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Jegu J.,University of Strasbourg | Jegu J.,Hopitaux Universitaires Of Strasbourg | Belot A.,Service de Biostatistique | Belot A.,Institute of Veille Sanitaire | And 13 more authors.
Oral Oncology

Objective: To provide head and neck squamous cell carcinoma (HNSCC) survival estimates with respect to patient previous history of cancer. Materials and methods: Data from ten French population-based cancer registries were used to establish a cohort of all male patients presenting with a HNSCC diagnosed between 1989 and 2004. Vital status was updated until December 31, 2007. The 5-year overall and net survival estimates were assessed using the Kaplan-Meier and Pohar-Perme estimators, respectively. Multivariate Cox regression models were used to assess the effect of cancer history adjusted for age and year of HNSCC diagnosis. Results: Among the cases of HNSCC, 5553 were localized in the oral cavity, 3646 in the oropharynx, 3793 in the hypopharynx and 4550 in the larynx. From 11.0% to 16.8% of patients presented with a previous history of cancer according to HNSCC. Overall and net survival were closely tied to the presence, or not, of a previous cancer. For example, for carcinoma of the oral cavity, the five-year overall survival was 14.0%, 5.9% and 36.7% in case of previous lung cancer, oesophagus cancer or no cancer history, respectively. Multivariate analyses showed that previous history of cancer was a prognosis factor independent of age and year of diagnosis (p <.001). Conclusion: Previous history of cancer is strongly associated with survival among HNSCC patients. Survival estimates based on patients' previous history of cancer will enable clinicians to assess more precisely the prognosis of their patients with respect to this major comorbid condition. © 2015 Elsevier Ltd. All rights reserved. Source

Molinie F.,Registre des cancers de Loire Atlantique Vendee | Leux C.,Registre des cancers de Loire Atlantique Vendee | Delafosse P.,Registre des cancers de lIsere | Ayrault-Piault S.,Registre des cancers de Loire Atlantique Vendee | And 8 more authors.

Waiting times are key indicators of a health's system performance, but are not routinely available in France. We studied waiting times for diagnosis and treatment according to patients' characteristics, tumours' characteristics and medical management options in a sample of 1494 breast cancers recorded in population-based registries. The median waiting time from the first imaging detection to the treatment initiation was 34 days. Older age, co-morbidity, smaller size of tumour, detection by organised screening, biopsy, increasing number of specimens removed, multidisciplinary consulting meetings and surgery as initial treatment were related to increased waiting times in multivariate models. Many of these factors were related to good practices guidelines. However, the strong influence of organised screening programme and the disparity of waiting times according to geographical areas were of concern. Better scheduling of diagnostic tests and treatment propositions should improve waiting times in the management of breast cancer in France. © 2013 Elsevier Ltd. Source

Tretarre B.,Registre des tumeurs de lHerault | Molinie F.,Registre des cancers de Loire Atlantique Vendee | Woronoff A.-S.,Registre des Tumeurs du Doubs et du Territoire de Belfort | Bossard N.,Service de Biostatistiques | And 12 more authors.
Gynecologic Oncology

Objective The aim of this epidemiological study was to describe the incidence, mortality and survival of ovarian cancer (OC) in France, according to age, period of diagnosis, and histological type. Methods Incidence and mortality were estimated from 1980 to 2012 based on data in French cancer registries and from the Centre for Epidemiology of Causes of Death (CépiDc-Inserm) up to 2009. Net survival was estimated from registry data using the Pohar-Perme method, on cases diagnosed between 1989 and 2010, with date of last follow-up set at 30 June 2013. Results In 2012, 4615 cases of OC were diagnosed in France, and 3140 women died from OC. World population age-standardized incidence and mortality rates declined by respectively 0.6% and 1.2% per year between 1980 and 2012. Net survival at 5 years increased slightly, from 40% for the period 1989-1993 to 45% for the period 2005-2010. Net survival varied considerably according to histological type. Germ cell tumors had better net survival at 10 years (81%) compared to epithelial tumors (32%), sex cord-stromal tumors (40%) and tumors without biopsy (8%). Conclusions Our study shows a decline in incidence and mortality rates from ovarian cancer in France between 1980 and 2012, but net survival remains poor overall, and improved only slightly over the whole study period. © 2015 Elsevier Inc. Source

Uhry Z.,Institute of Veille Sanitaire | Hedelin G.,University of Strasbourg | Colonna M.,Registre des cancers de lIsere | Asselain B.,University Pierre and Marie Curie | And 9 more authors.
Statistical Methods in Medical Research

This work presents a brief overview of Markov models in cancer screening evaluation and focuses on two specific models. A three-state model was first proposed to estimate jointly the sensitivity of the screening procedure and the average duration in the preclinical phase, i.e. the period when the cancer is asymptomatic but detectable by screening. A five-state model, incorporating lymph node involvement as a prognostic factor, was later proposed combined with a survival analysis to predict the mortality reduction associated with screening. The strengths and limitations of these two models are illustrated using data from French breast cancer service screening programmes. The three-state model is a useful frame but parameter estimates should be interpreted with caution. They are highly correlated and depend heavily on the parametric assumptions of the model. Our results pointed out a serious limitation to the five-state model, due to implicit assumptions which are not always verified. Although it may still be useful, there is a need for more flexible models. Over-diagnosis is an important issue for both models and induces bias in parameter estimates. It can be addressed by adding a non-progressive state, but this may provide an uncertain estimation of over-diagnosis. When the primary goal is to avoid bias, rather than to estimate over-diagnosis, it may be more appropriate to correct for over-diagnosis assuming different levels in a sensitivity analysis. This would be particularly relevant in a perspective of mortality reduction estimation. © The Author(s), 2010. Source

Uhry Z.,Institute of Veille Sanitaire | Hedelin G.,University of Strasbourg | Colonna M.,Registre des cancers de lIsere | Asselain B.,University Pierre and Marie Curie | And 13 more authors.
Cancer Epidemiology

Introduction: This study aimed at modelling the effect of organized breast cancer screening on mortality in France. It combined results from a Markov model for breast cancer progression, to predict number of cases by node status, and from relative survival analyses, to predict deaths. The method estimated the relative risk of mortality at 8 years, in women aged 50-69, between a population screened every two years and a reference population. Methods: Analyses concerned cases diagnosed between 1990 and 1996, with a follow-up up to 2004 for the vital status. Markov models analysed data from 3 screening programs (300,000 mammographies) and took into account opportunistic screening among participants to avoid bias in parameter's estimates. We used survival data from cancers in the general population (n = 918, 7 cancer registries) and from screened cancers (n = 565, 3 cancer registries), after excluding a subgroup of screened cases with a particularly high survival. Sensitivity analyses were performed. Results: Markov model main analysis lacked of fit in two out of three districts. Fit was improved in stratified analyses by age or district, though some lack of fit persisted in two districts. Assuming 10% or 20% overdiagnosed screened cancers, mortality reduction was estimated as 23% (95% CI: 4, 38%) and 19% (CI: -3, 35%) respectively. Results were highly sensitive to the exclusion in the screened cancers survival analysis. Conversely, RR estimates varied moderately according to the Markov model parameters used (stratified by age or district). Conclusion: The study aimed at estimating the effect of screening in a screened population compared to an unscreened control group. Such a control group does not exist in France, and we used a general population contaminated by opportunistic screening to provide a conservative estimate. Conservative choices were systematically adopted to avoid favourable estimates. A selection bias might however affect the estimates, though it should be moderate because extreme social classes are under-represented among participants. This modelling provided broad estimates for the effect of organized biennial screening in France in the early nineteen-nineties. Results will be strengthened with longer follow-up. © 2010 Elsevier Ltd. Source

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