Prevalence of Low-Cost Generic Program Use in a Nationally Representative Cohort of Privately Insured Adults

Emulating the GRADE trial using real world data: retrospective comparative effectiveness study

OBJECTIVE

To emulate the GRADE (Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study) trial using real world data before its publication. GRADE directly compared second line glucose lowering drugs for their ability to lower glycated hemoglobin A1c (HbA1c).

DESIGN

Observational study.

SETTING

OptumLabs® Data Warehouse (OLDW), a nationwide claims database in the US, 25 January 2010 to 30 June 2019.

PARTICIPANTS

Adults with type 2 diabetes and HbA1c 6.8-8.5% while using metformin monotherapy, identified according to the GRADE trial specifications, who also used glimepiride, liraglutide, sitagliptin, or insulin glargine.

MAIN OUTCOME MEASURES

The primary outcome was time to HbA1c ≥7.0%. Secondary outcomes were time to HbA1c >7.5%, incident microvascular complications, incident macrovascular complications, adverse events, all cause hospital admissions, and all cause mortality. Propensity scores were estimated using the gradient boosting machine method, and inverse propensity score weighting was used to emulate randomization of the treatment groups, which were then compared using Cox proportional hazards regression.

RESULTS

8252 people were identified (19.7% of adults starting the study drugs in OLDW) who met eligibility criteria for the GRADE trial (glimepiride arm=4318, liraglutide arm=690, sitagliptin arm=2993, glargine arm=251). The glargine arm was excluded from analyses owing to small sample size. Median times to HbA1c ≥7.0% were 442 days (95% confidence interval 394 to 480 days) for glimepiride, 764 (741 to not calculable) days for liraglutide, and 427 (380 to 483) days for sitagliptin. Liraglutide was associated with lower risk of reaching HbA1c ≥7.0% compared with glimepiride (hazard ratio 0.57, 95% confidence interval 0.43 to 0.75) and sitagliptin (0.55, 0.41 to 0.73). Results were consistent for the secondary outcome of time to HbA1c >7.5%. No significant differences were observed among treatment groups for the remaining secondary outcomes.

CONCLUSIONS

In this emulation of the GRADE trial, liraglutide was statistically significantly more effective at maintaining glycemic control than glimepiride or sitagliptin when added to metformin monotherapy. Generating timely evidence on medical treatments using real world data as a complement to prospective trials is of value.

Macrolide Treatment Failure due to Drug–Drug Interactions: Real-World Evidence to Evaluate a Pharmacological Hypothesis

Macrolide antibiotics have received criticism concerning their use and risk of treatment failure. Nevertheless, they are an important class of antibiotics and are frequently used in clinical practice for treating a variety of infections. This study sought to utilize pharmacoepidemiology methods and pharmacology principles to estimate the risk of macrolide treatment failure and quantify the influence of their pharmacokinetics on the risk of treatment failure, using clinically reported drug–drug interaction data. Using a large, commercial claims database (2006–2015), inclusion and exclusion criteria were applied to create a cohort of patients who received a macrolide for three common acute infections. Furthermore, an additional analysis examining only bacterial pneumonia events treated with macrolides was conducted. These criteria were formulated specifically to ensure treatment failure would not be expected nor influenced by intrinsic or extrinsic factors. Treatment failure rates were 6% within the common acute infections and 8% in the bacterial pneumonia populations. Regression results indicated that macrolide AUC changes greater than 50% had a significant effect on treatment failure risk, particularly for azithromycin. In fact, our results show that decreased or increased exposure change can influence failure risk, by 35% or 12%, respectively, for the acute infection scenarios. The bacterial pneumonia results were less significant with respect to the regression analyses. This integration of pharmacoepidemiology and clinical pharmacology provides a framework for utilizing real-world data to provide insight into pharmacokinetic mechanisms and support future study development related to antibiotic treatments.

Predictors of cardio-kidney complications and treatment failure in patients with chronic kidney disease and type 2 diabetes treated with SGLT2 inhibitors

BACKGROUND

Clinical practice guidelines recommend sodium-glucose co-transporter 2 inhibitors (SGLT2is) to mitigate adverse kidney and cardiovascular outcomes in patients with type 2 diabetes (T2D), including patients with comorbid chronic kidney disease (CKD), also referred to as diabetic kidney disease (DKD), who are at even higher risk. In this study, we sought to identify predictors of cardio-kidney events, cardio-kidney complications, and treatment failure (i.e., addition/initiation of a new T2D class, insulin, or discontinuation of SGLT2is) after new initiation of SGLT2is in patients with CKD and T2D (DKD).

METHODS

In this retrospective cohort study, we identified adult patients with DKD who initiated SGLT2is between April 1, 2012, and June 30, 2019, in Optum claims data. Outcome rates per 1000 person-years (PY) are reported with 95% confidence intervals (CIs). Cox proportional hazards regression identified patient characteristics associated with each outcome.

RESULTS

The study population consisted of 6389 initiators of SGLT2is. The rate of CV hospitalization was 26.0 (95% CI 21.6, 30.4) per 1000 PY. Baseline characteristics associated with higher risk of CV hospitalization included age, atrial fibrillation, peripheral vascular disease (PVD), and cancer. The rate of kidney hospitalization was 12.0 (95% CI 9.0, 15.0) per 1000 PY. The risk increased significantly with baseline evidence of heart failure, hyperkalemia, respiratory failure, depression, and use of loop diuretics. In total, 55.0% of all SGLT2i initiators discontinued treatment during the follow-up period. The rate of treatment failure was 510.5 (95% CI 492.9, 528.1) per 1000 PY. Analysis of key time-dependent SGLT2i-associated adverse events showed that experiencing diabetic ketoacidosis and volume depletion were associated with risk of treatment failure.

CONCLUSIONS

Our study demonstrated high rates of residual cardio-kidney outcomes and treatment failure in patients with DKD treated with SGLT2is. Patients with high baseline CV risk and the presence of certain conditions, such as atrial fibrillation, PVD, and heart failure, were at higher risk for cardio-kidney events. Further research is needed to assess the potential relationship between adverse events and SGLT2i treatment failure.

Association of Oral Anticoagulants and Verapamil or Diltiazem With Adverse Bleeding Events in Patients With Nonvalvular Atrial Fibrillation and Normal Kidney Function

IMPORTANCE

Direct oral anticoagulants (DOACs) are purported to have fewer drug-drug interactions than warfarin. However, potential interactions with coprescribed medications are still a safety concern. Verapamil hydrochloride and diltiazem hydrochloride are combined P-glycoprotein (P-gp) and CYP3A4 inhibitors and may be associated with increases in the risk of bleeding with DOACs.

OBJECTIVE

To evaluate the risk of bleeding with DOACs and verapamil or diltiazem using an active comparator design.

DESIGN, SETTING, AND PARTICIPANTS

A comparative effectiveness active comparator cohort study was conducted using US population-based data (2010-2015) analyzed between January 1 and July 15, 2019. Data were obtained on 48 442 patients with nonvalvular atrial fibrillation who had received an index prescription of dabigatran, rivaroxaban, or apixaban between October 19, 2010, through June 30, 2015, with final follow-up on October 1, 2015. Analysis was restricted to individuals with no history of kidney disease who were receiving standard doses of the DOACs.

EXPOSURES

Patients with initial prescriptions of DOACs who were receiving verapamil or diltiazem were compared with those receiving amlodipine or metoprolol.

MAIN OUTCOMES AND MEASURES

Overall and gastrointestinal major, moderate, and minor bleeding using primary or secondary diagnoses. Hazard ratios and 95% CIs were estimated using inverse probability of treatment weights in Cox proportional hazards regression models.

RESULTS

Of the 48 442 patients reviewed, analysis was conducted on 1764 patients receiving DOACs with verapamil or diltiazem compared with 3105 receiving amlodipine and 1793 patients receiving DOACs with verapamil or diltiazem compared with 3224 receiving metoprolol. Depending on the comparison, approximately 60% of the cohort were younger than 65 years and male, which differed by treatment group. Rivaroxaban and apixaban were not associated with increased rates of bleeding for patients receiving verapamil or diltiazem compared with those receiving amlodipine or metoprolol. Among patients receiving dabigatran etexilate, the overall bleeding rate was 52% higher (hazard ratio, 1.52; 95% CI, 1.05-2.20) with verapamil or diltiazem vs amlodipine and 43% higher (hazard ratio, 1.43; 95% CI, 1.02-2.00) vs metoprolol. Bleeding rates for dabigatran with verapamil or diltiazem were higher overall for other bleeding types (244.9 vs 158.4 per 1000 person-years; adjusted hazard ratios of overall GI bleeding: 2.16; 95% CI, 1.30-3.60; minor bleeding: 1.56; 95% CI, 1.07-2.27; and minor GI bleeding: 2.16; 95% CI, 1.29-3.63). Sensitivity analyses showed consistent results for dabigatran when used with verapamil and diltiazem, with magnitudes ranging from 50% to 100% increased hazard rates and no significant results for apixaban or rivaroxaban.

CONCLUSIONS AND RELEVANCE

Current US prescribing information only recommends prescribing changes with dabigatran and P-gp inhibitors with lower kidney function. This study found increased bleeding risk associated with dabigatran when used concomitantly with the P-gp inhibitors verapamil and diltiazem in individuals with normal kidney function. Clinicians and patients may need to consider these drug-drug interactions when choosing oral anticoagulation.

Paradox of glycemic management: multimorbidity, glycemic control, and high-risk medication use among adults with diabetes

INTRODUCTION

Glycemic targets and glucose-lowering regimens should be individualized based on multiple factors, including the presence of comorbidities. We examined contemporary patterns of glycemic control and use of medications known to cause hypoglycemia among adults with diabetes across age and multimorbidity.

RESEARCH DESIGN AND METHODS

We retrospectively examined glycosylated hemoglobin (HbA_1c) levels and rates of insulin/sulfonylurea use as a function of age and multimorbidity using administrative claims and laboratory data for adults with type 2 diabetes included in OptumLabs Data Warehouse, 1 January 2014 to 31 December 2016. Comorbidity burden was assessed by counts of any of 16 comorbidities specified by guidelines as warranting relaxation of HbA_1c targets, classified as being diabetes concordant (diabetes complications or risk factors), discordant (unrelated to diabetes), or advanced (life limiting).

RESULTS

Among 194 157 patients with type 2 diabetes included in the study, 45.2% had only concordant comorbidities, 30.6% concordant and discordant, 2.7% only discordant, and 13.0% had ≥1 advanced comorbidity. Mean HbA_1c was 7.7% among 18-44 year-olds versus 6.9% among ≥75 year-olds, and was higher among patients with comorbidities: 7.3% with concordant only, 7.1% with discordant only, 7.1% with concordant and discordant, and 7.0% with advanced comorbidities compared with 7.4% among patients without comorbidities. The odds of insulin use decreased with age (OR 0.51 (95% CI 0.48 to 0.54) for age ≥75 vs 18-44 years) but increased with accumulation of concordant (OR 5.50 (95% CI 5.22 to 5.79) for ≥3 vs none), discordant (OR 1.72 (95% CI 1.60 to 1.86) for ≥3 vs none), and advanced (OR 1.45 (95% CI 1.25 to 1.68) for ≥2 vs none) comorbidities. Conversely, sulfonylurea use increased with age (OR 1.36 (95% CI 1.29 to 1.44) for age ≥75 vs 18-44 years) but decreased with accumulation of concordant (OR 0.76 (95% CI 0.73 to 0.79) for ≥3 vs none), discordant (OR 0.70 (95% CI 0.64 to 0.76) for ≥3 vs none), but not advanced (OR 0.86 (95% CI 0.74 to 1.01) for ≥2 vs none) comorbidities.

CONCLUSIONS

The proportion of patients achieving low HbA_1c levels was highest among older and multimorbid patients. Older patients and patients with higher comorbidity burden were more likely to be treated with insulin to achieve these HbA_1c levels despite potential for hypoglycemia and uncertain long-term benefit.

Association of Cumulative Multimorbidity, Glycemic Control, and Medication Use With Hypoglycemia-Related Emergency Department Visits and Hospitalizations Among Adults With Diabetes

IMPORTANCE

Severe hypoglycemia is a serious and potentially preventable complication of diabetes, with some of the most severe episodes requiring emergency department (ED) care or hospitalization. A variety of health conditions increase the risk of hypoglycemia. People with diabetes often have multiple comorbidities, and the association of such multimorbidity with hypoglycemia risk in the context of other risk factors is uncertain.

OBJECTIVE

To examine the associations of age, cumulative multimorbidity, glycated hemoglobin (HbA1c) level, and use of glucose level-lowering medication with hypoglycemia-related ED visits and hospitalizations.

DESIGN, SETTING, AND PARTICIPANTS

Cohort study of claims and laboratory data from OptumLabs Data Warehouse, an administrative claims database of commercially insured and Medicare Advantage beneficiaries in the United States. Participants were adults (aged ≥18 years) with diabetes who had an available HbA1c level result in 2015. Data from January 1, 2014, to December 31, 2016, were analyzed. Final analyses were conducted from December 2017 to September 2018.

MAIN OUTCOMES AND MEASURES

This study calculated rates of hypoglycemia-related ED visits and hospitalizations during the year after the index HbA1c level was obtained, stratified by patient demographic characteristics, diabetes type, comorbidities (from 16 guideline-specified high-risk conditions), index HbA1c level, and glucose level-lowering medication use. The association of each variable with hypoglycemia-related ED and hospital care was examined using multivariable Poisson regression analysis overall and by diabetes type.

RESULTS

The study cohort was composed of 201 705 adults with diabetes (mean [SD] age, 65.8 [12.1] years; 102 668 [50.9%] women; 118 804 [58.9%] white; mean [SD] index HbA1c level, 7.2% [1.5%]). Overall, there were 9.06 (95% CI, 8.64-9.47) hypoglycemia-related ED visits and hospitalizations per 1000 persons per year. The risk of hypoglycemia-related ED visits and hospitalizations was increased by age 75 years or older (incidence rate ratio [IRR], 1.56 [95% CI, 1.23-2.02] vs 18-44 years), black race/ethnicity (IRR, 1.30 [95% CI, 1.16-1.46] vs white race/ethnicity), lower annual household income (IRR, 0.63 [95% CI, 0.53-0.74] for ≥$100 000 vs <$40 000), number of comorbidities (increasing from IRR of 1.66 [95% CI, 1.42-1.95] in the presence of 2 comorbidities to IRR of 4.12 [95% CI, 3.07-5.51] with ≥8 comorbidities compared with ≤1), prior hypoglycemia-related ED visit or hospitalization (IRR, 6.60 [95% CI, 5.77-7.56]), and glucose level-lowering treatment regimen (IRR, 6.73 [95% CI, 4.93-9.22] for sulfonylurea; 12.53 [95% CI, 8.90-17.64] for basal insulin; and 27.65 [95% CI, 20.32-37.63] for basal plus bolus insulin compared with other medications). Independent of these factors, having type 1 diabetes was associated with a 34% increase in the risk of hypoglycemia-related ED visits or hospitalizations (IRR, 1.34 [95% CI, 1.15-1.55]). The index HbA1c level was associated with hypoglycemia-related ED visits and hospitalizations when both low (IRR, 1.45 [95% CI, 1.12-1.87] for HbA1c level ≤5.6% vs 6.5%-6.9%) and high (IRR, 1.24 [95% CI, 1.02-1.50] for HbA1c level ≥10%).

CONCLUSIONS AND RELEVANCE

In this cohort study of adults with diabetes, the risk of an ED visit or hospitalization for hypoglycemia appeared to be highest among patients with type 1 diabetes, multiple comorbidities, prior severe hypoglycemia, and sulfonylurea and/or insulin use. At-risk patients may benefit from individualized treatment regimens to decrease their risk of hypoglycemia.

Real-world adherence for direct oral anticoagulants in a newly diagnosed atrial fibrillation cohort: does the dosing interval matter?

Background

Differences in adherence may represent drug properties (e.g. dosing interval) or patient experiences while on treatment. Adherence to direct oral anticoagulants (DOACs) in nonvalvular atrial fibrillation (NVAF) is important to maintain effectiveness over the course of treatment.

Methods

This was a retrospective cohort study using 2009–2015 Truven Health MarketScan Databases. New initiators of dabigatran, rivaroxaban, and apixaban with NVAF were identified. Twelve months of continuous enrollment before treatment was required to assess demographics and medical history. Proportion of days cover (PDC) was used to measure adherence at 3, 6, 9 and 12-month. Gaps in therapy and treatment switches were also evaluated. Logistic regression was used to compare high adherence (PDC ≥0.80).

Results

A total of 14,864 dabigatran, 16,005 rivaroxaban, and 8078 apixaban users were identified. Apixaban users had the highest adherence overall, with mean PDC at 3, 6, 9, and 12-months of 0.83, 0.76, 0.72, and 0.69, while dabigatran had the lowest adherence of 0.78, 0.67, 0.61, and 0.57. Adherence to DOACs increased with increased stroke risk scores. Adherence was also higher when first days supplied was > 30 days compared to 30 days and when filled via mail order pharmacies. Switching was highest among dabigatran users. Apixaban users were the most likely to have high adherence versus dabigatran (OR = 1.73, 95% CI = 1.60–1.88) and versus rivaroxaban (OR = 1.24, 95% CI = 1.14–1.34) at 12-months.

Conclusions

Apixaban users had the highest overall adherence despite twice-daily dosing versus once-daily dosing for rivaroxaban. These findings can be useful for formulary decision-making and when assessing treatment options.

Recurrent hospitalizations for severe hypoglycemia and hyperglycemia among U.S. adults with diabetes

AIMS

Examine 30-day readmissions for recurrent hypoglycemia and hyperglycemia in a national cohort of adults with diabetes.

METHODS

Retrospective analysis of data from OptumLabs Data Warehouse for all adults with diabetes hospitalized January 1, 2009 to December 31, 2014 with a principal diagnosis of hypoglycemia or hyperglycemia. We examined the rates and risk factors of 30-day readmissions for hypoglycemia and hyperglycemia.

RESULTS

After 6419 index hypoglycemia hospitalizations, 1.2% were readmitted for recurrent hypoglycemia, 0.2% for hyperglycemia, and 8.6% for other causes. Multimorbidity was the strongest predictor of recurrent hypoglycemia. After 6872 index hyperglycemia hospitalizations, 4.0% were readmitted for recurrent hyperglycemia, 0.4% for hypoglycemia, and 5.4% for other causes. Recurrent hyperglycemia was less likely in older patients (OR 0.6, 95% CI 0.5-0.9 for 45-64 vs. <45 years) and with the addition of a new glucose-lowering medication at index discharge (OR 0.40; 95% CI 0.2-0.7). New hypoglycemia readmissions were most likely among patients ≥75 years (OR 13.3, 95% CI 2.4-73.4, vs. <45 years).

CONCLUSIONS

Patients hospitalized for hyperglycemia are often readmitted for recurrent hyperglycemia, while patients hospitalized for hypoglycemia are generally readmitted for unrelated causes. Early recognition of high risk patients may identify opportunities to improve post-discharge management and reduce these events.

Hypoglycemia Among Patients with Type 2 Diabetes: Epidemiology, Risk Factors, and Prevention Strategies

PURPOSE OF REVIEW

Hypoglycemia is the most common and often treatment-limiting serious adverse effect of diabetes therapy. Despite being potentially preventable, hypoglycemia in type 2 diabetes incurs substantial personal and societal burden. We review the epidemiology of hypoglycemia in type 2 diabetes, discuss key risk factors, and introduce potential prevention strategies.

RECENT FINDINGS

Reported rates of hypoglycemia in type 2 diabetes vary widely as there is marked heterogeneity in how hypoglycemia is defined, measured, and reported. In randomized controlled trials, rates of severe hypoglycemia ranged from 0.7 to 12 per 100 person-years. In observational studies, hospitalizations or emergency department visits for hypoglycemia were experienced by 0.2 (patients treated without insulin or sulfonylurea) to 2.0 (insulin or sulfonylurea users) per 100 person-years. Patient-reported hypoglycemia is much more common. Over the course of 6 months, 1-4% non-insulin users reported need for medical attention for hypoglycemia; 1-17%, need for any assistance; and 46-58%, any hypoglycemia symptoms. Similarly, over a 12-month period, 4-17% of insulin-treated patients reported needing assistance and 37-64% experienced any hypoglycemic symptoms. Hypoglycemia is most common among older patients with multiple or advanced comorbidities, patients with long diabetes duration, or patients with a prior history of hypoglycemia. Insulin and sulfonylurea use, food insecurity, and fasting also increase hypoglycemia risk. Clinical decision support tools may help identify at-risk patients. Prospective trials of efforts to reduce hypoglycemia risk are needed, and there is emerging evidence supporting multidisciplinary interventions including treatment de-intensification, use of diabetes technologies, diabetes self-management, and social support. Hypoglycemia among patients with type 2 diabetes is common. Patient-centered multidisciplinary care may help proactively identify at-risk patients and address the multiplicity of factors contributing to hypoglycemia occurrence.

Antihyperglycemic Medications: A Claims-Based Estimate of First-line Therapy Use Prior to Initialization of Second-line Medications

OBJECTIVE

The American Diabetes Association recommends metformin as first-line therapy for type 2 diabetes. However, nonadherence to antihyperglycemic medication is common, and a clinician could confuse nonadherence with pharmacologic failure, potentially leading to premature prescribing of second-line therapies. We measured metformin use prior to second-line therapy initialization.

RESEARCH DESIGN AND METHODS

This retrospective cross-sectional study used unidentifiable member claims data from individuals covered from 2010 to 2015 by Aetna, a U.S. health benefits company. Beneficiaries with two physician claims or one hospitalization with a type 2 diabetes diagnosis were included. Recommended use of metformin was measured by the proportion of days covered over 60 days. Through sensitivity analysis, we varied estimates of the percentage of beneficiaries who used low-cost generic prescription medication programs.

RESULTS

A total of 52,544 individuals with type 2 diabetes were eligible. Of 22,956 patients given second-line treatment, only 1,875 (8.2%) had evidence of recommended use of metformin in the prior 60 days, and 6,441 (28.0%) had no prior claims evidence of having taken metformin. At the top range of sensitivity, only 49.5% patients could have had recommended use. Patients were more likely to be given an additional second-line antihyperglycemic medication or insulin if they were given their initial second-line medication without evidence of recommended use of metformin (P < 0.001).

CONCLUSIONS

Despite published guidelines, second-line therapy often is initiated without evidence of recommended use of first-line therapy. Apparent treatment failures, which may in fact be attributable to nonadherence to guidelines, are common. Point-of-care and population-level processes are needed to monitor and improve guideline adherence.

Adherence to Rivaroxaban, Dabigatran, and Apixaban for Stroke Prevention for Newly Diagnosed and Treatment-Naive Atrial Fibrillation Patients: An Update Using 2013-2014 Data

BACKGROUND

Few studies have assessed adherence to non-vitamin K antagonist oral anticoagulants (NOACs), especially using contemporary data now that multiple NOACs are available.

OBJECTIVE

To compare adherence and treatment patterns among NOACs for stroke prevention in patients with nonvalvular atrial fibrillation (NVAF).

METHODS

Incident and treatment-naive NVAF patients were identified during 2013-2014 from a large claims database in this retrospective cohort study. Patients were included who initiated rivaroxaban, dabigatran, or apixaban within 30 days after diagnosis. Adherence to the index medication and adherence to any oral anticoagulant was assessed using the proportion of days covered (PDC) at 3, 6, and 9 months. The number of switches and gaps in therapy were also evaluated. Analyses were stratified by stroke risk scores, and a logistic regression model was used to control for factors that may predict high adherence.

RESULTS

Dabigatran had lower adherence (PDC = 0.76, 0.64, 0.57) compared with rivaroxaban (PDC = 0.83, 0.73, 0.66; P < 0.001) and apixaban (PDC = 0.82, 0.72, 0.66; P < 0.001) at 3, 6, and 9 months of follow-up and twice the number of switches to either other anticoagulants or antiplatelet therapy. Adherence was higher overall as stroke risk increased, and dabigatran had consistently lower adherence compared with the other NOACs. Multivariable logistic regression predicting PDC ≥ 0.80 showed rivaroxaban users with higher odds of high adherence compared with dabigatran or rivaroxaban across all time periods. Adjusted analyses showed that increasing age and comorbid hypertension and diabetes were associated with higher adherence.

CONCLUSIONS

In this real-world analysis of adherence to NOACs, rivaroxaban and apixaban had favorable unadjusted adherence profiles compared with dabigatran, while rivaroxaban users had higher odds of high adherence (PDC ≥ 0.80) among the NOACs in adjusted analyses. Clinicians and managed care organizations should consider the implications of lower adherence on clinical outcomes and quality assessment.

DISCLOSURES

This project was supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through grant number UL1TR000117. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The authors have nothing to disclose. Study concept and design were contributed by Brown and Shewale. Brown and Talbert collected the data, and data analysis was performed primarily by Brown, along with Shewale and Talbert. The manuscript was written primarily by Brown, along with Shewale, and revised by all the authors.

Low-Cost Generic Program Use by Medicare Beneficiaries: Implications for Medication Exposure Misclassification in Administrative Claims Data

BACKGROUND

Administrative claims data are used for a wide variety of research and quality assurance purposes; however, they are prone to medication exposure misclassification if medications are purchased without using an insurance benefit. Low-cost generic drug programs (LCGPs) offered at major chain pharmacies are a relatively new and sparsely investigated source of exposure misclassification. LCGP medications are often purchased out of pocket; thus, a pharmacy claim may never be submitted, and the exposure may go unobserved in claims data. As heavy users of medications, Medicare beneficiaries have much to gain from the affordable medications offered through LCGPs. This use may put them at increased risk of exposure misclassification in claims data. Many high-risk medications (HRMs) and medications tracked for adherence and utilization quality metrics are available through LCGPs, and exposure misclassification of these medications may impact the quality assurance efforts reliant on administrative claims data. Presently, there is little information regarding the use of these programs among a geriatric population.

OBJECTIVES

To (a) quantify the prevalence of LCGP users in a nationally representative population of Medicare beneficiaries; (b) compare clinical and demographic characteristics of LCGP users and nonusers; (c) assess determinants of LCGP use and medications acquired through these programs; and (d) analyze patterns of LCGP use during the years 2007-2012.

METHODS

This study relied on data from the Medical Expenditure Panel Survey (MEPS) from 2007 to 2012. The first 3 objectives were completed with a cohort of individuals in the most recent MEPS panel, while the fourth objective was completed with a separate cohort composed of individuals who participated in MEPS from 2007 to 2012. Inclusion in either study cohort required that individuals were Medicare beneficiaries aged 65 years or greater, used at least 1 prescription drug during their 2-year panel period, and participated in all 5 rounds of data collection during their panel period. MEPS captures medication utilization by surveying individuals on current and previous medication use and verifies this information at the pharmacy level, so prescription fills can be observed irrespective of payment by an insurer or a filed claim. Pharmaceutical utilization was assessed at the individual level for each year of the study period, and LCGP use was recorded as a binary variable for each individual. An LCGP medication fill was identified if the total cost of the drug was paid out of pocket and matched the cost of medications listed on LCGP formularies available from major pharmacy retailers during these years. Cohort demographics and characteristics of interest included age, gender, race, employment status, marital status, family income level, education level, residence in a metropolitan statistical area, geographic region, prescription drug coverage, Medicare type, comorbidities, number of unique medications used, and number of medication fills. Comparisons were made between users and nonusers using chi-square and t-tests. Multivariable logistic regression was used to identify factors associated with LCGP use.

RESULTS

From the most recent MEPS panel, 1,861 individuals were included in the study cohort, of which 53.5% were observed to be LCGP users. The 995 LCGP users in this cohort represented over 20 million Medicare beneficiaries who used LCGPs from 2011 to 2012. Significant differences between LCGP users and nonusers existed in terms of race, educational attainment, comorbidity burden, type of Medicare insurance, number of unique medications used, and number of medication fills. Each additional unique medication filled increased the odds of LCGP use by 12% (95% CI = 1.09-1.14). Individuals with insurance in addition to Medicare (i.e., Tricare/Veteran's Affairs or Medicaid) had less than half the odds of using LCGPs compared with those with Medicare or Medicare managed care insurance coverage only. The proportion of LCGP users and the proportion of LCGP fills out of all medications available through LCGPs increased from 2007 to 2012.

CONCLUSIONS

There is a high rate of LCGP use among Medicare beneficiaries aged 65 years or greater. Claims-based research and quality assurance programs focusing on the benefits and harms of medications available through these programs are at risk of underestimating the true medication exposure in this population and should account for this possibility in sensitivity analyses. Managed care organizations should incentivize the reporting of LCGP medication use or make adjustments to generic medication benefit structures to more effectively capture true medication exposure.

DISCLOSURES

No direct sources of funding were used to conduct this study. Data acquisition was supported by the University of Kentucky Center for Clinical and Translational Science through funding from NIH NCATS grant #UL1TR000117. Brown is the Humana-Pfizer Research Fellow at the Institute for Pharmaceutical Outcomes & Policy at the University of Kentucky College of Pharmacy and is provided salary from these corporations. However, neither company provided any direct funding for the current study nor provided any input or guidance for the design, methods, or drafting of the manuscript. Pauly has no financial disclosures or conflicts of interest. Portions of these results were presented at the 20th International Society for Pharmacoeconomics and Outcomes Research International Meeting; May 16-20, 2015; Philadelphia, Pennsylvania. Study concept and design were primarily contributed by Brown, along with the other authors. Brown took the lead in data collection and interpretation, along with Pauly and Talbert. All authors participated in the writing and revision of the manuscript.

Adherence to Rivaroxaban, Dabigatran, and Apixaban for Stroke Prevention in Incident, Treatment-Naïve Nonvalvular Atrial Fibrillation

BACKGROUND

Non-vitamin K antagonist oral anticoagulants (NOACs) are widely used for prevention of stroke secondary to nonvalvular atrial fibrillation (NVAF). Increased use of NOACs is partially a result of simplified regimens compared with warfarin, which has been associated with poor adherence and persistence to therapy. Few studies have assessed adherence to NOACs, especially using contemporary data now that multiple NOACs are available.

OBJECTIVE

To evaluate adherence to NOACs in a cohort of newly diagnosed NVAF patients who are commercially insured.

METHODS

Incident, treatment-naïve NVAF patients were identified in 2013 from a large claims database. Patients were included who initiated rivaroxaban, dabigatran, or apixaban within 30 days after diagnosis. Subjects were required to have 12 months of pre-index information to assess demographic and clinical characteristics (comorbidities, CHA₂ DS₂-VASc, and HAS-BLED scores). Adherence to the index medication and adherence to any oral anticoagulant was assessed using proportion of days covered (PDC) at 3, 6, and 9 months. The number of switches and gaps in therapy were also evaluated. Analyses were stratified by stroke risk scores, and a logistic regression model was used to control for factors that may predict high adherence (PDC ≥ 0.80).

RESULTS

A total of 3,455 rivaroxaban, 1,264 dabigatran, and 504 apixaban users were included with no major clinical or demographic differences between groups. At 3, 6, and 9 months of follow-up, dabigatran had lower adherence (PDC = 0.77, 0.67, and 0.62) compared with rivaroxaban (PDC = 0.84, 0.75, and 0.70; P < 0.001) and apixaban (PDC = 0.82, 0.75, and 0.71; P < 0.001), as well as nearly twice the number of switches to either other anticoagulants or antiplatelet therapy. At 9 months, 55.0% of rivaroxaban initiators had PDC ≥ 0.80, which was comparable with 56.8% for apixaban and significantly greater than 46.7% for dabigatran (P < 0.001). Adherence was higher overall as stroke risk increased and showed dabigatran had consistently lower adherence compared with the other NOACs. Overall adherence to any oral anticoagulants, allowing for switches to another NOAC or warfarin, was not dependent on the index medication (9-month PDC = 0.74, 0.71, and 0.74 for rivaroxaban, dabigatran, and apixaban initiators). Adjusted analyses showed that increasing age and comorbid hypertension and diabetes were associated with higher adherence. Compared with rivaroxaban, dabigatran initiators had nearly 30% lower odds of being adherent to their index medication, and no differences were observed between apixaban and rivaroxaban. At 9 months, there were no differences between NOACs for overall adherence to oral anticoagulants.

CONCLUSIONS

In this real-world analysis of adherence to NOACs, rivaroxaban and apixaban had favorable profiles compared with dabigatran, and rivaroxaban appeared to have higher overall adherence among the NOACs. Clinicians and managed care organizations should consider the implications of lower adherence on clinical outcomes as well as quality assessment.

DISCLOSURES

The project described was supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through grant number UL1TR000117. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Brown reports receiving a training fellowship from Human and Pfizer. Study concept and design were contributed by Brown and Shewale. Talbert took the lead in data collection, along with Brown, and data interpretation was primarily performed by Brown, along with Shewale. The manuscript was written primarily by Brown, along with Shewale, and revised by all the authors.

Empirical Assessment of the Impact of Low-Cost Generic Programs on Adherence-Based Quality Measures

In the United States, federally-funded health plans are mandated to measure the quality of care. Adherence-based medication quality metrics depend on completeness of administrative claims data for accurate measurement. Low-cost generic programs (LCGPs) cause medications fills to be missing from claims data as medications are not adjudicated through a patient’s insurance. This study sought to assess the magnitude of the impact of LCGPs on these quality measures. Data from the 2012–2013 Medical Expenditure Panel Survey (MEPS) were used. Medication fills for select medication classes were classified as LCGP fills and individuals were classified as never, sometimes, and always users of LCGPs. Individuals were classified based on insurance type (private, Medicare, Medicaid, dual-eligible). The proportion of days covered (PDC) was calculated for each medication class and the proportion of users with PDC ≥ 0.80 was reported as an observed metric for what would be calculated based on claims data and a true metric which included missing medication fills due to LCGPs. True measures of adherence were higher than the observed measures. The effect’s magnitude was highest for private insurance and for medication classes utilized more often through LCGPs. Thus, medication-based quality measures may be underestimated due to LCGPs.

Quantification of missing prescriptions in commercial claims databases: results of a cohort study

PURPOSE

This study aims to quantify the magnitude of missed dispensings in commercial claims databases.

METHODS

A retrospective cohort study has been used linking PharMetrics, a commercial claims database, to a prescription database (LRx) that captures pharmacy dispensings independently of payment method, including cash transactions. We included adults with dispensings for opioids, diuretics, antiplatelet medications, or anticoagulants. To determine the degree of capture of dispensings, we calculated the number of subjects with the following: (1) same number of dispensings in both databases; (2) at least one dispensing, but not all dispensings, missed in PharMetrics; and (3) all dispensings missing in PharMetrics. Similar analyses were conducted using dispensings as the unit of analysis. To assess whether a dispensing in LRx was in PharMetrics, the dispensing in PharMetrics had to be for the same medication class and within ±7 days in LRx.

RESULTS

A total of 1 426 498 subjects were included. Overall, 68% of subjects had the same number of dispensings in both databases. In 13% of subjects, PharMetrics identified ≥1 dispensing but also missed ≥1 dispensing. In 19% of the subjects, PharMetrics missed all the dispensings. Taking dispensings as the unit of analysis, 25% of the dispensings present in LRx were not captured in PharMetrics. These patterns were similar across all four classes of medications. Of the dispensings missing in PharMetrics, 48% involved a subject who had >1 health insurance plan.

CONCLUSIONS

Commercial claims databases provide an incomplete picture of all prescriptions dispensed to patients. The lack of capture goes beyond cash transactions and potentially introduces substantial misclassification bias. © 2017 The Authors. Pharmacoepidemiology & Drug Safety Published by John Wiley & Sons Ltd.

The Prevalence and Predictors of Low-Cost Generic Program Use in a Nationally Representative Uninsured Population

The uninsured population has much to gain from affordable sources of prescription medications. No prior studies have assessed the prevalence and predictors of low-cost generic drug programs (LCGP) use in the uninsured population in the United States. A cross-sectional study was conducted using data from the Medical Expenditure Panel Survey (MEPS) during 2007–2012 including individuals aged 18 and older who were uninsured for the entire 2-year period they were in MEPS. The proportions of LCGP fills and users was tracked each year and logistic regression was used to assess significant factors associated with LCGP use. A total of 8.3 million uninsured individuals were represented by the sample and 39.9% of these used an LCGP. Differences between users and non-users included higher age, gender, year of participation, and number of medications filled. The proportion of fills and users via LCGPs increased over the 2007–2012 study period. Healthcare providers, especially pharmacists, should make uninsured patients aware of this source of affordable medications.

Incidence and Risk Factors of Thromboembolism with Multiple Myeloma in the Presence of Death as a Competing Risk: An Empirical Comparison of Statistical Methodologies

Multiple myeloma (MM) has an inherent high risk of thromboembolic events associated with patient as well as disease- and treatment-related factors. Previous studies have assessed the association of MM-related thromboembolism using "traditional" Kaplan-Meier (KM) and/or Cox proportional hazard (PH) regression. In the presence of high incidence of death, as would be the case in cancer patients with advanced age, these statistical models will produce bias estimates. Instead, a competing risk framework should be used. This study assessed the baseline patient demographic and clinical characteristics associated with MM-related thromboembolism and compared the cumulative incidence and the measures of association obtained using each statistical approach. The cumulative incidence of thromboembolism was 9.2% using the competing risk framework and nearly 12% using the KM approach. Bias in the measures of covariate risk associations was highest for factors related to risk of death such as increased age (75% bias) and severe liver disease (50%) for the Cox PH model compared to the competing risk model. These results show that correct specification of statistical techniques can have a large impact on the results obtained.

Utilization of free medication samples in the United States in a nationally representative sample: 2009-2013

BACKGROUND

Manufacturers provide free sample medications as a means to increase use of branded medications. Sample use varies year-to-year as branded product patents expire and new products come to market.

OBJECTIVE

This study sought to describe the use of sample medications during 2009-2013 and assess individual characteristics associated with sample use.

METHODS

Data from the 2009-2013 U.S. Medical Expenditure Panel Survey (MEPS) were used. MEPS asks participants whether they received each medication they are taking as a sample. The top 10 medications and medication classes used each year by volume were identified as well as the proportion of people who used at least one sample medication. The proportion of new initiators of medications were also classified as the percent who received a sample for the specific medication. Logistic regression was used to assess individual demographics, insurance, and medication characteristics associated with use.

RESULTS

Prevalence of sample use ranged from 9.3% in 2009 to 6.2% in 2013. The most widely used sample medications included statins during 2009-2011, which changed to inhaled β-agonists in 2012-2013, as atorvastatin became available as a generic. The overall volume of the top 10 free sample medications decreased by one-third over this study period. In 2013, 12.6% of new insulin analog users and 11.0% of new oral contraceptive users receive these medications through samples. Regression analysis showed that U.S. Medicaid- and Medicare-insured persons were less likely to use samples compared to those with private insurance.

CONCLUSIONS

Sample medication use has decreased as generic medications are becoming more used in the U.S.

Competing Risks Analysis of Cancer-associated Recurrent Thrombosis, Major Bleeds, and Death in a Geriatric Cohort

Background: Individuals with cancer are at an increased risk of venous thromboembolism (VTE). There is a continued increased risk of recurrent VTE after the initial event as well as increased bleed risk related to VTE treatment. Objectives: This study sought to observe the incidence of recurrent VTE, major bleeding, and death in a geriatric oncology population during treatment for a cancer-associated VTE. Methods: We utilized an insurance claims database of Medicare Advantage beneficiaries 65 and older. The index VTE was identified and individuals were followed up to 180 days to observe an outcome event. Treatment groups were classified among those receiving warfarin, low-molecular weight heparins (LMWH), vena cava (VC) filters with or without anticoagulation, or no treatment. Treatment groups were compared on baseline demographic and clinical characteristics and an inverse probability of treatment weight was used to balance these factors between the groups. A competing risks, time-to-event analysis was performed including treatment only models as well as adjusted models with additional covariates. Causespecific hazards ratios (HRs) and their 95% confidence intervals were reported. Results: Treatment groups differed on baseline variables including age, comorbidities, and tumor sites. After balancing the treatment groups on baseline characteristics, those receiving LMWHs had no difference in recurrent VTE compared to warfarin but had less than half the risk of major bleeding (HR=0.48 [0.27-0.85]). Those receiving VC filters had increased risk of all outcome events relative to warfarin. Conclusions: Patients over the age of 65 with cancer are at a high risk of experiencing recurrent VTE and major bleeding during treatment for a cancer-associated VTE. These results are consistent with United States guidelines which recommend LMWHs over warfarin for treatment and secondary prevention of VTE.