A Refill Adherence Algorithm for Multiple Short Intervals to Estimate Refill Compliance (ReComp)

Health Services Research and Development Northwest Center of Excellence, Seattle, Washington 98101, USA.
Medical Care (Impact Factor: 3.23). 07/2007; 45(6):497-504. DOI: 10.1097/MLR.0b013e3180329368
Source: PubMed


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.

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    • "A sensitivity analysis was performed on patients who were most adherent to diabetes medications. Adherence was based on medication possession ratios which were calculated as Refill Compliance (ReComp) scores for the six months prior to the outcome period [26]. Patients were only included in the sensitivity analysis if they had a ReComp score of at least 0.8. "
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    ABSTRACT: Background Thiazolidinediones are oral diabetes medications that selectively activate peroxisome proliferator-activated receptor gamma and have potent anti-inflammatory properties. While a few studies have found improvements in pulmonary function with exposure to thiazolidinediones, there are no studies of their impact on asthma exacerbations. Our objective was to assess whether exposure to thiazolidinediones was associated with a decreased risk of asthma exacerbation. Methods We performed a cohort study of diabetic Veterans who had a diagnosis of asthma and were taking oral diabetes medications during the period of 10/1/2005 – 9/30/2006. The risk of asthma exacerbations and oral steroid use during 10/1/2006 – 9/30/2007 was compared between patients who were prescribed thiazolidinediones and patients who were on alternative oral diabetes medications. Multivariable logistic regression and negative binomial regression analyses were used to characterize this risk. A sensitivity analysis was performed, restricting our evaluation to patients who were adherent to diabetes therapy. Results We identified 2,178 patients who were on thiazolidinediones and 10,700 who were not. Exposure to thiazolidinediones was associated with significant reductions in the risk of asthma exacerbation (OR = 0.79, 95% CI, 0.62 – 0.99) and oral steroid prescription (OR = 0.73, 95% CI 0.63 – 0.84). Among patients who were adherent to diabetes medications, there were more substantial reductions in the risks for asthma exacerbation (OR = 0.64, 95% CI 0.47 – 0.85) and oral steroid prescription (OR = 0.68, 95% CI 0.57 – 0.81). Conclusions Thiazolidinediones may provide a novel anti-inflammatory approach to asthma management by preventing exacerbations and decreasing the use of oral steroids.
    Allergy Asthma and Clinical Immunology 07/2014; 10(1):34. DOI:10.1186/1710-1492-10-34 · 2.03 Impact Factor
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    • "It has been demonstrated that RC can be documented in as many as three different measures: (1) availability of drug as measured by a ratio of time, (2) availability of a drug as measured at a certain fixed time, (3) or the time between refills [36]. Our study used the latter of the three and this measure has commonly been referred to a medication gap [37]. It has previously been shown that the time between refills is the easiest and most useful measurement to determine RC [36]. "
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    ABSTRACT: Background. Antiepileptic drugs (AEDs) noncompliance is associated with increased risk of seizures and morbidity in seizure disorder patients. Objective. To identify risk factors that correlated to higher levels of morbidity, measured by emergency room (ER) utilization by seizure disorder members taking AED. Methods. Patients with primary or secondary diagnosis of seizures, convulsions, and/or epilepsy and prescribed AEDs during an 11-month period were included in the study. Variables were analyzed using multivariate statistical analysis including logistic regression. Results. The study identified 201 members. No statistical significance (NS) between age, gender, number of tablets, type of drug, or other risk factors was associated with increased mortality. Statistical significance resulted with medication compliance review of 0-14 days, 15-60 days, and 61+ days between refills. 68% of patients with ER visit had noncompliance refill between 0 and 14 days compared to 52% of patients in non-ER group (P = 0.04). Contrastingly, 15% of ER group had refills within 15-60 days compared with 33% of non-ER group (P = 0.01). There was NS difference between two groups when noncompliance was greater than 60 days (P = 0.66). Conclusions. The study suggests that careful monitoring of pharmaceutical refill information could be used to identify AED noncompliance in epileptic patients.
    02/2014; 2014:734689. DOI:10.1155/2014/734689
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    • "Adherence to OHAs was estimated at the patient level using ReComp, a validated medication-possession ratio (MPR) algorithm described previously [18,19]. MPR has been shown to be correlated with hemoglobin A1c values in patients with diabetes [20]. "
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    ABSTRACT: Background Although oral hypoglycemic agents (OHAs) are an essential element of therapy for the management of type 2 diabetes, OHA adherence is often suboptimal. Pharmacists are increasingly being integrated into primary care as part of the move towards a patient-centered medical home and may have a positive influence on medication use. We examined whether the presence of pharmacists in primary care clinics was associated with higher OHA adherence. Methods This retrospective cohort study analyzed 280,603 diabetes patients in 196 primary care clinics within the Veterans Affairs healthcare system. Pharmacists presence, number of pharmacist full-time equivalents (FTEs), and the degree to which pharmacy services are perceived as a bottleneck in each clinic were obtained from the 2007 VA Clinical Practice Organizational Survey—Primary Care Director Module. Patient-level adherence to OHAs using medication possession ratios (MPRs) were constructed using refill data from administrative pharmacy databases after adjusting for patient characteristics. Clinic-level OHA adherence was measured as the proportion of patients with MPR >= 80%. We analyzed associations between pharmacy measures and clinic-level adherence using linear regression. Results We found no significant association between pharmacist presence and clinic-level OHA adherence. However, adherence was lower in clinics where pharmacy services were perceived as a bottleneck. Conclusions Pharmacist presence, regardless of the amount of FTE, was not associated with OHA medication adherence in primary care clinics. The exact role of pharmacists in clinics needs closer examination in order to determine how to most effectively use these resources to improve patient-centered outcomes including medication adherence.
    BMC Health Services Research 11/2012; 12(1):391. DOI:10.1186/1472-6963-12-391 · 1.71 Impact Factor
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