Increasing Cervical Cancer Rates Among Women Age 35-54 Years in Canada: Age-Specific Cervical Cancer Incidence Trends in Canada, 1992-2022
Bibliographic record
Abstract
PURPOSE: The vast majority of cervical cancer is preventable through human papillomavirus vaccination and screening with cytology or DNA testing. After decades of progress, recent cervical cancer trends in Western populations show a plateau or modest increase in incidence rates. Further investigation is required to understand the drivers of these emerging trends. In this study, we examined age-specific cervical cancer incidence rates in Canada from 1992 to 2022. METHODS: Data were obtained from the Canadian Cancer Registry maintained by Statistics Canada, which included cancer cases, population counts, and incidence rates of cervical cancer by age and province for the period 1992 to 2022. Joinpoint regression analysis was used to estimate temporal incidence trends across age groups. RESULTS: Cervical cancer incidence rates in Canada decreased among women age 25-34 years and those 65 years and older since 1992. Incidence rates among women age 35-44 years and 45-54 years have increased by 1.1% (95% CI, 0.5 to 2.5) and 1.6% (95% CI, -0.1 to 8.6) per year since 2001 and 2012, respectively. In 2022, the highest incidence rate of cervical cancer was among women age 35-44 years (18.1 per 100,000 women), which is comparable with rates in 1992. CONCLUSION: Cervical cancer incidence rates have been increasing in recent years among women age 35-54 years. This cohort may be falling into a cancer prevention gap. Targeted public health interventions are warranted to address the rising incidence of cervical cancer among this cohort of Canadian women.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".