Prevalence of and risk factors for excess weight gain in pregnancy: a cross-sectional study using survey data
Bibliographic record
Abstract
BACKGROUND: Maternal weight gain during pregnancy is required for fetal development; however, excess gestational weight gain is associated with increased maternal and neonatal morbidity. We aimed to determine the proportion of Canadian women who gained excess weight during pregnancy and to identify risk factors for excess gestational weight gain. METHODS: Self-reported data on maternal weight gain were collected from the 2015/16 and 2017/18 cycles of the Canadian Community Health Survey (CCHS), a cross-sectional population-based survey. We included females aged 15 to 54 years with data on height, prepregnancy weight and gestational weight gain. We defined excess gestational weight gain in terms of preconception body mass index (BMI) according to the 2009 guideline of the US Institute of Medicine. We used logistic regression to evaluate potential risk factors for excess gestational weight gain. RESULTS: Of 1 335 615 Canadian women (weighted from approximately 9300 survey respondents), 422 043 (32%) gained excess weight during pregnancy. Women with obesity had 33% lower odds of gaining excess weight relative to women with overweight (odds ratio 0.67, 95% confidence interval 0.48-0.94). Risk factors for excess gestational weight gain were lower education level, white or Indigenous identity, smoking, mood disorder, anxiety disorder and Canadian citizenship. INTERPRETATION: One-third of Canadian women in this survey had excess gestational weight gain during pregnancy, and women with obesity had lower odds of gaining excess weight during pregnancy relative to women with overweight. Strategies are needed to reduce the proportion of Canadian women who gain excess weight during pregnancy, regardless of preconception BMI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".