Modeling the health and economic implications of adopting a 1-dose 9-valent human papillomavirus vaccination program in adolescents in low/middle-income countries: An analysis of Indonesia
Bibliographic record
Abstract
BACKGROUND: Recent evidence suggests that 1 dose of the human papillomavirus (HPV) vaccine may have similar effectiveness in reducing HPV infection risk compared to 2 or 3 doses. OBJECTIVE: To evaluate the public health impact and cost-effectiveness of implementing a 1-dose or a 2-dose program of the 9-valent HPV vaccine in a low- and middle-income country (LMIC). METHODS: We adapted a dynamic transmission model to the Indonesia setting, and conducted a probabilistic sensitivity analysis using distributions reflecting the uncertainty in levels and durability of protection of a 1-dose that were estimated under a Bayesian framework incorporating 3-year vaccine efficacy data from the KEN SHE trial (base-case) and 10 year effectiveness data from the India IARC study (alternative analysis). Scenarios included different coverage levels targeted at girls-only, or girls and boys. Costs and benefits were computed over 100 years from a national single-payer perspective. RESULTS: Depending on the coverage and target population, the median number of cancer cases avoided in 2-dose programs ranged between 600,000-2,100,000, compared to 200,000-600,000 in 1-dose programs. The 1-dose programs are unlikely to be cost-effective compared to 2-dose programs even at low willingness-to-pay (WTP) thresholds. The girls-only 2-dose program tends to be cost-effective at lower WTP thresholds, particularly in scenarios with high coverage, dose price and discount rate, while the girls and boys 2-dose program is cost-effective at higher WTP thresholds. In the alternative analysis, 1-dose programs have higher probability of being cost-effective compared to the base-case, particularly for low WTP thresholds (less than 0.5 GDP) and for high coverage, dose price and discount rate. CONCLUSION: Adoption of 1-dose programs with 9-valent vaccine in an LMIC resulted in more vaccine-preventable HPV-related cancer cases than 2-dose programs. The 2-dose programs were more likely to be cost-effective than 1-dose programs for a wide range of WTP thresholds and scenarios.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".