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Editorial
This editorial highlights the importance of age-period-cohort modeling to better understand the evolving global epidemiological burden of calcific aortic valve disease.
The burden of calcific aortic valve disease (CAVD) has been the subject of growing attention over the last decades due to its frequency in the elderly and the considerable development of transcatheter aortic valve implantation (TAVI), which has become the favoured intervention for aortic stenosis in the elderly. Epidemiologic studies provide consistent estimations of the prevalence of moderate or severe aortic stenosis over 3% after the age of 75 with a steep age-related increase.1 However, little is known on the impact of the evolution of diagnostic investigations and therapeutic methods on mortality. Investigations and treatments have been subject to important changes over the last decades, although their implementation differs across countries.2 Changing epidemiology of degenerative heart valve diseases has been the subject of prior analyses of the global burden of disease (GBD) database.3 The paper by Shu et al. completes prior analyses of the GBD database in that it uses age- period- cohort modelling.4 Taking into account, period and cohort trends is particularly appropriate since GBD data were collected over a 29-year time period. Age–period cohort analysis provides further insights in the burden of CAVD as compared to the conventional study of the evolution of all-age rates or age-standardized rates of CAVD over time. Besides age effect, period effect reflects changes in patient management and cohort effect reflects the timing of diagnosis and treatment. Additional strength of the paper by Shu et al. is to be performed on more than 200 countries with a wide panel of socio-demographic characteristics and healthcare systems. This highlights the added value of in-depth analysis of the relationship between temporal, geographical and socio-economic related variables, and CAVD burden in terms of prevalence, mortality, and disability-adjusted life years (DALY).
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Bernard Iung (2023) studied this question.
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