Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America.
PLoS Med. 2010 Apr 13;7(4):e1000262. doi: 10.1371/journal.pmed.1000262.
One of the fundamental building blocks for determining the burden of disease in populations is to reliably measure the level and pattern of mortality by age and sex. Where well-functioning registration systems exist, this task is relatively straightforward. Results from many civil registration systems, however, remain uncertain because of a lack of confidence in the completeness of death registration. Incomplete registration systems mean not all deaths are counted, and resulting estimates of death rates for the population are then underestimated. Death distribution methods (DDMs) are a suite of demographic methods that attempt to estimate the fraction of deaths that are registered and counted by the civil registration system. Although widely applied and used, the methods have at least three types of limitations. First, a wide range of variants of these methods has been applied in practice with little scientific literature to guide their selection. Second, the methods have not been extensively validated in real population conditions where violations of the assumptions of the methods most certainly occur. Third, DDMs do not generate uncertainty intervals.
In this paper, we systematically evaluate the performance of 234 variants of DDM methods in three different validation environments where we know or have strong beliefs about the true level of completeness of death registration. Using these datasets, we identify three variants of the DDMs that generally perform the best. We also find that even these improved methods yield uncertainty intervals of roughly +/- one-quarter of the estimate. Finally, we demonstrate the application of the optimal variants in eight countries.
There continues to be a role for partial vital registration data in measuring adult mortality levels and trends, but such results should only be interpreted alongside all other data sources on adult mortality and the uncertainty of the resulting levels, trends, and age-patterns of adult death considered. Please see later in the article for the Editors' Summary.
确定人群疾病负担的基本组成部分之一是可靠地测量按年龄和性别划分的死亡率水平和模式。在功能完善的登记系统中,这项任务相对简单。然而,由于对死亡登记的完整性缺乏信心,许多民事登记系统的结果仍然不确定。不完整的登记系统意味着并非所有死亡都被统计在内,因此对人口死亡率的估计值就会被低估。死亡分布方法(DDM)是一套人口学方法,试图估计民事登记系统登记和统计的死亡人数的比例。尽管这些方法被广泛应用和使用,但至少有三种类型的局限性。首先,这些方法的变体在实践中得到了广泛应用,但很少有科学文献来指导它们的选择。其次,这些方法尚未在真实人口条件下得到广泛验证,而在这些条件下,方法的假设肯定会被违反。第三,DDM 不会生成不确定性区间。
在本文中,我们系统地评估了 234 种 DDM 方法变体在三种不同的验证环境中的性能,在这些环境中,我们知道或强烈相信死亡登记的真实完整程度。使用这些数据集,我们确定了三种 DDM 变体,它们通常表现最好。我们还发现,即使是这些改进的方法也会产生大约 +/-四分之一估计值的不确定性区间。最后,我们在八个国家演示了最佳变体的应用。
部分生命登记数据在衡量成人死亡率水平和趋势方面仍有一定作用,但此类结果应仅与其他所有成人死亡率数据来源以及考虑到的死亡率水平、趋势和成人死亡年龄模式的不确定性一起解释。请在文章后面查看编辑摘要。