Although cancer survivors are known to be at greater risk of developing second primary cancer (SPC), SPC incidence estimates in France are thus far lacking. We used a multivariate approach to compute these estimates and analyzed the effect of patient characteristics (gender, age at diagnosis, first cancer site, year of diagnosis and follow-up) on SPC risk.

When studying the influence of socioeconomic position (SEP) on health from data where individual-level SEP measures may be missing, ecological measures of SEP may prove helpful. In this paper, we illustrate the best use of ecological-level measures of SEP to deal with incomplete individual level data. To do this we have taken the example of a study examining the relationship between SEP and breast cancer (BC) stage at diagnosis. Using population based-registry data, all women over 18 years newly diagnosed with a primary BC in 2007 were included. We compared the association between advanced stage at diagnosis and individual SEP containing missing data with an ecological level SEP measure without missing data. We used three modelling strategies, 1/ based on patients with complete data for individual-SEP (n = 1218), or 2/ on all patients (n = 1644) using an ecological-level SEP as proxy for individual SEP and 3/ individual-SEP after imputation of missing data using an ecological-level SEP. The results obtained from these models demonstrate that selection bias was introduced in the sample where only patients with complete individual SEP were included. This bias is redressed by using ecological-level SEP to impute missing data for individual SEP on all patients. Such a strategy helps to avoid an ecological bias due to the use of aggregated data to infer to individual level. When individual data are incomplete, we demonstrate the usefulness of an ecological index to assess and redress potential selection bias by using it to impute missing individual SEP.