In contrast to the traditional approach that uses total mortality rates, we want to gain additional insight into the past development of mortality by concentrating on a more detailed breakdown of mortality data, namely by causes of death. We work with the data from five developed countries (USA, Japan, France, England and Wales, and Australia), two sexes, and split the mortality rates into five main groups of causes of death (Infectious&Parasitic, Cancer, Circulatory diseases, Respiratory diseases, and External causes). As it was shown in Arnold and Sherris (2016), these time series of cause-specific mortality rates are cointegrated and so, there exist long-run equilibrium relationships between them. While the previous research focused on the stationary part of the system of cause-specific mortality rates, in the present paper we study its non-stationary part. For this we explicitly extract common stochastic trends from the original variables and compare them across the different datasets. By testing cointegration assumptions about these trends, we are able to get a better representation and understanding of how cause-specific death rates are evolving. We believe that common patterns emerging from such analysis could indicate a link to more fundamental biological processes such as aging.

与使用总死亡率的传统方法相反，我们希望通过专注于更详细的死亡率数据细分（即按死因）来获得对过去死亡率发展的更多见解。我们使用来自五个发达国家（美国，日本，法国，英格兰和威尔士和澳大利亚），两个性别的数据进行工作，并将死亡率分为五个主要死因类别（传染性和寄生虫性，癌症，循环系统疾病，呼吸系统疾病） ，以及外部原因）。正如Arnold和Sherris（2016）所示，这些因因死亡率的时间序列是协整的，因此它们之间存在长期的均衡关系。虽然先前的研究集中在特定原因死亡率系统的固定部分，在本文中，我们研究其非平稳部分。为此，我们从原始变量中显式提取常见的随机趋势，并在不同的数据集中进行比较。通过测试关于这些趋势的协整假设，我们可以更好地表示和理解特定原因死亡率的变化情况。我们认为，此类分析中出现的常见模式可能表明与更基本的生物学过程（例如衰老）有联系。