable components may exist that determine treatment and outcome. It follows that the true value of the ATT is only identified if E½Yð0ÞjT ¼ 1  E½Yð0Þ jT ¼ 0 ¼ 0. This condition ensures that no selection bias is present [35]. Hence, one central assumption regarding the estimation of average treatment effects is unconfoundedness, fulfilling the conditional independence assumption (CIA). It implies that any variable that has a simultaneous influence on treatment assignment and potential outcomes has to be observed by the researcher [35].

Long-term sick leave and the impact of a graded… Table 1 Summary statistics: means with and without weighting

Return-to-work program

Unweighted

Duration sickness in days

Weighted

No

Yes

No

Yes

151.33

198.51

164.46

198.51

Reference: age \30

0.096

0.075

0.073

0.075

30 B age \ 40

0.149

0.142

0.141

0.142

40 B age \ 50

0.310

0.355

0.355

0.355

50 B age \ 60

0.354

0.376

0.378

0.376

Age C 60

0.091

0.052

0.053

0.052

Female

0.478

0.511

0.510

0.511

Part-time working

0.232

0.213

0.214

0.213

1. Quintile sickness benefit per month

0.197

0.115

0.115

0.115

2. Quintile sickness benefit per month 3. Quintile sickness benefit per month

0.203 0.200

0.160 0.213

0.158 0.213

0.160 0.213

4. Quintile sickness benefit per month

0.198

0.249

0.247

0.249

5. Quintile sickness benefit per month

0.200

0.263

0.266

0.263

0.144

0.184

0.185

0.184

Reference

Voluntarily insured Sickness [ 42 days previous year

0.089

0.075

0.075

0.075

Eastern Germany

0.126

0.101

0.102

0.101

Reference: low education

0.076

0.062

0.062

0.062

Middle education

0.746

0.731

0.730

0.731

Higher education

0.178

0.207

0.207

0.207

1. Quintile ISEI 08

0.186

0.132

0.131

0.132

2. Quintile ISEI 08

0.213

0.198

0.197

0.198

3. Quintile ISEI 08

0.201

0.220

0.219

0.220

4. Quintile ISEI 08

0.198

0.221

0.220

0.221

5. Quintile ISEI 08

0.202

0.229

0.233

0.229

Hospital stay ICD_05: mental disorders

0.524 0.210

0.651 0.246

0.605 0.241

0.651 0.246

ICD_13: musculoskeletal disorders

0.273

0.277

0.275

0.277

ICD_19: injuries

0.149

0.113

0.114

0.113

Reference: other ICD chapters

0.101

0.069

0.070

0.069

Unemployment rate area level

8.623

8.219

8.200

8.219

Reference: company size \50

0.402

0.319

0.319

0.319

Company size 50–249

0.281

0.290

0.292

0.290

Company size C250

0.317

0.391

0.389

0.391

Reference

Typically, the path into program participation is not observable directly. Put differently: it is unknown whether there exist certain attributes that drive the decision to participate or not. Selection could be influenced for instance by personal characteristics, physician contacts and medical or health-related information as well as work-related factors. Claims data include many observable factors for all of these three mentioned groups of variables. For the group of personal characteristics socio-economic factors such as age, sex, education, income, or insurance status are available. The group of work-related variables consists of

working-time, past sickness episodes, company size3, and unemployment rate. Regarding physician and medical information, the data at hand includes ICD diagnoses, health care utilization and expenditures, morbidity and physician status (GP or specialist). All these factors contain information that contributes to an approximation of an individual’s health status: medical expenditures, specialist 3

The size of the company (i.e. of the operating site) is based upon the definition of the European Commission on small and medium enterprises [36].

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visits, medical diagnosis and comorbidity capture information on an individual’s health status. It follows that even if we cannot observe health status directly, our available data provides us with useful information to shed light on an individual’s health. Hence, our strategy is to estimate weights to capture the effect of individual health in the selection process. For the selection process, we apply a logit model to estimate the probability of participating in the program. The resulting propensity scores can then be used for weighting the duration estimation [37]. It specifies the conditional probability that an individual is in the treatment group based on the independent variables. Assuming that the CIA holds, we calculate the average treatment effect on the treated (ATT) [37–39] in the duration analysis by using the weights from the propensity scores. The weights from the propensity scores are calculated as follows [37, 40]: wi ¼ Ti þ ð1  Ti Þ

e^i 1  e^i

ð2Þ

Again, T = 0, 1 is the treatment indicator and e^i the estimated propensity score. For T = 1 it follows a weight e^i of one, for the control group (T = 0) the weight is 1^ ei . This means that members of the control group are weighted in such a way that their characteristics correspond to those of the treatment group.4 This calculation of weights is denoted as ‘‘weighting by the odds’’ [37]. Our computed weights are used in the estimation of the sickness duration to correct for a selection bias. Estimation is carried out using a Cox proportional hazard model (see [41, 42]), which is a semi-parametric approach without explicit definition of the so-called baseline hazard: hi ðtÞ ¼ k0 ðtÞ expðb1 xi1 þ    þ bk xik Þ

ð3Þ

Equation (3) is the relation between the hazard of the individual hi and the explanatory factors xij. The parameter k0 is the time-dependent baseline or average hazard. Its interpretation is that it resembles the hazard of an individual with all covariates taking a value of zero. The advantage of the Cox model is that no further assumptions about the functional form of the baseline hazard are necessary. The only requirement is that it is restricted to a strictly positive range. The labeling proportional hazard represents the fact that the hazard of each individual is a fixed share of the hazard of another individual: hi ðtÞ ¼ expðb1 ðxi1  xj1 Þ þ    þ bk ðxik  xjk ÞÞ hj ðtÞ

ð4Þ

It is obvious from Eq. (4) that the baseline hazard drops out of this ratio of individual hazards. Furthermore, as the explanatory factors are not time-dependent, the same is true for the hazard ratio. In other words, the share of risks is proportional over time. Violation of this assumption is present if explanatory variables interact with time. For the Cox model, one assumes instead that the effect of an explanatory variable is constant over time. Without taking interactions with time into account, the estimated effect for an independent factor is the average effect over time. To test the proportionality assumption, Schoenfeld residuals for each of the explaining factors are estimated and their correlation with time is computed [42]. If the assumption of proportionality is valid then the Schoenfeld residual is independent of time. For any explanatory factor with a significant correlation between the Schoenfeld residual and time, the interaction with time is included into the regression model. If this interaction is significantly different from zero, a violation of the assumption exists and the interaction has to be accounted for in the estimation. Generally, it is possible to model interactions with a continuous measure (duration of sickness) as well as with dichotomous time [43]. The latter case is relevant if a possible non-linearity exists between explanatory factor and time, which can be included by dividing the sickness time into discrete periods. We follow this approach for the explanatory factor ‘participant in the RTW program’.

Results Independent variables in the selection equation encompass socio-economic information, health care utilization and company information.5 The latter group comprises the number of staff members and the unemployment rate based on the county of the company location on the 31st December 2010. Medical information includes hospital stays longer than 14 days, physician visits and ICD diagnoses. As a measure of morbidity or health status for the previous year, we apply the Elixhauser comorbidity score [44, 45]. Moreover, contacts with sickness funds prior to the participation decision are included. The probability of taking part in the RTW program rises significantly with sickness benefits and socio-economic status but is lower if there was a long-term sickness episode in the previous year (Table 2). Most medical diagnoses have a significant impact compared to the reference group, except for ICD 11 (digestive system) and ICD 19 (injuries). Medical services

4

To calculate the ATE, a so-called inverse probability of treatment iÞ weighting (IPTW) can be used [37]. The formula is: wi ¼ Te^ii þ ð1T 1e^i . For participants, the weight is wi ¼ 1=^ ei , and for the members of the control group wi ¼ 1=ð1  e^i Þ.

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5

The results of the logit model are presented in Table 2. All statistical analyses are based on SAS 9.3/Enterprise Guide 5.1.

Long-term sick leave and the impact of a graded… Table 2 Logit estimation of participating in the RTW program Variable labelsa

Estimate

Standard error

Pr [ Chi-square

Intercept

-3.4185

0.1204

\0.0001

30 B age \ 40

-0.0083

0.0646

40 B age \ 50

0.0808

0.0591

50 B age \ 60

-0.0278

Age C60

Odds ratio

95 % Wald confidence limits

0.8973

0.992

0.874

1.126

0.1714

1.084

0.966

1.217

0.0595

0.6404

0.973

0.866

1.093

-0.6818

0.0814

\0.0001

0.506

0.431

0.593

Female

0.3570

0.0360

\0.0001

1.429

1.332

1.533

Part-time working 2. Quintile sickness benefit per month

0.0246 0.3780

0.0421 0.0533

0.5596 \0.0001

1.025 1.459

0.944 1.314

1.113 1.620

3. Quintile sickness benefit per month

0.7289

0.0543

\0.0001

2.073

1.864

2.305

4. Quintile sickness benefit per month

0.8973

0.0573

\0.0001

2.453

2.192

2.745

5. Quintile sickness benefit per month

0.9526

0.0670

\0.0001

2.592

2.274

2.956

Voluntarily insured

-0.0400

0.0479

0.4036

0.961

0.875

1.055

1–3 cases of sickness previous year

0.0290

0.0332

0.3817

1.029

0.965

1.099

4–6 cases of sickness previous year

0.0331

0.0507

0.5131

1.034

0.936

1.142

More than 7 cases of sickness previous year

0.0630

0.0978

0.5192

1.065

0.879

1.290

Sickness [ 42 days previous year

-0.1751

0.0546

0.0013

0.839

0.754

0.934

Eastern Germany

-0.0007

0.0501

0.9883

0.999

0.906

1.102

Middle education

-0.0775

0.0606

0.2013

0.925

0.822

1.042

Higher education

-0.1036

0.0732

0.1571

0.902

0.781

1.041

2. Quintile ISEI 08

0.1765

0.0498

0.0004

1.193

1.082

1.315

3. Quintile ISEI 08

0.1589

0.0533

0.0028

1.172

1.056

1.301

4. Quintile ISEI 08 5. Quintile ISEI 08

0.1311 0.1446

0.0525 0.0566

0.0124 0.0106

1.140 1.156

1.029 1.034

1.264 1.291

ICD_02: neoplasms

0.6430

0.0799

\0.0001

1.902

1.626

2.225

ICD_05: mental disorders

0.5598

0.0613

\.0001

1.750

1.552

1.974

ICD_06: nervous system

0.4278

0.0956

\0.0001

1.534

1.272

1.850

ICD_09: circulatory system

0.5244

0.0814

\0.0001

1.689

1.440

1.982

-0.1732

0.1056

0.1011

0.841

0.684

1.034

0.4908

0.0595

\0.0001

1.634

1.454

1.836

ICD_11: digestive system ICD_13: musculoskeletal disorders ICD_18: not classified symptoms

0.3795

0.0964

\0.0001

1.462

1.210

1.766

ICD_19: injuries

0.1583

0.0671

0.0183

1.172

1.027

1.336

ICD_21: contact with health services

0.2347

0.0744

0.0016

1.265

1.093

1.463

-0.0670

0.0367

0.0677

0.935

0.870

1.005

8–12 physician visits first 42 days of sickness

0.1103

0.0412

0.0074

1.117

1.030

1.211

13 or more physician visits first 42 days of sickness

0.1967

0.0516

0.0001

1.217

1.100

1.347

Hospital stay [14 days first 42 days of sickness

0.7371

0.0469

\0.0001

2.090

1.906

2.291

4–7 physician visits first 42 days of sickness

Health expenditure first 42 days of sickness in € 1000 Health expenditure first 42 days of sickness in € 1000 squared Main diagnosis by a specialist

0.0696

0.0062

\0.0001

1.072

1.059

1.085

-0.0010 -0.1078

0.0002 0.0301

\0.0001 0.0003

0.999 0.898

0.999 0.846

0.999 0.952

1.1937

0.0511

\0.0001

3.299

2.985

3.647

Unemployment rate area level

-0.0388

0.0049

\0.0001

0.962

0.953

0.971

Elixhauser score [0

Contact with sickness fund

-0.0435

0.0316

0.1683

0.957

0.900

1.019

Company size 50–249

0.1938

0.0367

\0.0001

1.214

1.130

1.304

Company size C250

0.2747

0.0361

\0.0001

1.316

1.226

1.413

123

U. Schneider et al. Table 2 continued Variable labelsa R2

b

Max-rescaled R2 N a

Estimate

Standard error

Pr [ Chi-square

Odds ratio

95 % Wald confidence limits

0.0832 c

0.1238 28.856

b

ICD chapters abbreviated. ICD reference: chapters with a share lower than 4 % n o2=N Lð0Þ R2: Generalized R2: Cox and Snell: R2 ¼ 1  Lð ^ bÞ n o2=N

c

Max-rescaled R2: Adjustment of the generalized R2 by Nagelkerke: R2 ¼

like a hospital stay longer than 14 days within the first 42 days of sickness and a contact with the sickness fund increases the participation probability. The same is true for a company size larger than 50 employees. The Elixhauser score, our measure of comorbidities in the previous year, shows no significant impact. The main estimation of the Cox proportional hazard model uses the calculated weights from the propensity scores of the logit model to account for a potential selection bias into the program. According to our aim to analyze the full return to work and to assess the impact of the RTW program on sickness time, the estimated program effects can be interpreted as the average treatment effect on the treated (ATT) as long as no unobservables drive the selection into the RTW program. The dependent variable is the duration of sickness. To incorporate the circumstance that the beginning of the sickness spell can be somewhere between 1st October 2010 and 31st January 2011, we control for a so-called late entry [42]. From the comparison of the weighted and unweighted means, the effect of the weighting by the odds becomes obvious, as means of participants remain unchanged whereas those of non-participants are adapted (Table 1). The test regarding a potential violation of the proportionality assumption (based on the calculated Schoenfeld residuals) suggests a violation for the following variables: age[60, voluntary insurance, sick leave[42 days, hospital stay, ICD chapters 05 and 19 and RTW program. For the latter variable, we use a dichotomization of sickness duration based on 15 dummy variables that each encompasses an interval of 30 days.6 For the other variables, the interaction with sickness duration is included in the estimation. Incorporated in the estimation of the Cox proportional hazard model are the core variables in Table 1 and the 6

In addition, we used a linear interaction as well as a quadratic or cubic interaction. As these implementations designate the functional form of the interaction, we preferred the more flexible dummy approach.

123

1

Lð0Þ ^ LðbÞ

1Lð0Þ2=N

interactions with time. Overall, among the 28,856 observations that enter the regression, 2082 cases are rightcensored. Detailed results are presented in Table 3. The first part of Table 3 shows the results of the core variables, the second part the interaction effects. Under the assumption of unconfoundedness, the estimated effects would represent the average treatment effect on the treated (ATT), i.e. the average impact of the RTW program e.g. on the participants’ sickness duration in the considered period. In contrast, causal interpretations would not be feasible if the assumption of unconfoundedness does not hold. Compared to the reference group, elderly insured show a lower hazard ratio (\1). Overall, we find a significant age gradient. The significant negative coefficient for women indicates that they return later to work than men. Regarding higher sickness benefits, we find a significant hazard ratio larger than one. The same is true for the explanatory variable voluntary insurance. For education, we find significant effects only for some of the factors, e.g. higher education. Regarding ISEI quintiles as a proxy for socioeconomic status, all four variables show a significantly positive effect. All coefficients concerning the medical condition are highly significant. ICD diagnoses 13 and 19 show a hazard ratio larger than one whereas for ICD 05 it is smaller than one. For those on sick leave with a mental illness, return to work appears to be delayed compared to the reference group (other ICD chapters). If the insured is working in a company with more than 250 employees, sickness duration is reduced compared to small companies, which may reflect the effects of an occupational health management. Looking at the interactions with time to correct for a violation of the proportional hazard assumption, we find no significant coefficients for the interactions of age C60 and voluntary insurance with sickness duration. Hence, a violation can be rejected for these variables. The estimated effect of the return-to-work program is non-linear over time (see Fig. 1). The horizontal line at the

Long-term sick leave and the impact of a graded… Table 3 Estimation of RTW effect on sickness duration Labela

Parameter estimate

Standard error

Pr [ ChiSq

Hazard ratio

30 B age \ 40

-0.2097

0.0397

\0.0001

0.811

40 B age \ 50

-0.2656

0.0361

\0.0001

0.767

50 B age \ 60

-0.3895

0.0364

\0.0001

0.677

-0.4806

0.0748

\0.0001

0.618

-0.0775

0.0216

0.0003

0.925

Part-time working

0.0258

0.0256

0.3142

1.026

2. Quintile sickness benefit per month

0.0720

0.0344

0.0366

1.075

3. Quintile sickness benefit per month

0.0974

0.0343

0.0045

1.102

4. Quintile sickness benefit per month

0.1263

0.0357

0.0004

1.135

5. Quintile sickness benefit per month

0.0791

0.0412

0.0550

1.082

0.2063 -0.2877

0.0407 0.0583

\0.0001 \0.0001

1.229 0.750

Age C 60 Female

Voluntarily insured Sickness [42 days previous year Eastern Germany

0.1079

0.0306

0.0004

1.114

Middle education

0.0357

0.0378

0.3445

1.036

Higher education

0.0900

0.0452

0.0462

1.094

2. Quintile ISEI 08

0.0696

0.0314

0.0267

1.072

3. Quintile ISEI 08

0.1031

0.0334

0.0020

1.109

4. Quintile ISEI 08

0.1051

0.0328

0.0014

1.111

5. Quintile ISEI 08

0.0738

0.0353

0.0368

1.077

Hospital stay

-0.4364

0.0301

\0.0001

0.646

ICD_05: mental disorders

-0.6448

0.0400

\0.0001

0.525

ICD_13: musculoskeletal disorders

0.2301

0.0226

\0.0001

1.259

ICD_19: injuries

0.4740

0.0449

\0.0001

1.606

-0.0036

0.0030

0.2368

0.996

Company size 50–249

Unemployment rate area level

0.0147

0.0226

0.5161

1.015

Company size C250

0.0706

0.0220

0.0013

1.073

Age C60 * sick days Voluntarily insured * sick days

0.0035 0.0011

0.0033 0.0020

0.2912 0.5885

1.003 1.001

Sickness [ 42 days * sick days

0.0138

0.0027

\0.0001

1.014

Hospital stay * sick days

0.0093

0.0015

\0.0001

1.009

ICD_05 * sick days

0.0187

0.0018

\0.0001

1.019

ICD_19 * sick days

-0.0050

0.0027

0.0699

0.995 0.094

RTW * \ 60 sick days

-2.3629

0.0718

\0.0001

RTW * 60–89 sick days

-0.5394

0.0365

\0.0001

0.583

RTW * 90–119 sick days

-0.0443

0.0324

0.1717

0.957

RTW * 120–149 sick days

0.0724

0.0347

0.0371

1.075

RTW * 150–179 sick days

0.1027

0.0404

0.0110

1.108

RTW * 180–209 sick days

0.2357

0.0466

\0.0001

1.266

RTW * 210–239 sick days

0.2050

0.0562

0.0003

1.228

RTW * 240–269 sick days

0.3128

0.0632

\0.0001

1.367

RTW * 270–299 sick days

0.3434

0.0718

\0.0001

1.410

RTW * 300–329 sick days RTW * 330–359 sick days

0.4118 0.4867

0.0798 0.0856

\0.0001 \0.0001

1.510 1.627

RTW * 360–389 sick days

0.5117

0.0928

\0.0001

1.668

RTW * 390–419 sick days

0.3395

0.1052

0.0013

1.404

RTW * 420–449 sick days

0.4392

0.1063

\0.0001

1.551

RTW * 450–479 sick days

0.4394

0.1127

\0.0001

1.552

RTW * sick days C480

0.7119

0.1050

\0.0001

2.038

123

U. Schneider et al.

Table 3 continued Labela Generalized R2

Parameter estimate b

b

Hazard ratio

0.1266

N a

Pr [ ChiSq

Standard error

28.856 ICD chapters abbreviated. ICD reference: other ICD chapters n o2=N Lð0Þ Generalized R2: Cox and Snell: R2 ¼ 1  Lð ^ bÞ

for the TK. The remaining categories serve as a reference. The corresponding hazard ratios are depicted in Fig. 2. There exist obvious differences. For ICD chapter 19, a positive but not significant effect of the RTW program can only be found from 240 days on. Further, between 420 and 449 days, we find a negative effect (HR \ 1). One reason behind this result may be related to the large heterogeneity of diagnoses summarized in this chapter comprising injuries and accidents. The most distinct effect can be found for mental disorders. Here, the hazard ratio is significantly larger than one from 90 days on. Taking the average length of program participation into account (41 days for ICD 05), for people on sick leave with an ICD 05-diagnosis, an offer to participate in the RTW should be made shortly after the 6-week-period of continued remuneration to realize the positive program effects, on average. The effects in ICD chapter 13 are less clear, especially for larger sickness durations, the RTW program has no significant effect. For the reference group (other ICD chapters), a positive effect can be found, beginning with 180 days of sickness. For all

value one reflects the point where there is no difference in the sickness duration between participants and non-participants (hazard ratio = 1). Moreover, the dotted lines designate the 95 % confidence interval for the estimated hazard ratio. The first two interactions show a hazard ratio significantly lower than one, the third one (90–120 days) is not significantly different from one. These significantly negative regression coefficients for the interaction of RTW and sickness spells shorter than 90 days indicate that participation may lead to longer sickness spells. From 120 days on, the hazard ratio is significantly larger than one. Those individuals with long sick leave duration who participate in the RTW program show a faster return to regular work on average. Moreover, the hazard ratio does not increase monotonically. Instead, we find local minima at intervals starting at 210 and 390 days. While this result reflects the estimated effects for the whole sample, Fig. 2 shows the results after stratification with respect to the ICD chapters 5, 13 and 19. These three ICD chapters are those with the highest expenditure shares Fig. 1 Hazard ratio of the RTW program

Hazard ratio of the graded return-to-work programm over time 2.5

hazard ratio

2.0

1.5

1.0

0.5

0.0

0+

47

9

9

44

9

95 % Upper and Lower Confidence Limit

48

0-

0-

41

9

9

9

123

45

42

0-

35

32

9

9

9

29

38

0-

0-

0-

23

26

0-

09

9

9

20

17

14

19

9

9

0-

0-

0-

-1

-8

-5

0-

39

36

33

30

27

24

21

18

15

12

90

60

43

sick days Hazard ratio

Long-term sick leave and the impact of a graded…

Hazard ratio of the graded return-to-work programm over time by seleted ICD chapters ICD 05: mental disorders

ICD 13: musculoskeletal system

ICD 19: injury, poisoning

other ICD chapters

3

2

hazard ratio

1

0

3

2

1

0

0+ 48 79 4 045 49 4 042 19 4 039 8 9 3 036 9 35 033 29 3 030 9 29 027 69 2 02 4 39 2 021 0 9 2 018 7 9 1 015 4 9 1 012 9 1 -1 90 9 -8 60 9 -5 43 0+ 48 79 4 045 49 4 042 19 4 039 89 3 036 59 3 033 9 32 030 99 2 027 69 2 024 39 2 021 09 2 018 79 1 015 49 1 012 9 1 -1 90 9 -8 60 9 -5

43

sick days Hazard ratio

95 % Upper and Lower Confidence Limit

Fig. 2 Hazard ratio of the RTW program by ICD diagnosis

samples in Figs. 1 and 2, a rise in the hazard ratio can be observed at the end of the 517-day period, which could be related to the maximum payment duration of sickness benefits of 78 weeks. The results presented above can be discussed in the presence of a strong correlation between the income-related factors such as sickness benefits, the index of socioeconomic status and voluntary insurance. The latter factor is directly related to earned income as it is only possible to opt out of the SHI system if the earned income is above the income threshold for compulsory insurance (€49.500 p.a. in 2011). It has yet to be proved whether the correlation has — besides a loss in efficiency of the estimation — an impact on the results of the interactions between participation in the RTW program and time. Table 4 presents the estimated hazard ratios for the RTW program — the main interesting effect — for combinations of income-related explanatory factors as a robustness check. The results in the estimated hazard rates and standard errors differ only slightly in comparison to Table 3. Hence, the latter results can be viewed as robust with respect to variation in

income-related factors. Together with the question concerning robustness, we can ask for guidance with respect to model selection. Therefore, we use information criteria [Akaike (AIC) and Schwarz/Baysian Criterion (BIC)] as measures of goodness of fit [38]. The difference between these measures is that the BIC puts more weight on a parsimonious specification. Starting with the AIC, the preferred specification is the one with all income-related factors included, whereas the BIC would choose voluntary insurance as the only income-related explanatory factor. In the presented analysis, we followed the AIC and included all factors of interest. The difference in the information criteria finally depends on the consideration of estimated parameters in both measures.

Discussion and outlook Graded return-to-work programs allowing a stepwise reentry into the working environment after long-term absenteeism are legally coded in the German Social Code

123

U. Schneider et al. Table 4 Robustness of the results and variations in income-related variables RTW * sickness daysa 43–59 60–89 90–119 120–149 150–179 180–209 210–239 240–269

Sickness benefits only

Voluntarily insured only

ISEI only

Sickness benefits and voluntarily insured

Sickness benefits and ISEI

Voluntarily insured and ISEI

Without income variables

All income variables

0.0942

0.0942

0.0942

0.0942

0.0942

0.0942

0.0942

0.0941

(0.0068)

(0.0068)

(0.0068)

(0.0068)

(0.0068)

(0.0068)

(0.0068)

(0.0068)

0.5838 (0.0213)

0.5839 (0.0213)

0.5846 (0.0213)

0.5830 (0.0213)

0.5839 (0.0213)

0.5838 (0.0213)

0.5847 (0.0213)

0.5831 (0.0213)

0.9572

0.9578

0.9597

0.9561

0.9578

0.9579

0.9593

0.9566

(0.0311)

(0.0311)

(0.0311)

(0.0310)

(0.0311)

(0.0311)

(0.0311)

(0.0310)

1.0761

1.0764

1.0794

1.0743

1.0771

1.0767

1.0788

1.0751

(0.0373)

(0.0373)

(0.0374)

(0.0373)

(0.0374)

(0.0374)

(0.0374)

(0.0373)

1.1095

1.1099

1.1136

1.1073

1.1106

1.1103

1.1129

1.1082

(0.0448)

(0.0448)

(0.0449)

(0.0447)

(0.0448)

(0.0448)

(0.0449)

(0.0447)

1.2685

1.2679

1.2727

1.2650

1.2695

1.2680

1.2723

1.2658

(0.0591)

(0.0591)

(0.0593)

(0.0590)

(0.0592)

(0.0591)

(0.0593)

(0.0590)

1.2309

1.2293

1.2340

1.2272

1.2315

1.2290

1.2340

1.2275

(0.0692)

(0.0691)

(0.0693)

(0.0690)

(0.0692)

(0.0691)

(0.0693)

(0.0690)

1.3720

1.3694

1.3746

1.3673

1.3721

1.3685

1.3753

1.3673

(0.0867)

(0.0866)

(0.0869)

(0.0864)

(0.0867)

(0.0865)

(0.0869)

(0.0864)

270–299

1.4149

1.4120

1.4176

1.4101

1.4148

1.4106

1.4190

1.4098

300–329

(0.1015) 1.5152

(0.1013) 1.5113

(0.1017) 1.5185

(0.1012) 1.5095

(0.1015) 1.5154

(0.1012) 1.5104

(0.1018) 1.5196

(0.1012) 1.5095

(0.1209)

(0.1207)

(0.1212)

(0.1205)

(0.1210)

(0.1206)

(0.1213)

(0.1205)

1.6339

1.6270

1.6373

1.6264

1.6345

1.6267

1.6375

1.6269

(0.1398)

(0.1392)

(0.1401)

(0.1392)

(0.1398)

(0.1392)

(0.1401)

(0.1392)

330–359 360–389 390–419 420–449 450–479

1.6764

1.6665

1.6810

1.6669

1.6779

1.6670

1.6803

1.6681

(0.1554)

(0.1546)

(0.1559)

(0.1546)

(0.1556)

(0.1546)

(0.1558)

(0.1547)

1.4114

1.4027

1.4154

1.4032

1.4127

1.4030

1.4147

1.4042

(0.1484)

(0.1475)

(0.1488)

(0.1476)

(0.1485)

(0.1476)

(0.1487)

(0.1477)

1.5581

1.5491

1.5638

1.5501

1.5599

1.5494

1.5632

1.5514

(0.1655)

(0.1646)

(0.1661)

(0.1648)

(0.1657)

(0.1647)

(0.1660)

(0.1649)

1.5537

1.5484

1.5576

1.5502

1.5556

1.5492

1.5564

1.5518

(0.1750)

(0.1745)

(0.1754)

(0.1747)

(0.1752)

(0.1746)

(0.1753)

(0.1749)

2.0314

2.0296

2.0338

2.0357

2.0339

2.0312

2.0312

2.0379

(0.2130)

(0.2131)

(0.2131)

(0.2137)

(0.2132)

(0.2132)

(0.2130)

(0.2139)

AICb

226603.54

226549.91

226607.97

226540.31

226597.89

226542.35

226623.00

226536.07

SBCb R2c

226947.74 0.1242

226877.71 0.1257

226952.17 0.1241

226900.90 0.1262

226974.87 0.1246

226902.94 0.1262

226934.42 0.1234

226929.44 0.1266

480?

Coefficients represent hazard ratios of the interactions; standard errors in parentheses, N = 28.856 a

First column: interactions between RTW and time

b

AIC Akaike information criterion, SBC Schwarz/Baysian criterion

c

Generalized R2

Book and are part of company integration management. Although established 25 years ago and practiced in various forms by different sickness funds for more than 40 years, there is a lack of empirical evidence regarding the program effects. The central question is whether participation in the program results in a faster and permanent return to work. Up to now, empirical evidence for Germany has only been

123

available for the return-to-work program after a rehab treatment. This program is financed by Statutory Pension Insurance and results cannot be transferred to the SHI because the underlying sample differs in many respects, for instance gender. Empirical evidence from other European countries suggests that there exists a positive program effect after exceeding a certain threshold of sick days

Long-term sick leave and the impact of a graded…

(between several weeks and 120–150 days). The study at hand is therefore the first analysis of a graded return-towork program in the German SHI using claims data from a large German sickness fund. Since 1st October 2010, data on participation in the program has been systematically included. Among the insured with a long-term sick leave starting between 1st October 2010 and 31st January 2011, about one quarter participated in the program. This relatively low participation rate may be due to lack of knowledge about the program: either employees do not have the necessary information or are in a bad health status that prohibits participation, employers fail to establish effective occupational health management, or physicians do not consider this alternative. One major challenge in analyzing RTW data is a possible selection bias into the program. To account for selection effects, we used a weighting procedure by first estimating the probability of participation and second including transformed propensity scores into the analysis of absenteeism duration. Our chosen procedure to first estimate the weights from a logit regression that enter into the Cox regression is only valid if any variable with a simultaneous influence on treatment assignment and potential outcomes is observable (CIA). This assumption itself is not testable. The open question is whether there exist unobservable factors that drive selection into the RTW program. One such factor may be individual health. In empirical studies, true health is a latent variable, and therefore an individual’s health status has to be approximated by selected indicators. In the analysis at hand, we controlled for different factors like physician status (GP versus specialist), demand and expenditure for physician services (visits and hospital stays), and comorbidity (Elixhauser score as a measure of patient’s past health status) that included information on the patient’s health status. Nevertheless, some uncertainty remains regarding the existence of unobservables in the selection process. Besides dealing with sample selection, a second issue to handle depends on the assumption of proportionality of the hazard functions in the Cox regression. For the variables under suspicion of violating this assumption, we introduced interactions with time, the duration of the sickness episode. For our main variable of interest, we dichotomize time into 30-day intervals to account for any non-linearity in the effect of the RTW program. As a result, our approach shows a positive effect of the program for more than 120 days of sickness. The study presents first evidence about the advantages of participation in a return-to-work program using claims data for Germany. Our results stress that the duration of sickness episodes is related to program participation. The estimated positive impact depends on sickness time above a threshold of 120–150 days. Stratification with

respect to ICD diagnoses shows an even stronger effect for mental disorders (90 days) whereas for ICD chapter 19 (injuries) the program seems to be effective only after more than 240 days of sickness, a result that emphasizes the strong heterogeneity among the cases in this diagnostic chapter. One striking result is that program participation seems to go along with longer sickness periods for a large group of individuals, namely those with spells shorter than 90–120 days. One reason for this may be that participants are still classified as sick during program participation and receive sickness benefits, even if they work part time in the RTW program. There may be good reasons why participation of this subgroup in the program is nevertheless effective. Firstly, by gradually increasing the workload, the risk of a relapse may be reduced. Secondly, employers are able to accompany the RTW process at an early stage. Thirdly, even if total sickness benefits paid to this group seem to be higher than without participation, the reduced relapse risk may save future sickness benefits and medical expenditures. For a deeper discussion of the estimated longer sickness periods for those with spells up to 90–120 days, one has to take into account that some individuals may take part in the program with a high chance of returning into work even if they would not have participated. As pointed out, participation would presumably lead to c.p. longer sickness episodes. Descriptive statistics showed that the time difference from beginning of sick leave to program start as well as program duration is shorter for this group than for those with longer sickness spells. Therefore, there is a correlation of shorter program participation with an early entry into the program. Nevertheless, an individual starting full-time working directly after long-term illness up to 90–120 days has a shorter total sickness absence compared to an individual starting part-time working in a RTW program. The reason for this is that the time in the program has to be added to the sickness duration. For individuals with spells above 120 days, results suggest that return to work would have taken much longer, on average, without such a program. We would expect shorter sickness episodes as workload would rise only slowly up to the point where the employee is fully able to work. While the participation decision is observable, it is not possible from our results to draw conclusions about the isolated effects of a hypothetical participation and nonparticipation. However, from the data at hand, we know that all observed participants were eligible for the program and only those individuals should apply for the RTW program for which a full recovery of the ability to work is plausible, which has to be attested by their physician. Therefore, all participants showed good employment prospects for a return to work.

123

U. Schneider et al.

Further analyses should focus on potential unobservables affecting the participation in the program and on the time after returning to work. The first task centers around the central methodological question of the study at hand: what factors drive the participation decision and how can we account for unobserved heterogeneity? For the second task, one might ask whether participants in the RTW program show higher or lower relapse rates into sickness than non-participants. Are they healthier, or do they differ with respect to their demand for medical care? Generally, one key question remaining is how to bring the right participants into the program and how to give them the required information for their decision to participate. This means identifying those potential participants with the highest net-revenue from participation. In our sample, only about one-fourth of long-term sick individuals take part in the program that has been in action for some decades. Whether non-participation is caused by a bad health status or a lack of information about the program remains unclear from the claims data used. One possibility of further promoting the program is to provide information to patients, physicians and employers. Our analysis showed that in particular for small companies, participation share is low. In addition, employees in small enterprises show longer sickness durations compared to other companies (see Table 3). Finally, better implementation of company integration management might be central in promoting the return-to-work program.

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