BACKGROUND: Although many patient, physician, and payment predictors of adherence have been described, knowledge of their relative strength and overall ability to explain adherence is limited. OBJECTIVES: To measure the contributions of patient, physician, and payment predictors in explaining adherence to statins. RESEARCH DESIGN: Retrospective cohort study using administrative data. SUBJECTS: A total of 14,257 patients insured by Horizon Blue Cross Blue Shield of New Jersey who were newly prescribed a statin cholesterol-lowering medication. MEASURES: Adherence to statin medication was measured during the year after the initial prescription, based on proportion of days covered. The impact of patient, physician, and payment predictors of adherence were evaluated using multivariate logistic regression. The explanatory power of these models was evaluated with C statistics, a measure of the goodness of fit. RESULTS: Overall, 36.4% of patients were fully adherent. Older patient age, male gender, lower neighborhood percent black composition, higher median income, and fewer number of emergency department visits were significant patient predictors of adherence. Having a statin prescribed by a cardiologist, a patient's primary care physician, or a US medical graduate were significant physician predictors of adherence. Lower copayments also predicted adherence. All of our models had low explanatory power. Multivariate models including patient covariates only had greater explanatory power (C = 0.613) than models with physician variables only (C = 0.566) or copayments only (C = 0.543). A fully specified model had only slightly more explanatory power (C = 0.633) than the model with patient characteristics alone. CONCLUSIONS: Despite relatively comprehensive claims data on patients, physicians, and out-of-pocket costs, our overall ability to explain adherence remains poor. Administrative data likely do not capture many complex mechanisms underlying adherence.
BACKGROUND: Although many patient, physician, and payment predictors of adherence have been described, knowledge of their relative strength and overall ability to explain adherence is limited. OBJECTIVES: To measure the contributions of patient, physician, and payment predictors in explaining adherence to statins. RESEARCH DESIGN: Retrospective cohort study using administrative data. SUBJECTS: A total of 14,257 patients insured by Horizon Blue Cross Blue Shield of New Jersey who were newly prescribed a statin cholesterol-lowering medication. MEASURES: Adherence to statin medication was measured during the year after the initial prescription, based on proportion of days covered. The impact of patient, physician, and payment predictors of adherence were evaluated using multivariate logistic regression. The explanatory power of these models was evaluated with C statistics, a measure of the goodness of fit. RESULTS: Overall, 36.4% of patients were fully adherent. Older patient age, male gender, lower neighborhood percent black composition, higher median income, and fewer number of emergency department visits were significant patient predictors of adherence. Having a statin prescribed by a cardiologist, a patient's primary care physician, or a US medical graduate were significant physician predictors of adherence. Lower copayments also predicted adherence. All of our models had low explanatory power. Multivariate models including patient covariates only had greater explanatory power (C = 0.613) than models with physician variables only (C = 0.566) or copayments only (C = 0.543). A fully specified model had only slightly more explanatory power (C = 0.633) than the model with patient characteristics alone. CONCLUSIONS: Despite relatively comprehensive claims data on patients, physicians, and out-of-pocket costs, our overall ability to explain adherence remains poor. Administrative data likely do not capture many complex mechanisms underlying adherence.
Authors: Jessica M Franklin; William H Shrank; Joyce Lii; Alexis K Krumme; Olga S Matlin; Troyen A Brennan; Niteesh K Choudhry Journal: Health Serv Res Date: 2015-04-16 Impact factor: 3.402