110 An exploration of 11 years of seasonality in diagnoses at GOSH. (15th December 2021)

• Record Type: Journal Article
• Authors: Bowyer, Stuart A; Bryant, William A; Booth, John; Briggs, Lydia; Key, Daniel; Shah, Mohsin; Spiridou, Anastassia; Sebire, Neil J
• Is Part Of: Archives of disease in childhood. Volume 106(2021)Supplement 3
• Page Start: A41
• Page End: A41
• Publication Date: 2021-12-15
• Subjects: Children -- Diseases -- Periodicals; Infants -- Diseases -- Periodicals; 618.920005
• DOI: 10.1136/archdischild-2021-gosh.110
• Languages: English
• ISSNs: 0003-9888

Abstract

Abstract : Background: With the extensive impact of the COVID-19 pandemic and subsequent government interventions on the development, diagnosis and treatment of illnesses, building an understanding of 'typical' diagnosis trends at GOSH is critical for predicting future demands and potential clinical challenges. Seasonality analysis is an effective method with which one can explore, model and predict the occurrence of events over time when – as with many common diagnoses at GOSH – they generally exhibit a periodic trend over the year. Methods: To investigate diagnosis seasonality at GOSH, we have extracted all diagnoses recorded in the Legacy and Epic systems, since the year 2010. We have developed an analytics pipeline that uses these data to compute historical rates for any given diagnosis, or group of diagnoses. Based on these diagnosis rates, our pipeline applies a widely used regressive, multiplicative, seasonal decomposition model with integrated model evaluation. Results: For the analysis, a total of 3, 480, 887 diagnosis events were considered across 29, 529 patients between receiving a diagnosis between 1 st January 2010 and 30 th September 2021. This exploration presents data on many of the common diagnoses at GOSH that exhibit a clear seasonal trend in combination with a statistically significant deviation from that trend since March 2020, likely due to the pandemic. In addition, we illustrate how the available data and model allow us to predict the diagnostic shortfall during the same period.