Income loss after a cancer diagnosis in Germany: An analysis based on the socio-economic panel survey

Cancer Med. 2021 Jun;10(11):3726-3740. doi: 10.1002/cam4.3913. Epub 2021 May 10.

Abstract

Background and aims: Cancer treatments often require intensive use of healthcare services and limit patients' ability to work, potentially causing them to become financially vulnerable. The present study is the first attempt to measure, on the German national level, the magnitude of absolute income loss after a cancer diagnosis.

Methods: This study analyzes data from the Socio-Economic Panel (SOEP) survey, one of the largest and most comprehensive household surveys in Germany, consisting of approximately 20,000 individuals, who are traced annually. The empirical strategy consists of ordinary least squares (OLS) and multinomial logistic estimators to measure changes in job income, work status, working hours, and pension as a result of reporting a cancer diagnosis for the period between 2009 and 2015. Sample consistency checks were conducted to limit measurement error biases.

Results: Our results show that job incomes dropped between 26% and 28% within the year a cancer diagnosis was reported. The effect persisted for two years after the diagnosis and was no longer observable after four years. The finding was linked to an increased likelihood of unemployment and a reduction of working hours by 24%. Pension levels, on the other hand, were not affected by a cancer diagnosis.

Conclusions: These findings suggest that many cancer patients are exposed to financial hardship in Germany, particularly when the cancer diagnosis occurs during their working age and before requirements to obtain a pension are met. Further research seems warranted to identify particularly vulnerable patient groups.

Keywords: cancer diagnosis; financial burden; income loss; socio-economic panel.

MeSH terms

  • Cost of Illness*
  • Economic Factors
  • Financial Stress
  • Germany
  • Humans
  • Income / statistics & numerical data*
  • Income / trends
  • Least-Squares Analysis
  • Logistic Models
  • Neoplasms / diagnosis*
  • Pensions / statistics & numerical data
  • Time Factors
  • Unemployment / statistics & numerical data
  • Unemployment / trends