title: [Professional status and development of morbidity of health insurance patients in the period 1990 to 2003. A longitudinal Analysis of Routine Data from the Gmünder Ersatzkasse].
abstract: In Germany there are only insufficient data both with regard to general morbidity events as well as with regard to the effects of specific professions on the morbidity process. For this reason the data of the Gmuender Ersatzkasse (GEK) have been analysed with a longitudinal design for the period 1990 to 2003. Our sample includes all employed members of the GEK who had been insured as of January 1, 1990 and were between 30 and 59 years old at this baseline. The total sample comprises 129,173 men and 13,567 women. The statistical analysis was performed with the statistical package "Transitional Data Analysis" (TDA) which was developed especially for longitudinal data. The analysis includes both cumulated morbidity rates as well as transition rates (Cox regression). The analysis shows that the cumulated morbidity rates of the seven diseases are higher for women than for men in the age category 30-39 years. In the age categories 40-49 and 50-59 years the cumulated morbidity rates are higher for men than for women. With regard to the occupations, the highest morbidity rates are found for manual occupations and services without special skills. The lowest rates are found for professions with high skills, engineers and managers. The results of the longitudinal analysis show distinctive social gradients. For occupations with lower skills the morbidity rates are about 100 percent higher than those of occupations with higher skills. Longitudinal analyses on the basis of health insurance fund data can make an important contribution to the monitoring of health and morbidity and should therefore be conducted also by other health insurance funds.
authors.affiliation: Abteilung Gesundheitspolitik, Arbeits- und Sozialmedizin, Zentrum für Sozialpolitik, Universität Bremen. atimm@zes.uni-bremen.de
authors.forename: A
authors.initials: A
authors.lastname: Timm
authors.forename: U
authors.initials: U
authors.lastname: Helmert
authors.forename: R
authors.initials: R
authors.lastname: Müller
mesh_terms: D000328:Adult; D000367:Age Factors; D003627:Data Interpretation, Statistical; D004651:Employment; D005260:Female; D005858:Germany; D006760:Hospitalization; D006801:Humans; D007348:Insurance, Health; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D009017:Morbidity / Q000639:trends*; D015999:Multivariate Analysis; D009790:Occupations; D016016:Proportional Hazards Models; D011446:Prospective Studies; D012044:Regression Analysis; D012307:Risk Factors; D012494:Sampling Studies; D012737:Sex Factors; D013997:Time Factors