Associations of Childhood Eczema Severity: A US Population-Based Study
Bibliographic record
Abstract
BACKGROUND: Little is known about the predictors of eczema severity in the US population. OBJECTIVES: We sought to determine the distribution and associations of childhood eczema severity in the United States. METHODS: We analyzed the data from the 2007 National Survey of Children's Health, a prospective questionnaire-based study of a nationally representative sample of 91,642 children (range, 0-17 years). RESULTS: The prevalence of childhood eczema was 12.97% (95% confidence interval [95% CI], 12.42-13.53); 67.0% (95% CI, 64.8-69.2) had mild disease, 26.0% (95% CI, 23.9-28.1) had moderate disease, and 7.0% (95% CI, 5.8-8.3) had severe disease. There was significant statewide variation of the distribution of eczema severity (Rao-Scott χ, P = 0.004), with highest rates of severe disease in Mid-Atlantic and Midwestern states. In univariate models, eczema severity was increased with older age, African American and Hispanic race/ethnicity, lower household income, oldest child in the family, home with a single mother, lower paternal/maternal education level, maternal general health, maternal/paternal emotional health, dilapidated housing, and garbage on the streets. In multivariate survey logistic regression models using stepwise and backward selection, moderate-to-severe eczema was associated with older age, lower household income, and fair or poor maternal health but inversely associated with birthplace outside the United States. CONCLUSIONS: These data indicate that environmental and/or lifestyle factors play an important role in eczema severity.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".