Epidemiology of Current Asthma in Children Under 18: A Two-Decade Overview Using National Center for Health Statistics (NCHS) Data
Notice bibliographique
Résumé
OBJECTIVE: This study conducted a comprehensive two-decade analysis of current asthma among children under 18 in the United States using National Center for Health Statistics (NCHS) data. The primary objective was to assess the prevalence of current asthma, evaluate temporal trends, and identify disparities based on gender, age, insurance status, household poverty levels, and race/ethnicity. METHODS: Data spanning 2003-2019 from NCHS were analyzed, focusing on current asthma prevalence among children under 18. Age-adjusted prevalence rates were calculated and stratified by various factors, including gender, age groups, health insurance status, poverty levels, and race/ethnicity. RESULTS: The study revealed substantial disparities in current asthma prevalence. Over the two-decade period, the overall prevalence of current asthma fluctuated. It increased from 2003 (8.5%) to 2009 (9.6%) and then decreased by 2019 (7.0%). Gender disparities were evident, with males (9.9%) consistently reporting a higher prevalence than females (7.5%). Older children aged between 10-17 years (10.4%) consistently had a higher prevalence of asthma than younger children aged 0-4 (5.3%) and 5-9 years (9.5%). Children with Medicaid insurance (11.2%) had the highest prevalence, followed by insured (8.9%), privately insured (7.7%), and uninsured children (6.1%). Children living below the federal poverty level (FPL) consistently reported the highest prevalence (11.3%), while children above 400% of the FPL (7.1%) had the lowest prevalence. Racial disparities were observed, with Black children (14.3%) having higher asthma prevalence, followed by White (7.6%) and Asian children (5.4%). CONCLUSION: The study highlights significant disparities in current asthma prevalence over the two-decade period analyzed. While the overall prevalence showed fluctuations, it generally increased from 2003 to 2009 and then decreased by 2019. Gender disparities were evident, with males consistently reporting a higher prevalence compared to females. Older children in the 10-17 age group consistently had a higher asthma prevalence than younger age groups. Moreover, disparities based on insurance status and income levels were also apparent, with children on Medicaid and those living below the FPL reporting higher asthma prevalence. Racial disparities were observed, with Black children having the highest prevalence, followed by White and Asian children. These findings emphasize the importance of addressing these disparities and tailoring interventions to improve asthma management and prevention across different demographic groups.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 ».