Citation

Scantlebury R, Rowlands G, Durbaba S, Schofield P, Sidhu K, Ashworth M. Br. J. Gen. Pract. 2015; 65(639): e649-54.

Affiliation

Department of Primary Care and Public Health Sciences, King's College London, London.

Copyright

(Copyright © 2015, Royal College of General Practitioners)

DOI

10.3399/bjgp15X686893

PMID

26412841

Abstract

BACKGROUND: Demand for England's accident and emergency (A&E) services is increasing and is particularly concentrated in areas of high deprivation. The extent to which primary care services, relative to population characteristics, can impact on A&E is not fully understood.

AIM: To conduct a detailed analysis to identify population and primary care characteristics associated with A&E attendance rates, particularly those that may be amenable to change by primary care services. DESIGN AND SETTING: This study used a cross-sectional population-based design. The setting was general practices in England, in the year 2011-2012.

METHOD: Multivariate linear regression analysis was used to create a model to explain the variability in practice A&E attendance rates. Predictor variables included population demographics, practice characteristics, and measures of patient experiences of primary care.

RESULTS: The strongest predictor of general practice A&E attendance rates was social deprivation: the Index of Multiple Deprivation (IMD-2010) (β = 0.3. B = 1.4 [95% CI =1.3 to 1.6]), followed by population morbidity (GPPS responders reporting a long-standing health condition) (β = 0.2, B = 231.5 [95% CI = 202.1 to 260.8]), and knowledge of how to contact an out-of-hours GP (GPPS question 36) (β = -0.2, B = -128.7 [95% CI =149.3 to -108.2]). Other significant predictors included the practice list size (β = -0.1, B = -0.002 [95% CI = -0.003 to -0.002]) and the proportion of patients aged 0-4 years (β = 0.1, B = 547.3 [95% CI = 418.6 to 676.0]). The final model explained 34.4% of the variation in A&E attendance rates, mostly due to factors that could not be modified by primary care services.

CONCLUSION: Demographic characteristics were the strongest predictors of A&E attendance rates. Primary care variables that may be amenable to change only made a small contribution to higher A&E attendance rates.

Language: en