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PhD Scholar, Department of Pharmacy Practice, Victorian College of Pharmacy, Monash University, Parkville, Victoria, Australia
PhD Scholar, Department of Pharmacy Practice, Victorian College of Pharmacy, Monash University
Biostatistician, Department of Epidemiology and Preventive Medicine, Faculty of Medicine, Monash University
Lecturer and Director of International Development, Department of Pharmacy Practice, Victorian College of Pharmacy, Monash University; Senior Pharmacist, Pharmacy Department, The Alfred Hospital, Commercial Road, Melbourne, VIC, Australia
Senior Lecturer, Department of Pharmacy Practice, Victorian College of Pharmacy, Monash University
Senior Lecturer, Department of Pharmacy Practice, Victorian College of Pharmacy, Monash University
Reprints: Dr. Stewart, Department of Pharmacy Practice, Victorian College of Pharmacy, Monash University, 381 Royal Parade, Parkville, VIC 3052, Australia, fax 61-3-9903 9629, Kay.Stewart{at}vcp.monash.edu.au
BACKGROUND: Medication lists offer an alternative source of data on comorbidities and disease burden.
OBJECTIVE: To develop and validate the Medication-Based Disease Burden Index (MDBI).
METHODS: A list of medications corresponding to the leading causes of global death was pilot tested and finalized by an expert panel. The resulting index was tested on drug regimens of patients at risk of medication misadventure. Criterion validity of the index was established against Charlson's index and Chronic Disease Score (CDS). Sensitivity, specificity, predictive validity, convergent and discriminant validity, and interrater and test-retest reliabilities of the index were also assessed.
RESULTS: The MDBI consisting of specific medications for 20 chronic medical conditions and corresponding disability weightings was developed. The MDBI was tested on 317 patients with mean ± SD Charlson's index scores of 2.8 ± 2.2 and CDS scores of 7.3 ± 2.8. Mean MDBI scores (0.33 ± 0.28) demonstrated significant correlations with Charlson's index scores (r = 0.31; p < 0.001) and CDS (r = 0.53; p < 0.001). MDBI had satisfactory sensitivity and high specificity. Age of the patients and number of medications had significant correlation with the MDBI scores, but the MDBI scores were not significantly different in males and females. MDBI scores could successfully predict death and planned or unplanned readmissions (OR = 4.7, 95% CI 1.4 to 15.5; p = 0.01). MDBI demonstrated high inter-rater (intraclass correlation coefficient [ICC] = 0.99) and test-retest reliabilities (ICC = 0.98).
CONCLUSIONS: Initial testing suggests that MDBI could offer an alternative low-cost and convenient method for quantifying disease burden and predicting health outcomes.
Key Words: comorbidity, medication-based disease burden index
Published Online, March 28, 2006. www.theannals.com, DOI 10.1345/aph.1G204
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