Self-reported Human Papillomavirus Vaccination and Vaccine Effectiveness Among Men Who Have Sex with Men: A Quantitative Bias Analysis
Bibliographic record
Abstract
BACKGROUND: Self-report of human papillomavirus (HPV) vaccination has ~80-90% sensitivity and ~75-85% specificity. We measured the effect of nondifferential exposure misclassification associated with self-reported vaccination on vaccine effectiveness (VE) estimates. METHODS: Between 2017-2019, we recruited sexually active gay, bisexual, and other men who have sex with men aged 16-30 years in Canada. VE was derived as 1-prevalence ratio × 100% for prevalent anal HPV infection comparing vaccinated (≥1 dose) to unvaccinated men using a multivariable modified Poisson regression. We conducted a multidimensional and probabilistic quantitative bias analysis to correct VE estimates. RESULTS: Bias-corrected VE estimates were relatively stable across sensitivity values but differed from the uncorrected estimate at lower values of specificity. The median adjusted VE was 27% (2.5-97.5th simulation interval = -5-49%) in the uncorrected analysis, increasing to 39% (2.5-97.5th simulation interval = 2-65%) in the bias-corrected analysis. CONCLUSION: A large proportion of participants erroneously reporting HPV vaccination would be required to meaningfully change VE estimates.
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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 | 0.137 | 0.280 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".