Moving toward the Elimination of Cervical Cancer: Modelling the Health and Economic Benefits of Increasing Uptake of Human Papillomavirus Vaccines
Bibliographic record
Abstract
Background: The human papillomavirus (hpv) is a common sexually transmitted infection and a primary cause of cervical cancer. The Government of Canada has set a target of reaching 90% hpv vaccine coverage among adolescents by 2025. Here, we examine hpv vaccine uptake in school-based immunization programs across Canada and explore how achieving the 90% target could affect the future incidence of cervical cancer, mortality, and health system expenditures in a cohort of Canadian women. Methods: Data for hpv vaccine uptake in the most recent reported school year available in each jurisdiction were provided in 2017 by jurisdictional school-based immunization programs and were used to estimate a national weighted average of 67%. The OncoSim microsimulation model (version 2.5) was used to compare 3 different levels of hpv vaccine uptake (0%, 67%, 90%) on health and economic outcomes for a hypothetical cohort of all 5- to 10-year-old girls in Canada in 2015. Results: Vaccine uptake for girls in school-based programs varied from 55.0% to 92.0% in the jurisdictions reviewed. The OncoSim model projects that increasing uptake to 90% from 67% would result in a 23% reduction in cervical cancer incidence rates (to 3.1 cases from 4.0 cases per 100,000, averaged across the lifetime of the cohort) and a 23% decline in the average annual mortality rate (to 1.0 deaths from 1.3 deaths per 100,000). Finally, the model projects that the health system will incur a cost of $9 million (1% increase) during the lifetime of the cohort if uptake is increased to 90% from 67%. Costs are discounted (1.5%) and expressed in 2016 Canadian dollars. Costs reflect the payer perspective. Conclusions: Our model shows that increasing hpv vaccine uptake to 90% from current levels for girls in school-based immunization programs could result in substantial reductions in the future incidence and mortality rates for cervical cancer in Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".