Stochastic Mediation Contrasts in Epidemiologic Research: Interpregnancy Interval and the Educational Disparity in Preterm Delivery
Bibliographic record
Abstract
Low maternal education is consistently associated with increased risk of preterm delivery (PTD). The interpregnancy interval (IPI), defined as the time between the date of a previous birth and the conception date of the index pregnancy, may mediate this relationship. We estimated controlled direct effects to assess whether hypothetical interventions designed to increase IPIs would reduce the educational disparity in PTD. We introduce a technique for estimating controlled direct effects under interventions that set only some persons in the population to a specific mediator value. We used data from 847,618 singleton livebirths occurring in Quebec, Canada, between 1989 and 2010. Compared with mothers with some university education (≥14 years), mothers with less than high school (<11 years), high school (11 years), and some college (12-13 years) had excess PTD risks of 2.6% (95% confidence interval (CI): 2.4, 2.8), 1.5% (95% CI: 1.4, 1.7), and 1.0% (95% CI: 0.9, 1.1), respectively. Risk differences under an intervention corresponding to the Healthy People 2020 objective of reducing the number of mothers with IPIs less than 18 months by 3% were no different from those for the total relationship. Our results suggest that interventions designed to increase the length of short IPIs will yield no important change in the PTD disparity by maternal educational level.
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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 | 0.110 | 0.280 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".