Person:

Brennan, Troyen

BACKGROUND: Classifying medication adherence is important for efficiently targeting adherence improvement interventions. The purpose of this study was to evaluate the use of a novel method, group-based trajectory models, for classifying patients by their long-term adherence. RESEARCH DESIGN: We identified patients who initiated a statin between June 1, 2006 and May 30, 2007 in prescription claims from CVS Caremark and evaluated adherence over the subsequent 15 months. We compared several adherence summary measures, including proportion of days covered (PDC) and trajectory models with 2-6 groups, with the observed adherence pattern, defined by monthly indicators of full adherence (defined as having >/=24 d covered of 30). We also compared the accuracy of adherence prediction based on patient characteristics when adherence was defined by either a trajectory model or PDC. RESULTS: In 264,789 statin initiators, the 6-group trajectory model summarized long-term adherence best (C=0.938), whereas PDC summarized less well (C=0.881). The accuracy of adherence predictions was similar whether adherence was classified by PDC or by trajectory model. CONCLUSIONS: Trajectory models summarized adherence patterns better than traditional approaches and were similarly predicted by covariates. Group-based trajectory models may facilitate targeting of interventions and may be useful to adjust for confounding by health-seeking behavior.

Background. Dabigatran, rivaroxaban, and apixaban have been approved for use in patients with atrial fibrillation based upon randomized trials demonstrating their comparable or superior efficacy and safety relative to warfarin. Little is known about their adoption into clinical practice, whether utilization is consistent with the controlled-trials on which their approval was based, and how their use has affected health spending for patients and insurers. Study design. We used medical and prescription claims data from a large insurer to identify patients with non-valvular atrial fibrillation who were prescribed an oral anticoagulant in 2010-2013. We plotted trends in medication initiation over time, assessed corresponding insurer and patient out-of-pocket spending, and evaluated the cumulative number and cost of anticoagulants. We identified predictors of novel anticoagulant initiation using multivariable logistic models. Finally, we estimated the difference in total drug expenditures over 6 months for patients initiating warfarin vs. a novel anticoagulant. Results. 6,893 patients with atrial fibrillation initiated an oral anticoagulant during the study period. By the end of the study period, novel anticoagulants accounted for 62% of new prescriptions and 98% of anticoagulant-related drug costs. Female sex, lower household income and higher (CHADS_2), (CHA_2DS_2-VASC), and HAS-BLED scores were significantly associated with lower odds of receiving a novel anticoagulant (p<0.001 for each). Average combined patient and insurer anticoagulant spending in the first 6 months after initiation was more than $900 greater for patients initiating a novel anticoagulant. Conclusions. This study demonstrates rapid adoption of novel anticoagulants into clinical practice, particularly among patients with lower (CHADS_2) and HAS-BLED scores, and high health care cost consequences. These findings provide important directions for future comparative and cost-effectiveness research.

Objectives. We assessed the relationship between individual characteristics and receipt of oseltamivir (Tamiflu) in the United States during the H1N1 pandemic and other flu seasons. Methods. In a cohort of individuals enrolled in pharmacy benefit plans, we used a multivariate logistic regression model to measure associations between subscriber characteristics and filling a prescription for oseltamivir during 3 flu seasons (October 2006–May 2007, October 2007–May 2008, and October 2008–May 2010). In 19 states with county-level influenza rates reported, we controlled for disease burden. Results. Approximately 56 million subscribers throughout the United States were included in 1 or more study periods. During pandemic flu, beneficiaries in the highest income category had 97% greater odds of receiving oseltamivir than those in the lowest category (P < .001). After we controlled for disease burden, subscribers in the 2 highest income categories had 2.18 and 1.72 times the odds of receiving oseltamivir compared with those in the lowest category (P < .001 for both). Conclusions. Income was a stronger predictor of oseltamivir receipt than prevalence of influenza. These findings corroborate concerns about equity of treatment in pandemics, and they call for improved approaches to distributing potentially life-saving treatments.

OBJECTIVE: To determine the impact of FDA safety communications regarding the weight loss medications sibutramine and orlistat. METHODS: The 2008 to 2011 pharmacy claims data from CVS Caremark were used to determine the effect of the relevant FDA warnings on (1) use of sibutramine and orlistat, (2) their rates of discontinuation, and (3) substitution to an alternate weight loss medication in the 3-month period following discontinuation. RESULTS: The use of sibutramine, orlistat, or phentermine declined from 45 users per 100,000 Caremark enrollees in May 2008 to 24 users per 100,000 enrollees in December 2010. In the time series analyses of overall use of medications, a very small decline in the trend of use of sibutramine after the FDA communication (0.000002% per month decline after the communication; P < 0.001) was found. However, rates of discontinuation of sibutramine and orlistat were similar before and after relevant FDA communications (all P values >0.1 for both level and trend changes post-warning). Patients discontinuing sibutramine post-communication increased use of phentermine at a rate of 0.004% per month after discontinuation (P = 0.01). CONCLUSION: From 2008 to 2010, use of prescription weight loss medications was low and declined over time. FDA communications regarding the safety of these medications had limited effect on use.

Background: Statins are effective in preventing cardiovascular events, but patients do not fully adhere to them. Objective: To determine whether patients are more adherent to generic statins versus brand-name statins (lovastatin, pravastatin, or simvastatin) and whether greater adherence improves health outcomes. Design: Observational, propensity score–matched, new-user cohort study. Setting: Linked electronic data from medical and pharmacy claims. Participants: Medicare beneficiaries aged 65 years or older with prescription drug coverage between 2006 and 2008. Intervention: Initiation of a generic or brand-name statin. Measurements: Adherence to statin therapy (measured as the proportion of days covered [PDC] up to 1 year) and a composite outcome comprising hospitalization for an acute coronary syndrome or stroke and all-cause mortality. Hazard ratios (HRs) and absolute rate differences were estimated. Results: A total of 90 111 patients who initiated a statin during the study was identified; 83 731 (93%) initiated a generic drug, and 6380 (7%) initiated a brand-name drug. The mean age of patients was 75.6 years, and most (61%) were female. The average PDC was 77% for patients in the generic group and 71% for those in the brand-name group (P < 0.001). An 8% reduction in the rate of the clinical outcome was observed among patients in the generic group versus those in the brand-name group (HR, 0.92 [95% CI, 0.86 to 0.99]). The absolute difference was −1.53 events per 100 person-years (CI, −2.69 to −0.19 events per 100 person-years). Limitation: Results may not be generalizable to other populations with different incomes or drug benefit structures. Conclusion: Compared with those initiating brand-name statins, patients initiating generic statins were more likely to adhere and had a lower rate of a composite clinical outcome. Primary Funding Source: Teva Pharmaceuticals.

OBJECTIVE: The objective of this study was to compare treatment persistence and rates of seizure-related events in patients who initiate antiepileptic drug (AED) therapy with a generic versus a brand-name product. METHODS: We used linked electronic medical and pharmacy claims data to identify Medicare beneficiaries who initiated one of five AEDs (clonazepam, gabapentin, oxcarbazepine, phenytoin, zonisamide). We matched initiators of generic versus brand-name versions of these drugs using a propensity score that accounted for demographic, clinical, and health service utilization variables. We used a Cox proportional hazards model to compare rates of seizure-related emergency room (ER) visit or hospitalization (primary outcome) and ER visit for bone fracture or head injury (secondary outcome) between the matched generic and brand-name initiators. We also compared treatment persistence, measured as time to first 14-day treatment gap, between generic and brand-name initiators. RESULTS: We identified 19,760 AED initiators who met study eligibility criteria; 18,306 (93%) initiated a generic AED. In the matched cohort, we observed 47 seizure-related hospitalizations and ER visits among brand-name initiators and 31 events among generic initiators, corresponding to a hazard ratio of 0.53 (95% confidence interval, 0.30 to 0.96). Similar results were observed for the secondary clinical endpoint and across sensitivity analyses. Mean time to first treatment gap was 124.2days (standard deviation [sd], 125.8) for brand-name initiators and 137.9 (sd, 148.6) for generic initiators. SIGNIFICANCE: Patients who initiated generic AEDs had fewer adverse seizure-related clinical outcomes and longer continuous treatment periods before experiencing a gap than those who initiated brand-name versions.

PURPOSE: Trajectory models have been shown to (1) identify groups of patients with similar patterns of medication filling behavior and (2) summarize the trajectory, the average adherence in each group over time. However, the association between adherence trajectories and clinical outcomes remains unclear. This study investigated the association between 12-month statin trajectories and subsequent cardiovascular events. METHODS: We identified patients with insurance coverage from a large national insurer who initiated a statin during January 1, 2007 to December 31, 2010. We assessed medication adherence during the 360 days following initiation and grouped patients based on the proportion of days covered (PDC) and trajectory models. We then measured cardiovascular events during the year after adherence assessment. Cox proportional hazards models were used to evaluate the association between adherence measures and cardiovascular outcomes; strength of association was quantified by the hazard ratio, the increase in model C-statistic, and the net reclassification index (NRI). RESULTS: Among 519 842 statin initiators, 8777 (1.7%) had a cardiovascular event during follow-up. More consistent medication use was associated with a lower likelihood of clinical events, whether adherence was measured through trajectory groups or PDC. When evaluating the prediction of future cardiovascular events by including a measure of adherence in the model, the best model reclassification was observed when adherence was measured using three or four trajectory groups (NRI = 0.189; 95% confidence interval: [0.171, 0.210]). CONCLUSIONS: Statin adherence trajectory predicted future cardiovascular events better than measures categorizing PDC. Thus, adherence trajectories may be useful for targeting adherence interventions. Copyright (c) 2015 John Wiley & Sons, Ltd.