Bimal V. Patel, PharmD, MS

 To investigate the impact of Part D coverage gap reform on diabetes medication adherence.

 Retrospective data analysis based on pharmacy claims data from a national pharmacy benefit manager.

We used a difference-in-difference-indifference method to evaluate the impact of coverage gap reform on adherence to diabetes medications. Two cohorts (2010 and 2011) were constructed to represent the last year before Affordable Care Act (ACA) reform and the first year after reform, respectively. Each patient had 2 observations: 1 before and 1 after entering the coverage gap. Patients in each cohort were divided into groups based on type of gap coverage: no coverage, partial coverage (generics only), and full coverage.

 Following ACA reform, patients with no gap coverage and patients with partial gap coverage experienced substantial drops in copayments in the coverage gap in 2011. Their adherence to diabetes medications in the gap, measured by percentage of days covered, improved correspondingly (2.99 percentage points, 95% confidence interval [CI] 0.49-5.48, 

 = .019 for patients with no coverage; 6.46 percentage points, 95% CI 3.34-9.58, 

 <.0001 for patients with partial coverage). Patients with full coverage also had lower copayments in the gap in 2011. However, their adherence did not increase (—0.13 percentage point, 

 In the first year of ACA coverage gap reform, copayments in the gap decreased substantially for all patients. Patients with no coverage and patients with partial coverage in the gap had better adherence in the gap in 2011.

The Affordable Care Act mandated that Part D coverage gap be shrunk starting from 2011. The reform had an important impact on adherence to diabetes medications in the gap.

		 The coverage gap reform decreased copayments for diabetes medications substantially in the coverage gap for all patients.

		 Patients with no coverage and patients with partial coverage in the gap had significant improvements in adherence.

		 Patients with full coverage in the gap had unchanged adherence to diabetes medications in the coverage gap in 2011.

Medicare Part D was implemented in 2006 to provide prescription drug benefits for Medicare beneficiaries. The implementation of Part D has led to many positive changes: reduced out-of-pocket expenses, increased medication fills, improved adherence, decreased medical spending, and fewer avoidable hospitalizations.

One controversial aspect of the Part D benefit design is the coverage gap, commonly referred to as the “donut hole.” Patients with a Part D plan that has a standard defined benefit have a copayment of 25% on prescription drugs after they pay the initial deductible. After their total pharmacy spending reaches the coverage gap limit ($2830 in 2010), patients have a 100% copayment for their drugs until they reach the catastrophic limit ($6440 in 2010). In the catastrophic stage patients pay 5% or $2.60 per prescription.

The lack of coverage in the donut hole raises concern that the increase in copayment may have an adverse effect on drug utilization. Research has shown that reaching the coverage gap decreases medication fills

 and lowers adherence to essential medications.

 In response to these concerns, the Affordable Care Act (ACA), passed in March 2010, includes a provision to shrink the coverage gap. Starting in 2011, the copayment for brand drugs in the coverage gap was reduced 50%, and the copayment for generic drugs was reduced 7%. The copayments for brand drugs and generic drugs in the gap will be further reduced after 2012. In the year of 2020, the donut hole will be completely closed with a 25% copayment for both brand and generic drugs.

The closure of the donut hole represents another significant change for Medicare Part D. A solid understanding of the latest reform is critical for the design of Part D benefits. This research contributes by investigating the impacts of the 2011 coverage gap reform on adherence to diabetes medications. We focused on diabetes because it is a highly prevalent disease among Medicare beneficiaries. It is estimated that in 2010, the prevalence rates for people aged 65 to 74 years and >75 years were 20.7% and 18.9%, respectively. The diabetes prevalence rates for the 2 groups are expected to become 30.1% and 32.7% in 2050.

 Effective management of diabetes is vital to the success of Medicare.

There are 4 types of Part D plans: standard defined benefit, actuarially equivalent, basic alternative, and enhanced alternative. Of the 4 types of plans, the actuarially equivalent and basic alternative plans are similar to the standard defined benefit plan in coverage. Patients with an enhanced alternative plan can pay a higher premium for better coverage, such as having coverage in the gap and/or covering more drugs than standard defined benefit plans.

The year 2011 is the first year of coverage gap reform mandated by the ACA. In 2011, patients reaching the gap received a 50% copayment reduction for brand drugs and a 7% copayment reduction for generic drugs. For patients whose Part D plans provided coverage in the gap, discounts were applied to their copayments directly. For example, suppose a patient who had coverage while in the gap in 2010 paid a 25% copayment for both brand and generic drugs in the coverage gap. In 2011, this same patient would pay a 12.5% copayment for brand drugs and a 23.25% copayment for generic drugs in the coverage gap.

Data for this research came from pharmacy claim data from MedImpact HealthCare Systems Inc’s book of business. Med- Impact is a national pharmacy benefit manager. The research data included a 2010 cohort and a 2011 cohort. The 2010 cohort represents the last year before ACA reform and the 2011 cohort represents the first year after ACA reform.

Patients in both cohorts were required to be continuously enrolled in the same health plan for 2 years: 1 year before the cohort year and the cohort year. Patients with diabetes were identified as having at least 2 diabetes drug claims in both the cohort year and the year before the cohort year. It was important to require patients to have used diabetes drugs before the cohort year so that we had a full year to measure adherence in the cohort year. The inclusion of 1 year of data before the cohort year also was important to construct a comorbidity index. All patients had to be at least 65 years old in their cohort year. Patients eligible for a low-income subsidy were removed because their copayment structures are different.

Each data cohort included patients with no coverage, partial coverage (ie, generic coverage only), and full coverage in the gap. In this research, patients with partial and full coverage all came from enhanced alternative plans. Patients with no coverage came from standard defined benefit, actuarially equivalent, basic alternative, and enhanced alternative plans. Theoretically, patients in actuarially equivalent, basic alternative, and enhanced alternative plans could have coverage gap limits different from those of standard defined benefit plans. However, that was not the case in this research. We checked and confirmed that all patients in the data set had the same coverage gap limits.

These identification steps generated 20,709 patients in the 2010 cohort and 

2 patients in the 2011 cohort. We selected only patients who entered the coverage gap but did not enter the catastrophic level to make it easier to compare adherence before the gap with adherence in the gap.

This exclusion led to 6828 patients in the 2010 cohort and 6124 patients in the 2011 cohort.

The outcome variable was adherence to diabetes medications. Diabetes is a long-term and progressive disease. Adherence to diabetes medication is critical for maintaining glycemic control and slowing down disease progression. Poor adherence can lead to inadequate glycemic control, higher probabilities of hospitalization, higher medical cost, and higher mortality.

Adherence is measured by proportion of days covered (PDC), defined as the number of days covered by at least 1 diabetes medication divided by the length of the study period based on medication fill date and days of supply. The PDC methodology has been used in many studies.

The value of PDC is bounded between 0.0 and 1.0.

Difference-in-Difference-in-Difference Method

This research used a difference-in-difference-in-difference method to identify the impact of coverage gap reform on adherence to diabetes medications. In this model, year 2010 data and year 2011 data were used to represent the periods before and after coverage gap reform, respectively. Each patient in a cohort year had 2 observations: one before the coverage gap and the other in the coverage gap. Patients with no coverage or partial coverage were the treatment group; patients with full coverage were the control group. In a straightforward difference- in-difference-in-difference model, the effect of coverage gap reform was captured by the interaction of coverage gap, year 2011, and the type of coverage in the coverage gap.

One problem of a straightforward difference-in-difference-in-difference model is that the outcome variable, PDC, was bounded between 0 and 1. In this research, the mean PDC value was around 80% and the distribution was highly skewed. This can be problematic in an ordinary least squares regression. One way to address this issue is to transform the outcome variable as the change of PDC after reaching the coverage gap, defined as PDC in the coverage gap minus PDC before the gap. After the transformation, the model can be written as:

 equals PDC in the coverage gap minus PDC before the coverage gap for each patient, 

 is the dummy variable indicating no coverage in the gap, 

 is the dummy variable indicating partial coverage in the gap, and 

 is the dummy variable indicating the 2011 cohort.

 are the coefficients of interest. They represent the change in adherence in the coverage gap in response to coverage gap reform for patients with no coverage and partial coverage, respectively.

Through the transformation, each patient’s idiosyncratic propensity to be adherent was removed. Patients’ unchanged characteristics such as age, sex, and geographical location were also removed. As a consequence, the inclusion of covariates such as age and sex was not necessary. The main model was run without covariates. We included covariates in the model as part of the robustness analysis. Control variables included age, sex, geographical location, whether insulin was used in the last year, and comorbidities. Information on patient demographics was obtained from patients’ eligibility files. Information on the use of insulin was available from the previous year’s pharmacy claim file. Comorbidities were measured by RxRisk, a comorbidity measure based on pharmacy claims in the prior year.

Each RxRisk category indicates whether a patient took certain classes of medications (eg, those for hypertension and hyperlipidemia) in the previous year. Only RxRisk categories with prevalence rates higher than 5% were included in the study.

One notable difference between our outcome variable and the comparable measure in the Centers for Medicare & Medicaid Services (CMS) Five-Star Quality Rating System for Medicare Advantage Plans is that the CMS star rating uses only oral diabetes medications in its calculation. This research uses all diabetes medications including insulin. A robustness check was performed at the end of the study by removing patients with a history of insulin use from the model. This robustness check was important also because patients receiving insulin likely had either advanced type 2 diabetes or type 1 diabetes. These patients can be different from patients who do not use insulin, who tend to be patients in early stage of type 2 diabetes.

An alternative model was to use a dichotomous variable of adherence as the outcome variable, with a value of 1 assigned to patients having 

80% PDC. This dichotomous variable of adherence is used frequently in adherence studies.

 One problem of using a dichotomous outcome variable is that the interpretation of interaction terms in a logit/probit model is not straightforward. The interaction effect varies by patients. It can be positive or negative depending on many factors.

 This research uses a continuous outcome variable for ease of interpretation.

A generalized estimating equation was used to model the effect of coverage gap reform. The model was adjusted for health plan clustering effect to account for potential similarities of patients within the same health plan. The whole statistical analysis was performed with SAS 9.3 (SAS Institute, Cary, North Carolina).

Articles

The Part D coverage gap reform in 2011 improved adherence to diabetes medications in the coverage gap.

Effects of Coverage Gap Reform on Adherence to Diabetes Medications

To evaluate the impact of Medicare Part D coverage gap (donut hole) on adherence to diabetes medications.

 Retrospective cohort analysis based on pharmacy claims data.

The sample included 12,881 Medicare Part D beneficiaries with diabetes who entered the coverage gap in 2008. Sample patients had 3 different levels of coverage in the donut hole: no coverage, generic drug coverage only, and both generic and brand-name drug coverage. Adherence was measured by the proportion of days covered. We used a difference-in-difference model to evaluate the effect of coverage gap on adherence.

 In the donut hole, the average copayment for diabetes medications increased substantially for beneficiaries with no coverage and beneficiaries with generic drug coverage only, whereas the average copayment for beneficiaries with both generic and brand-name medication coverage declined slightly. Compared with beneficiaries with full coverage of both generic and brand-name drugs, beneficiaries with no coverage (odds ratio[OR] = 0.617, 

 <.0001, 95% confidence interval [CI] = 0.523, 0.728) and beneficiaries with generic drug coverage only (OR = 0.702, 

 <.0001, 95% CI = 0.604, 0.816) were significantly less likely to be adherent after entering the donut hole. The difference between having generic coverage and no coverage was not significant (

 The coverage gap in the Medicare Part D program has a significant negative impact on medication adherence among beneficiaries with diabetes. Availability of brand-name drug coverage in the donut hole is critical to adherence to diabetes medications.

The Medicare Part D coverage gap has a significant negative impact on adherence to diabetes medications.

The average copayment of diabetes medications increased substantially for beneficiaries with no coverage or only generic coverage in the donut hole. 

Beneficiaries with no coverage or only generic coverage in the donut hole were significantly less likely to be adherent to diabetes medications in the donut hole. 

Offering only generic coverage in the donut hole has a limited impact on patients' adherence to diabetes medications.

	The Medicare Part D program, introduced on January 1, 2006, provides prescription drug coverage for Medicare beneficiaries. One unique feature of the Part D benefit design is the coverage gap (or donut hole). The defined standard benefit in 2008 started with a $275 deductible and a 25% copayment for drug spending between $275 and $2510. After the initial coverage period, beneficiaries entered a coverage gap, in which they paid 100% of the drug cost, until their true out-of-pocket drug spending reached the catastrophic limit of $4050 (or total drug spending of $5726.25). Under the catastrophic coverage, beneficiaries pay the greater of a 5% or a $2.25/$5.60 (generic/brand-name) copayment.

	The donut hole is a controversial component of the Part D design because a lack of benefit may negatively impact patients’ drug utilization and subsequent clinical outcomes. Studies have shown that beneficiaries of private drug benefit plans with annual benefit limits are more likely to discontinue their medications and have a higher rate of hospitalization compared with beneficiaries who have no benefit limits.

 Sun et al reported that the donut hole was associated with reductions in medications used for potentially disabling and life-threatening conditions.

 Another study by Zhang et al indicated that beneficiaries with no coverage in the donut hole decreased their use of monthly prescriptions by about 14%.

	In this study, we examine the effects of the Part D coverage gap on adherence to diabetes medications. We focused on diabetes because diabetes is a serious chronic medical condition with considerable morbidity and mortality. The prevalence rate of diabetes is high among senior citizens (23.1%), compared with 7.8% in the US population.

 In 2007, over half of the $174 billion economic cost of diabetes was attributed to people 65 years and older, and a large portion of these costs were borne by Medicare.

	Previous studies on the Part D coverage gap generally used the number of prescriptions filled as the outcome variable.

 It is difficult to link the number of prescriptions with important outcomes such as hospitalizations and glycosylated hemoglobin (A1C) levels to help decision making in Part D benefit design. Our research focused on adherence to  diabetes medications, a critical component in the management of diabetes. Aggressive glycemic management, as measured by A1C, can reduce long-term complications

 and medical costs associ-ated with diabetes treatment.

 Poor adherence to diabetes medications is a major contributor to unsatisfactory glycemic control

 and increases the risk of adverse health events associated with diabetes.

 Inadequate adherence to diabetes medications in the donut hole could have serious long-term consequences for both Medicare and Medicare beneficiaries.

	This study is a retrospective cohort analysis based on prescription drug claims data provided by MedImpact Healthcare Systems, Inc, a national pharmacy benefit management company. Compared with the national data, Part D beneficiaries in MedImpact had the same sex distribution but were less likely to be under age 65 years (

	The sample in this research included patients over age 65 years who were continuously enrolled in a Medicare Part D plan from January 1, 2007, to December 31, 2008, and had at least 2 diabetes medication claims in each year. This study required patients to use diabetes medications in 2007 to ensure that all patients had used diabetes medications prior to the study period, January 1, 2008, to December 31, 2008. Utilization data in 2007 were used to acquire comorbidity information. Patients who received low-income subsidies for their premium and copayment were excluded from the sample because of their unique benefit structure.

	A total of 35,426 patients were identified using these criteria. Among these patients, 37.10% reached the donut hole in 2008 but did not reach the catastrophic limit, 13.38% reached both the donut hole and the catastrophic limit, 49.44% never reached the donut hole, and the remaining 0.09% reached the catastrophic limit directly without entering the donut hole. We selected only patients who had reached the donut hole but not the catastrophic limit for this research.

 The final sample contained 12,881 patients from more than 30 different health plans.

	Part D patients can have different types of Part D plans, including the defined standard benefit (DSB) plan, the actuarially equivalent (AE) plan, the basic alternative (BA) plan, and the enhanced alternative (EA) plan. The AE and BA plans are equivalent to DSB plans in value and do not provide coverage in the donut hole. The EA plans charge a supplemental premium and provide supplemental benefits. Many EA plans cover either generic drugs only or both generic and brand-name drugs as supplemental benefits.

	In our analysis, we designated 3 groups of Part D beneficiaries based on their coverage in the donut hole. The first group consisted of beneficiaries with no donut hole coverage (“no coverage” group). This group included beneficiaries with DSB, AE, or BA plans and beneficiaries with EA plans that did not offer coverage in the donut hole. The second group comprised beneficiaries with only generic drug coverage in the donut hole (“generic coverage only” group) from EA plans. The third group included beneficiaries with both generic and brand-name drug coverage in the donut hole (“full coverage” group) from EA plans. All Part D plans identified in this research had $2510 and $5726.25 for the start and end of the donut hole.

	The outcome measure of interest was adherence to diabetes medications. In this analysis, adherence to diabetes medications was measured by the proportion of days covered (PDC), calculated as the number of days covered by at least 1 diabetes medication in a certain period of time, divided by the number of days in that period. To calculate a PDC, each day in that period was evaluated as covered or as not covered by a medication based on fill date and days of supply. If a new claim for the same diabetes medication occurred before the end date of the last claim, the starting date of the new claim was pushed forward to the end date of the last claim. We calculated 2 PDCs for each patient: one for the period before reaching the coverage gap (pregap PDC) and the other for the period after reaching the coverage gap (postgap PDC). Patients with a PDC of 80% or more were classified as adherent.

In addition to a dichotomous adherence variable, we used continuous PDC as the outcome variable to test the robustness of the results.

	Control variables include age, sex, whether patient used insulin in the prior 12 months, geographic region (Northeast, South, Midwest, and West), and comorbidities in the prior 12 months. Comorbidity indicators were constructed based on the RxRisk model of comorbid conditions.

 A series of dummy variables for each major comorbid condition were

	We used a difference-in-difference regression analysis to measure the effect of coverage gap on adherence to diabetes medications. The difference-in-difference method used patients with full coverage in the donut hole as the control group because these patients experienced little copayment change in the donut hole. The experimental group consisted of patients in the other 2 groups (no coverage and generic coverage only), who experienced substantial copayment increases in the donut hole.

	Two challenges in measuring the effect of the donut hole are that (1) patients may self-select different levels of coverage and (2) they may have different propensities for being adherent to diabetes medications. The difference-in-difference method addresses this issue by having repeated observations for the same patient in both the pregap period and postgap periods. A group fixed effect was used to control for the permanent differences across different groups, and a time fixed effect was used to control for the secular trend within the study period. The true donut hole effect is the difference in adherence to diabetes medications between the control and the experimental groups in the donut hole, adjusting for the difference between the control and the experimental groups before entering the donut hole.

presents the patients’ characteristics. The average age for the entire sample was 74.97 years, and 48.06% of patients in the sample were male. A typical patient spent around 104 days in the donut hole. The vast majority of patients in the sample lived in the Midwest (82.85%). The most significant comorbidities were hypertension (87.41%), hyperlipidemia (81.54%), and heart disease/hypertension (69.47%).

	About a third (33.47%) of patients used insulin in the prior 12 months. Among patients who used insulin in 2007, the percentages using short-acting, intermediate-acting, long-acting, and mixed insulin were 32.27%, 25.68%, 55.56%, and 27.67%, respectively. These categories are not mutually exclusive because a patient could have used more than 1 type of insulin in 2007.

	In the sample, 19.63% of the 12,881 beneficiaries had no coverage in the donut hole, 20.08% of beneficiaries were covered for generic drugs only, and 54.28% of beneficiaries had full coverage. Patients with no coverage in the donut hole were slightly older (75.40 years) than patients with generic coverage only (74.85 years) and patients with full coverage (74.87 years). Patients with generic coverage only had the longest stay in the donut hole (107.96 days), and patients with no coverage in the donut hole spent the shortest time in the donut hole (99.91 days); patients with full coverage fell in between (103.54 days).

 shows the adherence rates and average copayments for patients in the sample. The adherence rate before reaching the donut hole for the no-coverage group was 0.82, which was close to the adherence rates of 0.81 for the group with generic coverage only and 0.85 for the group with full coverage. After reaching the donut hole, the adherence rate of patients with no coverage dropped 8 percentage points to 0.74. Similarly, the adherence rate of patients with generic coverage dropped 6 percentage points to 0.75. In contrast, patients with full coverage maintained their adherence rate at 0.85 in the donut hole.

	The coverage gap had a substantial impact on copayment. The average copayment (per 30 days of supply, the same thereafter) of all diabetes medications before reaching the donut hole was $18.63 for patients with no coverage, $18.63 for patients with generic coverage only, and $16.48 for patients with full coverage. After reaching the donut hole, the average copayment increased to $69.20 (a 271% increase) for the group with no coverage and to $67.18 (a 261% increase) for the group with generic coverage only. Partially because of the increased use of 90 days of supply, the average copayment for patients with full drug coverage actually decreased slightly to $14.33 after reaching the donut hole.

	Table 3 breaks down the change in average copayment by generic and brand-name diabetes medications. For patients with no coverage, the average copayment for generic drugs almost doubled after reaching the donut hole ($12.13 vs $6.42). In contrast, the average copayment for generic drugs did not change for patients with generic coverage only ($6.64

	vs $6.52) and even decreased from $6.37 to $4.63 for patients with full coverage. The average copayment for brand-name drugs quadrupled after reaching the donut hole for patients with no coverage ($126.05 vs $31.79) and patients with generic coverage only ($133.34 vs $32.47).

 presents the changes in the market share by diabetes medication classes before and in the donut hole. Market share was defined as the sum of days of supply in each class of diabetes medications divided by the sum of days of supply for all diabetes medications. Overall, the market shares of different diabetes medications changed little before and in the donut hole. There was no evidence that patients with no brand-name drug coverage tried to increase the use of genericdrugs in response to increases in copayment for brand-name drugs in the donut hole. In fact, the market share of insulin and dipeptidyl peptidase-4 inhibitors, 2 classes of medications dominated by brand-name medications, increased in the donut hole for patients in all 3 groups.

 Difference-in-Difference Regression Analysis

The coverage gap in the Medicare Part D program has a significant negative impact on adherence to diabetes medications.

Part D Coverage Gap and Adherence to Diabetes Medications

 To evaluate the impact of utilization management methods on use of smoking cessation medication.

Retrospective cohort analysis of pharmacy claims data.

 The study population included patients at least 18 years of age, naive to varenicline, and with a rejected varenicline claim between January 2007 and April 2008 because of 1 of 4 utilization management restrictions: drug not covered, prior authorization, step therapy, or quantity limits. The outcome variable was whether patients used any smoking cessation medication including varenicline, bupropion, and nicotine replacement therapy (prescribed and over-the-counter) within 6 months of the rejected varenicline claim. Multivariable logistic regression was conducted to evaluate the probability of filling a prescription for any smoking cessation medication.

 A total of 15,597 patients were identified. Within 6 months after the rejected claims, 8393 (53.8%) patients filled at least 1 smoking cessation medication, 7864 (93.7%) of whom filled varenicline. Compared with quantity limits, the odds ratios for filling any smoking cessation medication after the rejected varenicline claim were 0.01 (95% confidence interval [CI], 0.01-0.02) for varenicline not covered, 0.07 (95% CI, 0.06-0.09) for step therapy, and 0.18 (95% CI, 0.16-0.20) for prior authorization. Higher out-of-pocket expense was associated with a lower probability of filling any smoking cessation medication.

 About half of the patients in this study did not fill any smoking cessation medication following a rejected varenicline claim. It is important to address this treatment gap in support of patients seeking smoking cessation therapy.

This research examines the use of smoking cessation medication among patients whose varenicline claims were rejected because of 1 of 4 utilization management restrictions (drug not covered, prior authorization, step therapy, or quantity limits). Six months after the rejected varenicline claim:

Only 53.8% of patients filled a prescription for any smoking cessation medication. 

Of patients who did fill a prescription, 93.7% received varenicline and 6.3% received other smoking cessation medication without getting varenicline. 

Higher out-of-pocket expense was associated with a lower filling rate for any smoking cessation medication.

Smoking is a serious health problem associated with significant mortality, morbidity, and costs. It is known to be a cause of many deadly diseases such as cancer, stroke, heart diseases, and lung diseases.

The Centers for Disease Control and Prevention estimated that smoking or exposure to smoking led to 443,000 premature deaths annually from 2000 to 2004 in the United States. Three leading causes of smoking-related death were lung cancer, ischemic heart disease, and chronic obstructive pulmonary disease. The total  annual economic burden of smoking was $193 billion per year in the United States during 2001 to 2004, with $96 billion for direct healthcare costs and $97 billion for productivity losses.

It was estimated that if all smokers covered by Medicaid stopped smoking, Medicaid could save $9.7 billion every year after 5 years.

Further, at a global level, the World Bank has estimated that smoking accounts for about 6% to 15% of total health expenditures in developed countries.

 Although smoking cessation (SC) is extremely beneficial, it is known to be difficult. Among all of the untreated smokers who try to quit smoking, only 4% to 7% are likely to be successful.

Current clinical guidelines call for aggressive treatment of tobacco dependence through a combination of clinical counseling and medication interventions to improve the success rates.

Seven medications have been identified as first-line treatment for smoking, including bupropion SR, nicotine gum, nicotine inhaler, nicotine lozenge, nicotine nasal spray, nicotine patch, and varenicline.

 Unfortunately, SC treatments have been severely underused. The National Health Interview Survey shows that of smokers attempting to quit smoking in 2000, only 21.7% used any pharmacotherapy and only 1.3% used behavioral counseling.6 Clinical guidelines call for coverage of tobacco dependence treatments to improve the use of SC treatments and cessation rates.

 Research on the impact of benefit design on SC medication is limited. Existing studies generally focused on the effects of copayments on SC medication use.

 The impact of utilization management methods such as prior authorization, step therapy, restrictive formulary, and quantity limits is not well understood. Previous research on rejected claims in other therapeutic areas showed that some patients did not fill prescriptions after their claims were rejected. For example, research on step-therapy restrictions on angiotensin-receptor blockers showed that 12 months after a claim for an angiotensinreceptor blocker was rejected because of step therapy, 7% of patients did not have any antihypertensive medication claims.

Another study showed that up to 70% of patients did not try to overcome a prior-authorization restriction after having a rejected claim for a cyclooxygenase-2  inhibitor. 

If a large percentage of patients did not fill any SC medication after being denied an SC medication claim, such an observation should raise concerns for health plans and policy makers. In the long run, decreased use of SC medication could lead to worse health outcomes and higher healthcare costs.

 We investigated the relationship between utilization management for varenicline and the initiation of therapy with SC medication. Specifically, this study reports the proportion of patients who actually used SC medication 6 months after a varenicline claim rejection and examines factors that influenced patients’ decision to use SC medication. We focusedon varenicline because utilization management of SC medication generally focuses on this drug, as it is the only newly approved SC medication in over a decade. In this study, 94.3% of the patients whose claims for SC medication were rejected because of utilization management had prescriptions for varenicline. Sensitivity analysis was performed to include patients whose claims for other prescribed SC medication were rejected.

 A retrospective cohort data analysis was conducted based on MedImpact’s claims database. MedImpact is a large private national pharmacy benefits management company. The claims database contains detailed information of the adjudication process, including rejected, reversed, and approved claims. If a patient decides to pay cash to purchase a medication after a claim is rejected because of utilization management, he or she receives a network discount, typically around 10% to 15%, and the claim is recorded in the data set.

 The target study population included patients who attempted to fill prescriptions for varenicline that were rejected because of utilization management. The study population met the following inclusion criteria:

 • At least 1 rejected varenicline claim from January 2007 to April 2008 because of 1 of 4 utilization management restrictions: drug not covered, prior authorization, step therapy, or quantity limits.

 • No approved or rejected varenicline claims before the identification period.

 • Continuously enrolled in the same health plan 1 year before and at least 183 days after the initial rejected varenicline claim.

 • Had coverage for over-the-counter (OTC) SC medication.

 • Age 18 years or older at the time of attempting to fill a varenicline claim.

 This research focused on patients whose varenicline claims were rejected because of 1 of 4 utilization management restrictions: drug not covered, prior authorization, step therapy, or quantity limits. These 4 restrictions accounted for approximately 80.9% (140,228 of 173,314) of the total number of rejected varenicline claims (

). The remaining varenicline claims were turned down for reasons unrelated to benefit design such as member benefit expired, incorrect spelling, not a network pharmacy, etc.

 Patients whose claims for varenicline were rejected because the drug was not covered had a closed formulary in which medications not listed in the formulary were excluded from coverage. Closed formularies are commonly used by health plans to control costs.

Patients who had an open formulary, in which medications not in the preferred formulary were covered with a higher copayment, were not included in this analysis because their varenicline claims were not rejected.

 Patients whose claims were rejected because of prior authorization usually were required to enroll in an approved counseling and behavior-modification program. Both clinical guidelines and the prescribing information for varenicline recommend the use of clinical counseling along with varenicline to improve cessation rates.

 For this reason, many health plans require patients to enroll in a clinical counseling program before varenicline can be dispensed. Upon enrollment in such a program, patients receive a certificate of enrollment that they then can fax to health plans to meet the prior-authorization requirement and obtain varenicline. Prescribing physicians also can certify this enrollment on behalf of patients to help them obtain the drug.

 The step-therapy restriction typically requires patients to try bupropion or nicotine replacement therapy before trying varenicline. Health plans usually impose the steptherapy requirement to encourage patients to use a low-cost alternative. If a significant portion of patients can be diverted to low-cost alternatives, health plans can save on medication costs.

 Finally, the quantity-limit restriction usually restricts days of supply of the initial varenicline claim to fewer than 21 to 34 days, depending on the health plan. Health plans usually put a quantity limit on SC medication to avoid waste. For example, some patients may stop using varenicline, like other SC medication, after several days. In that case, the rest of the medication could be wasted if a large quantity of varenicline were dispensed.

 The outcome variable was whether patients used any SC medication within 6 months of the rejected varenicline claim. In general, the intent of utilization management is to encourage patient compliance with clinical guidelines or to channel patients to use cheaper alternatives, rather than to deter patients from initiating SC pharmacotherapy treatment altogether. For this reason, the percentage of patients using any SC medication was used as a measure of success for these tools. The SC medication considered in this research included any form of bupropion, prescribed and OTC nicotine replacement therapy, and varenicline.

 Price Index for Smoking Cessation Medication

 When a patient’s claim for an SC medication is rejected, he or she may consider alternative medications. The health plan’s generosity in covering SC medication may be an important factor in the patient’s decision. We constructed a price index to quantify health plan’s coverage of SC medication.

 A price index is the average out-of-pocket (OOP) cost for SC medication weighted by the number of claims. It can be interpreted as the OOP cost for a market basket of SC medication. The 

) provides the technical details about the price index.

 Multivariable logistic regression modeling was done to examine factors that could influence patient response to a rejected varenicline claim. The covariates included age, sex, type of insurance, geographic locations, comorbidities, type of benefit restrictions, price index, and history of use of bupropion or nicotine replacement therapy before the rejected varenicline claim. Comorbidities were measured by RxRisk, a method to determine a patient’s risk profile based on pharmacy data only.

Only comorbidities that influenced more than 5% of the population were included in the final regression analysis.

About half of the patients in this study did not fill any smoking cessation medication following a rejected claim for varenicline.

Bimal V. Patel, PharmD, MS

Articles

Effects of Coverage Gap Reform on Adherence to Diabetes Medications

Part D Coverage Gap and Adherence to Diabetes Medications

Utilization Management for Smoking Cessation Pharmacotherapy: Varenicline Rejected Claims Analysis