Interpregnancy and interbirth intervals and all-cause, cardiovascular-related and cancer-related maternal mortality: findings from a large population-based cohort study
Bibliographic record
Abstract
INTRODUCTION: Scarce research is available regarding the association between interbirth intervals (IBI) and long-term maternal health outcomes, particularly cardiovascular disease (CVD) mortality. We aimed to assess whether IBIs were associated with all-cause, CVD-related and cancer-related mortality. METHODS: We conducted a cohort study in the setting of the Jerusalem Perinatal Study. Women with at least two consecutive singleton live births in 1964-1976 (N=18 294) were followed through 2016. IBIs were calculated as the interval between women's first and second cohort birth. We estimated associations between IBIs and mortality using Cox's proportional hazards models, adjusting for age, parity, maternal education, maternal origin and paternal socioeconomic status. Date of last menstrual period was available for a subset of women. We assessed the interpregnancy interval (IPI) for these women and compared the models using IPI and IBI. RESULTS: During 868 079 years of follow up (median follow-up: 49.0 years), 3337 women died. Women with IBIs <15 months had higher all-cause mortality rates (HR 1.18; 95% CI 1.05 to 1.33) compared to women with 33-month to 68-month IBIs (reference category). IBI and CVD mortality appeared to have a J-shaped association; IBIs of <15, 15-20, 21-2626-2632, 33-68 and ≥69 months had HRs of 1.44, 1.40, 1.33, 1.14, 1.00 and 1.30, respectively. No substantial association was found with cancer mortality. Models using IPIs and those using IBI were similar. CONCLUSION: Our results support the WHO recommendations for IPIs of ≥24 months and add additional evidence regarding long-term CVD mortality.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".