Impact of the COVID‐19 pandemic on infant feeding practices in the United States: Food insecurity, supply shortages and deleterious formula‐feeding practices
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic increased food insecurity among US households, however, little is known about how infants, who rely primarily on human milk and/or infant formula, were impacted. We conducted an online survey with US caregivers of infants under 2 years of age (N = 319) to assess how the COVID-19 pandemic impacted breastfeeding, formula-feeding and household ability to obtain infant-feeding supplies and lactation support (68% mothers; 66% White; 8% living in poverty). We found that 31% of families who used infant formula indicated that they experienced various challenges in obtaining infant formula, citing the following top three reasons: the formula was sold out (20%), they had to travel to multiple stores (21%) or formula was too expensive (8%). In response, 33% of families who used formula reported resorting to deleterious formula-feeding practices such as diluting formula with extra water (11%) or cereal (10%), preparing smaller bottles (8%) or saving leftover mixed bottles for later (11%). Of the families who fed infants human milk, 53% reported feeding changes directly as a result of the pandemic, for example, 46% increased their provisioning of human milk due to perceived benefits for the infant's immune system (37%), ability to work remotely/stay home (31%), concerns about money (9%) or formula shortages (8%). Fifteen percent of families who fed human milk reported that they did not receive the lactation support they needed and 4.8% stopped breastfeeding. To protect infant food and nutrition security, our results underscore the need for policies to support breastfeeding and ensure equitable and reliable access to infant formula.
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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 | 0.001 | 0.001 |
| 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".