1502 Geospatial visualisation of emergency department attendance rates and their associations with deprivation and non-urgent attendances. Issue 12 (22nd November 2022)

• Record Type: Journal Article
• Authors: Sutton-Klein, Joanna; Lewis, Jen; Shephard, Neil; Campbell, Ric; Stone, Tony; O'Keeffe, Colin; Mason, Suzanne
• Is Part Of: Emergency medicine journal. Volume 39:Issue 12(2022)
• Page Start: A965
• Page End: A966
• Publication Date: 2022-11-22
• Subjects: Emergency medicine -- Periodicals; 616.02505
• DOI: 10.1136/emermed-2022-RCEM2.6
• Languages: English
• ISSNs: 1472-0205

Abstract

Abstract : Aims, Objectives and Background: Attendances at emergency departments in England continue to increase above the capacity of the urgent and emergency care system. There is significant variability in the rates of attendance at emergency departments across different localities. The aim of this study is to model the association of deprivation and non-urgent attendances with locality-based emergency department attendance rates. The secondary aim is to create an interactive data visualisation tool to engage stakeholders, clinicians, and the public with the research. Method and Design: We undertook a retrospective, observational study using routinely collected emergency department attendance data from Yorkshire and the Humber (population 5.4 million) between January 2013 and March 2017. We calculated average annual age and sex standardised attendance rates at emergency departments for small localities known as lower layer super output areas. The association between emergency department attendance rates, deprivation and non-urgent attendances was examined using multivariable linear and logistic regression models, which were adjusted for travel time to the nearest emergency department. The data was visualised to create an interactive choropleth map using R. Results and Conclusion: The analytical sample included 6, 389, 383 attendances across 2, 880 localities, with a median age and sex standardised annual emergency department attendance rate of 308 per 1000 population (interquartile range 130). The fully adjusted linear regression model was significant (Adjusted R2 = 0.648, F (7, 2872)=757, p<0.001 ). Higher locality-based emergency department attendance rates were significantly predicted by each increasing decile of deprivation (β =14.8, p=0.002), each minute less of travel time to the emergency department (β = 7.86, p<0.001) and each percent greater proportion of low acuity attendances (β = 8.61, p<0.001). A large proportion of the variability in emergency department attendance rates in different geographical areas can be explained by deprivation levels and proportion of non-urgent attendances. This provides an opportunity for targeted interventions to reduce emergency department attendances.