Arbeitslosigkeit und ihre Auswirkungen auf die Gesundheit

Kroll, Lars Eric; Müters, Stephan; Lampert, Thomas

doi:10.1007/s00103-015-2282-7

Cited by 124 publications

(23 citation statements)

References 39 publications

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“…It is well documented that the unemployed are in worse health compared to their employed counterparts (Kroll, Müters, and Lampert 2016). However, there are three distinct but not mutually exclusive explanations for this difference: social causation, indirect selection and direct (or health) selection.…”

Section: Introductionmentioning

confidence: 99%

What explains the negative effect of unemployment on health? An analysis accounting for reverse causality

Krug

Eberl

2018

Research in Social Stratification and Mobility

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The unemployed are often in poorer health than their employed counterparts. This cross-sectional correlation is often attributed to a causal effect of unemployment on health. Resent research analyzing longitudinal data, however, often supports alternative explanations, such as spurious correlation and/or selection of unhealthy workers into unemployment (i.e., reverse causality). In this paper, we apply a dynamic panel data estimator (system GMM) to account for both unobserved confounders and reverse causality. Despite some evidence for health selection, we still find strong support for the causality thesis. Furthermore, we show that the adverse health effect is partially explained by the loss of self-perceived social status due to unemployment but not by the loss of household income or social contacts.

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Section: Introductionmentioning

confidence: 99%

What explains the negative effect of unemployment on health? An analysis accounting for reverse causality

Krug

Eberl

2018

Research in Social Stratification and Mobility

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“…Auch Präventionsangebote oder Selektivverträge von Krankenkassen können mit regionalisierten Daten deutlich zielgerichteter ausgestaltet werden, wovon schließlich die Versicherten profitieren. Unabdingbar dafür sind jedoch valide Kennziffern zur Häufigkeit von Krankheiten auf kleinräumiger Ebene (Mangiapane 2014;Nolting 2015;Klauber et al 2012 (Behrens und Noll 2006;Mielck 2008;Maier et al 2012Maier et al , 2013Sundmacher und Busse 2012;Burgdorf und Sundmacher 2014;Kroll et al 2016;Storz-Pfennig 2012).…”

Section: Nutzen Kleinräumiger Darstellungen Des Gesundheitszustandesunclassified

Schätzung kleinräumiger Krankheitshäufigkeiten für die deutsche Bevölkerung anhand von Routinedaten am Beispiel von Typ-2-Diabetes

Breitkreuz

Brückner

Burgard

et al. 2019

AStA Wirtsch Sozialstat Arch

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“…Demgegenüber stehen jedoch die positiven Aspekte der Arbeit, wie Selbstverwirklichung, soziale Unterstützung auf der Arbeit sowie die verbesserte Möglichkeit der Teilhabe am sozialen und gesellschaftlichen Leben [10,11]. In vielen Studien konnte gezeigt werden, dass Arbeitslosigkeit oder ein niedriger Sozialstatus sowohl mit einem schlechteren subjektiven als auch schlechteren ob-jektiven Gesundheitszustand assoziiert sind [12,13], sodass der Einfluss sozioökonomischer Merkmale auf die Prävalenz psychischer Erkrankungen belegt ist. Wenn die moderne und sich stetig verändernde Arbeitswelt tatsächlich verstärkt psychisch krankmacht, dann müsste sich dies auch in einem vergleichsweise erhöhten Anstieg der Prävalenz psychischer Erkrankungen bei Erwerbstätigen im Vergleich zu nicht-Erwerbstätigen im Zeitablauf äußern.…”

Section: Hintergrundunclassified

Die Häufigkeit von Depressionsdiagnosen nach Versichertengruppen im Zeitverlauf – eine Routinedatenanalyse der Jahre 2006–2015

Stahmeyer

Kuhlmann

Eberhard

2018

Psychother Psych Med

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Depressive disorders are one of the most common type of mental disorders and are associated with a significant loss of social functioning and decrease in quality of life. While survey data from the Robert Koch-Institute show no increase in the prevalence of depression during recent years, data from social insurance agencies demonstrate an increasing social significance. Many people argue that changes in working conditions are a major reason for increasing depression diagnoses. Aim of the current study was to analyze the prevalence of depression diagnoses in health insurance data (administrative prevalence) for different types of insured persons and to examine the development over time with particular regard to working and non-working insured people. We conducted a cross-sectional analysis of administrative depression prevalence for the years 2006 to 2015 using F32 and F33 diagnoses. Analyses were stratified by sex and insured persons were grouped in (1.) employees, (2.) self-employed, (3.) spouses with insurance coverage as family member, (4.) pensioners, (5.) unemployed people receiving unemployment benefit I, (6.) people receiving unemployment benefit II and (7.) students and trainees. During the 10-year period, we found an increase in administrative prevalence from 12.0% in 2006 (women: 16.3%; men: 6.8%) to 16.3% in 2015 (women: 21.3%; men: 10.9%). In 2015, highest administrative prevalence rates were observed in people receiving unemployment benefit I or II and in pensioners. The lowest rates were found in students and trainees as well as self-employed. Employees had significantly lower administrative prevalence rates than people receiving unemployment benefit. We observed large increases in administrative prevalence over time in all groups of insured persons. The results highlight the increasing social significance of depressive disorders. A comparatively greater increase in the working population was not observed. A long-term health objective should be to further improve psychiatric care.

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Arbeitslosigkeit und ihre Auswirkungen auf die Gesundheit

Cited by 124 publications

References 39 publications

What explains the negative effect of unemployment on health? An analysis accounting for reverse causality

What explains the negative effect of unemployment on health? An analysis accounting for reverse causality

Schätzung kleinräumiger Krankheitshäufigkeiten für die deutsche Bevölkerung anhand von Routinedaten am Beispiel von Typ-2-Diabetes

Die Häufigkeit von Depressionsdiagnosen nach Versichertengruppen im Zeitverlauf – eine Routinedatenanalyse der Jahre 2006–2015

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