The lidA Cohort Study (German Cohort Study on Work, Age, Health and Work Participation) was set up to investigate and follow the effects of work and work context on the physical and psychological health of the ageing workforce in Germany and subsequently on work participation. Cohort participants are initially employed people subject to social security contributions and born in either 1959 (n = 2909) or 1965 (n = 3676). They were personally interviewed in their homes in 2011 and will be visited every 3 years. Data collection comprises socio-demographic data, work and private exposures, work ability, work and work participation attitudes, health, health-related behaviour, personality and attitudinal indicators. Employment biographies are assessed using register data. Subjective health reports and physical strength measures are complemented by health insurance claims data, where permission was given. A conceptual framework has been developed for the lidA Cohort Study within which three confirmatory sub-models assess the interdependencies of work and health considering age, gender and socioeconomic status. The first set of the data will be available to the scientific community by 2015. Access will be given by the Research Data Centre of the German Federal Employment Agency at the Institute for Employment Research (http://fdz.iab.de/en.aspx).

Although secondary data analyses have been established in recent years in health research, explicit recommendations for standardized, transparent and complete reporting of secondary data analyses do not exist as yet. Therefore, between 2009 and 2014, a first proposal for a specific reporting standard for secondary data analysis was developed (STROSA 1). Parallel to this national process in Germany, an international reporting standard for routine data analysis was initiated in 2013 (RECORD). Nevertheless, because of the specific characteristics of the German health care system as well as specific data protection requirements, the need for a specific German reporting standard for secondary data analyses became evident. Therefore, STROSA was revised and tested by a task force of 15 experts from the working group Collection and Use of Secondary Data (AGENS) of the German Society for Social Medicine and Prevention (DGSMP) and the German Society for Epidemiology (DGEpi) as well as from the working group Validation and Linkage of Secondary Data of the German Network for Health Services Research (DNVF). The consensus STROSA-2 checklist includes 27 criteria, which should be met in the reporting of secondary data analysis from Germany. The criteria have been illustrated and clarified with specific explanations and examples of good practice. The STROSA reporting standard aims at stimulating a wider scientific discussion on the practicability and completeness of the checklist. After further discussions and possibly resulting modifications, STROSA shall be implemented as a reporting standard for secondary data analyses from Germany. This will guarantee standardized and complete information on secondary data analyses enabling assessment of their internal and external validity.

In the German health service research there exists, unlike in labour market research, no experience with the combination of personal primary and secondary data. One of the reasons for this is, among other things, data protection. The lidA cohort study analyses persons in employment (excluding civil servants and self-employed), born in 1959 and 1965. It is intended to give answers to questions from the health services research as well as from the labour market research. It relies on different primary and secondary data sources: survey data, process data from the Federal Employment Agency, aggregated and individual health insurance data. The experiences made in the lidA study, in addition to the data protection needs and the expenditures for the implementation, are summarised as "best practice". The procedure for the application process according to § 75 SGB X, the directive to develop data security concepts, the informed consent for the linkage of personal information are described and the importance of a transparent approach is explained. So far it has been shown that the preparation and approval process for the release of the actual data, both within the project consortium, but also with external parties such as health insurance companies or the responsible data protection officer, requires a major effort. In view of all the identified legal and organisational challenges, our findings should be extremely useful for answering different research questions in the fields of labour market and health services research. In combination with primary data, they may even represent a "gold standard" for epidemiology and health services research.

BackgroundClose, continuous and efficient collaboration between different professions and sectors of care is necessary to provide patient-centered care for individuals with mental disorders. The lack of structured collaboration between in- and outpatient care constitutes a limitation of the German health care system. Since 2012, a new law in Germany (§64b Social code book (SGB) V) has enabled the establishment of cross-sectoral and patient-centered treatment models in psychiatry. Such model projects follow a capitation budget, i.e. a total per patient budget of inpatient and outpatient care in psychiatric clinics. Providers are able to choose the treatment form and adapt the treatment to the needs of the patients. The present study (EVA64) will investigate the effectiveness, costs and efficiency of almost all model projects established in Germany between 2013 and 2016.Methods/designA health insurance data-based controlled cohort study is used. Data from up to 89 statutory health insurance (SHI) funds, i.e. 79% of all SHI funds in Germany (May 2017), on inpatient and outpatient care, pharmaceutical and non-pharmaceutical treatments and sick leave for a period of 7 years will be analyzed. All patients insured by any of the participating SHI funds and treated in one of the model hospitals for any of 16 pre-defined mental disorders will be compared with patients in routine care. Sick leave (primary outcome), utilization of inpatient care (primary outcome), utilization of outpatient care, continuity of contacts in (psychiatric) care, physician and hospital hopping, re-admission rate, comorbidity, mortality, disease progression, and guideline adherence will be analyzed. Cost and effectivity of model and routine care will be estimated using cost-effectiveness analyses. Up to 10 control hospitals for each of the 18 model hospitals will be selected according to a pre-defined algorithm.DiscussionThe evaluation of complex interventions is an important main task of health services research and constitutes the basis of evidence-guided advancement in health care. The study will yield important new evidence to guide the future provision of routine care for mentally ill patients in Germany and possibly beyond.Trial registrationThis study was registered in the database “Health Services Research Germany” (trial number: VVfD_EVA64_15_003713).

ZusammenfassungDas personenbezogene Verknüpfen verschiedener Datenquellen (Datenlinkage) für Forschungszwecke findet in den letzten Jahren in Deutschland zunehmend Anwendung. Jedoch fehlen hierfür konsentierte methodische Standards. Ziel dieses Beitrages ist es, solche Standards für Forschungsvorhaben zu definieren. Eine weitere Intention ist es, dem Lesenden eine Checkliste zur Bewertung geplanter Forschungsvorhaben und Artikel bereitzustellen. Zu diesem Zweck hat eine aus Mitgliedern verschiedener Fachgesellschaften zusammengesetzte Expertengruppe seit 2016 insgesamt 7 Leitlinien mit 27 konkreten Empfehlungen erstellt. Die Gute Praxis Datenlinkage beinhaltet die folgenden Leitlinien: (1) Forschungsziele, Fragestellung, Datenquellen und Ressourcen, (2) Dateninfrastruktur und Datenfluss, (3) Datenschutz, (4) Ethik, (5) Schlüsselvariablen und Linkageverfahren, (6) Datenprüfung/Qualitätssicherung sowie (7) Langfristige Datennutzung für noch festzulegende Fragestellungen. Jede Leitlinie wird ausführlich diskutiert. Zukünftige Aktualisierungen werden wissenschaftliche und datenschutzrechtliche Entwicklungen berücksichtigen.

ZusammenfassungDie Verknüpfung verschiedener Datenquellen, genannt Datenlinkage oder auch Record Linkage, zur Beantwortung von wissenschaftlichen Fragestellungen findet in den letzten Jahren in Deutschland vermehrt Anwendung. Jedoch mangelt es bisher an publizierten Erfahrungen. Neue Projekte erarbeiten sich in der Regel autark voneinander das notwendige Handwerkszeug. Daher hat sich eine Gruppe von Forschern zusammengefunden, um ihre Erfahrungen zum Datenlinkage in Deutschland als mögliche Hilfestellung bzw. Anregung für Projekte, Gutachter sowie Datenschützer und Ethikkommissionen zusammenzustellen. Ziel dieser ersten Bestandsaufnahme zum Datenlinkage ist es deshalb, eine Unterstützung für zukünftige Projekte zu liefern, die Daten aus Deutschland auf individueller Ebene verknüpfen möchten. Neben den (datenschutz-)rechtlichen Rahmenbedingungen werden dabei auch praxisorientiert die Arten des Datenlinkage, deren Anwendungsfelder und Ansätze zur Vermeidung von Fehlern anhand von Beispielen dargestellt.

The data linkage of health-related primary and secondary data provides new opportunities for health services research. The advantages of both data sources can be used synergistically, in this way their disadvantages can be overcome. In the context of the evaluation of a health intervention - the integrated health services project ('Gesundes Kinzigtal') - the conditions and requirements for an individualised data linkage of primary data (survey) and claims data of a statutory health insurance are described in this paper. The integration of secondary data permits us not only to assess the intervention concerning physical activity, nutrition and social participation of elderly people ('AGil') but, above all, also to measure and analyse the program effects on the utilisation of health care services. Recommendations regarding the data linkage of primary and secondary data in health services research are derived from the results and experiences of the AGil study. Suggestions are made concerning the suitable pseudonymisation algorithm for primary and secondary data, the matching method, approaches to reduce mismatching and their validation, as well as the legal basis for such a data linkage. Overall, an individualised data linkage of primary and secondary data does not pose any technical problems. Nevertheless a couple of data protection rules have to be followed; the data linkage offers a high knowledge insight to many health and epidemiological research questions and might be the new gold standard for health services research.

In Germany, research on health-care services addresses many topics within a regional context, and it predominantly uses a single (typically secondary) data sources for this purpose. The specific disadvantages and methodological challenges associated with these data sources may limit analysis. Various data sources break the data down by region and may be of interest in regional health-care research. Linking multiple data sources (data linkage) could therefore expand analysis options in this area. Researchers in this field are currently discussing various approaches for using data linkage to overcome the respective weaknesses of primary and secondary data. This contribution covers the various types of data linkage (on an aggregate or individual level) and their potentials and limitations in small area health services research. The focus lies on individual data linkage, which requires written informed consent. Taking into account methodological and particularly data protection challenges, conclusions are drawn regarding future application areas and options of small area health services research and specific examples are provided.