Abstract 847: Human papillomavirus (HPV) type distribution in multi-ethnic cohort of women: Implications for vaccination programs
Bibliographic record
Abstract
Abstract Introduction: HPV is linked to many genital and oropharyngeal cancers, and current HPV vaccines target 2 oncogenic HPV types (16 and 18), which are estimated to account for 70% of cervical cancer cases. We sought to determine type distributions among a multi-ethnic cohort of women to understand what proportion of infections are covered by current and proposed vaccines. Methods: We analyzed cervical specimens from a cohort of 536 non-pregnant women from four clinical centers in the United States and Canada. HPV genotyping was performed using the Linear Array® HPV Genotyping Test (Roche), which detects 37 HPV types. We calculated prevalence of HPV types (individually and grouped by those types included in the currently approved bivalent and quadrivalent vaccines and the nonavalent vaccine currently being developed) by age, race/ethnicity, and histology. Results: Overall the prevalence of any HPV type in the entire cohort was 57%. More than a quarter of all specimens showed infection with multiple HPV infections with 6 types being the most detected in a single specimen. The prevalence of oncogenic types on the array ranged from 36% among women with normal histology to 88% and 92% among those with low-grade and high-grade lesions, respectively. Among women with high-grade lesions the prevalence of types 16/18 was only 45% while the prevalence of types in the nonavalent vaccine was 85%. Prevalence of oncogenic types decreased by age group with women less than 30 having a prevalence of 63%, while in those over age 50 it was 34%. HPV16 was the most prevalent type among non-Hispanic white women (19%), but not among African-American (0%) or Hispanic (2%) women. HPV58 and HPV58/59 were the most common types among African-American and Hispanic women, respectively. Among non-Hispanic whites, 50% of prevalent oncogenic types were covered by current vaccines, while 81% would be covered by the nonavalent vaccine. In comparison, only 32% of infections among African-American and 29% among Hispanic women were covered by current vaccines, compared to 86% and 75%, respectively, for the nonavalent vaccine. In fact, African American women had 2.5-fold higher prevalence of HPV 58 compared to non-Hispanic white women. Of note, Asian women were more than four-times as likely to be infected with multiple HPV genotypes compared to non-Hispanic white women. Conclusions: Our findings suggest that a nonavalent vaccine would cover more of the prevalent HPV genotypes present across racial/ethnic groups when compared to current vaccines. These results also suggest that even though more infections occur among younger women (<30), a significant proportion of older women (>50) are also infected. Further, 14-25% of currently prevalent HPV types would still not be covered by next generation vaccines. Citation Format: Michael E. Scheurer, Hung N. Luu, Martial Guillaud, Jane Montealegre, Laura M. Dillon, Michele Follen, Karen Adler-Storthz. Human papillomavirus (HPV) type distribution in multi-ethnic cohort of women: Implications for vaccination programs. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 847. doi:10.1158/1538-7445.AM2015-847
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".