Age at natural menopause and its associated factors in Canada: cross-sectional analyses from the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
BACKGROUND: Early onset of menopause is associated with long-term disease and higher mortality risks. Research suggests that age at natural menopause (ANM) varies across populations. Little is known about factors that affect ANM in Canadian women. OBJECTIVE: This study aims to estimate the median ANM and examine factors associated with earlier ANM among Canadian women. METHODS: Baseline data from the Tracking cohort of the Canadian Longitudinal Study on Aging was used for this analysis. The relation of sociodemographic, lifestyle, and health-related factors with ANM was examined among 7,719 women aged 40 and above. Nonparametric Kaplan-Meier cumulative survivorship estimates were used to assess the timing of natural menopause. Univariate and multivariate Cox proportional hazard regression models were used to characterize ANM and its association with relevant covariates. RESULTS: Overall, median ANM was 51 years. Having no partner, low household income and education levels, current and former smoking, and cardiovascular disease were all associated with an earlier ANM, whereas current employment, alcohol consumption, and obesity were associated with later ANM. CONCLUSIONS: These findings provide a national estimate of ANM in Canada and show the importance of lifestyle factors and health conditions in determining menopausal age. These factors might help in risk assessment, prevention and early management of chronic disease risk during the menopausal transition.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".