Olga S. Matlin

Patterns of Initiation of Oral Anticoagulants in Patients with Atrial Fibrillation - Quality and Cost Implications

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. METHODS We used medical and prescription claims data from a large insurer to identify patients with nonvalvular 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 versus a novel anticoagulant. RESULTS There were 6893 patients with atrial fibrillation that 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 CHADS2, CHA2DS2-VASC, and HAS-BLED scores were significantly associated with lower odds of receiving a novel anticoagulant (P <.001 for each). Average combined patient and insurer anticoagulant spending in the first 6 months after initiation was more than

Comparative Effectiveness of Generic and Brand-Name Statins on Patient Outcomes: A Cohort Study

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 CHADS2 and HAS-BLED scores, and high health care cost consequences. These findings provide important directions for future comparative and cost-effectiveness research.

Untangling the relationship between medication adherence and post–myocardial infarction outcomes: Medication adherence and clinical outcomes

Context Some patients do not adhere to their prescribed statins and thus do not fully benefit from the decreases in blood lipid levels that these medications provide. Contribution This study found that, compared with those who initiated a brand-name statin, patients who initiated a generic statin had better adherence and fewer occurrences of a composite outcome that included death from any cause plus hospitalization for an acute coronary syndrome or stroke. Caution All patients were Medicare beneficiaries aged 65 years or older with prescription drug coverage. Implication The lower cost of generic statins allowed patients to adhere to the medication better. The Editors Statins are the most frequently prescribed drugs in the United States (1) and are effective in reducing low-density lipoprotein (LDL) cholesterol levels and cardiovascular events (24). Randomized, controlled trials have found that statins reduce the relative risk for major vascular events by 21% for each 1.0-mmol/L (39-mg/dL) reduction in LDL cholesterol level in patients at low risk for vascular disease (3). Patients assigned to statin therapy in trials tended to achieve reductions in LDL cholesterol level of 1.8 mmol/L (70 mg/dL) with doses used regularly in practice (2). However, a large body of evidence suggests that, in routine practice, patients do not fully adhere to statins and therefore may not receive their full benefit (5, 6). Approximately half of patients in ambulatory care settings discontinue statin therapy within 1 year of initiation (68). Medication nonadherence is a complex multifactorial process (9). Among its many determinants, drug cost may be one of the most easily modifiable (10). Reducing patient spending for prescription drugs can improve adherence (11, 12) and, in some cases, clinical outcomes (13). Generic drugs have been shown in small, short, randomized trials(14, 15) to be clinically equivalent to their brand-name counterparts, as required for approval by the U.S. Food and Drug Administration (FDA). They are usually less expensive than brand-name products and have been associated with better adherence (12). However, no study has investigated whether use of generic versus brand-name statins also leads to improved health outcomes (16). We sought to determine whether patients in a large cohort of Medicare beneficiaries were more adherent to therapy after initiating a generic statin versus a brand-name statin and whether this resulted in differences in health outcomes. Methods The study was designed by the authors and approved by the Institutional Review Board at Brigham and Womens Hospital. Study Cohort The study cohort comprised Medicare beneficiaries (aged 65 years) who had prescription drug coverage through either a stand-alone Medicare Part D plan or a retiree drug plan administered by CVS Caremark, a large national pharmacy benefits manager. For each patient, we linked claims for filled prescriptions to diagnostic, health care utilization, and demographic data from Medicare Parts A and B files and enrollment files. The cohort included patients who initiated a statin (lovastatin, pravastatin, or simvastatin) between 2006 and 2008 and had continuous Medicare and CVS Caremark eligibility in the 6 months before initiation. We restricted the cohort to patients initiating these drugs because they were the only statins for which generic versions were available in the United States during the study. Initiation was defined as a new (index) prescription for a study drug with no prescription for any single statin or statin combination product in the preceding 180 days. To maximize the generalizability of this comparative effectiveness study, we did not impose any other exclusion criteria. We classified patients as exposed to a generic or brand-name statin on the basis of the National Drug Code associated with the index prescription claim. We used the FDAs National Drug Code Directory (17) to determine the manufacturer of each drug and the FDAs Approved Drug Products with Therapeutic Equivalence Evaluations publication (18) to determine whether each manufacturers products were approved via a new drug application (brand-name) or abbreviated new drug application (generic). Outcomes and Follow-up The primary outcomes were adherence to the index statin and a composite cardiovascular outcome. Adherence was measured as the proportion of days covered (PDC) by the index statin up to 1 year after the index prescription date. The PDC is calculated by dividing the number of days of medication supplied by the number of days in a given interval (19). For each patient, the denominator interval began on the index date and ended at death, hospitalization, prescription for any other lipid-lowering drug (for example, a different statin or another lipid-lowering agent, such as a fibrate or bile acid sequestrant, although switches between brand-name and generic versions of the index statin were allowed), the end of the study (31 December 2008), or 365 days after the index prescription date, whichever occurred first. The numerator was the sum of the number of days in the interval for which medication was available based on the days supplied by each prescription. The primary clinical outcome comprised hospitalization for an acute coronary syndrome or stroke and all-cause mortality. We also examined each of these outcomes separately. We used a validated claims-based definition for each outcome, with positive predictive values ranging from 86% to 96% (2022). In the primary analysis, we followed patients from the day after index drug initiation until an occurrence of an event of interest, the end of the study (31 December 2008), or 365 days after initiation, whichever came first. Covariates We measured potential confounders in the 180-day baseline period preceding each patients index date. Demographic variables included age, sex, and race. Health service utilization variables included the number of unique drugs dispensed, number of hospitalizations, number of cardiovascular diagnoses, number of days in the hospital, number of physician office visits, and number of physician office visits with cardiovascular diagnoses. In addition to a comorbidity score that captured patients general health status (23), we determined whether patients had health care encounters with diagnoses for specific cardiovascular conditions (such as atrial fibrillation, congestive heart failure, or peripheral vascular disease) and other disorders (such as chronic obstructive pulmonary disease, diabetes, musculoskeletal conditions, and endocrine disease). Furthermore, we determined whether patients initiated statin treatment for primary or secondary prevention, with the latter defined as having been hospitalized for an acute coronary syndrome in the baseline period. We also measured use of preventive services, including screening mammography and vaccinations, to account for healthy-user effects (24, 25) and ascertained proxies of frailty, such as use of supplemental oxygen, to account for the propensity to stop preventive medications in patients who are very ill (26). Finally, we geocoded patients street addresses and linked them to U.S. census data at the block group level, which is the lowest level for which data are publicly available. We identified the unemployment rate and the median household income in each patients census block group as proxies for socioeconomic status (SES). Statistical Analysis We used propensity score matching (27) to mitigate confounding due to different characteristics between the brand-name and generic groups. The propensity score, which was estimated with a logistic regression model, was defined as a patients probability of receiving a generic statin versus a brand-name statin and was conditional on measured baseline covariates. We matched generic and brand-name drug recipients by using a nearest-neighbor algorithm and within calipers of 0.05 units on the propensity score scale in the primary analysis. Because the cohort included many more generic than brand-name drug recipients, we matched each patient in the brand-name group to as many patients as possible in the generic group with similar propensity scores within the specified caliper. We matched brand-name drug recipients only to recipients of the generic version of the same product (for example, brand-name and generic simvastatin) to compare patients who had initiated molecularly identical drugs. This ensured that differences in outcome rates between treatment groups could be attributed to the generic versus brand-name status rather than to differences among the 3 statins. To assess the performance of the propensity score matching process, we evaluated balance in each baseline covariate and overlap in propensity score distributions between treatment groups before and after matching. We used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% CIs. To account for the variable ratio matching, the Cox models were stratified by matching set. We also estimated rate differences. We performed several prespecified secondary, sensitivity, and subgroup analyses to assess the validity of our study assumptions. We altered our primary analysis by shortening (to 90 days) and lengthening (to 720 days) the maximum follow-up time, excluding events that occurred within 30 and 60 days after index drug initiation, and performing 1:1 fixed-ratio matching on the propensity score. Furthermore, outcome event rates were compared between recipients of the generic and brand-name versions of each drug separately. The primary analysis was also repeated separately for primary prevention and secondary prevention patients. We conducted an on treatment analysis in which we censored patients when they discontinued statin therapy, defined as a gap of more than 30 days without filling a statin prescription beyond the number of days supplied by the last prescription or switching to another lipid-lowering

Group-based Trajectory Models A New Approach to Classifying and Predicting Long-Term Medication Adherence

BACKGROUND Patients who adhere to medications experience better outcomes than their nonadherent counterparts. However, these observations may be confounded by patient behaviors. The level of adherence necessary for patients to derive benefit and whether adherence to all agents is important for diseases that require multiple drugs remain unclear. This study quantifies the relationship between medication adherence and post-myocardial infarction (MI) adverse coronary events. METHODS This is a secondary analysis of the randomized MI FREEE trial. Patients who received full prescription coverage were classified as adherent (proportion of days covered ≥80%) or not based upon achieved adherence in the 6 months after randomization. First major vascular event or revascularization rates were compared using multivariable Cox models adjusting for comorbidity and health-seeking behavior. RESULTS Compared with patients randomized to usual care, full coverage patients adherent to statin, β-blocker, or angiotensin-converting enzyme inhibitor/angiotensin receptor blocker were significantly less likely to experience the studys primary outcome (hazard ratio [HR] range 0.64-0.81). In contrast, nonadherent patients derived no benefit (HR range 0.98-1.04, P ≤ .01 for the difference in HRs between adherent and nonadherent patients). Partially adherent patients had no reduction in clinical outcomes for any of the drugs evaluated, although their achieved adherence was higher than that among controls. CONCLUSION Achieving high levels of adherence to each and all guideline-recommended post-MI secondary prevention medication is associated with improved event-free survival. Lower levels of adherence appear less protective.

Comparative Effectiveness of Generic and Brand-Name Statins on Patient Outcomes

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.

Cost-Effectiveness of Oral Anticoagulants for Treatment of Atrial Fibrillation

Context Some patients do not adhere to their prescribed statins and thus do not fully benefit from the decreases in blood lipid levels that these medications provide. Contribution This study found that, compared with those who initiated a brand-name statin, patients who initiated a generic statin had better adherence and fewer occurrences of a composite outcome that included death from any cause plus hospitalization for an acute coronary syndrome or stroke. Caution All patients were Medicare beneficiaries aged 65 years or older with prescription drug coverage. Implication The lower cost of generic statins allowed patients to adhere to the medication better. The Editors Statins are the most frequently prescribed drugs in the United States (1) and are effective in reducing low-density lipoprotein (LDL) cholesterol levels and cardiovascular events (24). Randomized, controlled trials have found that statins reduce the relative risk for major vascular events by 21% for each 1.0-mmol/L (39-mg/dL) reduction in LDL cholesterol level in patients at low risk for vascular disease (3). Patients assigned to statin therapy in trials tended to achieve reductions in LDL cholesterol level of 1.8 mmol/L (70 mg/dL) with doses used regularly in practice (2). However, a large body of evidence suggests that, in routine practice, patients do not fully adhere to statins and therefore may not receive their full benefit (5, 6). Approximately half of patients in ambulatory care settings discontinue statin therapy within 1 year of initiation (68). Medication nonadherence is a complex multifactorial process (9). Among its many determinants, drug cost may be one of the most easily modifiable (10). Reducing patient spending for prescription drugs can improve adherence (11, 12) and, in some cases, clinical outcomes (13). Generic drugs have been shown in small, short, randomized trials(14, 15) to be clinically equivalent to their brand-name counterparts, as required for approval by the U.S. Food and Drug Administration (FDA). They are usually less expensive than brand-name products and have been associated with better adherence (12). However, no study has investigated whether use of generic versus brand-name statins also leads to improved health outcomes (16). We sought to determine whether patients in a large cohort of Medicare beneficiaries were more adherent to therapy after initiating a generic statin versus a brand-name statin and whether this resulted in differences in health outcomes. Methods The study was designed by the authors and approved by the Institutional Review Board at Brigham and Womens Hospital. Study Cohort The study cohort comprised Medicare beneficiaries (aged 65 years) who had prescription drug coverage through either a stand-alone Medicare Part D plan or a retiree drug plan administered by CVS Caremark, a large national pharmacy benefits manager. For each patient, we linked claims for filled prescriptions to diagnostic, health care utilization, and demographic data from Medicare Parts A and B files and enrollment files. The cohort included patients who initiated a statin (lovastatin, pravastatin, or simvastatin) between 2006 and 2008 and had continuous Medicare and CVS Caremark eligibility in the 6 months before initiation. We restricted the cohort to patients initiating these drugs because they were the only statins for which generic versions were available in the United States during the study. Initiation was defined as a new (index) prescription for a study drug with no prescription for any single statin or statin combination product in the preceding 180 days. To maximize the generalizability of this comparative effectiveness study, we did not impose any other exclusion criteria. We classified patients as exposed to a generic or brand-name statin on the basis of the National Drug Code associated with the index prescription claim. We used the FDAs National Drug Code Directory (17) to determine the manufacturer of each drug and the FDAs Approved Drug Products with Therapeutic Equivalence Evaluations publication (18) to determine whether each manufacturers products were approved via a new drug application (brand-name) or abbreviated new drug application (generic). Outcomes and Follow-up The primary outcomes were adherence to the index statin and a composite cardiovascular outcome. Adherence was measured as the proportion of days covered (PDC) by the index statin up to 1 year after the index prescription date. The PDC is calculated by dividing the number of days of medication supplied by the number of days in a given interval (19). For each patient, the denominator interval began on the index date and ended at death, hospitalization, prescription for any other lipid-lowering drug (for example, a different statin or another lipid-lowering agent, such as a fibrate or bile acid sequestrant, although switches between brand-name and generic versions of the index statin were allowed), the end of the study (31 December 2008), or 365 days after the index prescription date, whichever occurred first. The numerator was the sum of the number of days in the interval for which medication was available based on the days supplied by each prescription. The primary clinical outcome comprised hospitalization for an acute coronary syndrome or stroke and all-cause mortality. We also examined each of these outcomes separately. We used a validated claims-based definition for each outcome, with positive predictive values ranging from 86% to 96% (2022). In the primary analysis, we followed patients from the day after index drug initiation until an occurrence of an event of interest, the end of the study (31 December 2008), or 365 days after initiation, whichever came first. Covariates We measured potential confounders in the 180-day baseline period preceding each patients index date. Demographic variables included age, sex, and race. Health service utilization variables included the number of unique drugs dispensed, number of hospitalizations, number of cardiovascular diagnoses, number of days in the hospital, number of physician office visits, and number of physician office visits with cardiovascular diagnoses. In addition to a comorbidity score that captured patients general health status (23), we determined whether patients had health care encounters with diagnoses for specific cardiovascular conditions (such as atrial fibrillation, congestive heart failure, or peripheral vascular disease) and other disorders (such as chronic obstructive pulmonary disease, diabetes, musculoskeletal conditions, and endocrine disease). Furthermore, we determined whether patients initiated statin treatment for primary or secondary prevention, with the latter defined as having been hospitalized for an acute coronary syndrome in the baseline period. We also measured use of preventive services, including screening mammography and vaccinations, to account for healthy-user effects (24, 25) and ascertained proxies of frailty, such as use of supplemental oxygen, to account for the propensity to stop preventive medications in patients who are very ill (26). Finally, we geocoded patients street addresses and linked them to U.S. census data at the block group level, which is the lowest level for which data are publicly available. We identified the unemployment rate and the median household income in each patients census block group as proxies for socioeconomic status (SES). Statistical Analysis We used propensity score matching (27) to mitigate confounding due to different characteristics between the brand-name and generic groups. The propensity score, which was estimated with a logistic regression model, was defined as a patients probability of receiving a generic statin versus a brand-name statin and was conditional on measured baseline covariates. We matched generic and brand-name drug recipients by using a nearest-neighbor algorithm and within calipers of 0.05 units on the propensity score scale in the primary analysis. Because the cohort included many more generic than brand-name drug recipients, we matched each patient in the brand-name group to as many patients as possible in the generic group with similar propensity scores within the specified caliper. We matched brand-name drug recipients only to recipients of the generic version of the same product (for example, brand-name and generic simvastatin) to compare patients who had initiated molecularly identical drugs. This ensured that differences in outcome rates between treatment groups could be attributed to the generic versus brand-name status rather than to differences among the 3 statins. To assess the performance of the propensity score matching process, we evaluated balance in each baseline covariate and overlap in propensity score distributions between treatment groups before and after matching. We used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% CIs. To account for the variable ratio matching, the Cox models were stratified by matching set. We also estimated rate differences. We performed several prespecified secondary, sensitivity, and subgroup analyses to assess the validity of our study assumptions. We altered our primary analysis by shortening (to 90 days) and lengthening (to 720 days) the maximum follow-up time, excluding events that occurred within 30 and 60 days after index drug initiation, and performing 1:1 fixed-ratio matching on the propensity score. Furthermore, outcome event rates were compared between recipients of the generic and brand-name versions of each drug separately. The primary analysis was also repeated separately for primary prevention and secondary prevention patients. We conducted an on treatment analysis in which we censored patients when they discontinued statin therapy, defined as a gap of more than 30 days without filling a statin prescription beyond the number of days supplied by the last prescription or switching to another lipid-lowering

Initial Choice of Oral Glucose-Lowering Medication for Diabetes Mellitus: A Patient-Centered Comparative Effectiveness Study

Background— New anticoagulants may improve health outcomes in patients with atrial fibrillation, but it is unclear whether their use is cost-effective. Methods and Results— A Markov state transition was created to compare 4 therapies: dabigatran 150 mg BID, apixaban 5 mg BID, rivaroxaban 20 mg QD, and warfarin therapy. The population included those with newly diagnosed atrial fibrillation who were eligible for treatment with warfarin. Compared with warfarin, apixaban, rivaroxaban, and dabigatran, costs were

Effect of Reminder Devices on Medication Adherence: The REMIND Randomized Clinical Trial

93 063,

Impact of medication adherence on absenteeism and short-term disability for five chronic diseases.

111 465, and

Five Features Of Value-Based Insurance Design Plans Were Associated With Higher Rates Of Medication Adherence

140 557 per additional quality-adjusted life year gained, respectively. At a threshold of