Modeling the epidemiological impact and cost-effectiveness of a combined schoolgirl HPV vaccination and cervical cancer screening program among Chinese women
Bibliographic record
Abstract
Human papillomavirus (HPV) infection is common in women and also the main cause of cervical cancer. Based on a dynamic compartmental model, we aimed to evaluate the population impact and cost-effectiveness of strategies that combined cervical cancer screening and HPV schoolgirl vaccination for Chinese women. The effectiveness of interventions was assessed by comparing modeled scenarios to the status quo, where a 3-y cervical cancer screening program remained at a 20% coverage and without a universal HPV vaccination program. Our study demonstrated that increasing screening coverage from 20% to 50% would reduce the high-risk HPV (HR-HPV) prevalence to 5.4%, whereas a universal schoolgirl vaccination program using the quadrivalent vaccine (qHPV) with a coverage of 50% would reduce the prevalence to 2.9% by 2069. Scaling-up the cervical screening coverage to 50% will prevent 16,012 (95% CI: 8,791 to 25,913) Disability-Adjusted Life-Years (DALYs) per year, with an incremental cost-effectiveness ratio (ICER) of US$ 10,958 (95% CI: $169 to $26,973)/DALY prevented. At the current qHPV price, vaccinating 50% of school girls will prevent 13,854 (95% CI: 8,355 to 20,776) DALYs/year, but the corresponding incremental cost-effectiveness ratio (ICER, US$ 83,043, 95% CI: $52,234 to $138,025) exceeds cost-effectiveness threshold (i.e., 3 times GDP per-capita of China: $30,792). The qHPV vaccine requires at least a 50% price reduction to be cost-effective. Vaccinating schoolgirls will result in a large population health benefit in the long term, but such a universal HPV vaccination program can only be cost-effective with a substantial price reduction.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".