Cervical Intraepithelial Neoplasia Rates in British Columbia Women: A Population-Level Data Linkage Evaluation of the School-Based HPV Immunization Program
Bibliographic record
Abstract
BACKGROUND: To understand real-world human papillomavirus (HPV) vaccine impact, continuous evaluation using population-based data is critical. We evaluated the early impact of the school-based HPV immunization program on cervical dysplasia in women in British Columbia, Canada. METHODS: Data linkage was performed using records from provincial cervical screening and immunization registries. Precancerous outcomes were compared between unvaccinated and HPV-vaccinated women born 1994-2005. Incidence rate, relative rate (RR), and vaccine effectiveness (VE), using unadjusted and adjusted Poisson regression of cytology (HSIL) and histopathology (CIN2, CIN3, and CIN2+) outcomes, were compared across vaccination status groups. RESULTS: Women who received a complete series of vaccine on schedule between age 9 and 14 years had an adjusted RR = 0.42 (95% confidence interval [CI], 0.31-0.57) for CIN2+ over 7 years of follow-up compared to unvaccinated women, resulting in a VE of 57.9% (95% CI, 43.2%-69.0%). Adjusted RR for HSIL was 0.53 (95% CI, .43-.64), resulting in a VE of 47.1% (95% CI, 35.6%-56.7%). CONCLUSION: Women vaccinated against HPV have a lower incidence of cervical dysplasia compared to unvaccinated women. Immunization between 9 and 14 years of age should be encouraged. Continued program evaluation is important for measuring long-term population impact.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".