Incidence of Breast Cancer in Younger Women: A Canadian Trend Analysis
Bibliographic record
Abstract
Purpose: Breast cancer (BC) incidence is increasing globally. Age-specific BC incidence trend analyses are lacking for women under age 50 in Canada. In this study, we evaluate the incidence trends in breast cancer in women under age 50 in Canada and compare them with corresponding trends among women 50 to 54. Methods: BC case counts were obtained from the National Cancer Incidence Reporting System (1984-1991) and the Canadian Cancer Registry (1992-2019) both housed at Statistics Canada. Population data were also obtained from Statistics Canada. Annual female BC age-specific incidence rates from 1984 to 2019 were derived for the following age groups: 20 to 29, 30 to 39, 40 to 49, 40 to 44, 45 to 49, and 50 to 54. Changes in trends in age-specific BC incidence rates, if any, and annual percent changes (APCs) for each identified trend, were determined using JoinPoint. Results: Statistically significant increasing trends in BC incidence rates were noted for almost all age groups: since 2001 for 20 to 29 (APC = 3.06%, P < .001); since 2009 for 30 to 39 (APC = 1.25%, P = .007); since 1984 for both 40 to 49 (APC = 0.26%, P < .001) and 40 to 44 (APC = 0.19%, P = .011), increased since 2015 for 40 to 49 (APC = 0.77%, P = .047); and since 2005 for 50 to 54 (APC = 0.38%, P = .022). Among women 45 to 49 there was a non-significant increase since 2005 (APC = 0.24, P = .058). Statistically significant average annualized increases in BC incidence rates were observed for each age group studied. Conclusions: Examining age-specific incidence rates formed a more complete picture of BC time trends with significant increasing trends in the incidence of BC among women in their 20s, 30s, 40s, and early 50s. A greater awareness regarding the increasing number of cases of BC in women younger than 50 is critical to allow for earlier diagnosis with its resultant reduced mortality and morbidity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".