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In order to implement disease-specific interventions in young age groups,policy makers in low- and middle-income countries require timely and accurateestimates of age- and cause-specific child mortality. High quality data is notavailable in settings where these interventions are most needed, but there is apush to create sample registration systems that collect detailed mortalityinformation. Current methods that estimate mortality from this data employmultistage frameworks without rigorous statistical justification thatseparately estimate all-cause and cause-specific mortality and are notsufficiently adaptable to capture important features of the data. We propose aflexible Bayesian modeling framework to estimate age- and cause-specific childmortality from sample registration data. We provide a theoretical justificationfor the framework, explore its properties via simulation, and use it toestimate mortality trends using data from the Maternal and Child HealthSurveillance System in China.

A flexible Bayesian framework to estimate age- and cause-specific child mortality over time from sample registration data