Abstract
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.
Funding Statement
Research reported in this publication was supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Number R21HD095451 and the National Institute of Mental Health of the National Institutes of Health under Award Number DP2MH122405. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This work was also supported in part by the Bill & Melinda Gates Foundation, Investment ID: OPP1172551.
Acknowledgments
The authors would like to thank the anonymous referees and the Editor, Beth Ann Griffin, for their constructive comments that improved the quality of this paper.
Citation
Austin E. Schumacher. Tyler H. McCormick. Jon Wakefield. Yue Chu. Jamie Perin. Francisco Villavicencio. Noah Simon. Li Liu. "A flexible Bayesian framework to estimate age- and cause-specific child mortality over time from sample registration data." Ann. Appl. Stat. 16 (1) 124 - 143, March 2022. https://doi.org/10.1214/21-AOAS1489
Information