The social determinants of child health: variations across health outcomes – a population-based cross-sectional analysis
Bibliographic record
Abstract
BACKGROUND: Disparities in child health outcomes persist despite advances in medical technology and increased global wealth. The social determinants of health approach is useful in explaining the disparities in health. Our objective in this paper is four-fold: (1) to test whether the income relationship (and the related income gradient) is the same across different child health outcomes; (2) to test whether the association between income and child health outcomes persists after controlling for other traditional socioeconomic characteristics of children and their family (education and employment status); (3) to test the role of other potentially mediating variables, namely parental mental health, number of children, and family structure; and (4) to test the interaction between income and education. METHODS: This population-based cross-sectional study used data from the 2003 US National Survey of Children's Health involving 102,353 children aged 0 to 17 years. Using multivariate logistic regression models, the association between household income, education, employment status, parental mental health, number of children, family structure and the following child health outcomes were examined: presence or absence of asthma, headaches/migraine, ear infections, respiratory allergy, food/digestive allergy, or skin allergy. RESULTS: While the associations of some determinants were found to be consistent across different health outcomes, the association of other determinants such as household income depended on the specific outcome. Controlling for other factors, a gradient association persisted between household income and a child having asthma, migraine/severe headaches, or ear infections with children more likely to have the illness if their family is closer to the federal poverty level. Potentially mediating variables, namely parental mental health, number of children, and family structure had consistent associations across health outcomes. CONCLUSION: There appears to be evidence of an income gradient for certain child health outcomes, even after controlling for other traditional measures of socioeconomic status. Our study also found evidence of an association between certain child health outcomes and potential mediating factors.
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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 | 0.004 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".