Impact of the COVID-19 pandemic on Infant Feeding Practices in the U.S.: Food Insecurity, Supply Shortages, and Deleterious Feeding Practices
Bibliographic record
Abstract
The COVID-19 pandemic drastically increased food insecurity among U.S. 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 U.S. caregivers of infants (<2 years) (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). Results suggest that the COVID-19 pandemic had a greater negative impact on formula-feeding families, largely due to formula shortages and financial strain. We found 31% of formula-feeding families indicated they experienced various challenges to obtaining formula, citing the following top three reasons: formula was sold out (20%), had to travel to multiple stores (21%), or too expensive (8%). In response, 33% of formula-feeding families reported resorting to potentially harmful feeding practices such as diluting formula with extra water (12%) or cereal (10%), preparing smaller bottles (8%), or feeding left-over/expired formula (11%). Only 15% of breastfeeding families reported feeding difficulties directly as a result of the pandemic—15% were reluctant to obtain lactation support and 9% weaned early. In fact, 51% of breastfeeding families reported increased provision of human milk to infants due to perceived benefits for the infant’s immune system (37%), ability to work remotely/stay home (31%), or concerns about money (8%) or formula shortages (8%). To protect infants from malnutrition in future crises, our results underscore the need for policies to support breastfeeding families and ensure equitable access to infant formula.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".