Quantifying impacts of the COVID-19 pandemic through life expectancy losses: a population-level study of 29 countries

Jm, Aburto; Schöley, Jonas; Zhang, L; Kashnitsky, Ilya; Rahal, Charles; Missov, Trifon I.; Mc, Mills; Jb, Dowd; Kashyap, Ridhi

doi:10.1101/2021.03.02.21252772

Cited by 48 publications

(50 citation statements)

References 44 publications

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“…Our estimates of the loss of life expectancy in Switzerland in 2020 are close to those obtained by Aburto et al [8], who reported a loss of almost 12 months for men, and slightly more than 6 months for women. Of note, these results were calculated using five broad age classes (0-14, 15-64, 65-74, 75-84, and 85+).…”

Section: Plos Onesupporting

confidence: 90%

“…Heuveline and Tzen [ 7 ] applied a similar procedure for 186 countries or territories, including Switzerland. In a preprint, Aburto et al [ 8 ] provided such estimates for 29 countries, including Switzerland, based on all-cause mortality. Given the potential importance of such analyses in the current (urgent) scientific and political debates on the COVID-19 pandemic, we believe that even results that are not entirely definitive deserve to be presented and published.…”

Section: Introductionmentioning

confidence: 99%

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A first analysis of excess mortality in Switzerland in 2020

Locatelli

Rousson

2021

PLoS ONE

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Objective To quantify excess all-cause mortality in Switzerland in 2020, a key indicator for assessing direct and indirect consequences of the COVID-19 pandemic. Methods Using official data on deaths in Switzerland, all-cause mortality in 2020 was compared with that of previous years using directly standardized mortality rates, age- and sex-specific mortality rates, and life expectancy. Results The standardized mortality rate was 8.8% higher in 2020 than in 2019, returning to the level observed 5–6 years before, around the year 2015. This increase was greater for men (10.6%) than for women (7.2%) and was statistically significant only for men over 70 years of age, and for women over 75 years of age. The decrease in life expectancy in 2020 compared to 2019 was 0.7%, with a loss of 9.7 months for men and 5.3 months for women. Conclusions There was an excess mortality in Switzerland in 2020, linked to the COVID-19 pandemic. However, as this excess only concerned the elderly, the resulting loss of life expectancy was restricted to a few months, bringing the mortality level back to 2015.

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Section: Plos Onesupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

A first analysis of excess mortality in Switzerland in 2020

Locatelli

Rousson

2021

PLoS ONE

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“…In the case of other death rates that increased during the COVID-19 pandemic, such as unintentional injuries, because they tend to be at younger ages than COVID-19 deaths, equating excess deaths to unreported COVID-19 would lead to some underestimation of years of lost life and, as a result, MUL. A similar gap can be observed considering estimates of changes in PLEB that are now available in a number of nations [45] and that differ more from initial estimates based on reported COVID-19 deaths only [46] than excess to COVID-19 death ratio would have suggested.…”

Section: Plos Onesupporting

confidence: 57%

The Mean Unfulfilled Lifespan (MUL): A new indicator of the impact of mortality shocks on the individual lifespan, with application to mortality reversals induced by COVID-19

Heuveline

2021

PLoS ONE

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Declines in period life expectancy at birth (PLEB) provide seemingly intuitive indicators of the impact of a cause of death on the individual lifespan. Derived under the assumption that future mortality conditions will remain indefinitely those observed during a reference period, however, their intuitive interpretation becomes problematic when period conditions reflect a temporary mortality “shock”, resulting from a natural disaster or the diffusion of a new epidemic in the population for instance. Rather than to make assumptions about future mortality, I propose measuring the difference between a period average age at death and the average expected age at death of the same individuals (death cohort): the Mean Unfulfilled Lifespan (MUL). For fine-grained tracking of the mortality impact of an epidemic, I also provide an empirical shortcut to MUL estimation for small areas or short periods. For illustration, quarterly MUL values in 2020 are derived from estimates of COVID-19 deaths that might substantially underestimate overall mortality change in affected populations. These results nonetheless illustrate how MUL tracks the mortality impact of the pandemic in several national and sub-national populations. Using a seven-day rolling window, the empirical shortcut suggests MUL peaked at 6.43 years in Lombardy, 8.91 years in New Jersey, and 6.24 years in Mexico City for instance. Sensitivity analyses are presented, but in the case of COVID-19, the main uncertainty remains the potential gap between reported COVID-19 deaths and actual increases in the number of deaths induced by the pandemic in some of the most affected countries. Using actual number of deaths rather than reported COVID-19 deaths may increase seven-day MUL from 6.24 to 8.96 years in Mexico City and from 2.67 to 5.49 years in Lima for instance. In Guayas (Ecuador), MUL is estimated to have reached 12.7 years for the entire month of April 2020.

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“…If the modeling goal is mortality forecasting, as under the counterfactual no-COVID scenario, then fitting seasonal regression models over all weeks of a year reduces the bias in the forecasted death counts. A lack of adjustment for continuous mortality improvements, as have been observed across Europe up until 2020 [15,2], explains the tendency of the average death rate model towards low estimates of excess. By simply averaging declining death rates over time, one overestimates expected mortality for the current year, thus underestimating excess.…”

Section: Discussionmentioning

confidence: 99%

“…A lack of adjustment for continuous mortality improvements, as have been observed across Europe up until 2020 [15, 2], explains the tendency of the average death rate model towards low estimates of excess. By simply averaging declining death rates over time, one overestimates expected mortality for the current year, thus underestimating excess.…”

Section: Discussionmentioning

confidence: 99%

Robustness and bias of European excess death estimates in 2020 under varying model specifications

Schöley

2021

Preprint

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Various procedures are in use to calculate excess deaths during the ongoing COVID-19 pandemic. Using weekly death counts from 20 European countries, we evaluate the robustness of excess death estimates to the choice of model for expected deaths and perform a cross-validation analysis to assess the error and bias in each model's predicted death counts. We find that the different models produce very similar patterns of weekly excess deaths but disagree substantially on the level of excess. While the exact country ranking along percent excess death in 2020 is sensitive to the choice of model the top and bottom ranks are robustly identified. On the country level, the 5-year average death rate model tends to produce the lowest excess death estimates, whereas high excess deaths are produced by the popular 5-year average death count and Euromomo-style Serfling models. Cross-validation revealed these estimates to be biased under a causal interpretation of "expected deaths had COVID-19 not happened."

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Quantifying impacts of the COVID-19 pandemic through life expectancy losses: a population-level study of 29 countries

Cited by 48 publications

References 44 publications

A first analysis of excess mortality in Switzerland in 2020

A first analysis of excess mortality in Switzerland in 2020

The Mean Unfulfilled Lifespan (MUL): A new indicator of the impact of mortality shocks on the individual lifespan, with application to mortality reversals induced by COVID-19

Robustness and bias of European excess death estimates in 2020 under varying model specifications

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