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. 2014 Feb 15:14:94.
doi: 10.1186/1471-2407-14-94.

The effect of patient characteristics on second primary cancer risk in France

Jérémie Jégu  1 , Marc Colonna, Laetitia Daubisse-Marliac, Brigitte Trétarre, Olivier Ganry, Anne-Valérie Guizard, Simona Bara, Xavier Troussard, Véronique Bouvier, Anne-Sophie Woronoff, Michel Velten
Affiliations

The effect of patient characteristics on second primary cancer risk in France

Jérémie Jégu et al. BMC Cancer. .

Abstract

Background: 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.

Methods: Data from ten French population-based cancer registries were used to establish a cohort of all patients diagnosed with a first cancer between 1989 and 2004 and followed up until December 31, 2007. The person-year approach was used to estimate standardized incidence ratios (SIRs) and excess absolute risks (EARs) of metachronous SPC. Multivariate Poisson regression models were then used to model SIRs and EARs separately by gender, adjusting for age, year of diagnosis, follow-up and first cancer site.

Results: Among the 289,967 followed-up patients with a first primary cancer, 21,226 developed a SPC. The SIR was of 1.36 (95% CI, 1.35-1.38) and the EAR was of 39.4 excess cancers per 10,000 person-years (95% CI, 37.4-41.3). Among male and female patients, multivariate analyses showed that age, year of diagnosis, follow-up and first cancer site were often independently associated with SIRs and EARs. Moreover, the EAR of SPC remained elevated during patient follow-up.

Conclusions: French cancer survivors face a dramatically increased risk of SPC which is probably related to the high rate of tobacco and alcohol consumption in France. Multivariate modeling of SPC risk will facilitate the construction of a tailored prediction tool to optimize SPC prevention and early detection strategies.

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