Stochastic models for cause-specific mortality

EPSRC研究领域统计和应用概率本项目旨在开发一个贝叶斯框架，以估计和预测多个国家的特定原因死亡率，同时考虑到不完整的数据。每一个记录在案的死亡都需要填写一份死因医疗证明，然后使用世界卫生组织（世卫组织）维持的《疾病和相关健康问题国际统计分类》（疾病分类）进行编码。原因特异性死亡率的可比性要求这些编码在不同时间保持不变-然而，ICD编码大约每十年改变一次。除了英格兰和威尔士的记录外，使用来自一系列国家的死亡登记数据将使我们能够借用有关死亡率趋势的信息，并纠正在不同时间点记录死亡原因的不一致之处。缺失数据框架将纳入贝叶斯模型规范，以说明ICD编码变化的影响以及对呼吸系统疾病死亡产生重大影响的不同编码做法。贝叶斯方法还将使我们能够以自然的方式整合先验信息（例如医疗质量的趋势，专家意见）和不确定性。为了估计和预测死因特异性死亡率，我们将在存在竞争风险的情况下建立一个Lee Carter型模型。广义加性模型可用于研究特定原因死亡率如何随年龄和队列等参数平稳变化。本项目期间开发的特定原因死亡率预测模型可能会引起众多利益相关者的兴趣，包括政府的社会和医疗保健规划;用于法定资金估值的公共和私人养老金计划;以及人寿保险行业，以支持其产品的准确储备和定价。

EPSRC Research Area Statistics and applied probabilityThis project aims to develop a Bayesian framework to estimate and forecast cause-specific mortality rates for multiple countries taking into account of incomplete data. Each recorded death requires a medical certificate of cause of death to be completed, which is then coded using the International Statistical Classification of Diseases and Related Health Problems (ICD) maintained by the World Health Organisation (WHO). Comparability of cause-specific mortality rates requires these codes to be constant across time - ICD codes have, however, changed roughly every decade. The use of death registration data from a range of countries in addition to the records for England and Wales will allow us to borrow information on mortality trends and correct for inconsistencies in the way causes of death were recorded at various points in time. A missing data framework will be incorporated into the Bayesian model specifications to account for the effects of changing ICD codes as well as different coding practices that have significantly affected deaths due to respiratory diseases. Bayesian methods will also enable us to incorporate prior information (e.g. trends in the quality of healthcare, expert opinion) and uncertainty in a natural manner. To estimate and forecast cause specific mortality rates, we will develop a Lee Carter type model in the presence of competing risks. Generalised additive models can be used to investigate how cause specific mortality rates vary smoothly with parameters such as age and cohort. The predictive model for the cause specific mortality developed during this project could be of interest to numerous stakeholders, including governments for social and health care planning; public and private pension schemes for use in statutory funding valuations; and the life insurance industry to support accurate reserving and pricing of their products.