A linear mixed model to estimate COVID-19-induced excess mortality

The Corona Virus Disease (COVID-19) pandemic has increased mortality in countries worldwide. To evaluate the impact of the pandemic on mortality, the use of excess mortality rather than reported COVID-19 deaths has been suggested. Excess mortality, however, requires estimation of mortality under nonpandemic conditions. Although many methods exist to forecast mortality, they are either complex to apply, require many sources of information, ignore serial correlation, and/or are influenced by historical excess mortality. We propose a linear mixed model that is easy to apply, requires only historical mortality data, allows for serial correlation, and down-weighs the influence of historical excess mortality. Appropriateness of the linear mixed model is evaluated with fit statistics and forecasting accuracy measures for Belgium and the Netherlands. Unlike the commonly used 5-year weekly average, the linear mixed model is forecasting the year-specific mortality, and as a result improves the estimation of excess mortality for Belgium and the Netherlands.

Automated summary

The COVID-19 pandemic has caused an increase in mortality worldwide. To assess the impact on mortality, researchers propose using excess mortality instead of reported COVID-19 deaths. However, estimating excess mortality requires considering non-pandemic mortality rates, and current methods have limitations. In this study, a linear mixed model is proposed, which improves excess mortality estimation by using historical mortality data, accounting for serial correlation, and reducing the influence of historical excess mortality.