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Estimating conditional vaccine effectiveness
1Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, Stanford University, Stanford, CA, 94305, USA. jioannid@stanford.edu.
This study introduces conditional vaccine effectiveness, measuring protection against severe COVID-19 outcomes after a less severe event. This concept aids personalized risk communication and public health decisions.
Area of Science:
- Epidemiology
- Immunology
- Public Health
Background:
- COVID-19 vaccine effectiveness is commonly assessed for hierarchical outcomes like infection, hospitalization, and death.
- Understanding protection against severe outcomes among those experiencing milder ones is crucial.
Purpose of the Study:
- To introduce and provide methods for calculating conditional vaccine effectiveness.
- To explore the application of conditional effectiveness in communicating vaccine benefits.
Main Methods:
- Development of formulas and a nomogram for calculating conditional effectiveness.
- Analysis of illustrative examples from recent COVID-19 vaccine effectiveness studies.
Main Results:
- Conditional effectiveness quantifies protection against severe outcomes (e.g., death) given a less severe outcome (e.g., infection).
- Effectiveness can vary over time and across different outcome severities.
- Formulas and a nomogram are provided for practical application.
Conclusions:
- Conditional effectiveness offers a nuanced view of vaccine benefits, useful for personalized communication.
- This metric can inform public health decision-making alongside infection fatality rates and epidemic activity.
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