Month-to-month all-cause mortality forecasting: a method allowing for changes in seasonal patterns

Forecasting of seasonal mortality patterns can provide useful information for planning health-care demand and capacity. Timely mortality forecasts are needed during severe winter spikes and/or pandemic waves to guide policy-making and public health decisions. In this article, we propose a flexible m...

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Bibliographic Details
Published in:American journal of epidemiology 2024-06, Vol.193 (6), p.898-907
Main Authors: Léger, Ainhoa-Elena, Rizzi, Silvia
Format: Article
Language:English
Subjects:
Cause of Death
COVID-19 - epidemiology
COVID-19 - mortality
Denmark - epidemiology
Europe - epidemiology
Forecasting - methods
Humans
Models, Statistical
Mortality - trends
Pandemics
Practice of Epidemiology
SARS-CoV-2
Seasons
Spain - epidemiology
Sweden - epidemiology
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Summary:Forecasting of seasonal mortality patterns can provide useful information for planning health-care demand and capacity. Timely mortality forecasts are needed during severe winter spikes and/or pandemic waves to guide policy-making and public health decisions. In this article, we propose a flexible method for forecasting all-cause mortality in real time considering short-term changes in seasonal patterns within an epidemiologic year. All-cause mortality data have the advantage of being available with less delay than cause-specific mortality data. In this study, we use all-cause monthly death counts obtained from the national statistical offices of Denmark, France, Spain, and Sweden from epidemic seasons 2012-2013 through 2021-2022 to demonstrate the performance of the proposed approach. The method forecasts deaths 1 month ahead, based on their expected ratio to the next month. Prediction intervals are obtained via bootstrapping. The forecasts accurately predict the winter mortality peaks before the COVID-19 pandemic. Although the method predicts mortality less accurately during the first wave of the COVID-19 pandemic, it captures the aspects of later waves better than other traditional methods. The method is attractive for health researchers and governmental offices for aiding public health responses because it uses minimal input data, makes simple and intuitive assumptions, and provides accurate forecasts both during seasonal influenza epidemics and during novel virus pandemics.
ISSN:0002-9262
1476-6256
1476-6256
DOI:10.1093/aje/kwae004