The use of end-quintile comparisons to identify under-servicing of the poor and over-servicing of the rich: a longitudinal study describing the effect of socioeconomic status on healthcare.
BACKGROUND
To demonstrate the use of end-quintile comparisons in assessing the effect of socio-economic status on hospital utilisation and outcomes in Western Australia.


METHODS
Hospital morbidity records were extracted from the WA Data Linkage System for the period 1994-99, with follow-up to the end of 2000. Multivariate modelling was used to estimate the effect of socio-economic status on hospital admission rates, average and total length of stay (LOS), cumulative incidence of readmission at 30 days and one year, and case fatality at one year.


RESULTS
The study demonstrated higher rate ratios of hospital admission in the more disadvantaged quintiles: rate ratios were 1.31 (95% CI 1.25-1.37) and 1.32 (1.26-1.38) in the first quintile (most disadvantaged) and the second quintile respectively, compared with the fifth quintile (most advantaged). There was a longer total LOS in the most disadvantaged quintile compared with quintile 5 (LOS ratio 1.24; 1.23-1.26). The risk of readmission at 30 days and one year and the risk of death at one year were also greater in those with greater disadvantage: the hazard ratios for quintiles 1:quintile 5 were 1.07 (1.05-1.09), 1.17 (1.16-1.18) and 1.10 (1.07-1.13) respectively. In contradiction to the trends towards higher hospital utilisation and poorer outcomes with increasing social disadvantage, in some MDC's the rate ratio of quintile 1:quintile 2 was less than 1, and quintile 4:quintile 5 was greater than 1. For all surgical admissions the most disadvantaged had a significantly lower admission rate than the second quintile.


CONCLUSIONS
This study has shown that the disadvantaged within Western Australia are more intensive users of hospital services but their outcomes following hospitalisation are worse, consistent with their health status. Instances of overuse in the least disadvantaged and under use in the most disadvantaged have also been identified.
Brameld
Kate J
KJ
Centre for Health Services Research, School of Population Health, The University of Western Australia, Crawley, Western Australia, 6009. kate@sph.uwa.edu.au
Holman
C D'Arcy J
CD
D000293:Adolescent; D000328:Adult; D000368:Aged; D002648:Child; D002675:Child, Preschool; D005260:Female; D006300:Health Services Misuse / Q000191:economics / Q000706:statistics & numerical data*; D006301:Health Services Needs and Demand / Q000706:statistics & numerical data; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D010343:Patient Admission / Q000706:statistics & numerical data; D016012:Poisson Distribution; D011203:Poverty; D012923:Social Class; D016896:Treatment Outcome; D035862:Vulnerable Populations / Q000706:statistics & numerical data*; D014904:Western Australia