Institute for Ageing and Health, Newcastle University, c/o Government Office for the North East, Citygate, Newcastle upon Tyne, NE1 4WH England, United Kingdom.
Exp Gerontol. 2010 Mar;45(3):180-7. doi: 10.1016/j.exger.2009.11.008. Epub 2009 Nov 29.
Historical human mortality curves display five phases, differing in dimensions with population, time and circumstance. Existing explanatory models describe some but not all of these, and modelling of entire curves has hitherto necessitated an assumption of multiple distributions. A new distribution, shown previously to describe survival in experimental animals, postulates empirically that net mortality risk comprises two components described as 'redundancy decay' and 'interactive risk'. The former is proposed effectively to set a 'program' for the increase of mortality risk with age, and may be a better measure of the 'rate of ageing' which has previously been assumed to determine the slope of semi-logarithmic mortality curves. Entire human mortality curves are shown here to be compatible with this single distribution given the assumption that individuals vary only with respect to the interactive risk parameter (k). Historical Swedish cohort data are modelled here exclusively through changes in k values, clustered increasingly toward the higher end of their range, while redundancy values are held constant. This pattern is compatible with the hypothesis that historical changes in human mortality may be explained purely in terms of interactive risks and without changes in the underlying pattern or rate of ageing.
历史上人类死亡率曲线呈现五个阶段，在人口、时间和环境方面存在差异。现有的解释模型描述了其中的一些，但不是全部，而且对整个曲线的建模迄今为止需要假设存在多个分布。一种新的分布，以前被证明可以描述实验动物的生存情况，从经验上假设净死亡率风险由两个组成部分组成，分别描述为“冗余衰减”和“交互风险”。前者有效地提出了一个“程序”，随着年龄的增长增加死亡率风险，并且可以更好地衡量以前假设决定半对数死亡率曲线斜率的“衰老率”。这里假设个体仅在交互风险参数 (k) 上存在差异，因此整个人类死亡率曲线都与该单一分布兼容。历史上的瑞典队列数据仅通过 k 值的变化进行建模，k 值逐渐聚类到其范围的较高端，而冗余值保持不变。这种模式与假设一致，即历史上人类死亡率的变化可以仅用交互风险来解释，而无需改变潜在的模式或衰老率。
