N M Vink1, O H Klungel, R P Stolk, P Denig. 1. Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Abstract
BACKGROUND: Several measures using prescription data have been developed for estimating medication refill adherence. Few studies have made direct comparisons, and little is known about the accuracy of these measures in patients on a multiple-drug regimen. PURPOSE: To compare different calculation methods using prescription data for assessing refill adherence. METHOD: An observational cohort study among type 2 diabetes patients was conducted. Adherence to oral glucose-lowering, antihypertensive and lipid-lowering medication was assessed for 2004. We calculated medication possession ratios in a flexible period (MPRF), per calendar year (MPRY) and gaps between refills (GAP) at drug class and therapeutic level. Individual review of drug prescription profiles was conducted to validate identified cases of suboptimal refill adherence. Differences in Area Under the Curve (AUC) of ROC-curves were calculated to compare the methods. RESULTS: Of the 3877 patients, 2969 (77%) patients received oral glucose-lowering medication, 2715 (70%) antihypertensives and 1797 (46%) lipid-lowering medication. Using cutoffs for MPR < 80% and GAP > 30 days, overall rates of suboptimal adherence for these drug classes were 32, 35 and 23% respectively. AUC for measures calculated at drug class level (range 0.85-0.90) were significantly larger than those calculated at therapeutic level (0.72-0.90). For oral glucose-regulating medication and antihypertensives, AUCs were largest for the MPRY and GAP measures (0.87-0.88). For lipid-lowering medication, the AUC was largest for the GAP measure (0.90). CONCLUSIONS: Differences between adherence measures were small and favoured calculation on drug class level. For multiple drug use, both MPRY and GAP were good measures for identifying suboptimal refill adherence.
BACKGROUND: Several measures using prescription data have been developed for estimating medication refill adherence. Few studies have made direct comparisons, and little is known about the accuracy of these measures in patients on a multiple-drug regimen. PURPOSE: To compare different calculation methods using prescription data for assessing refill adherence. METHOD: An observational cohort study among type 2 diabetespatients was conducted. Adherence to oral glucose-lowering, antihypertensive and lipid-lowering medication was assessed for 2004. We calculated medication possession ratios in a flexible period (MPRF), per calendar year (MPRY) and gaps between refills (GAP) at drug class and therapeutic level. Individual review of drug prescription profiles was conducted to validate identified cases of suboptimal refill adherence. Differences in Area Under the Curve (AUC) of ROC-curves were calculated to compare the methods. RESULTS: Of the 3877 patients, 2969 (77%) patients received oral glucose-lowering medication, 2715 (70%) antihypertensives and 1797 (46%) lipid-lowering medication. Using cutoffs for MPR < 80% and GAP > 30 days, overall rates of suboptimal adherence for these drug classes were 32, 35 and 23% respectively. AUC for measures calculated at drug class level (range 0.85-0.90) were significantly larger than those calculated at therapeutic level (0.72-0.90). For oral glucose-regulating medication and antihypertensives, AUCs were largest for the MPRY and GAP measures (0.87-0.88). For lipid-lowering medication, the AUC was largest for the GAP measure (0.90). CONCLUSIONS: Differences between adherence measures were small and favoured calculation on drug class level. For multiple drug use, both MPRY and GAP were good measures for identifying suboptimal refill adherence.
Authors: Michael J Stirratt; Jacqueline Dunbar-Jacob; Heidi M Crane; Jane M Simoni; Susan Czajkowski; Marisa E Hilliard; James E Aikens; Christine M Hunter; Dawn I Velligan; Kristen Huntley; Gbenga Ogedegbe; Cynthia S Rand; Eleanor Schron; Wendy J Nilsen Journal: Transl Behav Med Date: 2015-07-09 Impact factor: 3.046
Authors: Tomi Romppainen; Maria Rikala; Emma Aarnio; Maarit Jaana Korhonen; Leena K Saastamoinen; Risto Huupponen Journal: Eur J Clin Pharmacol Date: 2014-08-22 Impact factor: 2.953
Authors: Michael J Stirratt; Jeffrey R Curtis; Maria I Danila; Richard Hansen; Michael J Miller; C Ann Gakumo Journal: J Gen Intern Med Date: 2018-02 Impact factor: 5.128
Authors: Bruce Stuart; Amy Davidoff; Ruth Lopert; Thomas Shaffer; J Samantha Shoemaker; Jennifer Lloyd Journal: Health Serv Res Date: 2011-03-17 Impact factor: 3.402