Hospital Formula Supplementation Postbreastfeeding Initiation, Neighborhood Economy, and Race
Bibliographic record
Abstract
Importance: Breastfeeding supports lifelong health, but socioeconomic and racial disparities persist. Biases in hospital formula supplementation practices may be an underlying contributor. Objective: To examine whether nonmedically indicated hospital formula supplementation of term-born breastfed newborns is associated with neighborhood socioeconomic status and/or maternal race. Design, Setting, and Participants: Provincial registry data were used to build a cohort of all live births of term-born singleton infants who initiated breastfeeding in Ontario, Canada, hospitals from April 1, 2015 through March 31, 2021, and for whom prenatal screening data were available. Of 570 936 eligible births, 148 888 were excluded, primarily due to missing outcome data, preterm birth, or not initiating breastfeeding. These data were analyzed from December 2023 through October 2025. Exposures: The 2 exposures were socioeconomic status, derived by linking maternal postal codes with 2021 Ontario Marginalization Index neighborhood-level quintiles for material resources, and maternal race (Asian, Black, White, or other [Indigenous, multiracial, or unknown race]), determined from prenatal screening data. Main Outcome and Measure: The primary outcome was nonmedically indicated formula supplementation, determined from hospital feeding records. Results: This cohort included 422 048 maternal-infant dyads, 28% of whom were in the Asian racial group, 7% in the Black racial group, 59% in the White racial group, and 5% in the other racial group. Overall, 27% of infants received nonmedically indicated formula supplementation, with an increase from 23% to 32% over the study period. Participants in the Asian, Black, and other racial groups were more likely than those in the White group to be in the most marginalized socioeconomic quintile (20%, 43%, and 23% vs 16%). Risk of nonmedically indicated formula supplementation increased in a gradient across quintiles of increasing socioeconomic marginalization (quintile 5 vs quintile 1: adjusted relative risk [aRR], 1.68; 95% CI, 1.64-1.72) and was significantly elevated for the Asian (aRR, 2.69; 95% CI, 2.64-2.74), Black (aRR, 2.07; 95% CI, 2.01-2.13), and other (aRR, 1.43; 95% CI, 1.39-1.48) racial groups compared with the White group. Conclusions and Relevance: In this population-level analysis, nonmedically indicated formula supplementation prevalence was high and increased over time, with elevated risk associated with socioeconomic marginalization and maternal racialization. Increased hospital adherence to breastfeeding support guidelines is needed to improve health equity.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".