Using Historical Vital Statistics to Predict the Distribution of Under-Five Mortality by Cause - Rao, Chalapati (Marshfield Clinic, 2011)
Background: 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. Methods: 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. Results: 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. Conclusions: 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.