Abstract
The COVID-19 outbreak has called for renewed attention to the need for sound statistical analyses to monitor mortality patterns and trends over time. Excess mortality has been suggested as the most appropriate indicator to measure the overall burden of the pandemic in terms of mortality. As such, excess mortality has received considerable interest since the outbreak of COVID-19 began.Previous approaches to estimate excess mortality are somewhat limited, as they do not include sufficiently long-term trends, correlations among different demographic and geographic groups, or autocorrelations in the mortality time series. This might lead to biased estimates of excess mortality, as random mortality fluctuations may be misinterpreted as excess mortality.We propose a novel approach that overcomes the named limitations and draws a more realistic picture of excess mortality. Our approach is based on an established forecasting model that is used in demography, namely, the Lee-Carter model. We illustrate our approach by using the weekly age- and sex-specific mortality data for 19 countries and the current COVID-19 pandemic as a case study. Our findings show evidence of considerable excess mortality during 2020 in Europe, which affects different countries, age, and sex groups heterogeneously. Our proposed model can be applied to future pandemics as well as to monitor excess mortality from specific causes of death.
Highlights
The outbreak of COVID-19 has highlighted the need for sound and timely statistical analyses and monitoring of mortality patterns and trends
We first perform principal component analysis (PCA) on the logit-WASCSMR time series such that we obtain a set of Principal component (PC) with the ith PC being a linear combination of all logit-WASCSMRs: X 152 pi;w;y 1⁄4 λi; jμc;g;a;w;y; i 1⁄4 1; ...; 152; j1⁄41 where μc, g, a, w, y is the logit-WASCSMR of country c, sex g and age a, in calendar week w of year y and λi, j is the loading of the jth mortality variable on the ith PC
Our estimate of excess mortalities is more precise than previous approaches and shows the uncertainty of these estimates that is based both on the demography of some models do not consider the long-term trends in mortality at all, as they take the average values of the previous years
Summary
The outbreak of COVID-19 has highlighted the need for sound and timely statistical analyses and monitoring of mortality patterns and trends. While aggregate all-cause mortality data are increasingly being released, the timely reporting of cause-specific data by demographic subgroups is still underdeveloped. Such information would enable near real-time assessments of the excess mortality caused by specific diseases. The widespread statistical approaches used far for estimating excess mortality have been rather simplistic, as they rely on rather basic statistical measures, such as the number of deaths above an ex ante expected value, which do not include or consider the stochasticity or connections among the mortality developments of different demographic or geographic groups. Rather short time series are generally considered in the computations of excess mortality, which cannot sufficiently capture long-term trends
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