Cross-sectional analysis of the association between household food insecurity and mental health conditions in children aged 5–11 years in Canada
Bibliographic record
Abstract
BACKGROUND: Children living in food insecure households have poorer mental health outcomes compared with their food-secure peers; however, the relationship between the severity of food insecurity and diagnosed mental health conditions in young children remains unknown. This study examined the association between household food insecurity and reported diagnosed mental health conditions among children aged 5-11 years in Canada. METHODS: This study included 16 216 children aged 5-11 years living in Canada, from the 2019 Canadian Health Survey on Children and Youth. We measured household food insecurity using the Household Food Security Survey Module. We measured diagnosed mental health conditions by parent/caregiver report of health professional-diagnosed anxiety, depression, autism spectrum disorder or attention-deficit/hyperactive disorder. We developed a multivariable logistic regression model to assess the association between severities of food insecurity and mental health, controlling for potentially confounding variables. RESULTS: 17.0% of children lived in households reporting some level of food insecurity (5.4% marginal, 8.0% moderate and 3.6% severe). The prevalence of at least one diagnosed mental health condition in the same population was 10.9%. After adjusting for sociodemographic characteristics, children from marginal, moderate and severe food insecure households had a 1.39 (95% CI 0.99 to 1.97), 1.46 (95% CI 1.13 to 1.89) and 1.67 (95% CI 1.18 to 2.35) increased odds of having a diagnosed mental health condition, respectively. CONCLUSION: Household food insecurity is associated with an increased presence of diagnosed mental health conditions in children aged 5-11 years. This study adds to the body of research showing that social and economic inequities, including household food insecurity, negatively impact the health of children.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".