Method for redistributing ill-defined causes of death.

P. Grigoriev, Florian Bonnet, Elsa Perdrix
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Abstract

Analysis of causes of death is crucial for monitoring an epidemiological situation and for developing adequate policy responses. However, the comparability of cause-specific mortality data depends on the proportion of ill-defined deaths. To eliminate the bias resulting from the varying proportions of such causes over time and between populations, deaths from ill-defined causes need to be reassigned to other categories. We provide thorough documentation of and tools for the practical implementation of a regression-based method for redistributing ill-defined causes of death, as first proposed by Sully Ledermann in the 1950s. The method relies on subnational cause-specific mortality data to estimate unbiased death rates at both national and subnational levels. We refine Ledermann's method by elaborating on its mathematical properties, making additional adjustments, and evaluating the performance of the approach through simulations. To illustrate the practical application of the method, we rely on French subnational cause-of-death data and provide the R code for performing all calculations.
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重新分配不明确死因的方法。
死因分析对于监测流行病情况和制定适当的应对政策至关重要。然而,特定死因死亡率数据的可比性取决于死因不明死亡的比例。为了消除此类死因在不同时期和不同人群中所占比例不同而造成的偏差,需要将死因不明的死亡重新归入其他类别。苏利-莱德曼(Sully Ledermann)在 20 世纪 50 年代首次提出了一种基于回归的方法,用于重新分配定义不清的死因,我们为这种方法的实际应用提供了详尽的文件和工具。该方法依靠国家以下各级的特定死因死亡率数据来估算国家和国家以下各级的无偏见死亡率。我们对莱德曼的方法进行了改进,详细阐述了其数学特性,进行了额外的调整,并通过模拟评估了该方法的性能。为了说明该方法的实际应用,我们使用了法国的国家以下各级死因数据,并提供了执行所有计算的 R 代码。
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