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BACKGROUND: Although atopic diseases and associated co-morbidities are prevalent in children, little is known about racial differences in emergency department (ED) visitation. OBJECTIVE: We sought to examine racial differences in ED visitation among children with allergic comorbidities. METHODS: We conducted a retrospective study of patients (<21 years) who visited the ED at a large pediatric hospital for atopic dermatitis (AD), food allergy (FA), asthma, allergic rhinitis (AR), and eosinophilic esophagitis (EoE) from 2015 to 2019. We determined the probability of ED encounter-free using hazard ratios (HR) and time to recurrence (TTR) of ED encounter for patients identified as Black/African American (AA) and White/European American (EA). We assessed potentially underlying allergic, demographic, and place-based factors, and potential interactions between factors. RESULTS: A total of 30,894 patients (38% AA, 62% EA) had 83,078 ED encounters (38,378 first ED encounters, and 44,700 recurrent ED encounters) during the study period. Asthma and AR showed the highest rate of comorbidity in ED encounters in both AA and EA children. AA children exhibited higher HR for encounter following index AD and asthma encounters. We found an interaction between the type of insurance and race in ED encounters for AD, FA, AR, and EoE. In AA children, those insured by Medicaid demonstrated a higher HR for any encounter compared to those with commercial insurance. Conversely, in EA children, those with Medicaid insurance showed a lower HR compared to their commercially insured peers. Regardless of race, allergic comorbidity increased the HR of ED encounter (1.12-1.62) for all allergic diseases. At 5-years follow up, mean differences in TTR were shorter in AA children compared to EA children in AD, FA, and asthma. CONCLUSION: Identification of disease-specific racial disparities in ED visitation related to atopic diseases is a necessary first step toward the design and implementation of interventions capable of equitably reducing emergency care in atopic comorbid children.
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BACKGROUND: This study addresses the limited research on racial disparities in asthma hospitalization outcomes, specifically length of stay (LOS) and readmission, across the U.S. METHODS: We analyzed in-patient and emergency department visits from the All of Us Research Program, identifying various risk factors (demographic, comorbid, temporal, and place-based) associated with asthma LOS and 30-day readmission using Bayesian mixed-effects models. RESULTS: Of 17,233 patients (48.0% White, 30.7% Black, 19.7% Hispanic/Latino, 1.3% Asian, and 0.3% Middle Eastern and North African) with 82,188 asthma visits, Black participants had 20% shorter LOS and 12% higher odds of readmission, compared to White participants in multivariate analyses. Public-insured patients had 14% longer LOS and 39% higher readmission odds than commercially insured patients. Weekend admissions resulted in a 12% shorter LOS but 10% higher readmission odds. Asthmatics with chronic diseases had a longer LOS (range: 6-39%) and higher readmission odds (range: 9-32%) except for those with allergic rhinitis, who had a 23% shorter LOS. CONCLUSIONS: A comprehensive understanding of the factors influencing asthma hospitalization, in conjunction with diverse datasets and clinical-community partnerships, can help physicians and policymakers to systematically address racial disparities, healthcare utilization and equitable outcomes in asthma care.
Assuntos
Asma , Saúde da População , Fatores Raciais , Humanos , Asma/terapia , Teorema de Bayes , Tempo de Internação , Readmissão do Paciente , Estudos Retrospectivos , Estados Unidos/epidemiologiaRESUMO
En este artículo se presenta un software de código abierto, llamado CreaTools, cuyo principal objetivo es el procesar y facilitar la visualización de imágenes médicas. Este software flexible funciona en diferentes sistemas operativos (Linux, Mac OS X, Windows), se desarrolla en el lenguaje de programación C++ para asegurar una fácil integración de módulos C++ y proporciona a los usuarios herramientas computacionales para construir interfaces gráficas de usuario (GUI), incluidos los datos de entrada/salida (manejo de archivos), la visualización, la interacción y el procesamiento de datos. Este artículo muestra también la utilidad de CreaTools mediante un proyecto de investigación que consiste en la detección automática de lesiones arteriales. Los algoritmos desarrollados han sido implementados en una interfaz gráfica amigable con visualización 3D e interacción. Ejemplos de tales algoritmos incluyen la extracción de ejes de arterias y la generación de modelos descriptivos de arterias con lesiones y sin lesiones.