Transitions in the labor market after cancer: a comparison of self-employed workers and salaried staff.

PURPOSE: The aim of this study was to investigate whether the labor market mobility of a population of cancer survivors 2 years after diagnosis differed compared to the French general population by focusing on the differences between self-employed workers and salaried staff. METHODS: Coarsened exact matching was implemented to reduce the sampling bias introduced by the comparison of individuals from two different surveys. Then, labor market mobility was analyzed by estimating transition probability matrices from 2010 to 2012 under the framework of a continuous-time Markov technique and by estimating a two-step model. RESULTS: Salaried employees and self-employed workers from the general population were more likely to remain employed 2 years after 2010 compared to salaried employees and self-employed workers who survived cancer. There was no major difference between salaried and self-employed workers surviving cancer in terms of job retention. CONCLUSIONS: French workers surviving cancer face the same difficulties that were observed in the National Cancer Survey of 2004: unemployment and inactivity caused by the diagnosis of cancer. Among cancer survivors, self-employed workers do not seem to be particularly more affected by inactivity than salaried staff. However, unemployment insurance is not compulsory for them, contrary to salaried staff. In this regard, self-employed workers might be a more vulnerable group.

Cohort Creation and Visualization Using Graph Model in the PREDIMED Health Data Warehouse.

Grenoble Alpes University Hospital (CHUGA) is currently deploying a health data warehouse called PREDIMED [1], a platform designed to integrate and analyze for research, education and institutional management the data of patients treated at CHUGA. PREDIMED contains healthcare data, administrative data and, potentially, data from external databases. PREDIMED is hosted by the CHUGA Information Systems Department and benefits from its strict security rules. CHUGA's institutional project PREDIMED aims to collaborate with similar projects in France and worldwide. In this paper, we present how the data model defined to implement PREDIMED at CHUGA is useful for medical experts to interactively build a cohort of patients and to visualize this cohort.