Lifetime breastfeeding and mortality among parous women in the Mexican Teacher's Cohort
Bibliographic record
Abstract
OBJECTIVE: To assess the relationship between lifetime breastfeeding and all-cause and cause-specific mortality in Mexican women. METHODS: We used prospective data of 88 597 women from the Mexican Teachers' Cohort. Hazard ratios (HRs) were estimated from Cox regression models for total mortality, and a competing risk model was used for cause-specific mortality, adjusted for childhood/adolescence socioeconomic and lifestyle factors, age at first birth, and number of births. A dose-response relation was assessed using smoothed splines. RESULTS: Participants, on average, breastfed for 6 months per birth, with a mean total breastfeeding duration of 13 months. After a mean follow-up of 11 years, 1556 deaths were found. Compared with parous women who did not breastfeed, all-cause mortality was lower for women who breastfed (<6 months: 0.78, 95% confidence interval [CI] 0.67-0.91; for 6-12 months: 0.76, 95% CI 0.64-0.90; for 12-24 months: 0.73, 95% CI 0.62-0.86; and for >24 months: 0.77, 95% CI 0.64-0.91). The dose-response relationship between breastfeeding and all-cause mortality was non-linear (P = 0.023). This trend was consistent when accounting for single live births and excluding women with gestational conditions. Those who breastfed >6 months had lower risk of cardiovascular and breast cancer-specific mortality. CONCLUSION: Breastfeeding was associated with reduced all-cause, cardiometabolic, and breast cancer mortality in Mexican women. The findings support the need for comprehensive policies to support breastfeeding, considering the potential for significant public health benefits. Additionally, the study highlighted a substantial gap in breastfeeding practices in Mexico, with average durations far below international recommendations.
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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 | 0.000 | 0.000 |
| 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 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".