Rehabilitation vor Pflegebedürftigkeit im Alter: eine Analyse anhand von Routinedaten

Meinck, Matthias; Lübke, Norbert; Polak, U.

doi:10.1055/s-0033-1347233

Cited by 3 publications

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“…Häufig wurden dabei neben Daten der großen Ersatzkassen, wie der Techniker Krankenkasse (TK) [52,53], der Gmünder ErsatzKasse (GEK) bzw. der daraus nach der Fusion entstandenen BARMER GEK [54,55] oder der Deutschen Angestellten-Krankenkasse (DAK) [56,57], entweder Daten aller AOK-Versicherten [9][10][11][12] bzw. einzelner AOKen oder Bundesländer [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] genutzt.…”

Section: Diskussionunclassified

“…Dabei kamen sowohl Daten mehrerer bzw. aller AOK-Versicherten zum Einsatz [9][10][11][12] als auch regionale Analysen einzelner AOKen bzw. Bundesländer wie Baden-Württemberg [13,14], Bayern [15,16], Berlin [17,18], Hessen [19,20], Mecklenburg-Vorpommern [21,22], Niedersachsen [23,24], Nordrhein-Westfalen [25,26], Sachsen [27,28] oder Sachsen-Anhalt [29].…”

Section: Introductionunclassified

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Verschiedene Regionen, verschiedene Versichertenpopulationen? Soziodemografische und gesundheitsbezogene Unterschiede zwischen Krankenkassen

Hoffmann

Koller²

2015

Gesundheitswesen

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Analyses of health insurance claims data are getting more important in public health and health services research. Since there are several different health insurance funds in Germany, the specific characteristics of regional and socio-demographic population covered by a single fund has to be considered. The aim of this study is to evaluate the differences in socio-demographic and health-related variables between health insurance funds. This study is based on the GEDA-Study 2009 and 2010, 2 representative cross-sectional telephone surveys (n=42 534). We included socio-economic factors as well as information on area of residence and health-related variables to health status, health behavior and cardiovascular diseases. There are fewer privately insured persons in the eastern regions of Germany. Insurants of the public health insurances have a lower socio-economic status and many have a migration background. Similar results can be found for smoking, obesity and cardiovascular factors. These differences between funds were found in many regional analyses. Especially differences in socio-economic factors are constant between insurance funds and regions. Therefore, the results show that analyses of one single health insurance fund cannot be generalized to the whole population. To ensure precise estimates on health services, morbidity or quality monitoring, we need data sets that integrate more funds.

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