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Biodemographic Analyses of Longitudinal Data on Aging, Health, and Longevity: Recent Advances and Future Perspectives

机译:关于人口老龄化,健康和长寿的纵向数据的生物人口学分析:最新进展和未来展望

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Biodemography became one of the most innovative and fastest growing areas in demography. This progress is fueled by the growing variability and amount of relevant data available for analyses as well as by methodological developments allowing for addressing new research questions using new approaches that can better utilize the potential of these data. In this review paper, we summarize recent methodological advances in biodemography and their diverse practical applications. Three major topics are covered: (1) computational approaches to reconstruction of age patterns of incidence of geriatric diseases and other characteristics such as recovery rates at the population level using Medicare claims data; (2) methodological advances in genetic and genomic biodemography and applications to research on genetic determinants of longevity and health; and (3) biodemographic models for joint analyses of time-to-event data and longitudinal measurements of biomarkers collected in longitudinal studies on aging. We discuss how such data and methodology can be used in a comprehensive prediction model for joint analyses of incomplete datasets that take into account the wide spectrum of factors affecting health and mortality transitions including genetic factors and hidden mechanisms of aging-related changes in physiological variables in their dynamic connection with health and survival.
机译:生物人口统计学成为人口统计学领域最具创新性和发展最快的领域之一。可用于分析的相关数据的可变性和数量不断增长,以及方法学的发展推动了这一进展,方法学的发展允许使用可以更好地利用这些数据潜力的新方法来解决新的研究问题。在这篇综述文章中,我们总结了生物人口学及其在各种实际应用中的最新方法学进展。涵盖了三个主要主题:(1)使用Medicare索赔数据重建老年疾病发病年龄模式和其他特征(如人口水平的恢复率)的计算方法; (2)遗传和基因组生物人口学的方法学进展以及在长寿和健康遗传决定因素研究中的应用; (3)生物人口学模型,用于对事件数据进行时间分析以及对纵向研究中收集的生物标志物进行纵向测量。我们讨论如何将此类数据和方法用于不完整数据集的综合分析的综合预测模型中,其中要考虑影响健康和死亡率转变的各种因素,包括遗传因素和与衰老相关的生理变量变化的隐性机制。它们与健康和生存的动态联系。

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