Health and Economic Impact of Switching from a 4-Valent to a 9-Valent HPV Vaccination Program in the United States
Bibliographic record
Abstract
BACKGROUND: Randomized clinical trials have shown the 9-valent human papillomavirus (HPV) vaccine to be highly effective against types 31/33/45/52/58 compared with the 4-valent. Evidence on the added health and economic benefit of the 9-valent is required for policy decisions. We compare population-level effectiveness and cost-effectiveness of 9- and 4-valent HPV vaccination in the United States. METHODS: We used a multitype individual-based transmission-dynamic model of HPV infection and disease (anogenital warts and cervical, anogenital, and oropharyngeal cancers), 3% discount rate, and societal perspective. The model was calibrated to sexual behavior and epidemiologic data from the United States. In our base-case, we assumed 95% vaccine-type efficacy, lifelong protection, and a cost/dose of $145 and $158 for the 4- and 9-valent vaccine, respectively. Predictions are presented using the mean (80% uncertainty interval [UI] = 10(th)-90(th) percentiles) of simulations. RESULTS: Under base-case assumptions, the 4-valent gender-neutral vaccination program is estimated to cost $5500 (80% UI = 2400-9400) and $7300 (80% UI = 4300-11 000)/quality-adjusted life-year (QALY) gained with and without cross-protection, respectively. Switching to a 9-valent gender-neutral program is estimated to be cost-saving irrespective of cross-protection assumptions. Finally, the incremental cost/QALY gained of switching to a 9-valent gender-neutral program (vs 9-valent girls/4-valent boys) is estimated to be $140 200 (80% UI = 4200->1 million) and $31 100 (80% UI = 2100->1 million) with and without cross-protection, respectively. Results are robust to assumptions about HPV natural history, screening methods, duration of protection, and healthcare costs. CONCLUSIONS: Switching to a 9-valent gender-neutral HPV vaccination program is likely to be cost-saving if the additional cost/dose of the 9-valent is less than $13. Giving females the 9-valent vaccine provides the majority of benefits of a gender-neutral strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".