Abstract

Excess mortality is a measure of the increase in the number of deaths in a population during a certain time period compared to the expected values. This phenomenon can be triggered by a variety of natural or man-made disasters. Throughout the global COVID-19 pandemic, all countries experienced a rise in mortality rates, although not all deaths were directly caused by the coronavirus infection. Estimation of excess mortality during a pandemic, particularly stratified by the leading causes of death, is an important public health issue. The outcomes of these calculations, however, can vary significantly depending on the methodological approaches employed to estimate excess mortality.
 The aim of this study was to provide a systematic review of the analytical methods used by the international research community to quantify excess mortality during the COVID-19 pandemic. Full-text publications in both Russian and English, published between 2020 and 2022, that focused on assessing excess mortality during the COVID-19 were reviewed. The search for English-language publications was conducted in the MEDLINE database (www.pubmed.gov), while Russian-language publications were sourced from the scientific electronic library database eLIBRARY.RU (www.elibrary.ru). Out of the 725 publications initially identified, we included 83 original studies in this review. Among the various statistical methods employed to estimate excess mortality, the most utilized approaches were Poisson regression with correction for overdispersion and a range of adaptive models based on autoregression and integrated moving average. The selection of a specific model depended on factors such as the duration of the existing time series, its characteristics, and the forecasting interval.
 This review may serve as a resource for Russian-speaking researchers and analysts seeking guidance on selecting an appropriate analytical approach when examining excess deaths during the COVID-19 pandemic in Russia.

Full Text
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