Lower Adherence to Breastfeeding Recommendations in Mothers Treated With Antirheumatic and Antidepressant Medications
Bibliographic record
Abstract
BACKGROUND: Exclusive breastfeeding for 6 months is recommended, but breastfeeding safety data is insufficient for several medications. AIM: To determine if mothers treated with chronic medications are less likely to breastfeed. METHODS: For this secondary analysis, 6383 pregnant women in the MotherToBaby cohort recruited from the United States and Canada between 2010 and 2022 were included. Participants treated with antirheumatic medications (ARM), selective serotonin reuptake inhibitors (SSRIs), and asthma medications during pregnancy were divided into two groups based on their medication use: continuers and discontinuers. Breastfeeding initiation, supplementation with commercial milk formula, and discontinuation of breastfeeding before 6 months were compared between those exposed and unexposed to medication use. Adjusted risk and hazard ratios (aRR, aHR) and 95% Confidence Intervals (CI) were calculated with modified Poisson and Cox regressions adjusted for year, parity, socioeconomic status, body mass index, smoking, illicit drug use, race and ethnicity. RESULTS: The sample included 799 (12.5%) continuers and 475 (7.4%) discontinuers of ARM, 293 continuers (4.6%) and 63 (1.0%) discontinuers of SSRIs, and 217 (3.4%) continuers and 97 (1.5%) discontinuers of asthma medications. There were 4,439 (69.6%) participants who were unexposed to the study medications. Both ARM continuers and discontinuers were more likely to not breastfeed (aRRs 95% CI: 3.92 [3.03, 5.07] and 3.08 [2.19, 4.33]), to supplement (aRRs 95% CI: 1.12 [1.01, 1.26] and 1.25 [1.10, 1.43]) and stop breastfeeding before 6 months (aHRs 95% CI: 1.72 [1.29, 2.31] and 1.41 [0.92, 2.15]). SSRI continuers were more likely to supplement (aRR 95% CI: 1.26 [1.08, 1.47]). CONCLUSION: Participants treated with chronic medications, primarily ARMs, were less likely to breastfeed. Targeted lactation support for mothers with chronic illnesses is recommended along with development of breastfeeding safety data for these medications.
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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 | 0.001 | 0.006 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".