Background incidence rates from electronic healthcare databases for vaccine safety monitoring: review of challenges from the COVID-19 vaccination campaign and proposal for best practices
Bibliographic record
Abstract
Background incidence rates (BIRs) are essential for contextualizing adverse event rates in vaccine safety monitoring, particularly through observed-to-expected (O/E) analyses. The unprecedented rapid development of vaccines during the coronavirus disease 2019 (COVID-19) pandemic necessitated rigorous and continuous safety monitoring. While BIRs are traditionally obtained from literature reviews, the pandemic accelerated the large-scale generation of BIRs from electronic healthcare databases through various initiatives such as the vACCine covid-19 monitoring readinESS (ACCESS) and the Biologics Effectiveness and Safety (BEST) to support COVID-19 vaccine safety surveillance strategies. The Beyond COVID-19 Monitoring Excellence (BeCOME) initiative, launched in 2022, established seven working groups, including one focused on best practices for BIR generation and utilization in pharmacovigilance activities. The BeCOME BIR working group conducted a targeted literature review to enable focused analysis of challenges that emerged during large-scale vaccination campaigns through December 2023. The group also used structured group discussion following the nominal group technique over 10 months to develop consensus-based judgments on critical factors for BIR best practices. The findings were organized into four domains: key initiatives summary, pandemic-related challenges, implications for O/E analyses, and best practice recommendations. To identify key BIR initiatives supporting COVID-19 vaccine safety assessment, the review employed multiple strategies including scientific literature examination, public health authority website reviews, and reference list searches; data on study characteristics, limitations, challenges, and recommendations were extracted. The targeted review focused on five major initiatives, such as ACCESS and BEST, that generated BIRs for adverse events of special interest (AESIs) during the pandemic. The group identified persistent challenges during the vaccination campaign including timeliness constraints during rapid vaccine deployment, substantial heterogeneity across data sources, inconsistent case definitions, limited information for key subpopulations, and difficulties addressing emerging AESIs. These challenges directly impacted O/E analyses, potentially leading to biased safety signal assessments. We propose a coordinated action plan among key stakeholders to establish sustainable mechanisms for regular BIR delivery with methodological improvements, develop consensus on best practices for BIR selection, and secure resources to ensure pandemic preparedness. Implementing these recommendations will strengthen vaccine safety monitoring systems for both routine vaccination programs and future public health emergencies.
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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 | 0.123 | 0.347 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.058 | 0.044 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".