文章基本信息

标题：Death distribution methods for estimating adult mortality: Sensitivity analysis with simulated data errors
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作者：Kenneth Hill ; Danzhen You ; Yoonjoung Choi 等
期刊名称：Demographic Research
印刷版ISSN：1435-9871
电子版ISSN：1435-9871
出版年度：2009
卷号：21
页码：235-254
DOI：10.4054/DemRes.2009.21.9
出版社：Max Planck Institute for Demographic Research
摘要：TThe General Growth Balance (GGB) and Synthetic Extinct Generations (SEG) methods have been widely used to evaluate the coverage of registered deaths in developing countries. However, relatively little is known about how the methods behave in the presence of different data errors. This paper applies the methods (both singly and in combination) using non-stable populations of known mortality to which various data distortions in a variety of combinations have been applied. Results show that the methods work very well when the only errors in the data are those for which the methods were developed. For other types of error, performance is more variable, but on average, adjusted mortality estimates using the methods are closer to the true values than the unadjusted. The methods do surprisingly well in the presence of typical patterns of age misreporting, though GGB is more sensitive to coverage errors that change with age; the Basic SEG method (e.g. not adjusting for any slope with age of completeness estimates) is very sensitive to changes in census coverage; but once slope is adjusted for changing census, coverage has little effect. Fitting to the age range 5+ to 65+ is clearly preferable to fitting to 15+ to 55+. Both GGB and SEG are very sensitive to net migration, which is an Achilles heel for all of the methodologies in this paper. In populations not greatly affected by migration, our results suggest that an optimal strategy would be to apply GGB to estimate census coverage change, adjust for it and then apply SEG; in populations affected by migration, applying both GGB and SEG, fitting both to the age range 30+ to 65+, and averaging the results appears best.
关键词：adult mortality;death distribution methods;estimation;sensitivity analysis;simulation