Socio-demographic determinants of breastfeeding initiation and duration in US children: an analysis of NHANES (1999–2018)
Bibliographic record
Abstract
BACKGROUND: Despite an increase in the prevalence of breastfeeding initiation and breastfeeding duration over the past decades in the US, breastfeeding practices remain suboptimal and fall well below the Healthy People 2030 goals. We aimed to investigate the potential associations between socio-demographic factors and breastfeeding initiation and duration, with the goal of identifying factors that act as barriers or facilitators to breastfeeding practices. METHODS: A total of 7,629 US children aged 0 to 24 months were included from the National Health and Nutrition Examination Survey data between 1999 and 2018. Logistic regression was used to examine the relationship between socio-demographic factors and breastfeeding initiation and duration, with a heat map illustrating their combined associations. Meta-regression was employed to estimate the temporal trends in odds ratios (OR) for the effect of socio-demographic factors on breastfeeding outcomes. RESULTS: The overall prevalence of breastfeeding initiation and duration at 3, 6, and 12 months were lower among non-Hispanic Black children [adjusted OR (aOR): 0.42; 95% confidence interval (CI): 0.35, 0.51; 0.77; 0.63, 0.95; 0.58; 0.47, 0.71; 0.45; 0.32, 0.62] when compared with White children, while higher among high-income families (1.53; 1.31, 1.78; 1.24; 1.04, 1.48; 1.36; 1.15, 1.61; 1.23; 0.96, 1.59), mothers older than 25 years at delivery (1.68; 1.45, 1.95; 1.95; 1.63, 2.34; 1.88; 1.58, 2.23; 1.62; 1.26, 2.08), and nonsmoking mothers during pregnancy (2.65; 2.19, 3.21; 2.10; 1.60, 2.75; 2.52; 1.86, 3.41; 1.97; 1.26, 3.07) than among low-income families, mothers aged ≤ 25 years, and smoking mothers, respectively. When stratified by 10 consecutive cycles, the disparities in temporal trends among most socio-demographic factors remained stable, but the gaps among smoking status even widened over time. Heat map analysis further depicted the combined associations between these factors and breastfeeding practices, and found that children born to Black mothers, smoking mothers, mothers aged ≤ 25 years, and low-income families generally had lower odds of breastfeeding initiation and breastfeeding duration at 3, 6, and 12 months. CONCLUSIONS: Children born to socioeconomically disadvantaged families generally exhibited poorer breastfeeding practices. CLINICAL TRIAL NUMBER: Not applicable.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".