Trends in smoking and obesity prevalence at breast cancer diagnosis: results from a 35-year real-world observational study
Bibliographic record
Abstract
Breast cancer (BC) is the most commonly diagnosed cancer among women. Understanding long-term trends in modifiable risk factors at the time of diagnosis is essential to inform and evaluate the effectiveness of BC prevention strategies. This study assessed 35-year trends in the prevalence of two modifiable risk factors—obesity and smoking—among BC women. We conducted a retrospective cohort study including 14 595 women diagnosed with BC at the Centre des maladies du sein in Quebec City, Canada, between 1987 and 2021. Prevalence ratios (PRs) for obesity and smoking were estimated using multivariable log-binomial regression models adjusted for demographic and clinical characteristics. Over the 35-year period, obesity prevalence at diagnosis significantly increased [PR 1.19; 95% confidence interval (CI) 1.15-1.24; P < 0.0001], while smoking prevalence declined (PR 0.92; 95% CI 0.89-0.95; P < 0.0001). Stratified analyses showed more pronounced increase in obesity (PR 1.40; 95% CI 1.25-1.57; P < 0.0001) and decline in smoking (PR 0.82; 95% CI 0.78-0.86; P < 0.0001) among premenopausal women, compared with postmenopausal women (obesity: PR 1.16; 95% CI 1.11-1.21; P < 0.0001; smoking: PR 0.97; 95% CI 0.93-1.01; P = 0.1555). The rising prevalence of obesity among BC patients at diagnosis underscores the need to strengthen public health efforts targeting obesity prevention. While anti-smoking measures appear to have been effective, additional strategies are warranted to address the growing burden of obesity and its potential impact on BC incidence in Canada and elsewhere. • Smoking at diagnosis declined over 35 years, reflecting effective interventions. • Obesity rates rose, revealing a growing concern among breast cancer patients. • Changes in smoking and obesity were most apparent in premenopausal patients. • Obesity prevention—especially in young women—needs stronger focus.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".