Speaker: Austin Schumacher, Graduate Student, UW Biostatistics
Abstract: As investment increases in implementing age-targeted, disease-specific childhood interventions in data-scarce countries, effectiveness requires knowledge of the causes responsible for deaths in this important age group. Development of sample vital registration systems in low- and middle-income countries is progressing, motivating new methods to utilize this important data source. Current methods to model cause- and age-specific child mortality (i) estimate all-cause and cause-specific mortality in two separate frame- works, (ii) produce estimates separately and independently in each age group, and/or (iii) do not account for empirically observed correlations between causes. We propose a Bayesian modeling approach for the unified estimation of all-cause and cause-specific child mortality that accounts for correlations between cause- and age-specific mortality rates using sample registration data. We provide theoretical justification for this model, explore its properties via simulation, and use it to estimate age- and cause-specific mortality trends in sample registration data from China.