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A statistical analysis of vaccine-adverse event data
Jian-Jian Ren1, Tingni Sun2, Yongqun He3
1Statistics Program, Department of Mathematics, University of Maryland, College Park, 20742, MD, USA. jjren@umd.edu.
BMC Medical Informatics and Decision Making
|May 30, 2019
Summary
This study analyzed 24 years of Vaccine Adverse Event Reporting System (VAERS) data to find patterns between vaccines and adverse events. Results show symptom correlations vary by vaccine type, particularly for virus vaccines like flu shots.
Area of Science:
- Public Health
- Immunology
- Biostatistics
Background:
- The Vaccine Adverse Event Reporting System (VAERS) contains over 500,000 post-vaccination adverse event reports.
- VAERS data is complex, with large dimensions and multiple listings of vaccines and symptoms per report.
- Previous statistical analyses have not identified cross-board patterns in vaccine-related adverse symptoms.
Purpose of the Study:
- To identify cross-board patterns in the relationship between vaccines and reported adverse events.
- To develop methods for analyzing complex, large-scale VAERS data.
- To provide a foundation for further statistical modeling of vaccine safety data.
Main Methods:
- Utilized a 24-year partial VAERS dataset (1990-2013).
- Developed a neighboring method to handle data complexity from multiple vaccine/symptom listings.
- Employed data visualization techniques for pattern analysis.
Main Results:
- High-frequency symptoms are positively correlated.
- Symptom correlations differ between bacteria and virus vaccines; often positive for virus (especially flu) vaccines.
- No clear patterns observed between live versus inactive vaccines.
Conclusions:
- Identified cross-board patterns in vaccine-adverse event relationships.
- Enhances understanding of VAERS data complexity.
- Offers a starting point for advanced statistical analysis of vaccine safety.
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