Gavrilov Leonid A, Gavrilova Natalia S, Krut'ko Vyacheslav N
NORC at the University of Chicago; Computer Science and Control Federal Research Center, Russian Academy of Sciences.
NORC at the University of Chicago.
Living 100 Monogr. 2017 Jan;2017(1B). Epub 2017 Jul 27.
Knowledge of future mortality levels and trends is important for actuarial practice but poses a challenge to actuaries and demographers. The Lee-Carter method, currently used for mortality forecasting, is based on the assumption that the historical evolution of mortality at all age groups is driven by one factor only. This approach cannot capture an additive manner of mortality decline observed before the 1960s. To overcome the limitation of the one-factor model of mortality and to determine the true number of factors underlying mortality changes over time, we suggest a new approach to mortality analysis and forecasting based on the method of latent variable analysis. The basic assumption of this approach is that most variation in mortality rates over time is a manifestation of a small number of latent variables, variation in which gives rise to the observed mortality patterns. To extract major components of mortality variation, we apply factor analysis to mortality changes in developed countries over the period of 1900-2014. Factor analysis of time series of age-specific death rates in 12 developed countries (data taken from the Human Mortality Database) identified two factors capable of explaining almost 94 to 99 percent of the variance in the temporal changes of adult death rates at ages 25 to 85 years. Analysis of these two factors reveals that the first factor is a "young-age" or background factor with high factor loadings at ages 30 to 45 years. The second factor can be called an "oldage" or senescent factor because of high factor loadings at ages 65 to 85 years. It was found that the senescent factor was relatively stable in the past but now is rapidly declining for both men and women. The decline of the senescent factor is faster for men, although in most countries, it started almost 30 years later. Factor analysis of time series of age-specific death rates conducted for the oldest-old ages (65 to 100 years) found two factors explaining variation of mortality at extremely old ages in the United States. The first factor is comparable to the senescent factor found for adult mortality. The second factor, however, is specific to extreme old ages (96 to 100 years) and shows peaks in 1960 and 2000. Although mortality below 90 to 95 years shows a steady decline with time driven by the senescent factor, mortality of centenarians does not decline and remains relatively stable. The approach suggested in this paper has several advantages. First, it is able to determine the total number of independent factors affecting mortality changes over time. Second, this approach allows researchers to determine the time interval in which underlying factors remain stable or undergo rapid changes. Most methods of mortality projections are not able to identify the best base period for mortality projections, attempting to use the longest-possible time period instead. We observe that the senescent factor of mortality continues to decline, and this decline does not demonstrate any indications of slowing down. At the same time, mortality of centenarians does not decline and remains stable. The lack of mortality decline at extremely old ages may diminish anticipated longevity gains in the future.
了解未来的死亡率水平和趋势对精算实践很重要,但对精算师和人口统计学家来说是一项挑战。目前用于死亡率预测的Lee-Carter方法基于这样一种假设,即所有年龄组死亡率的历史演变仅由一个因素驱动。这种方法无法捕捉到20世纪60年代之前观察到的死亡率下降的累加方式。为了克服死亡率单因素模型的局限性,并确定随时间推移死亡率变化背后的真正因素数量,我们基于潜在变量分析方法提出了一种新的死亡率分析和预测方法。这种方法的基本假设是,死亡率随时间的大多数变化是少数潜在变量的表现,这些变量的变化导致了观察到的死亡率模式。为了提取死亡率变化的主要成分,我们对1900年至2014年期间发达国家的死亡率变化进行了因子分析。对12个发达国家(数据取自人类死亡率数据库)按年龄划分的死亡率时间序列进行因子分析,确定了两个因素,它们能够解释25至85岁成年人死亡率随时间变化的几乎94%至99%的方差。对这两个因素的分析表明,第一个因素是“年轻年龄”或背景因素,在30至45岁年龄组具有较高的因子载荷。第二个因素可称为“老年”或衰老因素,因为在65至85岁年龄组具有较高的因子载荷。研究发现,衰老因素在过去相对稳定,但现在男性和女性都在迅速下降。男性衰老因素的下降速度更快,尽管在大多数国家,它几乎在30年后才开始下降。对最年长者(65至100岁)按年龄划分的死亡率时间序列进行因子分析,在美国发现了两个因素,它们解释了极高龄人群死亡率的变化。第一个因素与在成人死亡率中发现的衰老因素相当。然而,第二个因素特定于极高龄(96至100岁),并在1960年和2000年出现峰值。尽管90至95岁以下的死亡率随着衰老因素的作用随时间稳步下降,但百岁老人的死亡率并未下降,而是保持相对稳定。本文提出的方法有几个优点。首先,它能够确定影响死亡率随时间变化的独立因素的总数。其次,这种方法使研究人员能够确定潜在因素保持稳定或经历快速变化的时间间隔。大多数死亡率预测方法无法确定死亡率预测的最佳基期,而是试图使用尽可能长的时间段。我们观察到死亡率的衰老因素继续下降,而且这种下降没有显示出任何放缓的迹象。与此同时,百岁老人的死亡率没有下降,而是保持稳定。极高龄人群死亡率缺乏下降可能会减少未来预期的寿命增长。