Changing Inequalities in Cervical Cancer: Modeling the Impact of Vaccine Uptake, Vaccine Herd Effects, and Cervical Cancer Screening in the Post-Vaccination Era
Bibliographic record
Abstract
BACKGROUND: Inequalities in cervical cancer may be increased following mass vaccination against the human papillomavirus (HPV) if girls with low vaccine uptake also have low future participation in cervical cancer screening. We evaluated how vaccine uptake distribution affects inequalities in squamous cell carcinoma (SCC) incidence between groups with different screening participation. METHODS: We used an individual-based transmission dynamic model of HPV infection and disease (HPV-ADVISE). Females were stratified by routine screening frequency. We modeled the impact of vaccination on SCC incidence rate differences (absolute inequality) and incidence rate ratios (relative inequality) between women who have routine screening intervals of <5 years (frequently screened), ≥5 years (underscreened), and who are never screened. We compared simulations with uniform vaccine uptake with scenarios with unequal vaccine uptake, in which never and underscreened women have lower vaccine uptake than frequently screened women. RESULTS: Absolute SCC inequalities between groups with different screening rates were predicted to decrease after vaccination, even when women with the lowest screening participation had the lowest vaccine uptake. Herd effects helped reduce absolute inequalities when vaccine uptake was unequal. Conversely, relative SCC inequalities remained unchanged or increased after vaccination. Results were robust to different overall vaccination coverages and sexual mixing scenarios. CONCLUSION: Though mass HPV vaccination is predicted to substantially decrease SCC incidence rates, never screened women will still have the highest disease burden after vaccination. IMPACT: To reduce both absolute and relative SCC inequalities, public health initiatives will need to address inequalities in both vaccine uptake and in cervical cancer screening participation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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) | 0.004 | 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".