Statistical Methods for Excess Mortality Estimation with Variable Data Availability and Completeness

Abstract

Reliable estimation of mortality is fundamental to demographic measurement and public health assessment. Excess mortality, the difference between observed and expected deaths, provides a comprehensive indicator of the total mortality impact of crises, encompassing both direct and indirect effects. The estimation of excess mortality across diverse countries and population subgroups, as well as the evaluation of the completeness of underlying death registration systems, presents significant methodological challenges. This dissertation develops statistical frameworks to improve the estimation, disaggregation, and validation of mortality measures in settings with incomplete or heterogeneous data, with a particular focus on the global impacts of the COVID-19 pandemic. The outline of the dissertation is as follows. In Chapter 2, we develop a model for estimating global and country-specific excess mortality during the COVID-19 pandemic using an overdispersed Poisson count framework. The approach jointly models total and expected deaths, incorporating both long-term trends and short-term seasonal variation, and extends estimation to countries without complete data through predictive log-linear and multinomial subnational models. These methods formed the basis for the World Health Organization’s global estimates of pandemic excess deaths for 194 countries. In Chapter 3, we extend this framework to the estimation of age- and sex-specific excess mortality. Expected death rates are modeled using an overdispersed Poisson regression with log-linear temporal trends and smooth age effects, while unobserved mortality rates are estimated through a reduced-dimensionality framework that leverages principal components analysis and country-level covariates. This chapter also investigates the sensitivity of excess mortality estimates to the specification of expected deaths and the choice of reference period. In Chapter 4, we address the problem of assessing the completeness of vital registration systems, particularly in low- and middle-income countries, by developing a probabilistic formulation of death distribution methods. This approach embeds the demographic balance equations relating deaths, births, and migration within a statistical framework that permits uncertainty quantification. We conclude with a discussion of future directions for this research. Together, these chapters contribute to a unified statistical foundation for mortality estimation, enhancing the accuracy, transparency, and interpretability of vital statistics across global contexts.