Characteristics of Pediatric Emergency Department Visits in the Past 22 Years in the United States (1997–2019)
Bibliographic record
Abstract
BACKGROUND: Pediatric emergency department (ED) visits are an important indicator of children's health and healthcare access. Understanding the factors influencing these visits, including socioeconomic status, insurance coverage, and geographic location, is essential for improving pediatric care delivery. OBJECTIVE: This study aims to analyze the characteristics of pediatric ED visits in the United States over 22 years (1997-2019), focusing on factors such as poverty levels, health insurance status, geographic region, and frequency of ED visits. METHODS: A retrospective analysis of data from the National Health Interview Survey (NHIS) was conducted for children aged 0-17 years who had visited an ED at least once in the past 22 years. The study examined factors such as income as a percentage of the poverty level, health insurance status (insured, private, Medicaid, and uninsured), and geographic region (Northeast, Midwest, South, and West). Statistical analysis evaluated trends and associations between these characteristics and ED visit frequencies over time. RESULTS: From 1997 to 2019, the percentage of children visiting the ED fluctuated between 16.7% and 22.4%. The highest rate was observed in 2002 (22.4%) and the lowest in 2015 (16.9%). Children under six years had the highest ED visit rates, peaking in 2006 at 28.2%. Male children consistently had higher ED visit rates than females. Among racial groups, Black children had the highest rates, while Asian children had the lowest. Hispanic children had elevated ED visit rates, peaking in 2009. Children below the poverty level had higher visit rates, while those with Medicaid had consistently higher visits. Regional trends showed higher visits in the South, and urban areas reported more visits than rural ones. Multiple ED visits declined over the years, especially for children under six. CONCLUSION: This study has disclosed that there is a significant relationship between pediatric ED visits and socioeconomic factors, insurance coverage, and geographic location. These findings emphasize the need for targeted interventions to address disparities in healthcare access and improve pediatric emergency care delivery across diverse populations.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".