Adherence to Glaucoma Medications Over 12 Months in Two US Community Pharmacy Chains.

UNLABELLED: This study determined the degree of adherence to medications for glaucoma among patients refilling prescriptions in community pharmacies. METHODS: Data abstracted from the dispensing records for 3615 adult patients (18 years or older, predominantly over 45) receiving glaucoma medications from two retail pharmacy chains (64 stores in total) were analyzed. From a 24-month historic data capture period, the 12-month levels of adherence were determined using standard metrics, the proportion of days covered (PDC) and the medication possession ratio (MPR). The overall 12-month mean PDC was only 57%, and the mean MPR was 71%. Using a criterion by which 80% coverage was considered satisfactory adherence, only 30% had satisfactory overall 12-month PDC coverage, and only 37% had satisfactory overall 12-month MPR coverage. Refill adherence increased with age and was highest in the 65-and-older age group (p < 0.001). Differential adherence was found across medication classes, with the highest satisfactory coverage seen for those taking alpha2-adrenergic agonists (PDC = 36.0%; MPR = 47.6%) down to those taking direct cholinergic agonists (PDC = 25.0%; MPR = 31.2%) and combination products (PDC = 22.7%; MPR = 31.0%). Adherence to glaucoma medications in the community setting, as measured by pharmacy refill data, is very poor and represents a critical target for intervention. Community pharmacists are well positioned to monitor and reinforce adherence in this population.

Random forests for classification in ecology.

Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature.