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The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
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A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
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Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy
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Short-term forecasts of expected deaths.

Silvia Rizzi1, James W Vaupel2

  • 1Interdisciplinary Centre on Population Dynamics, University of Southern Denmark, 5230 Odense M, Denmark.

Proceedings of the National Academy of Sciences of the United States of America
|March 27, 2021
PubMed
Summary

We developed a new method for short-term mortality forecasting. This approach revealed lower excess deaths in Denmark compared to Sweden during the initial COVID-19 wave, highlighting the impact of different policy responses.

Keywords:
Denmark and Swedencoronavirus pandemicexcess deathsmortality forecastingshort-term forecasting

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Area of Science:

  • Epidemiology
  • Demography
  • Public Health

Background:

  • The first wave of the COVID-19 pandemic caused significant mortality globally.
  • Assessing excess mortality is crucial for understanding the pandemic's true impact.
  • Denmark and Sweden adopted distinct strategies in response to COVID-19, offering a comparative study opportunity.

Purpose of the Study:

  • To introduce and validate a novel method for short-term mortality forecasting.
  • To estimate excess mortality in Denmark and Sweden during the first wave of the COVID-19 pandemic.
  • To compare the effectiveness of different policy interventions on mortality outcomes.

Main Methods:

  • Developed a forecasting method using the 'later/earlier ratio' for short-term predictions.
  • Applied the method to estimate expected deaths in the absence of COVID-19.
  • Calculated excess mortality by subtracting forecasted deaths from observed deaths, stratified by age and sex.
  • Compared mortality data between Denmark (strict lockdown) and Sweden (less strict measures).

Main Results:

  • Excess deaths were found to be lower in Denmark than in Sweden during the March-June 2020 period.
  • The proposed forecasting method demonstrated narrower prediction intervals and less bias compared to traditional 5-year averaging.
  • The 'later/earlier ratio' method proved effective for assessing the impact of major shocks on mortality.

Conclusions:

  • The 'later/earlier ratio' method is a simple yet effective tool for short-term mortality forecasting and impact assessment.
  • Different policy responses to COVID-19, such as varying lockdown severity, were associated with differences in excess mortality.
  • This forecasting approach has broader applicability for predicting and analyzing mortality, births, and economic activity with seasonal or periodic variations.