Mortalité par cancer du poumon chez les femmes françaises. Analyse de tendance et projection à l’aide d’un modèle âge-cohorte bayésien, de 1975 à 2014

Abstract

With 4,500 deaths in year 2000, female lung cancer mortality rates increased by 3% every year over the last two decades in France. This trend, not observed among males, is attributed to the regular increase of female smoking. In order to answer French Health decider's concerns, we estimated the future female lung cancer mortality rates and numbers of deaths for the next fifteen years, in France and its regions. Analyses were based on numbers of female deaths from lung cancer observed between 1975 and 1999, and on past and future population estimates for 1975-2014, at national and regional levels. Mortality rates and numbers of deaths in France and its regions by 5-year periods and 5-year age groups were given in the 1975-1999 death certificate data base, and were projected for 2000-2014. The analysis used a bayesian approach of the age-cohort model with auto-regressive constraints on parameters. Estimated mortality rates were standardized on truncated 20-85 + world population. French female lung cancer mortality increased by 3% every year between 1975 and 1999. In period 1995-1999, truncated 20-85 + mortality rates, and number of deaths per year were respectively 11.4 per 100,000 and 4,000. Mortality rates increased in all regions but variations were maximum in Corsica (+ 314%) and minimum in Auvergne (+ 37%). For the whole of France, the estimated truncated 20-85 + standardized rate, was respectively, 14.1 and 22.5 per 100,000 in period 2000-04 and period 2010-14, which represents a 60% increase between these two time periods. At the regional level, the maximum variation was found in Languedoc-Roussillon (107%), the minimum in Nord-Pas-de-Calais (40%). The bayesian approach of the age-cohort model is increasingly used because it produces stable projections, without having to include other cancer parameters. Nevertheless, it would be interesting to extend this model by incorporating a tobacco consumption component, in order to assess scenarios based on consumption decreases.