Emma J Aarnio1, Janne A Martikainen2, Arja Helin-Salmivaara3, Risto K Huupponen4, Juha E K Hartikainen5, Piia K Peura6, Maarit Jaana Korhonen7. 1. Department of Clinical Pharmacology, Tykslab, Turku University Hospital, Turku, Finland; School of Pharmacy, University of Eastern Finland, PO Box 1627, 70211 Kuopio, Finland. Electronic address: emma.aarnio@uef.fi. 2. School of Pharmacy, University of Eastern Finland, PO Box 1627, 70211 Kuopio, Finland. 3. Department of Pharmacology, Drug Development and Therapeutics, University of Turku, Turku, Finland; Unit of Primary Health Care, Hospital District of Helsinki and Uusimaa, Helsinki, Finland. 4. Department of Clinical Pharmacology, Tykslab, Turku University Hospital, Turku, Finland; Department of Pharmacology, Drug Development and Therapeutics, University of Turku, Turku, Finland. 5. Heart Center, Kuopio University Hospital, Kuopio, Finland; School of Medicine, University of Eastern Finland, Kuopio, Finland. 6. Finnish Medicines Agency, Kuopio, Finland. 7. Department of Pharmacology, Drug Development and Therapeutics, University of Turku, Turku, Finland; Department of Public Health, University of Turku, Turku, Finland.
Abstract
BACKGROUND: Although register-based studies on statin adherence are increasing, for administrative data, little is known about the explanatory power of the predictors that explain adherence. OBJECTIVE: The aim was to explore the ability of variables in administrative data to predict statin adherence in an unselected, universally insured population and, especially, to explore dispensation delay (time elapsed between prescription and dispensation) and out-of-pocket costs as explanatory factors. METHODS: Statin initiators who were aged 45 to 75 years in 2000-2004 (n = 247, 051) were identified in the Finnish Prescription Register. First-year statin adherence was measured as the proportion of days covered (PDC). The effect of variables related to patient, health care, and payment was assessed with multivariable logistic regression. The C statistic was used to evaluate the explanatory power of different models. RESULTS: Overall, 54.6% of the cohort had good adherence (PDC ≥ 80%). The explanatory power of all the models was low (C = 0.666 for the full model). The multivariable models, including only payment variables, had a greater explanatory power (C = 0.627) than models with only patient (C = 0.602) or health care (C = 0.548) variables. A shorter dispensation delay and lower out-of-pocket costs predicted better adherence. Of other patient-related variables, age, presence of acute coronary syndrome, and use of cardiovascular medications were significant predictors of adherence. Type of statin and the prescriber's workplace were also significantly associated with adherence. CONCLUSIONS: Models based on administrative data do not provide useful prediction of statin adherence. Of the individual predictors, long dispensation delay may serve as a practical tool for identifying patients at risk of poor adherence. Increases in out-of-pocket costs predict nonadherence.
BACKGROUND: Although register-based studies on statin adherence are increasing, for administrative data, little is known about the explanatory power of the predictors that explain adherence. OBJECTIVE: The aim was to explore the ability of variables in administrative data to predict statin adherence in an unselected, universally insured population and, especially, to explore dispensation delay (time elapsed between prescription and dispensation) and out-of-pocket costs as explanatory factors. METHODS: Statin initiators who were aged 45 to 75 years in 2000-2004 (n = 247, 051) were identified in the Finnish Prescription Register. First-year statin adherence was measured as the proportion of days covered (PDC). The effect of variables related to patient, health care, and payment was assessed with multivariable logistic regression. The C statistic was used to evaluate the explanatory power of different models. RESULTS: Overall, 54.6% of the cohort had good adherence (PDC ≥ 80%). The explanatory power of all the models was low (C = 0.666 for the full model). The multivariable models, including only payment variables, had a greater explanatory power (C = 0.627) than models with only patient (C = 0.602) or health care (C = 0.548) variables. A shorter dispensation delay and lower out-of-pocket costs predicted better adherence. Of other patient-related variables, age, presence of acute coronary syndrome, and use of cardiovascular medications were significant predictors of adherence. Type of statin and the prescriber's workplace were also significantly associated with adherence. CONCLUSIONS: Models based on administrative data do not provide useful prediction of statin adherence. Of the individual predictors, long dispensation delay may serve as a practical tool for identifying patients at risk of poor adherence. Increases in out-of-pocket costs predict nonadherence.
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