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Identifying patients with high pharmacy consumption by using the Adjusted Clinical Groups in a primary care setting

European Neuropsychopharmacology 19:S340-S341.

Badalona Serveis Assistencials SA, Directorate ofPlanning, Badalona. Barcelona, Spain

BACKGROUND: Prescription drug costs are rapidly increasing in most countries. The pharmaceutical expenditure in Catalonia, Spain is 25% of total health cost. Since patient complexity is a major determinant of expenditure, in order to control drug costs more effectively and with equity, methods for case mix adjustment should be considered. The adjusted clinical groups (ACGs) system was developed at the Johns Hopkins University in Baltimore. It estimates individual health status and risk for health service use based on age, gender and diagnoses assigned over a defined time interval, typically one year.

OBJECTIVE: To identify patients with high pharmacy consumption using the Adjusted Clinical Groups-Predictive Model (ACGPM) in a primary care setting.

METHODS: This is a cross-sectional observational study. All the attended patients by five primary care teams along the year 2006 were included. Electronic printed prescriptions are an overestimation ofprescribed drugs that are obtained from the pharmacy store and invoiced to CatSalut. A correction to the patient prescription cost was applied according to the deviation within each center of the prescription cost obtained from electronic files and the real expenditure invoiced. A conversion (mapping) from ICPC-2 codes to ICD-9-CM has been performed. For this process a working team (a documentalist, 2 clinical doctors and 2 consulters) was created. Main collected variables were age, sex, visits, morbidity and total costs for each patient, by adding fixed costs (structural) to variable costs (referrals, diagnostic tests, pharmacy). ACG-PM grouper (version 7.0) provides the following data for each patient: ACG category (n= 106), resource utilization band (RUB), American mean relative weights, probability of pharmacy consumption (pFC) for the next year and specific diseases with greater impact. To assess the model, ANOVA and multiple regression analysis were done; p < 0.05.

RESULTS: 83,873 patients were studied; with an average number of visits 8.0±8.1 and episodes 4.8±3.5 by patient/year 2006; mean age 41.4±23.0, and female sex 52.9%, p=O.OOO. Mean cost of care was 502.47±1,007.03€ and pharmacy prescription amounted to 268.55±603.99€. A high RUB (4-5) was found in 4,319 patients (5.3%). Characteristics of patients (n=386) with the highest PCF (>0.8) for the next year were: pharmacy costs €1,434.11±1,150.72; visits: 25.2±17.l; episodes: l3.0±4.3; age: 70.3±12.8; and diagnostics of ischemic heart disease (n = 1,637; PCF: 17.4), diabetes (n = 5,583; PCF: 10.5) and lipid disorders (n = 8,460; PCF: 12.25), p < 0.001. The explanatory power of the ACG classification system was 30.7% (Ln: 41.2%) for visits, 87.6% (Ln: 87.1%) for episodes and 21.3% (Ln: 39.9%) for pharmacy cost, p 0.001. The cross correlation matrix (Spearman’s rank correlation) is shown in the table below. Correlation matrix: Pharmacy cost with RUB and Visits was 0.472 and 0.507, respectively.

CONCLUSIONS: ACG-PM provides an acceptable estimation to identify patients with high health necessities, who could take benefit from preventive interventions. Health authorities must promote specific actions to improve information systems on the basis of case-mix.