Child Mortality Estimation: A Global Overview of Infant and Child Mortality Age Patterns in Light of New Empirical Data
Child Mortality Estimation: A Global Overview of Infant and Child Mortality Age Patterns in Light of New Empirical Data
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DOI：
10.1371/journal.pmed.1001299
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发表时间：
2012-08-01
期刊：
影响因子：
15.8
通讯作者：
Saabneh, Ameed
中科院分区：
文献类型：
--
作者：
Guillot, Michel;Gerland, Patrick;Saabneh, Ameed
背景：5岁以下儿童死亡率(出生到5岁之间死亡的概率，在文献中也表示为U5MR和（5)q(0)）是儿童健康的一个关键指标，但它隐藏了关于这种死亡率如何按年龄分布的重要信息。一个重要的区别是，5岁以下儿童死亡率中，1岁以下(（1)q(0)）与1岁及以上(（4)q(1)）的比例有多大。然而，在许多国家的情况下，由于各种类型的数据错误，这种区别往往难以确立。因此，通常的做法是根据(5)q(0)的观测值，借助于模型年龄模式来估计(1)q(0)和(4)q(1)。为此目的最常用的年龄模式是科尔和德梅尼系统和联合国系统。自从建立这些模型以来，已经有了许多关于五岁以下儿童死亡率的额外数据来源，从而有可能对婴儿和儿童死亡率的年龄模式进行一般性评价。在本文中，我们对(1)q(0)和(4)q(1)的经验值与模型年龄模式进行了系统的比较，并讨论了观察到的偏差是由于数据错误，还是它们反映了现有模型生命表中未解决的真实流行病学模式。方法和发现：我们使用了来自人类死亡率数据库的生命登记数据，来自世界生育调查和人口与健康调查项目的抽样调查数据，以及来自人口监测系统的数据。对于这些数据来源，我们将(1)q(0)和(4)q(1)的经验组合与Coale和Demeny提供的组合以及联合国模型年龄模式进行了比较。我们发现，总体而言，经验值相对较好地落在这些模型提供的范围内，但我们也发现了重要的例外。撒哈拉以南非洲国家倾向于表现出(4)q(1)相对于(1)q(0)的高值，这种模式似乎在很大程度上是由真正的流行病学原因引起的。虽然这种模式在西非是众所周知的，但我们观察到它比通常认为的更为普遍。我们还发现，虽然HIV/AIDS的出现可能导致了(4)q(1)的高相对值，但似乎并没有实质性地改变先前存在的模式。我们还确定了分布在世界不同地区的少数国家，它们相对于(1)q(0)表现出异常低的(4)q(1)值，这种模式不太可能仅仅是由数据错误引起的。最后，我们说明，随着人口死亡率的下降，婴儿和儿童死亡率的年龄模式发生变化是相对常见的。结论：现有模型似乎没有涵盖流行病学情况和轨迹的全部范围。因此，在(5)q(0)的基础上估算(1)q(0)和(4)q(1)时，应谨慎使用模型寿命表。此外，这种基于模型的估计过程假设(5)q(0)的输入值是正确的，这可能并不总是得到保证，特别是在调查数据的情况下。迫切需要系统地评估抽样调查中的数据误差及其对(1)q(0)和(4)q(1)年龄模式的影响，同时开发涵盖更广泛流行病学情况和轨迹的5岁以下儿童死亡率年龄模式模型。
Background: The under-five mortality rate (the probability of dying between birth and age 5 y, also denoted in the literature as U5MR and (5)q(0)) 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 ((1)q(0)) versus at age 1 y and above ((4)q(1)). 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 (1)q(0) and (4)q(1) on the basis of an observed value of (5)q(0). 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 (1)q(0) and (4)q(1) 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 (1)q(0) and (4)q(1) 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 (4)q(1) relative to (1)q(0), 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 (4)q(1), 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 (4)q(1) relative to (1)q(0), 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 (1)q(0) and (4)q(1) on the basis of (5)q(0). Moreover, this model-based estimation procedure assumes that the input value of (5)q(0) 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 (1)q(0) and (4)q(1) 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.