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papers

Distance to hospital and socioeconomic status influence secondary health care use

Published: June 1, 2013
Category: Bibliography > Papers
Authors: Borgquist L, Halling A, Zielinski A
Countries: Sweden
Language: null
Types: Care Management
Settings: Hospital

Scand J Prim Health Care 31:83-88.

Blekinge Centre of Competence, Karlskrona, Sweden

Objective. The aim of this study was to investigate how distance to hospital and socioeconomic status (SES) influence the use of secondary health care (SHC) when taking comorbidity into account.

Design and setting. A register-based study in Östergötland County. Subjects. The adult population of Östergötland County. Main outcome measures. Odds of SHC use in the population and rates of SHC use by patients were studied after taking into account comorbidity level assigned using the Adjusted Clinical Groups (ACG) Case-Mix System. The baseline for analysis of SES was individuals with the lowest education level (level 1) and the lowest income (1st quartile).

Results. The study showed both positive and negative association between SES and use of SHC. The risk of incurring SHC costs was 12% higher for individuals with education level 1. Individuals with income in the 2nd quartile had a 4% higher risk of incurring SHC costs but a 17% lower risk of emergency department visits. Individuals with income in the 4th quartile had 9% lower risk of hospitalization. The risk of using SHC services for the population was not associated with distance to hospital. Patients living over 40 km from hospital and patients with higher SES had lower use of SHC services.

Conclusions. It was found that distance to hospital and SES influence SHC use after adjusting for comorbidity level, age, and gender. These results suggest that GPs and health care managers should pay a higher degree of attention to this when planning primary care services in order to minimize the potentially redundant use of SHC.

PMID: 23301541

Population Markers,Age,Gender,Co-morbidity,Sweden,Hospitalization/statistics & numerical data,Middle Aged,Regression Analysis,Young Adult

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