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Linked vaccine adverse event data from VAERS for biomedical data analysis and longitudinal studies
Cui Tao1, Puqiang Wu2, Yi Luo3
1School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX USA.
Biodata Mining
|February 21, 2015
Summary
This study analyzed Vaccine Adverse Event Reporting System (VAERS) data from 1990-2013, identifying 83,148 vaccine-symptom pairs. Network analysis revealed dense vaccine-symptom associations, aiding vaccine safety research.
Area of Science:
- Public Health
- Data Science
- Pharmacovigilance
Background:
- Vaccines are crucial public health tools but can have adverse events.
- The Vaccine Adverse Event Reporting System (VAERS) contains extensive post-vaccination event data.
- VAERS data requires statistical analysis to establish causality between vaccines and reported symptoms.
Purpose of the Study:
- To represent vaccine-symptom correlations from VAERS using Resource Description Framework (RDF).
- To calculate Proportional Reporting Ratios (PRR) for vaccine-symptom pairs.
- To apply network analysis to VAERS data for longitudinal vaccine safety studies.
Main Methods:
- Extracted and processed 1990-2013 VAERS data.
- Calculated PRR for 83,148 unique vaccine-symptom pairs (75 vaccine types, 5,865 symptoms).
- Utilized network analysis on significant vaccine-symptom associations (PRR > 1).
Main Results:
- Identified 83,148 unique vaccine-symptom pairs from 1990-2013 VAERS data.
- Calculated yearly and overall PRR values for each pair.
- Found the vaccine-symptom association network to be dense, with nodes connected via few intermediaries.
Conclusions:
- RDF representation facilitates comprehensive vaccine safety analysis.
- Network analysis of VAERS data reveals complex vaccine-symptom relationships.
- This approach enhances the utility of VAERS data for vaccine safety research.
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