The importance of comorbidity in analysing patient costs in Swedish primary care
1 Ryd primary health care centre, Linköping, Sweden
2 General Practice, Department of Health and Society, Faculty of Health Sciences, Linköping University, Sweden
3 The Neurotec Department, Center for Family and Community Medicine, Karolinska Institutet, Stockholm, Sweden
4 Ödeshög primary health care centre, Ödeshög, Sweden
BMC Public Health 2006, 6:36 doi:10.1186/1471-2458-6-36Published: 16 February 2006
The objective was to explore the usefulness of the morbidity risk adjustment system Adjusted Clinical Groups® (ACG), in comparison with age and gender, in explaining and estimating patient costs on an individual level in Swedish primary health care. Data were retrieved from two primary health care centres in southeastern Sweden.
A cross-sectional observational study. Data from electronic patient registers from the two centres were retrieved for 2001 and 2002, and patients were grouped into ACGs, expressing the individual combination of diagnoses and thus the comorbidity. Costs per patient were calculated for both years in both centres. Cost data from one centre were used to create ACG weights. These weights were then applied to patients at the other centre. Correlations between individual patient costs, age, gender and ACG weights were studied. Multiple linear regression analyses were performed in order to explain and estimate patient costs.
The variation in individual patient costs was substantial within age groups as well as within ACG weight groups. About 37.7% of the individual patient costs could be explained by ACG weights, and age and gender added about 0.8%. The individual patient costs in 2001 estimated 22.0% of patient costs in 2002, whereas ACG weights estimated 14.3%.
ACGs was an important factor in explaining and estimating individual patient costs in primary health care. Costs were explained to only a minor extent by age and gender. However, the usefulness of the ACG system appears to be sensitive to the accuracy of classification and coding of diagnoses by physicians.