Nico Dragano1, Marvin Reuter2, Karin Halina Greiser3, Heiko Becher4, Hajo Zeeb5,6, Rafael Mikolajczyk7, Alexander Kluttig7, Michael Leitzmann8, Beate Fischer8, Karl-Heinz Jöckel9, Carina Emmel9, Gérard Krause10,11, Stefanie Castell10, Antje Damms-Machado3, Nadia Obi4, Tamara Schikowski12, Oliver Kuss13, Wolfgang Hoffmann14, Sabine Schipf14, Tobias Pischon15,16,17,18, Lina Jaeschke15, Lilian Krist19, Thomas Keil19,20,21, Wolfgang Lieb22, Bernd Holleczek23,24, Hermann Brenner24,25, Kerstin Wirkner26, Markus Loeffler26,27, Karin B Michels28, Claus-Werner Franzke28, Annette Peters29, Jakob Linseisen30,31, Klaus Berger32, Nicole Legath32, Wolfgang Ahrens33,34, Thomas Lampert35, Börge Schmidt9. 1. Institut für Medizinische Soziologie, Centre for Health and Society, Medizinische Fakultät, Heinrich-Heine-Universität Düsseldorf, Moorenstr. 5, 40225, Düsseldorf, Deutschland. dragano@med.uni-duesseldorf.de. 2. Institut für Medizinische Soziologie, Centre for Health and Society, Medizinische Fakultät, Heinrich-Heine-Universität Düsseldorf, Moorenstr. 5, 40225, Düsseldorf, Deutschland. 3. Abteilung Epidemiologie von Krebserkrankungen, DKFZ Heidelberg, Heidelberg, Deutschland. 4. Institut für Medizinische Biometrie und Epidemiologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Deutschland. 5. Abteilung Prävention und Evaluation, Leibniz-Institut für Präventionsforschung und Epidemiologie, BIPS GmbH, Bremen, Deutschland. 6. Health Sciences Bremen, Universität Bremen, Bremen, Deutschland. 7. Institut für Medizinische Epidemiologie, Biometrie und Informatik, Martin-Luther-Universität Halle-Wittenberg, Halle (Saale), Deutschland. 8. Institut für Epidemiologie und Präventivmedizin, Universität Regensburg, Regensburg, Deutschland. 9. Institut für Medizinische Informatik, Biometrie und Epidemiologie (IMIBE), Medizinische Fakultät, Universitätsklinikum Essen, Universität Duisburg-Essen, Essen, Deutschland. 10. Abteilung für Epidemiologie, Helmholtz-Zentrum für Infektionsforschung, Braunschweig, Deutschland. 11. Medizinische Hochschule Hannover (MHH), Hannover, Deutschland. 12. IUF - Leibniz Institut für umweltmedizinische Forschung gGmbH, Düsseldorf, Deutschland. 13. Institut für Biometrie und Epidemiologie, Deutsches Diabetes-Zentrum (DDZ), Leibniz-Zentrum für Diabetes-Forschung, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Deutschland. 14. Institut für Community Medicine, Universitätsmedizin Greifswald, Greifswald, Deutschland. 15. Forschergruppe Molekulare Epidemiologie, Max-Delbrück-Centrum für Molekulare Medizin in der Helmholtz-Gemeinschaft (MDC), Berlin, Deutschland. 16. Charité - Universitätsmedizin Berlin, Berlin, Deutschland. 17. Partnerstandort Berlin, Deutsches Zentrum für Herz-Kreislauf-Forschung (DZHK), Berlin, Deutschland. 18. MDC/BIH Biobank, Max-Delbrück-Centrum für Molekulare Medizin in der Helmholtz-Gemeinschaft (MDC) und Berlin Institute of Health (BIH), Berlin, Deutschland. 19. Institut für Sozialmedizin, Epidemiologie und Gesundheitsökonomie, Charité - Universitätsmedizin Berlin, Berlin, Deutschland. 20. Institut für Klinische Epidemiologie und Biometrie, Universität Würzburg, Würzburg, Deutschland. 21. Landesinstitut für Gesundheit, Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit (LGL), Bad Kissingen, Deutschland. 22. Institut für Epidemiologie, Christian-Albrechts-Universität Kiel, Kiel, Deutschland. 23. Krebsregister Saarland, Saarbrücken, Deutschland. 24. Abteilung Klinische Epidemiologie und Alternsforschung, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland. 25. Abteilung Präventive Onkologie, Deutsches Krebsforschungszentrum (DKFZ) und Nationales Centrum für Tumorerkrankungen (NCT), Heidelberg, Deutschland. 26. Leipziger Forschungszentrum für Zivilisationserkrankungen, Medizinische Fakultät, Universität Leipzig, Leipzig, Deutschland. 27. Institut für Medizinische Informatik, Statistik und Epidemiologie (IMISE), Medizinische Fakultät, Universität Leipzig, Leipzig, Deutschland. 28. Institut für Prävention und Tumorepidemiologie, Universitätsklinikum Freiburg, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Deutschland. 29. Institut für Epidemiologie, Helmholtz Zentrum München, München, Deutschland. 30. SFG Klinische Epidemiologie, Helmholtz Zentrum München, Neuherberg, Deutschland. 31. Lehrstuhl für Epidemiologie, UNIKA-T Augsburg, Ludwig-Maximilians-Universität München, Augsburg, Deutschland. 32. Institut für Epidemiologie und Sozialmedizin, Universität Münster, Münster, Deutschland. 33. Leibniz-Institut für Präventionsforschung und Epidemiologie, BIPS GmbH, Bremen, Deutschland. 34. Institut für Statistik, Universität Bremen, Bremen, Deutschland. 35. Abteilung für Epidemiologie und Gesundheitsmonitoring, Robert Koch-Institut, Berlin, Deutschland.
Abstract
BACKGROUND: In epidemiologic studies, standardised measurement of socio-demographic and employment-related factors is becoming increasingly important, as variables such as gender, age, education or employment status are factors influencing health and disease risks. AIMS: The article gives an overview of the scientific background and assessment of socio-demographic factors in the German National Cohort Study. In addition, the distribution of individual characteristics in the cohort as well as relationships with health-related measures are presented by way of example. MATERIAL AND METHODS: The analysis is based on the data of the first half of the baseline survey (n = 101,724). On this basis, we present the distribution of key socio-demographic characteristics and analyse relationships with exemplary selected health indicators (body mass index, self-reported health) to assess the validity of socio-demographic data measurements. RESULTS: On average, study participants were 52.0 years old (SD = 12.4). Of the participants, 53.6% were women, 54.3% had high education, 60.1% were married and 72% were employed while 3.4% were unemployed. Well-established correlations between socio-demographic factors and health could be reproduced with the German National Cohort data. For example, low education, old age and unemployment were associated with an increased prevalence of obesity and poor self-reported health. DISCUSSION: The German National Cohort provides a comprehensive measurement of socio-demographic characteristics. Combined with a wide range of health data and the longitudinal measurements available in the future, this opens up new opportunities for health science and social epidemiological research in Germany.
BACKGROUND: In epidemiologic studies, standardised measurement of socio-demographic and employment-related factors is becoming increasingly important, as variables such as gender, age, education or employment status are factors influencing health and disease risks. AIMS: The article gives an overview of the scientific background and assessment of socio-demographic factors in the German National Cohort Study. In addition, the distribution of individual characteristics in the cohort as well as relationships with health-related measures are presented by way of example. MATERIAL AND METHODS: The analysis is based on the data of the first half of the baseline survey (n = 101,724). On this basis, we present the distribution of key socio-demographic characteristics and analyse relationships with exemplary selected health indicators (body mass index, self-reported health) to assess the validity of socio-demographic data measurements. RESULTS: On average, study participants were 52.0 years old (SD = 12.4). Of the participants, 53.6% were women, 54.3% had high education, 60.1% were married and 72% were employed while 3.4% were unemployed. Well-established correlations between socio-demographic factors and health could be reproduced with the German National Cohort data. For example, low education, old age and unemployment were associated with an increased prevalence of obesity and poor self-reported health. DISCUSSION: The German National Cohort provides a comprehensive measurement of socio-demographic characteristics. Combined with a wide range of health data and the longitudinal measurements available in the future, this opens up new opportunities for health science and social epidemiological research in Germany.
Entities:
Keywords:
Body mass index; German National Cohort; Social epidemiology; Social inequality; Socio-economic position
Authors: Lilian Krist; Ahmed Bedir; Julia Fricke; Alexander Kluttig; Rafael Mikolajczyk Journal: BMC Med Res Methodol Date: 2021-08-23 Impact factor: 4.615