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Age-disaggregated health data is crucial for effective public health planningand monitoring. Monitoring under-five mortality, for example, requires highlydetailed age data since the distribution of potential causes of death variessubstantially within the first few years of life. Comparative researchers oftenhave to rely on multiple data sources yet, these sources often have agesaggregated at different levels, making it difficult to combine the data into asingle, coherent picture. To address this challenge in the context ofunder-five cause-specific mortality, we propose a Bayesian approach, thatcalibrates data with different age structures to produce unified and accurateestimates of the standardized age group distributions. We considerage-disaggregated death counts as fully-classified multinomial data and showthat by incorporating partially-classified aggregated data, we can construct animproved Bayes estimator of the multinomial parameters under theKullback-Leibler (KL) loss. We illustrate the method using both synthetic andreal data, demonstrating that the proposed method achieves adequate performancein imputing incomplete classification. Finally, we present the results ofnumerical studies examining the conditions necessary for obtaining improvedestimators. These studies provide insights and interpretations that can be usedto aid future research and inform guidance for practitioners on appropriatelevels of age disaggregation, with the aim of improving the accuracy andreliability of under-five cause-specific mortality estimates.

Bayesian Age Category Reconciliation for Age- and Cause-specific Under-five Mortality Estimates