A Bayesian approach to estimate the completeness of death registration

Abstract

Civil registration and vital statistics (CRVS) systems should be the primary source of mortality data for governments. Accurate 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 a Bayesian extension to the empirical completeness method to estimate the completeness of death registration in a population. The Bayesian models were developed using death registration data from 120 countries and 2748 country-years in 1970–2019 from the Global Burden of Disease (GBD) 2019 database. According to our findings, the credible intervals obtained under the proposed Bayesian models suggest that the covariates are reliable predictors of the logit of the completeness of death registration. To illustrate the effectiveness of our proposal, we applied the Bayesian models to estimate the completeness of death registration in the departments of Colombia in 2017. We found that most posterior mean estimates closely approximate to the observed value of completeness across the departments of Colombia and that, for nearly all departments, the observed completeness is within the credible intervals. The proposed Bayesian demographic models allow institutions to produce distributional estimates (e.g., posterior mean, quantiles, credible intervals, posterior probabilities) of the completeness of death registration at the subnational level, which can be used as evidence to strengthen their CRVS system.