  Civil registration and vital statistics (CRVS) systems should be the primary
source of mortality data for governments. Accurate and timely measurement of
the completeness of death registration helps inform interventions to improve
CRVS systems and to generate reliable mortality indicators. In this work we
propose 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 is
motivated for situations where demographic covariates can explain much of the
observed completeness but where unexplained within-country (i.e. by year) and
between-country variability also play an important role. The use of our
approach can allow institutions improve model parameter estimates and more
accurately predict completeness of death registration. Our models are based on
a dataset which uses Global Burden of Disease (GBD) death estimates based on
the GBD 2019 and comprises 120 countries and 2,748 country-years from 1970-2019
(Demographics Collaborators, 2020). To illustrate the effectiveness of our
proposal we consider the completeness of death registration in the departments
of Colombia in 2017.
