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. 2010 Apr 13;7(4):e1000262.
doi: 10.1371/journal.pmed.1000262.

What can we conclude from death registration? Improved methods for evaluating completeness

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What can we conclude from death registration? Improved methods for evaluating completeness

Christopher J L Murray et al. PLoS Med. .

Abstract

Background: 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 VSports手机版. .

Methods and findings: 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 V体育安卓版. 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. .

Conclusions: 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 V体育ios版. Please see later in the article for the Editors' Summary. .

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VSports注册入口 - Conflict of interest statement

Alan Lopez is on the Editorial Board of PLoS Medicine.

Figures (VSports app下载)

Figure 1
Figure 1. Three families of DDMs.
Figure 2
Figure 2. Simulated population model.
This schematic describes the evolution of the simulated population, where P 0 is the probability of remaining in the sample, P m is the probability of dying in the year given an age-specific probability ψ of dying in a single day (ζ is the fraction of time spent in the year in age group x 1), P ε(a,t) is the probability of migrating at age a and time t, and P b(a,t) is the probability of giving birth at age a and time t and only applies to the reproductive age groups.
Figure 3
Figure 3. An example of the effect of fertility and mortality evolution over time on the simulated population age-structure with no migration.
The initial age structure of Sweden's 1751 population is shown, as are 10-y incremental changes after applying the mortality and fertility rates of the simulated population.
Figure 4
Figure 4. The age pattern of in- and out-migration used to model migration in the simulated populations.
This age pattern is based on the average of a geographically diverse selection of countries with complete migration data as reported in the 1989 Demographic Yearbook.
Figure 5
Figure 5. Estimated relative completeness versus true relative completeness in the simulations, US counties with a population greater than 100,000, and large high-income countries for the three methods.
The distribution of true completeness for the US counties is artificially offset from 1 in order to better distinguish it graphically from high-income countries.
Figure 6
Figure 6. Relationship between median relative error and quartile of variance across (inconsistency between) the three families of DDM methods.
Figure 7
Figure 7. Relationship between median relative error and decile of diagnostics for GGB, SEG, and GGBSEG.
For GGB, the diagnostic is the R 2 of the regression of observed death rates on implied death rates. For SEG and GGBSEG, the diagnostic is the slope of the regression of age-specific completeness estimates on age.
Figure 8
Figure 8. Application of optimal DDMs to Canada, Switzerland, Korea, and Mexico.
Figure 9
Figure 9. Application of optimal DDMs to the Philippines, Paraguay, Thailand, and Tunisia.

References

    1. Murray CJ, Laakso T, Shibuya K, Hill K, Lopez AD. Can we achieve Millennium Development Goal 4? New analysis of country trends and forecasts of under-5 mortality to 2015. Lancet. 2007;370:1040–1054. - PubMed
    1. Obermeyer Z, Park C, Rajaratnam JK, Gakidou E, Lopez AD, et al. Measuring adult mortality using sibling survival: a new analytical method and new results for 44 countries, 1974–2006. PLoS Med. 2009;6:e1000260. doi: VSports - 10.1371/journal.pmed.1000260. - DOI - PMC - PubMed
    1. Mahapatra P, Shibuya K, Lopez AD, Coullare F, Notzon FC, et al. Civil registration systems and vital statistics: successes and missed opportunities. Lancet. 2007;370:1653–1663. - PubMed
    1. Mathers CD, Fat DM, Inoue M, Rao C, Lopez AD. Counting the dead and what they died from: an assessment of the global status of cause of death data. Bull World Health Organ. 2005;83:171–177. - PMC - PubMed
    1. Hill K. Estimating census and death registration completeness. Asian Pac Popul Forum. 1987;1:8–13. - PubMed

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