The Dynamics of Patient Visits to a Public Hospital Pediatric Emergency Department: A Time-Series Model. Issue 1 (January 2022)

• Record Type: Journal Article
• Authors: Almeida, Helena Seabra; Sousa, Margarida; Mascarenhas, Inês; Russo, Ana; Barrento, Manuel; Mendes, Manuel; Nogueira, Paulo; Trigo, Ricardo
• Is Part Of: Pediatric emergency care. Volume 38:Issue 1(2022)
• Publication Date: 2022-01
• Subjects: time-series model -- forecast -- humidex -- school calendar; Pediatric emergencies -- Periodicals; 618.92002505
• DOI: 10.1097/PEC.0000000000002235
• Languages: English
• ISSNs: 0749-5161

Abstract

Abstract : Background: The overcrowding of emergency departments (EDs) is an increasingly relevant public health problem. The main aims of this study were to identify and analyze temporal periodicities of a self-referred pediatric ED (PED), correlate them with meteorological and calendar variables and build a robust forecasting model. Methods: An 8-year administrative data set (2010–2017) of the daily number of admissions to the PED of a public hospital in Lisbon, Portugal, was used (n = 670, 379). A time-series model of the daily number of visits was built, including temporal periodicities, the Portuguese school calendar, and a meteorological comfort index (humidex). Results: Several temporal cycles were identified: 1 year (peak in January/February related to respiratory infections in younger children and infants), 6 months (peaks in May and October with an increase in the admissions of older children and adolescents with trauma, gastrointestinal infections and atopic symptoms), 4 months (related to annual school vacations), 1 week (lower admission values on Saturday), and half a week (low from Friday to Monday morning). School calendar and humidex were significantly correlated with daily admissions. The model yielded a mean absolute percentage error of 10.7% ± 1.10% when cross-validation was performed for the full data set. Conclusion: Although PED visits are multifactorial, they may be predicted and explained by a relatively small number of variables. Such a model may be easily reproduced in different settings and represents a relevant tool to improve quality in EDs through correctly adapting human resources to ED demand. Abstract : Supplemental digital content is available in the text.