Trends in Health Care Utilization among United States Children with Eczema by Age, Sex, Race, and Hispanic Ethnicity: National Health Interview Survey 2006–2018
Bibliographic record
Abstract
Abstract: Background: Higher health care utilization has been proven among US children with eczema than those without, but disparities may exist among sociodemographic subgroups. Objective: To determine health care utilization trends among children with eczema across sociodemographic factors. Methods: We included children (0–17 years old) from the US National Health Interview Survey 2006–2018. We calculated the survey-weighted health care utilization by determining proportion of children attending a well-child checkup, seen by a medical specialist, and seen by a mental health professional in the previous 12 months for children with and without eczema, by race (white, black, American Indian/Alaska Native, Asian, and multiracial), Hispanic ethnicity (yes/no), age (0–5, 6–10, 11–17), and gender (male/female) subgroups using SPSS complex samples. Joinpoint regression was used to estimate piecewise log-linear trends in the survey-weighted prevalence, annual percentage change, and disparities between subgroups. Results: We included 149,379 children—there was higher health care utilization in children with eczema than those without. However, when comparing the average annual percentage change (AAPC), white children had a significantly higher AAPC of “attending a well-child checkup” than black children. In addition, only white children showed a significantly increasing trend in being “seen by a medical specialist,” whereas all other minority race subgroups had stagnant trends. For those “seen by a mental health professional,” there were increasing trends only in the male and non-Hispanic subgroups out of all the sociodemographic subgroups. Conclusion: Improving awareness among primary care physicians to refer children with moderate-to-severe eczema to medical specialists (eg, allergists, dermatologists, and mental health/attention-deficit/hyperactivity disorder professionals) when necessary could improve quality of life and reduce emergency department visits—especially among minority race, Hispanic, and female 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".