CC BY-NC-ND 4.0 · Gesundheitswesen 2020; 82(S 01): S41-S51
DOI: 10.1055/a-0965-6777
Original Article
Eigentümer und Copyright ©Georg Thieme Verlag KG 2019

Occupation as a Proxy for Job Exposures? Routine Data Analysis Using the Example of Rehabilitation

Berufstätigkeit als Proxy für Arbeitsbelastungen? Routinedatenanalyse am Beispiel der Rehabilitation
1   Institute of Medical Sociology and Rehabilitation Science, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany
Sebastian Bernert
1   Institute of Medical Sociology and Rehabilitation Science, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany
Karla Spyra
1   Institute of Medical Sociology and Rehabilitation Science, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany
› Author Affiliations
Funding Both studies were funded by the German Federal Pension Insurance. The funder had no role in study design, data analysis, interpretation of data, preparation of the manuscript or in the decision to publish the results.
Further Information

Publication History

Publication Date:
28 October 2019 (online)


Aim of the study Job exposures are associated with health-related outcomes including sick leave and reduction in earning capacity. Rehabilitation of persons in working age aims primarily to secure or restore work capacity. Information concerning job exposures is, however, not directly available in routine data of healthcare payers. Since exposures relate to specific occupations and the current occupation is part of routine data, job exposures may be determined indirectly via job-exposure matrices (JEM). The aim of the study is to describe the possibilities and challenges of the representation of job exposures by the occupation according to routine data using the example of rehabilitation.

Methods The Scientific Use File ‘SUFRSDLV15B’ of the German Pension Insurance was analysed. We used data from n=1 242 171 persons in work with at least one completed medical rehabilitation between 2008 and 2015 (dataset 1). The occupation is coded according to KldB 88 or KldB 2010 (German Classification of Occupations). In addition, data from a nationwide survey with 2530 rehabilitation patients was available (dataset 2). Job exposures are operationalized by the Job Exposure Index via JEM. The relationship to the return-to-work prognosis at the end of rehabilitation (dataset 1) and to patient reported outcome measures (dataset 2) is described.

Results Information concerning the occupation is available for about 91% of rehabilitation measures of employed patients for the year prior to rehabilitation. At high levels of job exposures, the proportion of persons with a predicted working capacity in the last job of fewer than 3 h per day increased by a factor of 4 compared to low-level job exposures (23.5 vs. 6.1%). On the other hand, there is a low association only to reduced working capacity in the general labour market (2.9 vs. 2.4%). High-level job exposures are associated with self-reported, work-related impairments.

Conclusion The Job Exposure Index may offer a valid approach to depict occupation-related exposures. The index can be used in the analysis of routine data of the pension insurance and other social security funds, as well as in the linkage of individual assessment data with routine data containing the occupation, without any additional data collection effort. Due to its construction based on job classifications, it will not replace the assessment of individual burdens.


Ziel der Studie Arbeitsbelastungen sind mit verschiedenen Gesundheitsindikatoren wie Arbeitsunfähigkeit und Erwerbsminderung assoziiert. Primäres Ziel der Rehabilitation von Personen im erwerbsfähigen Alter ist meist die Sicherung bzw. Wiederherstellung der Erwerbsfähigkeit. Allerdings sind Arbeitsbelastungen in Routinedaten der Kostenträger nicht verfügbar. Da viele Belastungen typisch für einzelne Berufe sind und die Tätigkeit in Routinedaten dokumentiert ist, können Arbeitsbelastungen indirekt über die Berufstätigkeit dargestellt werden. Ziel der Arbeit ist es, Möglichkeiten und Grenzen der Abbildung von Arbeitsbelastungen durch die Tätigkeit gemäß Routinedaten am Beispiel der Rehabilitation zu beschreiben.

Methodik Das Scientific Use File „SUFRSDLV15B“ der Deutschen Rentenversicherung mit 1 242 171 Erwerbstätigen mit mindestens einer abgeschlossenen medizinischen Rehabilitation 2008–2015 wurde analysiert (Datensatz 1). Die Tätigkeit ist nach KldB 88 bzw. KldB 2010 (Klassifikation der Berufe) codiert. Zudem stehen Daten einer bundesweiten Befragung mit 2 530 Rehabilitanden zur Verfügung (Datensatz 2). Arbeitsbelastungen wurden mithilfe eines Index über die Berufstätigkeit durch Bildung von Job-Exposure-Matrizen operationalisiert. Der Zusammenhang zur Return-to-Work-Prognose am Ende der Rehabilitation (Datensatz 1) und zu selbstberichteten Beeinträchtigungen und Ressourcen (Datensatz 2) wird berichtet.

Ergebnisse Für etwa 91% der medizinischen Rehabilitationen bei Erwerbstätigen liegen für das Jahr vor Rehabilitation Informationen zur Berufstätigkeit und damit zum Arbeitsbelastungsindex vor. Bei hohen Arbeitsbelastungen war der Anteil mit einer prognostizierten Arbeitsfähigkeit im letzten Beruf von weniger als 3 Stunden täglich etwa um den Faktor 4 im Vergleich zu niedriger Arbeitsbelastung erhöht (23,5 vs. 6,1%). Hingegen besteht nur ein geringer Zusammenhang zur verminderten Leistungsfähigkeit auf dem allgemeinen Arbeitsmarkt (2,9 vs. 2,4%). Hohe Arbeitsbelastungen sind mit selbstberichteten, insbesondere berufsbezogenen Beeinträchtigungen assoziiert.

Schlussfolgerung Der Arbeitsbelastungsindex bietet die Möglichkeit, berufstypische Arbeitsbelastungen valide abzubilden. Der Index kann bei Analysen von Routinedaten der Sozialversicherungsträger sowie bei Verknüpfung von Primärdaten mit Routinedaten, welche die Berufstätigkeit enthalten, ohne zusätzlichen Erhebungsaufwand eingesetzt werden. Aufgrund seiner Konstruktion über die Berufstätigkeit kann der Index die Erhebung von individuellen Belastungen nicht ersetzen.

  • Literature

  • 1 Dragano N, Schneider L. Work related psychosocial factors and the risk of early disability pensioning: a contribution to assessing the need for rehabilitation. Rehabilitation 2011; 50: 28-36
  • 2 Liebers F, Brendler C, Latza U. Age- and occupation-related differences in sick leave due to frequent musculoskeletal disorders. Bundesgesundheitsblatt 2013; 56: 367-380
  • 3 Peter R, March S, du Prel J-B. Are status inconsistency, work stress and work-family conflict associated with depressive symptoms? Testing prospective evidence in the lidA study. Soc Sci Med 2016; 151: 100-109
  • 4 Rommel A, Varnaccia G, Lahmann N. et al. Occupational injuries in Germany: population-wide national survey data emphasize the importance of work-related factors. PLoS One 2016; 11: e0148798
  • 5 Backé EM, Seidler A, Latza U. et al. The role of psychosocial stress at work for the development of cardiovascular diseases: a systematic review. Int Arch Occup Environ Health 2012; 85: 67-79
  • 6 Roski C, Romppel M, Grande G. Risk factors for disability pensioning caused by mental disorders - A systematic review. Gesundheitswesen 2017; 79: 472-483
  • 7 Rose U, Müller G, Freude G. et al. Working conditions and mental health among salaried physicians: A nationwide comparison with a representative sample of employees. Gesundheitswesen 2019; 81: 382-390
  • 8 Dragano N, Wahrendorf M, Müller K. et al. Work and health inequalities: the unequal distribution of exposures at work in Germany and Europe. Bundesgesundheitsblatt 2016; 59: 217-227
  • 9 Kroll LE. Konstruktion und Validierung eines allgemeinen Index für die Arbeitsbelastung in beruflichen Tätigkeiten anhand von ISCO-88 und KldB-92. Methoden – Daten – Analysen 2011; 5: 63-90
  • 10 Niedhammer I, Bourgkard E, Chau N. Occupational and behavioural factors in the explanation of social inequalities in premature and total mortality: a 12.5-year follow-up in the Lorhandicap study. Eur J Epidemiol 2011; 26: 1-12
  • 11 Nübling M, Stößel U, Hasselhorn H-M. et al. Measuring psychological stress and strain at work: evaluation of the COPSOQ Questionnaire in Germany. GMS Psycho-Social Medicine 2006; 3: 1-14
  • 12 Boedeker W, Friedel H, Friedrichs M. et al. The impact of work on morbidity-related early retirement. J Public Health 2008; 16: 97-105
  • 13 Goldberg M, Kromhout H, Guénel P. et al. Job exposure matrices in industry. Int J Epidemiol 1993; 22: S10-S15
  • 14 Meyer S-C, Nelen A. Do occupational demands explain the educational gradient in health?. Bonn: Institute for the Study of Labor; 2014
  • 15 Bondo Petersen S, Flachs EM, Prescott EIB. et al. Job-exposure matrices addressing lifestyle to be applied in register-based occupational health studies. Occup Environ Med 2018; 75: 890-897
  • 16 Nübling M, Vomstein M, Haug A. et al. Are reference data from the COPSOQ database suitable for a JEM on psychosocial factors at work?. Zbl Arbeitsmed 2017; 67: 151-154
  • 17 Taeger D. Basic principles of a job exposure matrix. Zbl Arbeitsmed 2017; 67: 143-150
  • 18 Rijs KJ, van der Pas S, Geuskens GA. et al. Development and validation of a physical and psychosocial job-exposure matrix in older and retired workers. Ann Occup Hyg 2014; 58: 152-170
  • 19 Stegmann M. Meldeverfahren zur Sozialeversicherung. Änderung der Erfassung der Angaben über Bildung, Beruf und Beschäftigungsform im Meldeverfahren der Sozialversicherung. Deutsche Rentenversicherung 2009; 9: 487-500
  • 20 Paulus W, Matthes B. The German Classification of Occupations 2010 – Structure, Coding and Conversion Table. Nuremberg: Institute for Employment Research; 2013;
  • 21 Grobe T, Ihle P. Stammdaten und Versichertenhistorien. In: Swart E, Ihle P. et al. (Hrsg.) Routinedaten im Gesundheitswesen: Handbuch Sekundärdatenanalyse: Grundlagen, Methoden und Perspektiven. Bern: Huber; 2014: 28-37
  • 22 Eurofound. Sixth European Working Conditions Survey – Overview Report (2017 update). Luxembourg: Publications Office of the European Union; 2017
  • 23 International Labour Office. International Standard Classification of Occupations. Geneva: ILO; 2012
  • 24 Hasselhorn HM, Peter R, Rauch A. et al. Cohort profile: the lidA Cohort Study. A German Cohort Study on Work, Age, Health and Work Participation. Int J Epidemiol 2014; 43: 1736-1749
  • 25 Prigge M, Köhr M, Pfeiffer N. et al. Coding of occupational information in the baseline examination of the Gutenberg Health Study using the German Classification of Occupations KldB 2010 – presentation of the procedure and the data quality. Zbl Arbeitsmed 2014; 68: 153-161
  • 26 Wagner GG, Frick JR, Schupp J. The German Socio-Economic Panel study (SOEP) – evolution, scope and enhancements. Schmollers Jahrbuch 2007; 127: 139-169
  • 27 Lange C, Jentsch F, Allen J. et al. Data resource profile: German Health Update (GEDA). The health interview survey for adults in Germany. Int J Epidemiol 2015; 44: 442-450
  • 28 Nowossadeck E, Pohlner S, Kamtsiuris P. Utilization of medical rehabilitation services in Germany: a comparative analysis of survey and routine data. Gesundheitswesen 2017; 79: 1058-1064
  • 29 German Federal Pension Insurance. Rehabilitationsleistungen im Zeitablauf 2018. Berlin: 2018
  • 30 Brünger M, Spyra K. Prevalence of comorbid depressive symptoms in rehabilitation. Results from a cross-indication, nation-wide observational study. J Rehabil Med 2016; 48: 903-908
  • 31 Brünger M, Streibelt M, Schmidt C. et al. Psychometric testing of a generic assessment tool for the identification of biopsychosocial impairments in persons with an approval for medical rehabilitation. Rehabilitation 2016; 55: 175-181
  • 32 Brünger M, Spyra K. Importance of job demands for rehabilitation patients – application of an index according to occupations. Rehabilitation 2018; 57: 239-247
  • 33 Swart E, Gothe H, Geyer S. et al. Good Practice of Secondary Data Analysis (GPS): guidelines and recommendations. Gesundheitswesen 2015; 77: 120-126
  • 34 Hoffmann W, Latza U, Baumeister SE. et al. Guidelines and recommendations for ensuring Good Epidemiological Practice (GEP): a guideline developed by the German Society for Epidemiology. Eur J Epidemiol 2019; 34: 301-317
  • 35 Swart E, Bitzer EM, Gothe H. et al. A Consensus German Reporting Standard for Secondary Data Analyses, Version 2. Gesundheitswesen 2016; 78: e145-e160
  • 36 Rohrbach-Schmidt D. The BIBB/IAB- and BIBB/BAuA-Surveys of the working population on qualification and working conditions. Data and methodological reports. Bonn: Federal Institute for Vocational Education and Training (BIBB); 2009
  • 37 Kroll LE. Aktualisierung und erneute Validierung eines Index für Arbeitsbelastungen auf Basis von KldB-2010, KldB-92, ISCO-08 und ISCO-88. Gesundheitswesen 2015; 77: A363
  • 38 Rohrbach-Schmidt D, Hall A. BIBB/BAuA Employment Survey. Data and methodological reports. Bonn: Federal Institute for Vocational Education and Training (BIBB); 2013
  • 39 Kroll LE. Job Exposure Matrices (JEM) for ISCO and KldB (Version 2.0). Updated for ISCO-08 and KldB-2010 and including an additional Heavy Work Index. datorium 2015; DOI: 10.7802/1102.
  • 40 Stegmann M. Vergleichbarkeit der Berufklassifikationen öffentlicher Datenproduzenten und die Transformation in prominente sozialwissenschaftliche Klassifikationen und Skalen. DRV-Schriften 2005; Bd 55: 114-153
  • 41 German Federal Pension Insurance. Der ärztliche Reha-Entlassungbericht. Leitfaden zum einheitlichen Entlassungsbericht in der medizinischen Rehabilitation der gesetzlichen Rentenversicherung 2015. Berlin: 2015
  • 42 Dale AM, Ekenga CC, Buckner-Petty S. et al. Incident CTS in a large pooled cohort study: associations obtained by a Job Exposure Matrix versus associations obtained from observed exposures. Occup Environm Med 2018; 75: 501-506
  • 43 Madsen IEH, Gupta N, Budtz-Jorgensen E. et al. Physical work demands and psychosocial working conditions as predictors of musculoskeletal pain: a cohort study comparing self-reported and job exposure matrix measurements. Occup Environ Med 2018; 75: 752-758
  • 44 Hanvold TN, Sterud T, Kristensen P. et al. Mechanical and psychosocial work exposures: the construction and evaluation of a gender-specific job exposure matrix (JEM). Scand J Work Environ Health 2019; 45: 239-247
  • 45 Kroll LE, Müters S, Höbel J. et al. European Validation of ISCO-based Job Exposure Matrices using EWCS 2010. Eur J Public Health 2015; DOI: ckv175003.
  • 46 Hassoun L, Herrmann-Lingen C, Hapke U. et al. Association between chronic stress and blood pressure: findings from the German Health Interview and Examination Survey for Adults 2008–2011. Psychosom Med 2015; 77: 575-582
  • 47 Santi I, Kroll LE, Dietz A. et al. Occupation and educational inequalities in laryngeal cancer: the use of a job index. BMC Public Health 2013; 13: 1080
  • 48 Damm K, Lange A, Zeidler J. et al. Implementation of the new German job role code and its application in claims data analysis. Possibilities and limitations. Bundesgesundheitsblatt 2012; 55: 238-244
  • 49 March S, Iskenius M, Hardt J. et al. Methodological considerations for data linkage of primary and secondary data in occupational epidemiology studies. Bundesgesundheitsblatt 2013; 56: 571-578
  • 50 Hetzel C, Streibelt M. The return to work status one year after vocational retraining: is it an indicator for long term occupational participation?. Rehabilitation 2018; 57: 175-183
  • 51 Falk J, Haaf H-G, Brünger M. Rehabilitation of patients with peripheral arterial disease in the context of guideline recommendations. Rehabilitation 2019; 58: 225-233
  • 52 Fechtner S, Bethge M. Outpatient vs. inpatient rehabilitation: findings of a propensity score matched analysis. Rehabilitation 2017; 56: 372-378
  • 53 Holstiege J, Kaluscha R, Jankowiak S. et al. Associations of the employment status during the first 2 years following medical rehabilitation and long term occupational trajectories: implications for outcome measurement. Rehabilitation 2017; 56: 31-37
  • 54 Christiansen M, Schmidt JP, Shkel D. et al. A projection of the need for rehabilitation in Germany till 2040 based on demographic factors. Gesundheitswesen 2018; 80: 489-494
  • 55 Barth A, Aretz B, Doblhammer G. Risk of reduced earning capacity pension due to cardiovascular diseases after medical rehabilitation: An event history analysis based on German Statutory Pension Insurance data. Gesundheitswesen 2019; DOI: 10.1055/a-0832-2117.
  • 56 Köckerling E, Sauzet O, Hesse B. et al. Return to work after temporary disability pension. Gesundheitswesen 2019; DOI: 10.1055/a-0883-5276.
  • 57 Nivorozhkin A, Reims N, Zollmann P. et al. Vocational rehabilitation – comparing clients of the Federal Employment Agency and the German Pension Insurance. Rehabilitation 2018; 57: 149-156
  • 58 Antoni M, Ganzer A, vom Berge P. Sample of integrated labour market biographies (SIAB) 1975–2014. Nuremberg: Research Data Centre of the German Federal Employment Agency at the Institute for Employment Research; 2016
  • 59 Trappmann M, Beste J, Bethmann A. et al. The PASS panel survey after six waves. J Labour Market Res 2013; 46: 275-281
  • 60 Dannenmaier J, Ritter S, Jankowiak S. et al. Utilization of rehabilitation after disk surgery - A cross-sectoral analysis of claims data from Statutory Health Insurance and German Federal Pension Fund. Rehabilitation 2017; 56: 313-320
  • 61 Ritter S, Dannenmaier J, Jankowiak S. et al. Total hip and knee arthroplasty - utilization of postoperative rehabilitation. Rehabilitation 2018; 57: 248-255
  • 62 March S, Antoni M, Kieschke J. et al. Quo vadis data linkage in Germany? An initial inventory. Gesundheitswesen 2018; 80: e20-e31
  • 63 Kuntz B, Kroll LE, Hoebel J. et al. Time trends of occupational differences in smoking behaviour of employed men and women in Germany: results of the 1999–2013 microcensus. Bundesgesundheitsblatt 2018; 61: 1388-1398
  • 64 Swart E, Stallmann C, Schimmelpfennig M. et al. Gutachten zum Einsatz von Sekundärdaten für die Forschung zu Arbeit und Gesundheit. Berlin: German Federal Institute for Occupational Safety and Health (BAuA); 2018
  • 65 Bethge M, Spanier K, Peters E. et al. Self-reported work ability predicts rehabilitation measures, disability pensions, other welfare benefits, and work participation: longitudinal findings from a sample of German employees. J Occup Rehabil 2018; 28: 495-503
  • 66 Kessemeier F, Stockler C, Petermann F. et al. The significance of work motivation for rehabilitation success. Rehabilitation 2018; 57: 256-264
  • 67 Nübling R, Kaluscha R, Krischak G. et al. Outcome quality in medical rehabilitation: relationship between “Patient-Reported Outcomes” (PROs) and social security contributions. Rehabilitation 2017; 56: 22-30
  • 68 Streibelt M, Brünger M. How many work-related therapeutic services do patients with severe restrictions of work ability receive? Analysis of a representative rehabilitation sample across indications. Rehabilitation 2014; 53: 369-375
  • 69 Börsch-Supan A, Brandt M, Hunkler C. et al. Data resource profile: the Survey of Health, Ageing and Retirement in Europe (SHARE). Int J Epidemiol 2013; 42: 992-1001
  • 70 Mika T, Czaplicki C. SHARE-RV: Eine Datengrundlage für Analysen zu Alterssicherung, Gesundheit und Familie auf der Basis des Survey of Health, Ageing and Retirement in Europe und der Daten der Deutschen Rentenversicherung. RVaktuell 2010; 396-400