  In order to implement disease-specific interventions in young age groups,
policy makers in low- and middle-income countries require timely and accurate
estimates of age- and cause-specific child mortality. High quality data is not
available in settings where these interventions are most needed, but there is a
push to create sample registration systems that collect detailed mortality
information. Current methods that estimate mortality from this data employ
multistage frameworks without rigorous statistical justification that
separately estimate all-cause and cause-specific mortality and are not
sufficiently adaptable to capture important features of the data. We propose a
flexible Bayesian modeling framework to estimate age- and cause-specific child
mortality from sample registration data. We provide a theoretical justification
for the framework, explore its properties via simulation, and use it to
estimate mortality trends using data from the Maternal and Child Health
Surveillance System in China.
