Background: Individualbased measures for comorbidity are of increasing importance for planning and funding health care services. No measurement for individualbased healthcare costs exist in Sweden. The aim of this study was to validate the Johns Hopkins ACG Case-Mix System's predictive value of polypharmacy (regular use of 4 or more prescription medicines) used as a proxy for health care costs in an elderly population and to study if the prediction could be improved by adding variables from a population based study i.e. level of education, functional status indicators and health perception.
Methods: The Johns Hopkins ACG Case-Mix System was applied to primary health care diagnoses of 1402 participants (60-96 years) in a cross-sectional community based study in Karlskrona, Sweden (the Swedish National study on Ageing and Care) during a period of two years before they took part in the study. The predictive value of the Johns Hopkins ACG Case-Mix System was modeled against the regular use of 4 or more prescription medicines, also using age, sex, level of education, instrumental activity of daily living- and measures of health perception as covariates.
Results: In an exploratory biplot analysis the Johns Hopkins ACG Case-Mix System, was shown to explain a large part of the variance for regular use of 4 or more prescription medicines. The sensitivity of the prediction was 31.9%, whereas the specificity was 88.5%, when the Johns Hopkins ACG Case-Mix System was adjusted for age. By adding covariates to the model the sensitivity was increased to 46.3%, with a specificity of 90.1%. This increased the number of correctly classified by 5.6% and the area under the curve by 11.1%.
Conclusion: The Johns Hopkins ACG Case-Mix System is an important factor in measuring comorbidity, however it does not reflect an individual's capability to function despite a disease burden, which has importance for prediction of comorbidity. In this study we have shown that information on such factors, which can be obtained from short questionnaires increases the probability to correctly predict an individual's use of resources, such as medications.