Association Between Food Insecurity and Developmental Delay and Behavioral Problems in US Children 2 to 5 Years of Age
Bibliographic record
Abstract
OBJECTIVES: To investigate the relationship between food insecurity and developmental delay and/or behavior problems (DD/PB) in US children aged 2 to 5 years before the COVID-19 pandemic. METHODS: Data from 14,464 children aged 2 to 5 years from the National Survey of Children's Health from 2016 to 2017 were analyzed. Children with food insecurity came from families reporting they sometimes or often could not afford nutritious meals. Diagnosis of DD/PB by a professional was obtained through a caregiver report. RESULTS: A quarter of children aged 2 to 5 years lived in food insecure homes, and 9% were diagnosed with DD/PB. Children in food insecure households were more likely to be from minoritized populations publicly insured, with single parents, without high school education, living =< 130% Federal poverty line, and receiving supplemental nutrition assistance program (SNAP) benefits (all p < 0.001). Adjusting for age, sex, race, ethnicity, poverty, family structure, and parent education, children in food insecure households had 1.57 times the odds of being diagnosed with DD/PB compared with children in food secure households. In similarly adjusted models excluding poverty and stratified by SNAP use, homes not receiving SNAP maintained this association between food insecurity and diagnosis of DD/PB, whereas in homes receiving SNAP, the association was not significant. CONCLUSION: In this population-based study, US children aged 2 to 5 years in food insecure households were more likely to be diagnosed with DD/PB compared with those in food secure households. When stratified, there was no association between food insecurity and DD/PB among those receiving SNAP; the association remained for those not receiving SNAP. The potential long-term impact of this safety net program on our most vulnerable citizens must be considered as policymakers contemplate federal spending priorities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".