1065 Uncovering Ethnic/Racial Disparities in Pediatric Sleep Quality: Insights from the San Diego Sleep Survey
Bibliographic record
Abstract
Abstract Introduction Pediatric sleep questionnaires are essential tools for screening sleep disorders, especially where access to pediatric sleep specialists and laboratories is limited and often where underserved and minoritized populations receive care. Moreover, there is limited knowledge on the prevalence of sleep symptoms among representative race/ethnic pediatric groups in the U.S. To address this, we developed the San Diego Sleep Survey (SDSS) with the goal to assess a wide range of sleep symptoms within a clinical setting. This study aimed to identify potential differences in reported sleep symptoms between patient-reported ethnicities and racial groups. Methods Caregivers of patients referred to the Rady Children’s Hospital Sleep Center in San Diego, California, completed the SDSS via the Epic® Electronic Medical Record (EMR) system. The SDSS is a 51-item questionnaire utilizing a 4-point Likert scale (Never, Sometimes, Usually, Do Not Know) to provide detailed insights into sleep difficulties. Five domain scores are used to evaluate pediatric sleep issues: insomnia, sleep-disordered breathing (SDB), parasomnias, sleep hygiene, and daytime symptoms (DS), with lower scores indicating better sleep health. The survey is available in both English and Spanish, and demographics were extracted from the EMR. Results 1,362 patients completed the SDSS and PSQ from 2011 to 2021. The mean age was 8.174.62 years, and 554 (40.7%) were female. The cohort included 647 (47.5%) Hispanic, 558 (41.0%) Non-Hispanic White (NHW) patients, 85 (6.2%) Non-Hispanic Asian (NHA), and 72 (5.3%) Non-Hispanic Black (NHB). SDB scores were significantly lower among NHW children compared to Hispanic and NHB children (p < 0.01). In contrast, DS scores were significantly higher for NHW patients compared to Hispanic and NHB children. No differences in insomnia scores were observed across ethnic groups. Finally, sleep hygiene scores were significantly lower (p < 0.05) among NHW children compared to other groups. Conclusion The application of the SDSS in a large pediatric sleep clinic population revealed significant differences in reported sleep symptoms across children of various race/ethnicities. Although the sample included a smaller number of NHB children, the findings highlight substantial variations in symptom reporting by race/ethnicity, underscoring the impact of healthcare disparities on pediatric sleep health outcomes. Support (if any)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".