Characterisation of medical conditions of children with sickle cell disease in the USA: findings from the 2007–2018 National Health Interview Survey (NHIS)
Bibliographic record
Abstract
OBJECTIVES: We used the National Health Interview Survey (NHIS) data set to examine the prevalence of comorbid medical conditions; explore barriers to accessing healthcare and special educational services; and assess the associations between sickle cell disease (SCD) status and demographics/socioeconomic status (SES), and social determinants of health (SDoH) on comorbidities among children in the USA. DESIGN: Cross-sectional. SETTING: NHIS Sample Child Core questionnaire 2007-2018 data set. PARTICIPANTS: 133 481 children; presence of SCD was determined by an affirmative response from the adult or guardian of the child. MAIN OUTCOME MEASURES: Multivariate logistic regression was used to compare the associations between SCD status, SES and SDoH for various medical conditions for all races and separately for black children at p<0.05. RESULTS: 133 481 children (mean age 8.5 years, SD: 0.02), 215 had SCD and ~82% (weighted) of the children with SCD are black. Children with SCD were more likely to suffer from comorbid conditions, that is, anaemia (adjusted OR: 27.1, p<0.001). Furthermore, children with SCD had at least two or more emergency room (ER) visits (p<0.001) and were more likely to have seen a doctor 1-15 times per year (p<0.05) compared with children without SCD. Household income (p<0.001) and maternal education were lower for children with SCD compared with children without SCD (52.4% vs 63.5% (p<0.05)). SCD children with a maternal parent who has < / > High School degree were less likely to have no ER visits or 4-5 ER visits, and more likely to have 2-3 ER visits within 12 months. CONCLUSION: Children with SCD experienced significant comorbid conditions and have high healthcare usage, with black children being disproportionately affected. Moreover, maternal education status and poverty level illustrates how impactful SES can be on healthcare seeking behaviour for the SCD population. SDoH have significant implications for managing paediatric patients with SCD in clinical settings.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".