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Published on: January 8, 2020
Excess Mortality Estimation
Jon Wakefield1,2, Victoria Knutson2
1Department of Statistics, University of Washington, Seattle, USA.
Annual Review of Statistics and Its Application
|May 8, 2026
Summary
Estimating excess mortality, the difference between observed and expected deaths, is crucial for understanding public health crises. This study reviews methods for calculating excess mortality, especially when data is limited.
Area of Science:
- Public Health
- Epidemiology
- Biostatistics
Background:
- Estimating mortality during crises like pandemics or disasters is vital.
- Directly attributing deaths can be challenging; excess mortality (observed minus expected deaths) is a key metric.
- Data quality varies, particularly in low- and middle-income countries, necessitating advanced modeling.
Purpose of the Study:
- To review and describe methods for estimating excess mortality.
- To highlight challenges in data collection and modeling for mortality crisis events.
- To present a case study of excess mortality during the COVID-19 pandemic in the US.
Main Methods:
- Review of existing literature on excess mortality estimation.
- Discussion of modeling approaches for complete and incomplete vital registration systems.
- Application of methods to a US states case study during COVID-19.
Main Results:
- Excess mortality estimation is complex, especially with incomplete data.
- Modeling approaches are necessary to account for missing or unreliable mortality data.
- The COVID-19 pandemic demonstrated significant excess mortality across US states.
Conclusions:
- Accurate excess mortality estimation requires robust methodologies and reliable data.
- Addressing data gaps is critical for effective public health response during crises.
- Further research and improved data infrastructure are needed globally.
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