Frontiers in immunology · 2026
Development and application of the vaccine reporting quality score to evaluate the vaccine adverse event reporting system.
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Zhou Zongkai, Mu Zirong, Gu Nina, Sun Xu, Wu Zun, Tian Zuguo, Luo Wanhong
Аннотация
BACKGROUND AND AIMS: Ensuring the documentation quality of spontaneous reports in the Vaccine Adverse Event Reporting System (VAERS) is important for signal detection, yet targeted tools for evaluating vaccine-specific reporting quality remain lacking. This study proposes the Vaccine Reporting Quality Score (VRQS)-a transparent, exploratory evaluation system-and applies it to a large-scale VAERS dataset.
METHODS: The preliminary VRQS aggregates 13 items into two primary domains: Completeness (items 1-10) and Clinical Informativeness (items 11-13), using equal weighting as an initial exploratory index. Using the VAERS 2025 dataset, we established a strict 1-to-1 patient case unit of analysis (N = 40,285 unique case reports) to evaluate demographic, administrative, and clinical density. Multivariable linear and ordinal logistic regressions were executed using independent case characteristics, with all overlapping score components removed a priori to eliminate structural circularity.
RESULTS: The mean overall quality score was 9.04 ± 1.96, with 57.54% of unique reports falling into a medium-quality tier (scores 8-10). Product identifiers, such as vaccine type (97.92%), were highly prevalent, whereas informative laboratory data were present in only 16.18% of records. Distinct, descriptive documentation profiles emerged: spontaneous records displayed high completeness but low informativeness, whereas professional entries and serious adverse events (SAEs) featured dense clinical metrics alongside missing administrative checkfields. In re-specified, non-circular multivariable models, complex clinical factors such as pre-existing allergies (b=0.832, p<0.001) and concurrent medications (b=0.463, p<0.001) were independently associated with higher quality score.
CONCLUSION: The proposed VRQS framework demonstrates that administrative documentation density does not inherently correlate with clinical informativeness. Rather than acting as a validated tool for causality assessment, the VRQS provides a transparent, preliminary approach for report prioritization and screening pending formal external validation against global benchmarks.
METHODS: The preliminary VRQS aggregates 13 items into two primary domains: Completeness (items 1-10) and Clinical Informativeness (items 11-13), using equal weighting as an initial exploratory index. Using the VAERS 2025 dataset, we established a strict 1-to-1 patient case unit of analysis (N = 40,285 unique case reports) to evaluate demographic, administrative, and clinical density. Multivariable linear and ordinal logistic regressions were executed using independent case characteristics, with all overlapping score components removed a priori to eliminate structural circularity.
RESULTS: The mean overall quality score was 9.04 ± 1.96, with 57.54% of unique reports falling into a medium-quality tier (scores 8-10). Product identifiers, such as vaccine type (97.92%), were highly prevalent, whereas informative laboratory data were present in only 16.18% of records. Distinct, descriptive documentation profiles emerged: spontaneous records displayed high completeness but low informativeness, whereas professional entries and serious adverse events (SAEs) featured dense clinical metrics alongside missing administrative checkfields. In re-specified, non-circular multivariable models, complex clinical factors such as pre-existing allergies (b=0.832, p<0.001) and concurrent medications (b=0.463, p<0.001) were independently associated with higher quality score.
CONCLUSION: The proposed VRQS framework demonstrates that administrative documentation density does not inherently correlate with clinical informativeness. Rather than acting as a validated tool for causality assessment, the VRQS provides a transparent, preliminary approach for report prioritization and screening pending formal external validation against global benchmarks.