Gavrilov Leonid A, Gavrilova Natalia S
Research Associate at the Center on Aging, NORC at the University of Chicago, 1155 E. 60th St., Chicago, IL 60637.
N Am Actuar J. 2011;15(3):432-447. doi: 10.1080/10920277.2011.10597629.
Accurate estimates of mortality at advanced ages are essential to improving forecasts of mortality and the population size of the oldest old age group. However, estimation of hazard rates at extremely old ages poses serious challenges to researchers: (1) The observed mortality deceleration may be at least partially an artifact of mixing different birth cohorts with different mortality (heterogeneity effect); (2) standard assumptions of hazard rate estimates may be invalid when risk of death is extremely high at old ages and (3) ages of very old people may be exaggerated. One way of obtaining estimates of mortality at extreme ages is to pool together international records of persons surviving to extreme ages with subsequent efforts of strict age validation. This approach helps researchers to resolve the third of the above-mentioned problems but does not resolve the first two problems because of inevitable data heterogeneity when data for people belonging to different birth cohorts and countries are pooled together. In this paper we propose an alternative approach, which gives an opportunity to resolve the first two problems by compiling data for more homogeneous single-year birth cohorts with hazard rates measured at narrow (monthly) age intervals. Possible ways of resolving the third problem of hazard rate estimation are elaborated. This approach is based on data from the Social Security Administration Death Master File (DMF). Some birth cohorts covered by DMF could be studied by the method of extinct generations. Availability of month of birth and month of death information provides a unique opportunity to obtain hazard rate estimates for every month of age. Study of several single-year extinct birth cohorts shows that mortality trajectory at advanced ages follows the Gompertz law up to the ages 102-105 years without a noticeable deceleration. Earlier reports of mortality deceleration (deviation of mortality from the Gompertz law) at ages below 100 appear to be artifacts of mixing together several birth cohorts with different mortality levels and using cross-sectional instead of cohort data. Age exaggeration and crude assumptions applied to mortality estimates at advanced ages may also contribute to mortality underestimation at very advanced ages.
准确估计高龄人群的死亡率对于改进死亡率预测以及最年长老年人群体的人口规模预测至关重要。然而,估计极高龄人群的危险率给研究人员带来了严峻挑战:(1)观察到的死亡率减速可能至少部分是将具有不同死亡率的不同出生队列混合在一起的人为现象(异质性效应);(2)当老年时死亡风险极高时,危险率估计的标准假设可能无效;(3)非常年长的人的年龄可能被夸大。获得极端年龄死亡率估计值的一种方法是将活到极端年龄的人的国际记录汇总在一起,随后进行严格的年龄验证。这种方法有助于研究人员解决上述第三个问题,但由于将来自不同出生队列和国家的人的数据汇总在一起时不可避免的数据异质性,无法解决前两个问题。在本文中,我们提出了一种替代方法,该方法通过为更同质的单一年出生队列汇编数据,并在狭窄(每月)年龄间隔内测量危险率,从而有机会解决前两个问题。阐述了解决危险率估计第三个问题的可能方法。这种方法基于社会保障管理局死亡主文件(DMF)的数据。DMF涵盖的一些出生队列可以通过灭绝世代的方法进行研究。出生月份和死亡月份信息的可用性提供了一个独特的机会,可获得每个年龄月份的危险率估计值。对几个单一年灭绝出生队列的研究表明,高龄人群的死亡率轨迹在102 - 105岁之前遵循冈珀茨定律,没有明显减速。早期关于100岁以下年龄死亡率减速(死亡率偏离冈珀茨定律)的报告似乎是将几个具有不同死亡率水平的出生队列混合在一起并使用横截面数据而非队列数据的人为现象。年龄夸大以及应用于高龄死亡率估计的粗略假设也可能导致极高龄死亡率的低估。