Contribution of prepregnancy body mass index and gestational weight gain to adverse neonatal outcomes: population attributable fractions for Canada
Bibliographic record
Abstract
BACKGROUND: Low or high prepregnancy body mass index (BMI) and inadequate or excess gestational weight gain (GWG) are associated with adverse neonatal outcomes. This study estimates the contribution of these risk factors to preterm births (PTBs), small-for-gestational age (SGA) and large-for-gestational age (LGA) births in Canada compared to the contribution of prenatal smoking, a recognized perinatal risk factor. METHODS: We analyzed data from the Canadian Maternity Experiences Survey. A sample of 5,930 women who had a singleton live birth in 2005-2006 was weighted to a nationally representative population of 71,200 women. From adjusted odds ratios, we calculated population attributable fractions to estimate the contribution of BMI, GWG and prenatal smoking to PTB, SGA and LGA infants overall and across four obstetric groups. RESULTS: Overall, 6% of women were underweight (<18.5 kg/m(2)) and 34.4% were overweight or obese (≥25.0 kg/m(2)). More than half (59.4%) gained above the recommended weight for their BMI, 18.6% gained less than the recommended weight and 10.4% smoked prenatally. Excess GWG contributed more to adverse outcomes than BMI, contributing to 18.2% of PTB and 15.9% of LGA. Although the distribution of BMI and GWG was similar across obstetric groups, their impact was greater among primigravid women and multigravid women without a previous PTB or pregnancy loss. The contributions of BMI and GWG to PTB and SGA exceeded that of prenatal smoking. CONCLUSIONS: Maternal weight, and GWG in particular, contributes significantly to the occurrence of adverse neonatal outcomes in Canada. Indeed, this contribution exceeds that of prenatal smoking for PTB and SGA, highlighting its public health importance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".