Systematic review and meta-analysis of the association between maternal smoking in pregnancy and childhood overweight and obesity
Bibliographic record
Abstract
Background By 2020, it is predicted that 60 million children worldwide will be overweight. Maternal smoking in pregnancy has been suggested as a contributing factor. Our objective was to systematically review studies on this, thereby expanding the evidence base for this association. Methods Systematic review with meta-analysis, Prospero Registration number CRD42012002859. We searched PubMed, Embase, Global Health, Web of Science and the Grey literature. We included prevalence, cohort and cross-sectional studies involving full-term, singleton pregnancies. Published and unpublished studies through to 1 January 2015 in all languages, demonstrating an objective overweight outcome up until 18 years of age and data presented as an OR, were included. Quality assessment was undertaken using an adaption of the Newcastle-Ottawa scale. Statistical analysis was performed using Review Manager V.5.3. Findings The meta-analysis included 39 studies of 236 687 children from Europe, Australia, North America and South America and Asia. Maternal smoking in pregnancy ranged from 5.5% to 38.7%, with the prevalence of overweight from 6.3% to 32.1% and obesity from 2.6% to 17%. Pooled adjusted ORs demonstrated an elevated odds of maternal smoking in pregnancy for childhood overweight (OR 1.37, 95% CI 1.28 to 1.46, I 2 45%) and childhood obesity (OR 1.55, 95% CI 1.40 to 1.73, I 2 24%). Interpretation Our results demonstrate an association between maternal prenatal smoking and childhood overweight. This contributes to the growing evidence for the aetiology of childhood overweight, providing important information for policymakers and health professionals alike in planning cessation programmes or antismoking interventions for pregnant female smokers.
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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 | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.031 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".