A linear mixed model to estimate COVID‐19‐induced excess mortality. Issue 1 (22nd November 2021)

• Record Type: Journal Article
• Authors: Verbeeck, Johan; Faes, Christel; Neyens, Thomas; Hens, Niel; Verbeke, Geert; Deboosere, Patrick; Molenberghs, Geert
• Is Part Of: Biometrics. Volume 79:Issue 1(2023)
• Page Start: 417
• Page End: 425
• Publication Date: 2021-11-22
• Subjects: 5‐year weekly average -- COVID‐19 -- excess mortality -- linear mixed model; Biometry -- Periodicals; 570.15195
• DOI: 10.1111/biom.13578
• Languages: English
• ISSNs: 0006-341X

Abstract

Abstract: 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.