Epidemiological shifts and risk behaviours for oral and oropharyngeal cancers in multicultural population of British Columbia, Canada
Bibliographic record
Abstract
Although smoking prevalence in British Columbia (BC) is decreasing, numbers of oral cancers are increasing. This change may reflect new emerging risk factors, including an increase in human papillomavirus (HPV) infections and greater immigration from high-risk countries. Currently, in BC there is no data on the trends in oral cancer incidence and survival by ethnicity or by etiologically clustered oral cancer subsites (oral cavity cancers, OCC, which are predominantly tobacco related; and oropharyngeal cancers, OPC, which are predominantly HPV-related). Oral cancers were retrieved from BC Cancer Registry (BCCR) from 1980 to 2006 and the following information collected: names, demographic, tumor, treatment and outcome information. When specific information was not complete, chart review was done. South Asian (SA) or Chinese ethnicities were determined by using previously generated ethnic surname list. Age-adjusted incidence rates (AAIR), age-specific incidence rates (ASIR) and 5-year survival rates for these three populations were calculated by sex, grouping the cancers into etiologically clustered subsites. Calculations were done for each year from 1980 to 2006. An ethnographic study was then conducted to describe the patterns of access, use and perceptions of SA men towards chewing tobacco-containing betel quid (BQ). Extensive field work included participant observations and semi-structured interviews. We have for the first time shown that the incidence of HPV-related OPC has surpassed that of tobacco-related OCC in men. For female, the incidence rates of OPC increased and OCC unchanged. AAIR for OCC was highest in SA males and females while rates of OPC were highest in general population males and Chinese males. Survival rates for OCC were unchanged and for OPC improved in males. SA had poorest survival rates for OCC. Ethnographic findings revealed that among SA males chewing tobacco-containing BQ was viewed as a culturally accepted practice. Availability of BQ, perceived benefits of chewing, ability to conceal the habit, and a lack of awareness of health risks also supported chewing practices. These findings provide a strong foundation for continued work in this field aimed at identifying effective prevention and treatment strategies for oral cancer.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".