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高龄阶段的死亡率轨迹:关于美国老年死亡率不同数据来源的比较研究

Mortality Trajectories at Extreme Old Ages: A Comparative Study of Different Data Sources on U.S. Old-Age Mortality.

作者信息

Gavrilova Natalia S, Gavrilov Leonid A

机构信息

Center on Aging, NORC at the University of Chicago, 1155 E. 60th St., Chicago, IL 60637, USA.

出版信息

Living 100 Monogr. 2014;2014.

PMID:25664347
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4318539/
Abstract

The growing number of individuals living beyond age 80 underscores the need for accurate measurement of mortality at advanced ages. Our earlier published study challenged the common view that the exponential growth of mortality with age (Gompertz law) is followed by a period of deceleration, with slower rates of mortality increase (Gavrilov and Gavrilova 2011). This refutation of mortality deceleration was made using records from the U.S. Social Security Administration's Death Master File (DMF). Taking into account the significance of this finding for actuarial theory and practice, we tested these earlier observations using additional independent datasets and alternative statistical approaches. In particular, the following data sources for U.S. mortality at advanced ages were analyzed: (1) data from the Human Mortality Database (HMD) on age-specific death rates for 1890-99 U.S. birth cohorts, (2) recent extinct birth cohorts of U.S. men and women based on DMF data, and (3) mortality data for railroad retirees. In the case of HMD data, the analyses were conducted for 1890-99 birth cohorts in the age range 80-106. Mortality was fitted by the Gompertz and logistic (Kannisto) models using weighted nonlinear regression and Akaike information criterion as the goodness-of-fit measure. All analyses were conducted separately for men and women. It was found that for all studied HMD birth cohorts, the Gompertz model demonstrated better fit of mortality data than the Kannisto model in the studied age interval. Similar results were obtained for U.S. men and women born in 1890-99 and railroad retirees born in 1895-99 using the full DMF file (obtained from the National Technical Information Service, or NTIS). It was also found that mortality estimates obtained from the DMF records are close to estimates obtained using the HMD cohort data. An alternative approach for studying mortality patterns at advanced ages is based on calculating the age-specific rate of mortality change (life table aging rate, or LAR) after age 80. This approach was applied to age-specific death rates for Canada, France, Sweden and the United States available in HMD. It was found that for all 24 studied single-year birth cohorts, LAR does not change significantly with age in the age interval 80-100, suggesting no mortality deceleration in this interval. Simulation study of LAR demonstrated that the apparent decline of LAR after age 80 found in earlier studies may be related to biased estimates of mortality rates measured in a wide five-year age interval. Taking into account that there exists several empirical estimates of hazard rate (Nelson-Aalen, actuarial and Sacher), a simulation study was conducted to find out which one is the most accurate and unbiased estimate of hazard rate at advanced ages. Computer simulations demonstrated that some estimates of mortality (Nelson-Aalen and actuarial) as well as kernel smoothing of hazard rates may produce spurious mortality deceleration at extreme ages, while the Sacher estimate turns out to be the most accurate estimate of hazard rate. Possible reasons for finding apparent mortality deceleration in earlier studies are also discussed.

摘要

80岁以上老人数量的不断增加凸显了准确测量高龄死亡率的必要性。我们早期发表的研究对一种普遍观点提出了挑战,即死亡率随年龄呈指数增长(冈珀茨定律)之后会有一个减速期,死亡率增长速度会放缓(加夫里洛夫和加夫里洛娃,2011年)。对死亡率减速的这一反驳是利用美国社会保障管理局的死亡主文件(DMF)记录得出的。考虑到这一发现对精算理论和实践的重要性,我们使用额外的独立数据集和替代统计方法对这些早期观察结果进行了检验。具体而言,分析了以下美国高龄死亡率的数据源:(1)人类死亡率数据库(HMD)中1890 - 1899年美国出生队列的特定年龄死亡率数据,(2)基于DMF数据的美国男性和女性近期已灭绝出生队列,以及(3)铁路退休人员的死亡率数据。对于HMD数据,分析针对1890 - 1899年出生队列,年龄范围在80 - 106岁。使用加权非线性回归和赤池信息准则作为拟合优度度量,用冈珀茨模型和逻辑斯蒂(卡尼斯托)模型对死亡率进行拟合。所有分析均分别针对男性和女性进行。结果发现,对于所有研究的HMD出生队列,在研究的年龄区间内,冈珀茨模型对死亡率数据的拟合效果优于卡尼斯托模型。使用完整的DMF文件(从美国国家技术信息服务局,即NTIS获取)对1890 - 1899年出生的美国男性和女性以及1895 - 1899年出生的铁路退休人员进行分析,也得到了类似结果。还发现从DMF记录获得的死亡率估计值与使用HMD队列数据获得的估计值相近。研究高龄死亡率模式的另一种方法是基于计算80岁以后的特定年龄死亡率变化率(生命表老化率,即LAR)。该方法应用于HMD中加拿大、法国、瑞典和美国的特定年龄死亡率。结果发现,对于所有24个研究的单年出生队列,在80 - 100岁年龄区间内,LAR随年龄没有显著变化,这表明该区间内不存在死亡率减速。对LAR的模拟研究表明,早期研究中发现的80岁以后LAR的明显下降可能与在较宽的五年年龄区间内测量的死亡率估计偏差有关。考虑到存在几种危险率的经验估计方法(尼尔森 - 阿伦估计法、精算估计法和萨赫尔估计法),进行了一项模拟研究以找出哪种方法是高龄危险率最准确且无偏差的估计。计算机模拟表明,一些死亡率估计方法(尼尔森 - 阿伦估计法和精算估计法)以及危险率的核平滑处理在极端年龄可能会产生虚假的死亡率减速,而萨赫尔估计法被证明是最准确的危险率估计方法。还讨论了早期研究中发现明显死亡率减速的可能原因。

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