Population-level impact, herd immunity, and elimination after human papillomavirus vaccination: a systematic review and meta-analysis of predictions from transmission-dynamic models
Bibliographic record
Abstract
Background Modelling studies have been widely used to inform human papillomavirus (HPV) vaccination policy decisions; however, many models exist and it is not known whether they produce consistent predictions of population-level effectiveness and herd effects. We did a systematic review and meta-analysis of model predictions of the long-term population-level effectiveness of vaccination against HPV 16, 18, 6, and 11 infection in women and men, to examine the variability in predicted herd effects, incremental benefit of vaccinating boys, and potential for HPV-vaccine-type elimination. Methods We searched MEDLINE and Embase for transmission-dynamic modelling studies published between Jan 1, 2009, and April 28, 2015, that predicted the population-level impact of vaccination on HPV 6, 11, 16, and 18 infections in high-income countries. We contacted authors to determine whether they were willing to produce new predictions for standardised scenarios. Strategies investigated were girls-only vaccination and girls and boys vaccination at age 12 years. Base-case vaccine characteristics were 100% efficacy and lifetime protection. We did sensitivity analyses by varying vaccination coverage, vaccine efficacy, and duration of protection. For all scenarios we pooled model predictions of relative reductions in HPV prevalence (RR prev ) over time after vaccination and summarised results using the median and 10th and 90th percentiles (80% uncertainty intervals [UI]). Findings 16 of 19 eligible models from ten high-income countries provided predictions. Under base-case assumptions, 40% vaccination coverage and girls-only vaccination, the RR prev of HPV 16 among women and men was 0·53 (80% UI 0·46–0·68) and 0·36 (0·28–0·61), respectively, after 70 years. With 80% girls-only vaccination coverage, the RR prev of HPV 16 among women and men was 0·93 (0·90–1·00) and 0·83 (0·75–1·00), respectively. Vaccinating boys in addition to girls increased the RR prev of HPV 16 among women and men by 0·18 (0·13–0·32) and 0·35 (0·27–0·39) for 40% coverage, and 0·07 (0·00–0·10) and 0·16 (0·01–0·25) for 80% coverage, respectively. The RR prev were greater for HPV 6, 11, and 18 than for HPV 16 for all scenarios investigated. Finally at 80% coverage, most models predicted that girls and boys vaccination would eliminate HPV 6, 11, 16, and 18, with a median RR prev of 1·00 for women and men for all four HPV types. Variability in pooled findings was low, but increased with lower vaccination coverage and shorter vaccine protection (from lifetime to 20 years). Interpretation Although HPV models differ in structure, data used for calibration, and settings, our population-level predictions were generally concordant and suggest that strong herd effects are expected from vaccinating girls only, even with coverage as low as 20%. Elimination of HPV 16, 18, 6, and 11 is possible if 80% coverage in girls and boys is reached and if high vaccine efficacy is maintained over time. Funding Canadian Institutes of Health Research.
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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 | 0.023 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.039 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".