A Bayesian model for age at death with cohort effects

BACKGROUNDOngoing mortality trends affect the distribution of age at death, typically described by parametric models.Cohort effects can markedly perturb the distribution and reduce the fit of such models, and this needs to be specifically taken into account. OBJECTIVEThis study examines the integration of cohort effects in a three-component parametric model for the age-at-death distribution, applying it to data with significant cohort effects. METHODSWe employed a mixture model with a half-normal and two skew-normal components, adapted to a Bayesian framework to include multiplicative cohort effects.The model was applied to data from five Italian regions, with cohort effects estimated for the 1915-1925 cohorts. RESULTSIncorporating cohort effects significantly improved the model's fit.A notable finding of the comprehensive model is the shift in Italy from premature to middle-age mortality components over time.Our results also demonstrate the tendency for mortality structures to spatially homogenize over time in Italy. CONCLUSIONSThe study underscores the importance of including cohort effects in mortality models in order to provide a more detailed picture of mortality trends. CONTRIBUTIONThis work introduces a novel application of a Bayesian mixture model with cohort effects, offering enhanced tools for demographic analysis and new insights into the