Barriers to COVID-19 vaccine surveillance: the issue of under-reporting adverse events
Bibliographic record
Abstract
OBJECTIVES: This study investigated the reporting rates of adverse events following immunization (AEFIs) to the spontaneous reporting system (SRS) and its predictors among individuals with AEFIs after coronavirus disease 2019 (COVID-19) vaccination. METHODS: A cross-sectional, web-based survey was conducted from December 2, 2021 to December 20, 2021, recruiting participants >14 days after completion of a primary COVID-19 vaccination series. Reporting rates were calculated by dividing the number of participants who reported AEFIs to the SRS by the total number of participants who experienced AEFIs. We estimated adjusted odds ratios (aORs) using multivariate logistic regression to determine factors associated with spontaneous AEFIs reporting. RESULTS: Among 2,993 participants, 90.9% and 88.7% experienced AEFIs after the first and second vaccine doses, respectively (reporting rates, 11.6 and 12.7%). Furthermore, 3.3% and 4.2% suffered moderate to severe AEFIs, respectively (reporting rates, 50.5 and 50.0%). Spontaneous reporting was more prevalent in female (aOR, 1.54; 95% confidence interval [CI], 1.31 to 1.81); those with moderate to severe AEFIs (aOR, 5.47; 95% CI, 4.45 to 6.73), comorbidities (aOR, 1.31; 95% CI, 1.09 to 1.57), a history of severe allergic reactions (aOR, 2.02; 95% CI, 1.47 to 2.77); and those who had received mRNA-1273 (aOR, 1.25; 95% CI, 1.05 to 1.49) or ChAdOx1 (aOR, 1.62; 95% CI, 1.15 to 2.30) vaccines versus BNT162b2. Reporting was less likely in older individuals (aOR, 0.98; 95% CI, 0.98 to 0.99 per 1-year age increment). CONCLUSIONS: Spontaneous reporting of AEFIs after COVID-19 vaccination was associated with younger age, female sex, moderate to severe AEFIs, comorbidities, history of allergic reactions, and vaccine type. AEFIs under-reporting should be considered when delivering information to the community and in public health decision-making.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".