Characteristics of Pediatric Emergency Department Visits in the Past 22 Years in the United States (1997–2019)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».