Child Mortality Estimation: A Global Overview of Infant and Child Mortality Age Patterns in Light of New Empirical Data

Michel Guillot and colleagues did a systematic evaluation to assess what proportion of under-five mortality occurs below age one compared with at age one and above, to determine how much observed values deviate from so called “model age patterns” of under-five mortality Background: The under-five mortality rate (the probability of dying between birth and age 5 y, also denoted in the literature as U5MR and 5q0) is a key indicator of child health, but it conceals important information about how this mortality is distributed by age. One important distinction is what amount of the under-five mortality occurs below age 1 y (1q0) versus at age 1 y and above (4q1). However, in many country settings, this distinction is often difficult to establish because of various types of data errors. As a result, it is common practice to resort to model age patterns to estimate 1q0 and 4q1 on the basis of an observed value of 5q0. The most commonly used model age patterns for this purpose are the Coale and Demeny and the United Nations systems. Since the development of these models, many additional sources of data for under-five mortality have become available, making possible a general evaluation of age patterns of infant and child mortality. In this paper, we do a systematic comparison of empirical values of 1q0 and 4q1 against model age patterns, and discuss whether observed deviations are due to data errors, or whether they reflect true epidemiological patterns not addressed in existing model life tables. Methods and Findings: We used vital registration data from the Human Mortality Database, sample survey data from the World Fertility Survey and Demographic and Health Surveys programs, and data from Demographic Surveillance Systems. For each of these data sources, we compared empirical combinations of 1q0 and 4q1 against combinations provided by Coale and Demeny and United Nations model age patterns. We found that, on the whole, empirical values fall relatively well within the range provided by these models, but we also found important exceptions. Sub-Saharan African countries have a tendency to exhibit high values of 4q1 relative to 1q0, a pattern that appears to arise for the most part from true epidemiological causes. While this pattern is well known in the case of western Africa, we observed that it is more widespread than commonly thought. We also found that the emergence of HIV/AIDS, while perhaps contributing to high relative values of 4q1, does not appear to have substantially modified preexisting patterns. We also identified a small number of countries scattered in different parts of the world that exhibit unusually low values of 4q1 relative to 1q0, a pattern that is not likely to arise merely from data errors. Finally, we illustrate that it is relatively common for populations to experience changes in age patterns of infant and child mortality as they experience a decline in mortality. Conclusions: Existing models do not appear to cover the entire range of epidemiological situations and trajectories. Therefore, model life tables should be used with caution for estimating 1q0 and 4q1 on the basis of 5q0. Moreover, this model-based estimation procedure assumes that the input value of 5q0 is correct, which may not always be warranted, especially in the case of survey data. A systematic evaluation of data errors in sample surveys and their impact on age patterns of 1q0 and 4q1 is urgently needed, along with the development of model age patterns of under-five mortality that would cover a wider range of epidemiological situations and trajectories. Background: In 2000, world leaders agreed on eight Millennium Development Goals designed to end extreme poverty by 2015. The fourth of these goals—MDG 4—aims to reduce under-five mortality (the number of children who die before their fifth birthday) to a third of its 1990 level by 2015. A key indicator used to monitor progress towards this target is the under-five mortality rate (the probability of a child dying before his/her fifth birthday, also denoted as U5MR or 5q0). In developed countries, data collected through vital registration systems (which record all births and deaths) are used to calculate 5q0. However, developing countries, which are where most under-five deaths occur, rarely have vital registration systems, and 5q0 is estimated using data collected by programs such as the World Fertility Survey (WFS) and Demographic and Health Surveys (DHS), which conduct nationally representative surveys that ask a sample of women about their living and dead children. Why Was This Study Done?: Although 5q0 is a key indicator of child health, it conceals important information about the age distribution of child deaths. Public health experts need to know the distribution of 5q0 with respect to 1q0 (the probability that an infant will die before age one) and 4q1 (the probability that a child reaching age one will die below age five) to help them reduce child mortality. At a given level of 5q0, high values of 1q0 indicate high levels of death from congenital (inherited) anomalies and conditions that occur around the time of birth; these deaths can be reduced by improving the care of women during pregnancy and childbirth and the care of newborn babies. By contrast, at a given level of 5q0, high values of 4q1 indicate high levels of death from infectious diseases; these deaths can be reduced by, for example, introducing immunization programs. 1q0 and 4q1 are usually estimated from observed (empirical) values of 5q0 using the Coale and Demeny or United Nations (UN) “model life tables” (mathematical models of the variation of mortality with age), which were constructed in 1966 and 1982, respectively, using the best data available. Since their construction, additional sources of data about under-five mortality have become available; in this study, the researchers systematically compare global empirical values of 1q0 and 4q1 with values obtained using model life tables. What Did the Researchers Do and Find?: The researchers compared empirical combinations of 1q0 and 4q1 (estimated using vital registration data, WFS and DHS data, and data from Demographic Surveillance Sites in sub-Saharan Africa) with the combinations derived from 5q0 using the Coale and Demeny and UN model life tables. The empirical values mainly fell within the range provided by these tables, but there were important exceptions. For example, empirical values of 4q1 relative to 1q0 tended to be above the range provided by the model life tables for sub-Saharan African countries. This pattern was mainly because of epidemiological reasons (epidemiology is the study of disease patterns in populations), such as the occurrence of diseases such as malaria, measles, and diarrhea that generate excess mortality among children older than one year. Interestingly, the emergence of HIV does not seem to have substantially modified preexisting patterns of 1q0 versus 4q1. Importantly, the researchers also show that populations often experience changes in the age patterns of infant and child mortality as they experience an overall decline in mortality. What Do These Findings Mean?: These findings suggest that the existing model life tables do not cover the entire global range of epidemiological situations and trajectories and must, therefore, be used with caution for estimating 1q0 and 4q1 on the basis of 5q0. The development of new model age patterns of under-five mortality that cover a wider range of epidemiological situations should improve this situation, but a systematic analysis of data errors in sample surveys and the impact of such errors on estimates of 1q0 and 4q1 is also urgently needed to ensure that public health experts have access to accurate information on child mortality. Importantly, this overview shows that a wide range of 1q0 and 4q1 combinations can occur at a given level of 5q0. Because the level of 4q1 relative to 1q0 provides important information about the disease processes occurring in a population, this finding highlights the importance of determining 1q0 and 4q1 as well as 5q0 whenever possible. Additional Information: Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001299.