Addressing Racial Inequities in Breastfeeding in the Southern United States
Bibliographic record
Abstract
BACKGROUND: Race is a predictor of breastfeeding rates in the United States, and rates are lowest among African American infants. Few studies have assessed changes in breastfeeding rates by race after implementing the Ten Steps to Successful Breastfeeding (hereafter referred to as the Ten Steps), and none have assessed the association between implementation and changes in racial disparities in breastfeeding rates. Our goal was to determine if a hospital- and community-based initiative in the Southern United States could increase compliance with the Ten Steps, lead to Baby-Friendly designation, and decrease racial disparities in breastfeeding. METHODS: Hospitals in Mississippi, Louisiana, Tennessee, and Texas were enrolled into the Communities and Hospitals Advancing Maternity Practices initiative from 2014 to 2017 and received an intensive quality improvement and technical assistance intervention to improve compliance with the Ten Steps. Community partners and statewide organizations provided parallel support. Hospitals submitted monthly aggregate data stratified by race on breastfeeding, skin-to-skin care, and rooming in practices. RESULTS: The disparity in breastfeeding initiation between African American and white infants decreased by 9.6 percentage points (95% confidence interval 1.6–19.5) over the course of 31 months. Breastfeeding initiation increased from 66% to 75% for all races combined, and exclusivity increased from 34% to 39%. Initiation and exclusive breastfeeding among African American infants increased from 46% to 63% (P < .05) and from 19% to 31% (P < .05), respectively. Skin-to-skin care after cesarean delivery was significantly associated with increased breastfeeding initiation and exclusivity in all races; rooming in was significantly associated with increased exclusive breastfeeding in African American infants only. CONCLUSIONS: Increased compliance with the Ten Steps was associated with a decrease in racial disparities in breastfeeding.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".