Population-level health impact of hypothetical waning 1-dose human papillomavirus vaccination and 2-dose mitigation strategies in a high cervical cancer burden setting
Bibliographic record
Abstract
BACKGROUND: We simulated the impact of hypothetical waning scenarios of a 1-dose human papillomavirus (HPV) vaccination paired with switching to 2-dose mitigation strategies guided by empirical vaccine trial reporting timelines. METHODS: Using 2 independent mathematical models fitted to a high-burden setting, we projected the cumulative cervical cancer cases averted over 85 years for alternative HPV vaccination scenarios under 2 program adoption timelines: 1) de novo introduction of a 1-dose HPV vaccination and 2) a switch from an existing 2-dose HPV vaccination program to a 1-dose vaccination. We assumed 80% vaccination coverage with the bivalent vaccine and an average duration of a 1-dose HPV vaccine protection of either 30 or 25 years with 100% efficacy. We varied the eligible age group(s) at program introduction and the 2-dose mitigation (single-age cohort or multi-age cohort). If needed for mitigation, reintroduction of 2-dose vaccination was assumed to occur in 2036 (ie, 30 years after initiation of the Costa Rica Vaccine Trial). RESULTS: Under both vaccine adoption timelines, the models projected that countries could achieve the same level of health benefits by switching to 2 doses in 2036 using a multi-age cohort approach as with initiating a 2-dose or 1-dose vaccination program with no waning. With only a single-age cohort 2-dose mitigation approach, 98%-99% of cases would be prevented compared with the health benefits of 2 doses or a noninferior, durable 1 dose. CONCLUSIONS: Countries hesitant to adopt a 1-dose HPV vaccination program may have opportunities to leverage the benefits and efficiency of a 1-dose schedule while awaiting longer-term reporting from 1-dose durability studies, including Costa Rica Vaccine Trial.
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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 | 0.005 | 0.013 |
| 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 | 0.001 | 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".