Real-world impact and effectiveness assessment of the quadrivalent HPV vaccine: a systematic review of study designs and data sources
Bibliographic record
Abstract
Vaccine effectiveness and impact studies are typically observational, generating evidence after vaccine launch in a real-world setting. For human papillomavirus (HPV) vaccination studies, the variety of data sources and methods used is pronounced. Careful selection of study design, data capture and analytical methods can mitigate potential bias in such studies. We systematically reviewed the different study designs, methods, and data sources in published evidence (1/2007–3/2020), which assessed the quadrivalent HPV vaccine effectiveness and impact on cervical/cervicovaginal, anal, and oral HPV infections, anogenital warts, lesions in anus, cervix, oropharynx, penis, vagina or vulva, and recurrent respiratory papillomatosis. The rapid growth in access to real-world data allows global monitoring of effects of different public health interventions, including HPV vaccination programs. But the use of data which are not collected or organized to support research also underscore a need to develop robust methodology that provides insight of vaccine effects and consequences of different health policy decisions. To achieve the WHO elimination goal, we foresee a growing need to evaluate HPV vaccination programs globally. A critical appraisal summary of methodology used will provide timely guidance to researchers who want to initiate research activities in various settings.
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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 | 0.032 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".