[Good Practice of Secondary Data Analysis (GPS): guidelines and recommendations]. / Gute Praxis Sekundärdatenanalyse (GPS): Leitlinien und Empfehlungen.

In 2005, the Working Group for the Survey and Utilisation of Secondary Data (AGENS) of the German Society for Social Medicine and Prevention (DGSMP) and the German Society for Epidemiology (DGEpi) first published "Good Practice in Secondary Data Analysis (GPS)" formulating a standard for conducting secondary data analyses. GPS is intended as a guide for planning and conducting analyses and can provide a basis for contracts between data owners. The domain of these guidelines does not only include data routinely gathered by statutory health insurance funds and further statutory social insurance funds, but all forms of secondary data. The 11 guidelines range from ethical principles and study planning through quality assurance measures and data preparation to data privacy, contractual conditions and responsible communication of analytical results. They are complemented by explanations and practical assistance in the form of recommendations. GPS targets all persons directing their attention to secondary data, their analysis and interpretation from a scientific point of view and by employing scientific methods. This includes data owners. Furthermore, GPS is suitable to assess scientific publications regarding their quality by authors, referees and readers. In 2008, the first version of GPS was evaluated and revised by members of AGENS and the Epidemiological Methods Working Group of DGEpi, DGSMP and GMDS including other epidemiological experts and had then been accredited as implementation regulations of Good Epidemiological Practice (GEP). Since 2012, this third version of GPS is on hand and available for downloading from the DGEpi website at no charge. Especially linguistic specifications have been integrated into the current revision; its internal consistency was increased. With regards to contents, further recommendations concerning the guideline on data privacy have been added. On the basis of future developments in science and data privacy, further revisions will follow.

[How "representative" are SHI (statutory health insurance) data? Demographic and social differences and similarities between an SHI-insured population, the population of Lower Saxony, and that of the Federal Republic of Germany using the example of the AOK in Lower Saxony]. / Wie "repräsentativ" sind GKV-Daten? Demografische und soziale Unterschiede und Ähnlichkeiten zwischen einer GKV-Versichertenpopulation, der Bevölkerung Niedersachsens sowie der Bundesrepublik am Beispiel der AOK Niedersachsen.

Using data obtained from a statutory health insurance (AOK) in the federal state of Lower Saxony, this study examined whether there were differences between the insured population compared with that of Lower Saxony (Niedersachsen) and of Germany with respect to social structural characteristics. Data for the comparisons were provided by the statistical office of Germany, and all datasets were coded according to the same criteria. The differences in gender distribution and age distribution between the AOK, Lower Saxony, and Germany were small. The share of employed individuals among the insured compared with those of Lower Saxony and Germany did not differ for males, but it was lower in women. In the insured population a higher proportion of individuals had lower qualification levels than in Lower Saxony or in Germany; the number of individuals with higher qualifications was, however, sufficient to permit statistical analyses. There were differences in the distributions of social structural characteristics between the health insurance population on the one hand and the populations of Lower Saxony and of Germany on the other. Due to the high number of cases, it is nevertheless possible to analyze associations between social structural variables, health impairments, and patterns of health care utilization.