gms | German Medical Science

GMDS 2015: 60. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

06.09. - 09.09.2015, Krefeld

Income, education, or job grade – what factors drive the social gradient in cancer survival? Results of a prospective cohort study

Meeting Abstract

  • Susanne Singer - Institut für Medizinische Biometrie, Epidemiologie und Informatik der Johannes Gutenberg-Universität Mainz, Mainz, Deutschland
  • Jens-Uwe Stolzenburg
  • Kirsten Papsdorf
  • Oliver Krauß

GMDS 2015. 60. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS). Krefeld, 06.-09.09.2015. Düsseldorf: German Medical Science GMS Publishing House; 2015. DocAbstr. 087

doi: 10.3205/15gmds150, urn:nbn:de:0183-15gmds1503

Veröffentlicht: 27. August 2015

© 2015 Singer et al.
Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). Lizenz-Angaben siehe http://creativecommons.org/licenses/by/4.0/.


Gliederung

Text

Background: Registry based studies show that cancer patients living in socially deprived neighbourhoods are more likely to die early than people in more affluent areas. However, the individual socio-economic status is likely to differ from the area-defined socio-economic status (SES). Moreover, it is important to differentiate which factors of the SES drive the social gradient in health (education, income, or job grade). This study examined the effect of socio-economic status on cancer survival using individual data, separately for each SES factor.

Methods: In a prospective multi-centre cohort study, n=1633 cancer patients were enrolled. SES was measured at the time of diagnosis using the following indicators: school education, vocational training, job grade, job type, and income. The effect of SES on survival was measured for each indicator individually, adjusting for age, gender, medical characteristics, using Poisson regression. The additional effect of health behaviour (alcohol and tobacco consumption) was analysed in separate models.

Results: There was no evidence for an effect of school education and job grade on cancer survival. Patients with the highest level of vocational training were at decreased risk of dying (RR 0.7, 95% CI 0.5-0.9) compared to patients with no vocational training; patients with blue collar jobs were at increased risk (RR 1.2; 95% CI 1.0-1.5); income had a gradual effect (RR for the highest to lowest income 0.4, 95% CI 0.3-0.5). Adding health behaviour to the models did not change the effect estimates considerably.

Discussion: Cancer survival in Germany is related to socio-economic characteristics of the patients at the time of diagnosis. Patients with higher income, better vocational training, and white collar jobs survive longer, regardless of stage of disease at baseline.