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Civil registration and vital statistics (CRVS) systems should be the primarysource of mortality data for governments. Accurate and timely measurement ofthe completeness of death registration helps inform interventions to improveCRVS systems and to generate reliable mortality indicators. In this work wepropose the use of hierarchical Bayesian linear mixed models with Global-Local(GL) priors to estimate the completeness of death registration at global,national and subnational levels. The use of GL priors in this paper ismotivated for situations where demographic covariates can explain much of theobserved completeness but where unexplained within-country (i.e. by year) andbetween-country variability also play an important role. The use of ourapproach can allow institutions improve model parameter estimates and moreaccurately predict completeness of death registration. Our models are based ona dataset which uses Global Burden of Disease (GBD) death estimates based onthe GBD 2019 and comprises 120 countries and 2,748 country-years from 1970-2019(Demographics Collaborators, 2020). To illustrate the effectiveness of ourproposal we consider the completeness of death registration in the departmentsof Colombia in 2017.

A Bayesian approach to estimate the completeness of death registration