Racial and ethnic disparities in human papillomavirus‐associated cancer burden with first‐generation and second‐generation human papillomavirus vaccines
Bibliographic record
Abstract
BACKGROUND: In the United States, the burden of human papillomavirus (HPV)-associated cancers varies by racial/ethnic group. HPV vaccination may provide opportunities for primary prevention of these cancers. Herein, the authors projected changes in HPV-associated cancer burden among racial/ethnic groups under various coverage assumptions with the available first-generation and second-generation HPV vaccines to evaluate changes in racial/ethnic disparities. METHODS: Cancer-specific mathematical models simulated the burden of 6 HPV-associated cancers. Model parameters, informed using national registries and epidemiological studies, reflected sex-specific, age-specific, and racial/ethnic-specific heterogeneities in HPV type distribution, cancer incidence, stage of disease at detection, and mortality. Model outcomes included the cumulative lifetime risks of developing and dying of 6 HPV-associated cancers. The level of racial/ethnic disparities was evaluated under each alternative HPV vaccine scenario using several metrics of social group disparity. RESULTS: HPV vaccination is expected to reduce the risks of developing and dying of HPV-associated cancers in all racial/ethnic groups as well as reduce the absolute degree of disparities. However, alternative metrics suggested that relative disparities would persist and in some scenarios worsen. For example, when assuming high uptake with the second-generation HPV vaccine, the lifetime risk of dying of an HPV-associated cancer for males decreased by approximately 60%, yet the relative disparity increased from 3.0 to 3.9. CONCLUSIONS: HPV vaccines are expected to reduce the overall burden of HPV-associated cancers for all racial/ethnic groups and to reduce the absolute disparity gap. However, even with the second-generation vaccine, relative disparities will likely still exist and may widen if the underlying causes of these disparities remain unaddressed. Cancer 2016;122:2057-66. © 2016 American Cancer Society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".