Abstract 16216: Sociodemographic Disparities in Severity of Multisystem Inflammatory Syndrome in Children: The National MUSIC Study
Bibliographic record
Abstract
Introduction: The long-term outcomes after the Multisystem Inflammatory Syndrome in Children (MUSIC) study investigates sequelae in multisystem inflammatory syndrome (MIS-C) post-COVID across 33 US and Canadian sites. Among children with COVID-19, the adjusted MIS-C incidence rate ratios are higher in children who are Hispanic or Black. Our objective was to assess sociodemographic disparities associated with MIS-C severity and adverse outcomes. Methods: This multicenter cross-sectional study included US persons <21 years-old with MIS-C from 6/2020-1/2022. The primary composite outcome of greater illness severity included > 1 findings: vasoactive medications, cardiac dysfunction or elevated troponin, intubation, or mechanical support. Secondary outcomes were days from symptom onset to hospital admission and hospital length of stay (LOS). Predictor variables were patient distance to the hospital, neighborhood social deprivation index (SDI-higher score is worse), race/ethnicity, and non-primary English language. Covariates were age, insurance, asthma, and obesity. Multivariable models used backwards selection at significance p<0.05. Results: There were 1115 MIS-C patients, with a median age of 9 years old (IQR 5.6, 12.7), 39.2% female, 28.1% non-Hispanic Black, 27.8% Hispanic, 47.3% with public insurance, and a median SDI of 54 (IQR 25.0, 82.5). On multivariable analysis, increased odds of more severe illness included Hispanic ethnicity, odds ratio (OR) 1.5 (95% CI 1.1, 2.2); non-Hispanic Black race, OR 1.7 (95% CI 1.1, 2.4) compared to non-Hispanic whites; and ages 13-17 years, OR 3.7 (95% CI 2.4, 5.6) compared to those ages 0-5 years. Longer time to hospital admission was associated with lower SDI (i.e., less deprivation; p=0.009) and further distance from hospital (p=0.006). Risk factors for longer LOS were adolescent age (p=0.002) and non-Hispanic Black race (p=0.018). Asthma and obesity had no association with MIS-C severity. Conclusion: MIS-C severity and adverse outcomes are associated with sociodemographic factors, including being Black and/or Hispanic. Future studies should explore risk mechanisms , including systemic racism, environmental and genetic causes, quality of medical care, or multifactorial interactions.
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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 | 0.001 | 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) | 0.002 | 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".