Quantifying and communicating the burden of COVID-19

Cube, Maja von; Timsit, Jean‐François; Kammerlander, Andreas; Schumacher, Martin

doi:10.1186/s12874-021-01349-z

Cited by 4 publications

(1 citation statement)

References 24 publications

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“…Excess all-cause mortality would be a better indicator of COVID-19 impact on the health care system than the COVID-19 reported mortality rate. It provides a comparing context between different management strategies and can guide policy-making strategies more efficiently [ 9 , 10 ]. There was an increase in the all-cause mortality rate (excess death) in Iran since winter 2020 compared to the expected mortality rate based on previous year data, likely attributed to the COVID-19 pandemic [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Excess all-cause mortality and COVID-19 reported fatality in Iran (April 2013–September 2021): age and sex disaggregated time series analysis

et al. 2022

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Objective The actual impact of the pandemic on COVID-19 specific mortality is still unclear due to the variability in access to diagnostic tools. This study aimed to estimate the excess all-cause mortality in Iran until September 2021 based on the national death statistics. Results The autoregressive integrated moving average was used to predict seasonal all-cause death in Iran (R-squared = 0.45). We observed a 38.8% (95% confidence interval (CI) 29.7%–40.1%) rise in the all-cause mortality from 22 June 2020 to 21 June 2021. The excess all-cause mortality per 100,000 population were 178.86 (95% CI 137.2–220.5, M:F ratio = 1.3) with 49.1% of these excess deaths due to COVID-19. Comparison of spring 2019 and spring 2021 revealed that the highest percent increase in mortality was among men aged 65–69 years old (77%) and women aged 60–64 years old (86.8%). Moreover, the excess mortality among 31 provinces of Iran ranged from 109.7 (Hormozgan) to 273.2 (East-Azerbaijan) per 100,000 population. In conclusion, there was a significant rise in all-cause mortality during the pandemic. Since COVID-19 fatality explains about half of this rise, the increase in other causes of death and underestimation in reported data should be concerned by further studies.

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Section: Introductionmentioning

confidence: 99%

Excess all-cause mortality and COVID-19 reported fatality in Iran (April 2013–September 2021): age and sex disaggregated time series analysis

et al. 2022

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The population-attributable fraction for time-to-event data

Cube

Schumacher

Timsit

et al. 2022

International Journal of Epidemiology

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Background Even though the population-attributable fraction (PAF) is a well-established metric, it is often incorrectly estimated or interpreted not only in clinical application, but also in statistical research articles. The risk of bias is especially high in more complex time-to-event data settings. Methods We explain how the PAF can be defined, identified and estimated in time-to-event settings with competing risks and time-dependent exposures. By using multi-state methodology and inverse probability weighting, we demonstrate how to reduce or completely avoid severe types of biases including competing risks bias, immortal time bias and confounding due to both baseline and time-varying patient characteristics. Results The method is exemplarily applied to a real data set. Moreover, we estimate the number of deaths that were attributable to ventilator-associated pneumonia in France in the year 2016. The example demonstrates how, under certain simplifying assumptions, PAF estimates can be extrapolated to a target population of interest. Conclusions Defining and estimating the PAF in advanced time-to-event settings within a framework that unifies causal and multi-state modelling enables to tackle common sources of bias and allows straightforward implementation with standard software packages.

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Population age structure dependency of the excess mortality P-score

Ullrich-Kniffka,

Schöley

2024

Popul Health Metrics

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Background Since the outbreak of the COVID-19 pandemic, the excess mortality P-score has gained prominence as a measure of pandemic burden. The P-score indicates the percentage by which observed deaths deviate from expected deaths. As the P-score is regularly used to compare excess mortality between countries, questions arise regarding the age dependency of the measure. In this paper we present formal and empirical results on the population structure bias of the P-score with a special focus on cross-country comparisons during the COVID-19 pandemic in Europe. Methods P-scores were calculated for European countries for 2021, 2022, and 2023 using data from the 2024 revision of the United Nations’ World Population Prospects and the HMDs Short Term Mortality Fluctuations data series. The expected deaths for 2021, 2022, and 2023 were estimated using a Lee–Carter forecast model assuming pre-pandemic conditions. P-score differences between countries were decomposed using a Kitagawa-type decomposition into excess-mortality and structural components. To investigate the sensitivity of P-score cross-country rankings to differences in population structure we calculated the rank-correlation between age-standardized and classical P-scores. Results The P-score is an average of age-specific percent excess deaths weighted by the age-distribution of expected deaths. It can be shown that the effect of differences in the distribution of deaths only plays a marginal role in a European comparison. In most cases, the excess mortality effect is the dominant effect. P-score rankings among European countries during the COVID-19 pandemic are similar under both age-standardized and classical P-scores. Conclusions Although the P-score formally depends on the age-distribution of expected deaths, this structural component only plays a minor role in a European comparison, as the distribution of deaths across the continent is similar. Thus, the P-score is suitable as a measure of excess mortality in a European comparison, as it mainly reflects the differences in excess mortality. However, this finding should not be extrapolated to global comparisons, where countries could have very different death distributions. In situations were P-score comparisons are biased age-standardization can be applied as a solution.

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Quantifying and communicating the burden of COVID-19

Cited by 4 publications

References 24 publications

Excess all-cause mortality and COVID-19 reported fatality in Iran (April 2013–September 2021): age and sex disaggregated time series analysis

Excess all-cause mortality and COVID-19 reported fatality in Iran (April 2013–September 2021): age and sex disaggregated time series analysis

The population-attributable fraction for time-to-event data

Population age structure dependency of the excess mortality P-score

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