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. 2015 Oct;65(639):e649-54.
doi: 10.3399/bjgp15X686893.

Socioeconomic deprivation and accident and emergency attendances: cross-sectional analysis of general practices in England

Rachel Scantlebury  1 , Gillian Rowlands  2 , Stevo Durbaba  1 , Peter Schofield  1 , Kalwant Sidhu  1 , Mark Ashworth  1
Affiliations

Socioeconomic deprivation and accident and emergency attendances: cross-sectional analysis of general practices in England

Rachel Scantlebury et al. Br J Gen Pract. 2015 Oct.

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.

Keywords: accident and emergency department; general practice; primary health care; socioeconomic factors.

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Figures

Figure 1.
Figure 1.
Distribution of final dataset general practice A&E attendance rates per 1000 registered population.
See this image and copyright information in PMC

References

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