<scp>I</scp>mpacts of human papillomavirus vaccination for different populations: <scp>A</scp> modeling study
Bibliographic record
Abstract
affects the strength of herd protection and hence the impact of a vaccination program. Similar vaccination programs may therefore generate different levels of impact depending upon the population's pre-vaccination HPV prevalence. We used IARC's transmission model to estimate (i) the overall effectiveness of vaccination versus no vaccination in women aged 15-34 years measured as percent prevalence reduction (%PR) of HPV16 and (ii) the corresponding herd protection in populations with gender-equal or traditional sexual behavior and with different levels of sexual activity, corresponding to pre-vaccination HPV16 prevalence from 1 to 8% as observed worldwide. Between populations with different levels of gender-equal sexual activity, the highest difference in %PR under girls-only vaccination is observed at 40% coverage (91%PR vs. 48%PR for 1% and 8% pre-vaccination prevalence, respectively). HPV16 elimination is obtained with 55 and 97% coverage, respectively. To achieve desirable levels of HPV16 prevalence after vaccination, different levels of coverage are required in populations with different levels of pre-vaccination HPV16 prevalence, for example, in populations with gender-equal sexual behavior a decrease to 1/1000 HPV16 from pre-vaccination prevalence of 1 and 8% would require coverages of 37 and 96%, respectively. In traditional populations, corresponding coverages would need to be 28 and 93%, respectively. In conclusion, pre-vaccination HPV prevalence strongly influences herd immunity and helps predict the overall effectiveness of HPV vaccination.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".