What Can We Conclude from Death Registration? Improved Methods for Evaluating Completeness

Julie Rajaratnam and colleagues evaluate the performance of a suite of demographic methods that estimate the fraction of deaths registered and counted by civil registration systems, and identify three variants that generally perform the best.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. 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. 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. : Please see later in the article for the Editors' Summary Background: Accurate worldwide information on the levels and patterns of mortality (deaths) is essential for planning and monitoring global public-health initiatives. The gold standard method for collecting such information is death registration. In high-income countries, death registration is effectively 100% complete, but the situation in many developing countries is very different. In most African countries, for example, less than one-quarter of deaths are officially recorded. Although other data sources such as household surveys can be used to estimate mortality levels in such countries, partial registration data could provide useful information about mortality levels in developing countries if its completeness could be evaluated. One way to do this is to use demographic methods called “death distribution methods” (DDMs). Demography is the study of the size, growth, and other characteristics of human populations; DDMs compare the age distribution of recorded deaths (the relative proportion of deaths in each age group) with the age distribution of the population in which they occurred to provide a correction factor that can be used to calculate corrected mortality levels from registered deaths. DDMs are used by the World Health Organization to monitor adult mortality in nearly 100 countries. Why Was This Study Done?: Although widely used, few studies have compared the performance of the many available DDM variants, and DDMs have not been extensively validated by testing them in populations for which the completeness of death registration is known. In addition, because DDMs are mathematical in nature, they do not provide any indication of the uncertainty associated with the correction factors they yield. This means that public-health officials using estimates of mortality levels generated from partial registration data using DDMs have no idea of the limits between which the true mortality levels of their populations lie. In this study, the researchers systematically evaluate the performance of 234 DDM variants and use the optimal variants that they identify to analyze registration completeness over time in six developing countries. What Did the Researchers Do and Find?: The researchers constructed 234 DDM variants by combining each of three general types of DDMs with 78 different “age trims”; demographers often age-trim—drop older and/or younger age groups—when using DDMs to estimate correction factors for observed death rates. The researchers then evaluated the performance of the variants in three “validation” datasets for which the completeness of death registration is known—a microsimulation of a population of 10 million people followed for 150 years, population data from US counties between 1990 and 2000, and population data from high-income OECD (Organisation for Economic Co-operation and Development) countries with populations of more than 5 million between 1950 and 2000. Detailed analyses of the performance of the DDM variants with all three datasets identified three optimal DDMs, one of each type. However, even with these optimal DDMs, the uncertainty intervals associated with estimates of relative completeness of registration were roughly +/− one-quarter of the estimate. Finally, the researchers applied their optimal DDMs to six developing countries over time. This analysis showed that death registration for adults has been relatively complete since 1970 in Mexico, for example, whereas in Tunisia, death registration has improved from nearly 50% in 1965 to complete by 1980. It also indicated that the three DDMs can give consistent results in some contexts. What Do These Findings Mean?: By using multiple validation databases, these findings identify three optimal DDMs for the estimation of completeness of death registration. The researchers recommend that analysts apply all three methods when estimating the completeness of death registration data and look at the consistency of the results produced. They warn that the level of uncertainty associated with the estimation of completeness of registration means that results yielded by DDMs should be interpreted with considerable caution. In particular, they note that although correction factors provided by DDMs may be a good way of estimating mortality levels, the uncertainty in these factors may make them unsuitable for analyzing trends over time in mortality levels. Overall, the researchers conclude that partial death registration data have a role to play in measuring adult mortality levels, provided that they are analyzed alongside other data sources. Additional Information: Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000262.