Papers

Risk adjustment for hospital use using social security data: cross sectional small area analysis

Roy A Carr-Hill, professor, ^a James Q Jamison, director, ^b Dermot O'Reilly, deputy director, ^b Michael R Stevenson, statistician, ^b James Reid, epidemiologist, ^c Barry Merriman, research associate. ^b

^a Centre for Health Economics, University of York, York YO10 5DD, ^b Health and Social Care Research Unit, Queen's University Belfast, Institute of Clinical Science, Belfast BT12 6BJ, ^c Northern Ireland Cancer Registry, Queen's University Belfast

Correspondence to: J Q Jamison, Centre for Social Research, Queen's University Belfast, Belfast BT7 1NN j.jamison{at}qub.ac.uk

Objectives: To identify demographic and socioeconomic determinants of need for acute hospital treatment at small area level. To establish whether there is a relation between poverty and use of inpatient services. To devise a risk adjustment formula for distributing public funds for hospital services using, as far as possible, variables that can be updated between censuses.
Design: Cross sectional analysis. Spatial interactive modelling was used to quantify the proximity of the population to health service facilities. Two stage weighted least squares regression was used to model use against supply of hospital and community services and a wide range of potential needs drivers including health, socioeconomic census variables, uptake of income support and family credit, and religious denomination.
Setting: Northern Ireland.
Main outcome measure: Intensity of use of inpatient services.
Results: After endogeneity of supply and use was taken into account, a statistical model was produced that predicted use based on five variables: income support, family credit, elderly people living alone, all ages standardised mortality ratio, and low birth weight. The main effect of the formula produced is to move resources from urban to rural areas.
Conclusions: This work has produced a population risk adjustment formula for acute hospital treatment in which four of the five variables can be updated annually rather than relying on census derived data. Inclusion of the social security data makes a substantial difference to the model and to the results produced by the formula.