Rao Chalapati, Adair Timothy, Kinfu Yohannes
School of Population Health, University of Queensland, Herston QLD, Australia.
Clin Med Res. 2011 Jun;9(2):66-74. doi: 10.3121/cmr.2010.959. Epub 2010 Oct 25.
Cause-specific mortality data is essential for planning intervention programs to reduce mortality in the under age five years population (under-five). However, there is a critical paucity of such information for most of the developing world, particularly where progress towards the United Nations Millennium Development Goal 4 (MDG 4) has been slow. This paper presents a predictive cause of death model for under-five mortality based on historical vital statistics and discusses the utility of the model in generating information that could accelerate progress towards MDG 4.
Over 1400 country years of vital statistics from 34 countries collected over a period of nearly a century were analyzed to develop relationships between levels of under-five mortality, related mortality ratios, and proportionate mortality from four cause groups: perinatal conditions; diarrhea and lower respiratory infections; congenital anomalies; and all other causes of death. A system of multiple equations with cross-equation parameter restrictions and correlated error terms was developed to predict proportionate mortality by cause based on given measures of under-five mortality. The strength of the predictive model was tested through internal and external cross-validation techniques. Modeled cause-specific mortality estimates for major regions in Africa, Asia, Central America, and South America are presented to illustrate its application across a range of under-five mortality rates.
Consistent and plausible trends and relationships are observed from historical data. High mortality rates are associated with increased proportions of deaths from diarrhea and lower respiratory infections. Perinatal conditions assume importance as a proportionate cause at under-five mortality rates below 60 per 1000 live births. Internal and external validation confirms strength and consistency of the predictive model. Model application at regional level demonstrates heterogeneity and non-linearity in cause-composition arising from the range of under-five mortality rates and related mortality ratios.
Historical analyses suggest that under-five mortality transitions are associated with significant changes in cause of death composition. Sub-national differentials in under-five mortality rates could require intervention programs targeted to address specific cause distributions. The predictive model could, therefore, help set broad priorities for interventions at the local level based on periodic under-five mortality measurement. Given current resource constraints, such priority setting mechanisms are essential to accelerate reductions in under-five mortality.
特定病因死亡率数据对于规划干预项目以降低五岁以下儿童（以下简称“五岁以下儿童”）死亡率至关重要。然而，对于世界上大多数发展中地区而言，此类信息严重匮乏，尤其是在实现联合国千年发展目标4（MDG 4）进展缓慢的地区。本文基于历史生命统计数据提出了一个五岁以下儿童死亡率的预测死因模型，并讨论了该模型在生成有助于加速实现MDG 4的信息方面的作用。
分析了近一个世纪内从34个国家收集的超过1400个国家年的生命统计数据，以建立五岁以下儿童死亡率水平、相关死亡率以及四个病因组的比例死亡率之间的关系，这四个病因组分别为：围产期疾病；腹泻和下呼吸道感染；先天性异常；以及所有其他死因。开发了一个具有交叉方程参数限制和相关误差项的多元方程组系统，以根据给定的五岁以下儿童死亡率指标预测各病因的比例死亡率。通过内部和外部交叉验证技术检验了预测模型的强度。给出了非洲、亚洲、中美洲和南美洲主要地区的模拟特定病因死亡率估计值，以说明其在不同五岁以下儿童死亡率范围内的应用。
从历史数据中观察到了一致且合理的趋势和关系。高死亡率与腹泻和下呼吸道感染导致的死亡比例增加相关。在五岁以下儿童死亡率低于每1000例活产60例的情况下，围产期疾病作为比例死因具有重要意义。内部和外部验证证实了预测模型的强度和一致性。在区域层面的模型应用表明，五岁以下儿童死亡率范围和相关死亡率导致了病因构成的异质性和非线性。
历史分析表明，五岁以下儿童死亡率转变与死亡原因构成的显著变化相关。五岁以下儿童死亡率的次国家差异可能需要针对性解决特定病因分布的干预项目。因此，该预测模型有助于根据定期的五岁以下儿童死亡率测量为地方层面的干预设定广泛的优先事项。鉴于当前的资源限制状况，这种优先事项设定机制对于加速降低五岁以下儿童死亡率至关重要。
