Mortality and Excess Mortality: Improving FluMOMO.

Area of Science:

• Epidemiology
• Biostatistics
• Public Health

Background:

• FluMOMO is a universal formula for forecasting mortality in Europe, attributing deviations to flu or extreme temperatures.
• The model uses influenza activity and extreme temperatures as key variables.
• Previous models may lack crucial variables for accurate all-cause and flu mortality assessment.

Purpose of the Study:

• To evaluate the accuracy of the FluMOMO model for all-cause and flu mortality.
• To develop an improved mortality forecasting model incorporating demographic variables.
• To assess excess mortality in Portugal during 2009-2020, net of specific factors.

Main Methods:

• Utilized negative binomial-generalized linear models for weekly death estimations.
• Modeled weekly deaths as an autoregressive process (lag one and lead one week).
• Incorporated variables: previous week's deaths, influenza activity, population age, heat waves, flu season, COVID-19 deaths, and population exposure risk.

Main Results:

• FluMOMO overestimates flu-attributable deaths by 78%.
• Excess mortality in 2020, excluding COVID-19, heat waves, and aging, was found to be low or non-existent.
• The probability of dying from flu shows a decreasing trend, potentially due to increased flu vaccination.

Conclusions:

• The developed model provides a more accurate measure of all-cause and flu mortality than FluMOMO.
• Demographic factors like population aging and exposure to death risk are essential for accurate mortality modeling.
• Early COVID-19 mortality spikes may include unclassified COVID-19 deaths and are influenced by factors beyond flu and temperature.