Does breastfeeding duration decrease child obesity? An instrumental variables analysis
Bibliographic record
Abstract
BACKGROUND: Many studies have documented that breastfeeding is associated with a significant reduction in child obesity risk. However, a persistent problem in this literature is that unobservable confounders may drive the correlations between breastfeeding behaviors and child weight outcomes. OBJECTIVE: This study examines the effect of breastfeeding practices on child weight outcomes at age 2. METHODS: This study relied on population-based data for all births in Oregon in 2009 followed for two years. We used instrumental variables methods to exploit variations in breastfeeding by mothers immediately after delivery and the degree to which hospitals encouraged mothers to breastfeed in order to isolate the effect of breastfeeding practices on child weight outcomes. RESULTS: We found that for every extra week that the child was breastfed, the likelihood of the child being obese at age 2 declined by 0.82% [95% CI -1.8% to 0.1%]. Likewise, for every extra week that the child was exclusively breastfed, the likelihood of being obese declined by 0.66% [95% CI -1.4 to 0.06%]. While the magnitudes of effects were modest and marginally significant, the results were robust in a variety of specifications. CONCLUSION: The results suggest that hospital practices that support breastfeeding may influence childhood weight outcomes.
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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 | 0.014 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".