Income and neighbourhood deprivation in relation to obesity in urban dwelling children 0–12 years of age: a cross-sectional study from 2013 to 2019
Bibliographic record
Abstract
BACKGROUND: Childhood obesity is a major public health concern. This study evaluated the independent and joint associations of family-level income, neighbourhood-level income and neighbourhood deprivation, in relation to child obesity. METHODS: A cross-sectional study was conducted in children ≤12 years of age from TARGet Kids! primary care network (Greater Toronto Area, 2013-2019). Parent-reported family income was compared with median neighbourhood income and neighbourhood deprivation measured using the Ontario Marginalization Index. Children's height and weight were measured and body mass index (BMI) z-scores (zBMI) were calculated. ORs and 95% CIs were estimated for the three exposure variables separately using multilevel multinomial logistic regression models with zBMI categories as the outcome, adjusting in model 1 for age, sex, ethnicity and number of family members and in model 2 adding family income. A joint measure was derived combining income and deprivation measures. RESULTS: A total of 5962 children were included. Low family income (Q1 vs Q5: OR=4.69, 95% CI 2.65 to 8.29), low neighbourhood income (Q1 vs Q5: OR=2.18, 95% CI 1.33 to 3.58) and high neighbourhood deprivation (Q1 vs Q5: OR=2.45, 95% CI 1.52 to 3.95) were each associated with increased OR of child obesity. However, after adjustment for family income, the association for both neighbourhood income (OR=1.39, 95% CI 0.82 to 2.34) and deprivation (OR=1.56, 95% CI 0.94 to 2.58) and obesity was attenuated. Children from low-income families living in low-income or high deprivation neighbourhoods had higher OR of obesity. CONCLUSION: Child obesity was independently associated with low family-level income and a joint measure suggests that neighbourhood also matters. Socioeconomic inequalities at both individual and neighbourhood levels should be addressed in childhood obesity interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".