A refill adherence algorithm for multiple short intervals to estimate refill compliance (ReComp).
ABSTRACT There are many measures of refill adherence available, but few have been designed or validated for use with repeated measures designs and short observation periods.
To design a refill-based adherence algorithm suitable for short observation periods, and compare it to 2 reference measures.
A single composite algorithm incorporating information on both medication gaps and oversupply was created. Electronic Veterans Affairs pharmacy data, clinical data, and laboratory data from routine clinical care were used to compare the new measure, ReComp, with standard reference measures of medication gaps (MEDOUT) and adherence or oversupply (MEDSUM) in 3 different repeated measures medication adherence-response analyses. These analyses examined the change in low density lipoprotein (LDL) with simvastatin use, blood pressure with antihypertensive use, and heart rate with beta-blocker use for 30- and 90-day intervals. Measures were compared by regression based correlations (R2 values) and graphical comparisons of average medication adherence-response curves.
In each analysis, ReComp yielded a significantly higher R2 value and more expected adherence-response curve regardless of the length of the observation interval. For the 30-day intervals, the highest correlations were observed in the LDL-simvastatin analysis (ReComp R2 = 0.231; [95% CI, 0.222-0.239]; MEDSUM R2 = 0.054; [95% CI, 0.049-0.059]; MEDOUT R2 = 0.053; [95% CI, 0.048-0.058]).
ReComp is better suited to shorter observation intervals with repeated measures than previously used measures.
[Show abstract] [Hide abstract]
ABSTRACT: Medication adherence is a critical aspect of managing cardiometabolic conditions, including diabetes, hypertension, dyslipidemia, and heart failure. Patients who have multiple cardiometabolic conditions and multiple prescribers may be at increased risk for nonadherence. The purpose of this study was to examine the relationship between number of prescribers, number of conditions, and refill adherence to oral medications to treat cardiometabolic conditions. In this retrospective cohort study, 7933 veterans were identified with 1 to 4 cardiometabolic conditions. Refill adherence to oral medications for diabetes, hypertension, and dyslipidemia was measured using an administrative claims-based continuous multiple-interval gap (CMG) that estimates the percentage of days a patient did not possess medication. We dichotomized refill adherence for each condition as a CMG ≤20% for each year of analysis. Condition-specific logistic regression models estimated the relationship between refill adherence and number of cardiometabolic conditions and number of prescribers, controlling for demographic characteristics, other comorbidities, and a count of cardiometabolic drug classes used. Compared with patients with 1 prescriber, antihypertensive refill adherence was lower in patients seeing ≥4 prescribers (odds ratio [OR] = 0.69; 95% CI = 0.59-0.80), but the number of cardiometabolic conditions was not a significant predictor. Antidyslipidemia refill adherence was lower in patients seeing 3 prescribers (OR = 0.80; 95% CI = 0.70-0.92) or ≥4 prescribers (OR = 0.77; 95% CI = 0.64-0.91). Conversely, antidyslipidemia refill adherence improved with the number of cardiometabolic conditions, but differences were only statistically significant for ≥3 conditions (OR = 1.31; 95% CI = 1.09-1.57). In multivariate regression models, the number of conditions and number of prescribers were not significant predictors of refill adherence in the group of patients with diabetes. Effective management of care and medication regimens for complex patients remains an unresolved challenge, but these results suggest that medication refill adherence might be improved by minimizing the number of prescribers involved in a patient's care, at least for hypertension and dyslipidemia. © The Author(s) 2014.Annals of Pharmacotherapy 12/2014; 49(3). DOI:10.1177/1060028014563266 · 2.92 Impact Factor
[Show abstract] [Hide abstract]
ABSTRACT: Background In the United States, more than 25 million people have diabetes. Medication adherence is known to be important for disease control. However, factors that consistently predict medication adherence are unclear and the literature lacks patient perspectives on how health care systems affect adherence to oral hypoglycemic agents (OHAs). This study explored facilitators and barriers to OHA adherence by obtaining the perspectives of Veterans Affairs (VA) patients with OHA prescriptions.MethodsA total of 45 patients participated in 12 focus groups that explored a wide range of issues that might affect medication adherence. Participants were patients at clinics in Seattle, Washington; San Antonio, Texas; Portland, Oregon; Salem, Oregon, and Warrenton, Oregon.ResultsKey system-level facilitators of OHA adherence included good overall pharmacy service and several specific mechanisms for ordering and delivering medications (automated phone refill service, Web-based prescription ordering), as well as providing pillboxes and printed lists of current medications to patients. Barriers mirrored many of the facilitators. Poor pharmacy service quality and difficulty coordinating multiple prescriptions emerged as key barriers.ConclusionsVA patient focus groups provided insights on how care delivery systems can encourage diabetes medication adherence by minimizing the barriers and enhancing the facilitators at both the patient and system levels. Major system-level factors that facilitated adherence were overall pharmacy service quality, availability of multiple systems for reordering medications, having a person to call when questions arose, counseling about the importance of adherence and providing tools such as pillboxes and updated medication lists.BMC Health Services Research 11/2014; 14(1):533. DOI:10.1186/s12913-014-0533-1 · 1.66 Impact Factor
[Show abstract] [Hide abstract]
ABSTRACT: Background Studies in integrated health systems suggest that patients often accumulate oversupplies of prescribed medications, which is associated with higher costs and hospitalization risk. However, predictors of oversupply are poorly understood, with no studies in Medicare Part D. Objective The aim of this study was to describe prevalence and predictors of oversupply of antidiabetic, antihypertensive, and antihyperlipidemic medications in adults with diabetes managed by a large, multidisciplinary, academic physician group and enrolled in Medicare Part D or a local private health plan. Methods This was a retrospective cohort study. Electronic health record data were linked to medical and pharmacy claims and enrollment data from Medicare and a local private payer for 2006-2008 to construct a patient-quarter dataset for patients managed by the physician group. Patients’ quarterly refill adherence was calculated using ReComp, a continuous, multiple-interval measure of medication acquisition (CMA), and categorized as <0.80 = Undersupply, 0.80-1.20 = Appropriate Supply, >1.20 = Oversupply. We examined associations of baseline and time-varying predisposing, enabling, and medical need factors to quarterly supply using multinomial logistic regression. Results The sample included 2,519 adults with diabetes. Relative to patients with private insurance, higher odds of oversupply were observed in patients aged <65 in Medicare (OR=3.36, 95% CI=1.61-6.99), patients 65+ in Medicare (OR=2.51, 95% CI=1.37-4.60), patients <65 in Medicare/Medicaid (OR=4.55, 95% CI=2.33-8.92), and patients 65+ in Medicare/Medicaid (OR=5.73, 95% CI=2.89-11.33). Other factors associated with higher odds of oversupply included any 90-day refills during the quarter, psychotic disorder diagnosis, and moderate versus tight glycemic control. Conclusions Oversupply was less prevalent than in previous studies of integrated systems, but Medicare Part D enrollees had greater odds of oversupply than privately insured individuals. Future research should examine utilization management practices of Part D versus private health plans that may affect oversupply.Research in Social and Administrative Pharmacy 09/2014; 11(3). DOI:10.1016/j.sapharm.2014.09.002 · 2.35 Impact Factor