Human costs of the first wave of the COVID-19 pandemic in the major epicentres in Italy

Ghislandi, Simone; Muttarak, Raya; Sauerberg, Markus; Scotti, B.

doi:10.1553/populationyearbook2022.res2.1

Cited by 6 publications

(6 citation statements)

References 32 publications

(34 reference statements)

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“…By definition, excess mortality is the number of deaths from all causes in a given period relative to the number of deaths that would have normally been expected to occur in that period. For assessing the direct and indirect impacts of COVID-19 on overall mortality, excess mortality for all causes of deaths is a well-established indicator (Aburto et al, 2021;Aron et al, 2020;Ghislandi et al, 2021;Schöley, 2021).…”

Section: Introductionmentioning

confidence: 99%

Assessing excess mortality in Vienna and Austria after the first year of the COVID-19 pandemic

Bauer¹,

Speringer²,

Frühwirt³

et al. 2022

Populationyearbook

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In Austria, the first confirmed COVID-19 death occurred in early March 2020. Since then, the question as to whether and, if so, to what extent the COVID-19 pandemic has increased overall mortality has been raised in the public and academic discourse. In an effort to answer this question, Statistics Vienna (City of Vienna, Department for Economic Affairs, Labour and Statistics) has evaluated the weekly mortality trends in Vienna, and compared them to the trends in other Austrian provinces. For our analysis, we draw on data from Statistics Austria and the Austrian Agency for Health and Food Safety (AGES), which are published along with data on the actual and the expected weekly numbers of deaths via the Vienna Mortality Monitoring website. Based on the definition of excess mortality as the actual number of reported deaths from all causes minus the expected number of deaths, we calculate the weekly prediction intervals of the expected number of deaths for two age groups (0 to 64 years and 65 years and older). The temporal scope of the analysis covers not only the current COVID-19 pandemic, but also previous flu seasons and summer heat waves. The results show the actual weekly numbers of deaths and the corresponding prediction intervals for Vienna and the other Austrian provinces since 2007. Our analysis underlines the importance of comparing time series of COVID-19-related excess deaths at the sub-national level in order to highlight within-country heterogeneities.

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Section: Introductionmentioning

confidence: 99%

Assessing excess mortality in Vienna and Austria after the first year of the COVID-19 pandemic

Bauer¹,

Speringer²,

Frühwirt³

et al. 2022

Populationyearbook

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show abstract

“…First, this alternative interpretation applies equally well to very short reference periods. When mortality conditions change rapidly, one may want to track these changes with high frequency for periods shorter than a full year (e.g., Trias-Llimos, Riffe, and Bilal 2020 ; Ghislandi et al 2022 ). Due to the seasonality of mortality conditions, however, the forward-looking interpretation of life expectancy for reference periods shorter than a full year implies an implausible lifetime experience, looping conditions in some seasons while skipping conditions in other seasons.…”

Section: The Period Life Table As a Stationary Populationmentioning

confidence: 99%

Interpreting changes in life expectancy during temporary mortality shocks

Heuveline¹

2023

DemRes

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BACKGROUNDLife expectancy is a pure measure of the mortality conditions faced by a population, unaffected by that population's age structure. The numerical value of life expectancy also has an intuitive interpretation, conditional on some assumptions, as the expected age at death of an average newborn. This intuitive interpretation gives life expectancy a broad appeal. Changes in life expectancy are also routinely used to assess mortality trends. Interpreting these changes is not straightforward as the assumptions underpinning the intuitive interpretation of life expectancy are no longer valid. This is particularly problematic during mortality 'shocks,' such as during wars or pandemics, when mortality changes may be sudden, temporary, and contrary to secular trends. OBJECTIVEThis study aims to provide an alternative perspective on what changes in life expectancy measure that remains applicable during mortality shocks. CONCLUSIONSReturning to two different models that the period life table may represent, I show that a difference in life expectancy is typically interpreted from the synthetic cohort model as the difference in mean longevity between different birth cohorts. However, it can also be interpreted from the stationary population model as a measure of premature mortality in a death cohort. The latter, less common interpretation makes more sense for temporary declines in life expectancy induced by mortality shocks. The absolute change in life expectancy is then an age-standardized value of the average lifespan reduction for people dying during the mortality shock. CONTRIBUTIONTo clarify what a decline in life expectancy measures during mortality shocks is important, especially as demographers often assess the mortality impact of those shocks using this metric, which gets widely reported beyond demographers' inner circle.

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“…First, this alternative interpretation applies equally well to very short reference periods. When mortality conditions change rapidly, one may want to track these changes with high frequency, for periods shorter than a full year (e.g., Trias-Llimos Riffe, and Bilal 2020; Ghislandi et al 2022). Due to the seasonality of mortality conditions, however, the probabilistic interpretation of life expectancy for reference periods shorter than a full year implies an implausible lifetime experience, looping conditions in some seasons while skipping conditions in other seasons.…”

Section: The Period Life Table As a Stationary Populationmentioning

confidence: 99%

Interpreting Changes in Life Expectancy During Temporary Mortality Shocks

Heuveline

2022

Preprint

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Life expectancy is the most widely used measure of mortality, largely because of its intuitive interpretation as the expected age at death of an average individual. Changes in life expectancy are also used to assess mortality trends, but mortality change is incompatible with the assumptions underpinning that intuitive interpretation of life expectancy. To provide an alternative, I return to the two main interpretations of the period life table. The first interpretation, as a synthetic-cohort life table, allows for forward-looking probabilistic statements regarding the individual life course. The second interpretation, as stationary population characteristics, yields standardized demographic indicators of the (past) reference period. Interpreting life table indicators such as life expectancy in the first manner is often more appealing, but only the second one makes sense during temporary mortality shocks. The absolute change in life expectancy can then be interpreted as the standardized value of the average lifespan reduction in a past death cohort.

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Human costs of the first wave of the COVID-19 pandemic in the major epicentres in Italy

Cited by 6 publications

References 32 publications

Assessing excess mortality in Vienna and Austria after the first year of the COVID-19 pandemic

Assessing excess mortality in Vienna and Austria after the first year of the COVID-19 pandemic

Interpreting changes in life expectancy during temporary mortality shocks

Interpreting Changes in Life Expectancy During Temporary Mortality Shocks

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