Caswell Hal, Zarulli Virginia
Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, PO Box 94248, Amsterdam, 1090 GE, The Netherlands.
Interdisciplinary Center on Research and Education on Population Dynamics (InCent), University of Southern Denmark, Campusvej 55, Odense, DK-5230, Denmark.
Popul Health Metr. 2018 Jun 7;16(1):8. doi: 10.1186/s12963-018-0165-5.
Increases in human longevity have made it critical to distinguish healthy longevity from longevity without regard to health. Current methods focus on expectations of healthy longevity, and are often limited to binary health outcomes (e.g., disabled vs. not disabled). We present a new matrix formulation for the statistics of healthy longevity, based on health prevalence data and Markov chain theory, applicable to any kind of health outcome and which provides variances and higher moments as well as expectations of healthy life.
The model is based on a Markov chain description of the life course coupled with the moments of health outcomes ("rewards") at each age or stage. As an example, we apply the method to nine European countries using the SHARE survey data on the binary outcome of disability as measured by activities of daily living, and the continuous health outcome of hand grip strength.
We provide analytical formulas for the mean, variance, coefficient of variation, skewness and other statistical properties of healthy longevity. The analysis is applicable to binary, categorical, ordinal, or interval scale health outcomes. The results are easily evaluated in any matrix-oriented software. The SHARE results reveal familiar patterns for the expectation of life and of healthy life: women live longer than men but spend less time in a healthy condition. New results on the variance shows that the standard deviation of remaining healthy life declines with age, but the coefficient of variation is nearly constant. Remaining grip strength years decrease with age more dramatically than healthy years but their variability pattern is similar to the pattern of healthy years. Patterns are similar across nine European countries.
The method extends, in several directions, current calculations of health expectancy (HE) and disability-adjusted life years (DALYs). It applies to both categorical and continuous health outcomes, to combinations of multiple outcomes (e.g., death and disability in the formulation of DALYs) and to age- or stage-classified models. It reveals previously unreported patterns of variation among individuals in the outcomes of healthy longevity.
人类寿命的延长使得区分健康长寿和不考虑健康状况的长寿变得至关重要。当前的方法侧重于健康长寿的预期,并且通常仅限于二元健康结果(例如,残疾与非残疾)。我们基于健康患病率数据和马尔可夫链理论,提出了一种用于健康长寿统计的新矩阵公式,适用于任何类型的健康结果,并提供方差、高阶矩以及健康寿命的预期。
该模型基于对生命历程的马尔可夫链描述,以及每个年龄或阶段的健康结果(“回报”)的矩。例如,我们使用来自SHARE调查的数据,将该方法应用于九个欧洲国家,该数据涉及通过日常生活活动测量的残疾二元结果以及握力的连续健康结果。
我们提供了健康长寿的均值、方差、变异系数、偏度和其他统计特性的解析公式。该分析适用于二元、分类、有序或区间尺度的健康结果。结果可以在任何面向矩阵的软件中轻松评估。SHARE的结果揭示了预期寿命和健康寿命的常见模式:女性寿命更长,但处于健康状态的时间更少。关于方差的新结果表明,剩余健康寿命的标准差随年龄下降,但变异系数几乎恒定。剩余握力年数随年龄下降的幅度比健康年数更大,但其变异性模式与健康年数的模式相似。九个欧洲国家的模式相似。
该方法在几个方面扩展了当前健康预期寿命(HE)和伤残调整生命年(DALYs)的计算。它适用于分类和连续的健康结果、多个结果的组合(例如,在DALYs的公式中死亡和残疾)以及年龄或阶段分类模型。它揭示了健康长寿结果中个体间以前未报告的变异模式。