我国老年失能风险研究:基于删失数据半参转换模型

Disability Risk of Chinese Elderly People:A Semi-parametric Transformation Model Based on Censored Data

老年失能风险是人口老龄化背景下值得关注的重要问题。已有研究往往忽略失能数据的删失属性,因而无法充分利用数据信息。本文提出了一种新的老年失能风险建模方法,在考虑失能数据区间删失和右删失的情况下,对中国老年健康影响因素跟踪调查(CLHLS)数据按个体从健康状态转移到首次失能状态经历的时间进行分类,同时利用CLHLS死亡调查补充个体死亡前的失能信息。在此基础上,基于生存分析中的半参数转换失能时间模型,实现对老年失能率更准确的估计和预测。与传统模型相比,新模型将年龄、性别、教育水平等变量纳入模型,能够对不同特征人群的失能风险做更细致和精准的分析,为完善我国长期护理保险制度提供实证依据。

The disability risk among elderly people is an important problem in the context of population aging.Existing research often ignores the censoring properties of disability data and cannot make full use of micro-data information.This paper presents a new method for modeling the disability risk among old people.First,considering the interval censoring and right-censoring of disability data in the Chinese Longitudinal Healthy Longevity Survey(CLHLS),this paper classifies the data according to the transition time from the healthy state to the first disability state.This paper also uses the CLHLS death survey to add information on disability prior to an individual's death.This paper uses the semi-parametric transformation model in survival analysis to construct a model of disability time,which estimates and predicts the elderly disability rate more accurately.Compared with the traditional model,the new model can incorporate variables such as age,gender,and education level into the model,making a more detailed and accurate analysis of the disability risk for people with different characteristics.Our model provides an empirical basis for improving China's long-term care insurance system.