Nilay D. Shah, PhD

 To examine differences in the out-of-pocket costs for common generic drugs used to treat chronic conditions when individuals used their Medicare prescription drug plan (PDP) or when purchased through Walmart’s generic drug discount programs (GDDPs) from 2009 to 2017.

 A retrospective analysis of Medicare PDP Formulary files and Walmart’s GDDP retail drug lists from 2009 to 2017.

 We identified all generic drugs used to treat chronic conditions that were on Walmart’s GDDP retail drug list from 2009 to 2017. We then determined the out-of-pocket costs for each drug for each Medicare PDP and compared those costs with Walmart’s GDDP cash price.

 There were 62 and 43 generic medications used to treat common chronic diseases available through Walmart’s GDDP in 2009 and 2017, respectively. Across all PDPs, the median beneficiary out-of-pocket expenditure for a 30-day supply of the GDDP-available medications for chronic diseases decreased from $5.70 (interquartile range [IQR], $2.55-$7.98) in 2009 to $2.00 (IQR, $0.00-$4.00) in 2017 (

 <.001) Approximately three-fifths (60.2%) of PDPs required beneficiaries to pay out-of-pocket costs higher than those of Walmart’s GDDP in 2009, but only one-third (33.4%) did so in 2017.

 Although Medicare beneficiary out-of-pocket costs for commonly used generic drug prescriptions generally decreased over time, Medicare beneficiaries may still be paying more for the same drugs than they would through Walmart’s GDDP. Increased generic drug price transparency, including enforcing bans on gag clauses, is needed to ensure that Medicare beneficiaries obtain drugs using the most affordable options.

Retail generic drug discount programs (GDDPs) improve the affordability of generic medications, especially for individuals without pharmaceutical coverage. We characterized trends in the out-of-pocket costs required by Medicare prescription drug plans for generic drugs used for chronic diseases and compared them with the cash price of the drugs in Walmart’s GDDP from 2009 to 2017.

Out-of-pocket costs for these generic drugs have decreased over time.

Formularies have increasingly classified these drugs into tiers other than preferred generic tiers.

Opportunities to lower out-of-pocket costs still exist, as a substantial proportion of plans still require costs higher than GDDP cash prices for the same medications.

The 2003 Medicare Modernization Act established the Medicare Part D prescription drug benefit to improve access to and affordability of prescription medications for beneficiaries through government subsidies.

 Under the benefit, private plans negotiate with drug manufacturers and compete to enroll Medicare beneficiaries. To control prescription costs, Part D plans steer consumers toward certain drugs through coverage decisions, tier structure (which determines co-payments or coinsurance), and utilization management tools. Since its inception, Part D has grown from offering prescription drug coverage to 27.9 million beneficiaries in 2006 to 43.9 million in 2018,

 and it achieved comparable levels of utilization of generic drugs by enrollees in state Medicaid programs.

 However, billions of dollars in savings could be realized through greater generic substitution of brand-name medications among Medicare beneficiaries.

In 2006, the same year that the Part D benefit went into effect, large wholesale pharmacy chains, such as Walmart, began creating generic drug discount programs (GDDPs). These programs offer common generic medications for low out-of-pocket costs, usually $4 for a 30-day supply and $10 for a 90-day supply. This price can sometimes be lower than what patients would pay through their insurance coverage when purchasing the same medications and, therefore, leads to lower out-of-pocket costs. In 2007, 80% of beneficiaries filled prescriptions for generic medications available through GDDPs, but only 16.3% of those used the $4 GDDP

; the estimated total savings if beneficiaries had used GDDPs instead of their insurance was more than $5.7 billion, demonstrating the significant impact that GDDP utilization could have on prescription medication costs.

 A recent study of 2017 Medicare prescription drug plan (PDP) coverage of generic drugs used to treat cardiovascular disease (CVD) similarly demonstrated potential savings through GDDPs: 21% of PDPs required beneficiary out-of-pocket payments higher than Walmart’s GDDP cash price for a 30-day supply of the same drug.

 However, whether the proportion of PDPs with higher prices than those offered by GDDPs varies across geography, across other medication classes, or over time remains unknown. Accordingly, we used Medicare PDP data to characterize trends in Medicare beneficiary out-of-pocket expenditures for commonly used generic medications that treat any chronic disease and were available through Walmart’s GDDP from 2009 to 2017. We expect our findings to offer important insights for future policy efforts intended to further reduce costs for Medicare beneficiaries and to better clarify the role that GDDPs are likely to play in insurance cost sharing and formulary structure.

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We used data from the second quarters of 2009 through 2017 CMS PDP Formulary and Pricing files. These files provide information submitted by each Part D plan, including the associated formulary, tier assignment for all covered medications, co-payment type (eg, dollar co-payment vs percentage coinsurance), and co-payment amount. Because comprehensive Medicare PDP data to calculate out-of-pocket costs were not available until 2009 for 30-day supplies and 2014 for 90-day supplies, we limited our analysis to annual data from 2009 to 2017. We excluded Special Needs Plans, plans with incomplete or inaccurate data, and plans that operated in states for which Walmart did not guarantee its advertised cash price (generally $4 for a 30-day supply and $10 for a 90-day supply) for all of its GDDP medications through the entire time period of the analysis. Furthermore, we noted whether each plan was a Medicare Advantage (MA) plan, which covers Part A and Part B benefits in addition to prescription drug coverage, or a stand-alone Part D plan, which covers only prescription drugs.

We obtained information on drugs offered through Walmart’s GDDP from 2009 to 2017 directly from Walmart’s pharmacy website.

 We included all oral generic prescription medications used to treat chronic conditions that were not available over the counter. Because drugs could be added or removed from GDDP lists at any time, the drug sample varied from year to year.

Our primary outcome measure was the beneficiary out-of-pocket expenditure for generic prescription drugs for chronic conditions that were available through Walmart’s GDDP from 2009 to 2017. When calculating the median out-of-pocket expenditures, we adjusted for inflation using June 2017 as the index month and year,

 and we estimated the costs incurred by the beneficiary when filling a prescription with coverage for the first time in the year.

As a secondary outcome, we determined the proportion of plans requiring costs greater than Walmart’s GDDP price. For this outcome, we did not adjust for inflation because the GDDP cash price has remained the same since the inception of Walmart’s GDDP. Although nearly all included medications were offered by Walmart’s GDDP at $4 for a 30-day supply and $10 for a 90-day supply, for alendronate sodium, finasteride, and tamoxifen citrate, the 30-day supply cash price was $9 and the 90-day supply cash price was $24.

Other key variables of interest included presence of select care tier designations for certain drugs within plans, therapeutic classes, and geographic distribution of plans. In 2015, CMS increased the weight of adherence to some medications for hypertension, hyperlipidemia, and type 2 diabetes in calculating a plan’s star rating.

 Consequently, these medications (and other common drugs used for chronic conditions) were increasingly allocated into a select care tier, which has low to no out-of-pocket costs, with the goal of improving beneficiary medication adherence and allowing plans to score higher on the star rating measure. We grouped any drug listed in a select care tier with those in tier 1, which typically includes low-cost generic medications. The drugs listed under Walmart’s categorizations were classified into 3 therapeutic classes: cardiovascular, mental health, and endocrine. For geographic distribution, each state is assigned to 1 of 26 total MA regions and 1 of 34 stand-alone regions. All plans operating in MA or stand-alone regions composed of multiple states were assigned to all of those states; beneficiaries can enroll only in plans that operate in their state of residence.

We used descriptive analyses to calculate median out-of-pocket costs across all GDDP-available medications (30- and 90-day supply) offered by all plans each year between 2009 and 2017. We also compared each plan’s out-of-pocket cost for each drug with the Walmart GDDP cash price. Descriptive analyses were also applied to outcomes across geography.

Analyses were stratified by plan type (MA-PDP vs stand-alone PDP) and tier (tier 1 and select care tiers vs all other tiers). The Mann-Whitney 

 test was used to compare MA-PDPs and stand-alone PDPs within the same year. Because plan type and tier stratifications were independent of each other, we did not correct for multiple comparisons. We used the same test for comparing both outcomes in 2009 and 2017. We used the Wilcoxon signed-rank test to examine differences among the 3 therapeutic classes in a pairwise fashion with the Holm-Bonferroni method to correct for multiple comparisons, using a 

 value of .017 as the threshold for statistical significance.

As a sensitivity analysis, we used generalized estimating equations to examine whether within-plan clustering may be driving the results observed regarding median out-of-pocket expenses over time for 30-day supplies of GDDP chronic disease medications from 2009 to 2017. We included Medicaid region and Medicaid expansion status to account for factors that could influence out-of-pocket costs for generic drugs. Results of these analyses were consistent with our main analyses and are presented in the eAppendix material only (

Stata version 15.1 (StataCorp LLC; College Station, Texas) and RStudio version 1.1.423 (RStudio Inc; Boston, Massachusetts) were used for all analyses.

There were 3013 Medicare PDPs in 2009 and 2150 PDPs in 2017 included in our sample (

). Over this period, the composition of Medicare PDP types shifted toward MA-PDPs (51.6% MA-PDPs and 48.4% stand-alone PDPs in 2009; 71.3% MA-PDPs and 28.7% stand-alone PDPs in 2017), primarily because of a reduction in the number of stand-alone PDPs.

The number of medications available through Walmart’s GDDP and eligible for inclusion in our study decreased from 62 generic medications used to treat common chronic diseases in 2009 to 43 in 2017 (

). During this time period, Walmart added only 1 medication (finasteride) but removed 20. Our sample included medications for cardiovascular (32 and 20 medications in 2009 and 2017, respectively), psychiatric (17 and 12 medications in 2009 and 2017), and endocrine (13 and 11 medications in 2009 and 2017) disorders. By 2017, Walmart no longer offered digoxin, anticonvulsants, or selective serotonin reuptake inhibitors as part of the GDDP.

The median proportion of GDDP drugs for common chronic conditions covered in Medicare plans in either tier 1 or a select care tier decreased from 94.8% in 2009 to 60.6% in 2015, and then rose to 75.2% in 2017. By plan type, the median proportion decreased from 96.1% in 2009 to 57.5% in 2015, and then increased to 77.4% in 2017 for MA-PDPs; a similar trend occurred for stand-alone PDPs, from 93.5% in 2009 to 65.4% in 2015 to 69.6% in 2017 (

Across all plans, the median beneficiary out-of-pocket expenditure for a 30-day supply of these GDDP-available medications decreased 64.9%, from $5.70 (interquartile range [IQR], $2.55-$7.98) in 2009 to $2.00 (IQR, $0.00-$4.00) in 2017 (

). By plan type, costs decreased 64.9% for MA-PDPs, from $5.70 (IQR, $1.85-$7.98) in 2009 to $2.00 (IQR, $0.00-$4.00) in 2017 (

<.001), and 82.5% for stand-alone PDPs, from $5.70 (IQR, $2.64-$7.98) in 2009 to $1.00 (IQR, $0.69-$2.39) in 2017 (

Similar declines in median out-of-pocket costs were observed among both MA-PDPs and stand-alone PDPs for drugs in tier 1 or select care tiers and for drugs in all other tiers (

Proportion of Plans Requiring Beneficiary Out-of-Pocket Costs Greater Than GDDP Cash Prices

The median proportion of plans requiring beneficiary out-of-pocket costs greater than the GDDP cash price for a 30-day supply of medications was 60.2% (IQR, 58.5%-63.6%) in 2009 and 33.4% (IQR, 17.9%-55.1%) in 2017 (44.5% decrease; 

 <.001) (Table). By plan type, the proportion for MA-PDPs was 61.2% (IQR, 60.1%-62.9%) in 2009, which decreased to 41.0% (IQR, 22.7%-66.1%) in 2017 (33.0% decrease; 

 <.001). Similarly, the same proportions for stand-alone PDPs were 60.1% (IQR, 56.8%-65.4%) in 2009 and 11.8% (IQR, 3.3%-28.2%) in 2017 (80.4% decrease; 

Declines in proportion of plans requiring beneficiary co-payments more than the GDDP cash price were observed among drugs in tier 1 or select care tiers for both plan types (

 <.001) and among drugs in other tiers for only stand-alone PDPs, not MA-PDPs (

Across geographic areas, there was greater variation in out-of-pocket costs required by MA-PDPs than stand-alone PDPs and greater variation in 2009 than in 2017 (

). In 2009, the IQRs for out-of-pocket costs across states were $5.70 to $6.84 (range, $0-$11.40) for MA-PDPs and $5.70 to $5.70 (range, $5.70-$6.84) for stand-alone PDPs. In 2017, the IQRs were $2.00 to $3.00 (range, $0-$6.00) for MA-PDPs and $1.00 to $2.00 (range, $1.00-$2.00) for stand-alone PDPs.

In 2009, the IQRs for proportions of plans requiring co-payments greater than the GDDP cash price for a 30-day supply across states were 61.9% to 81.0% (range, 19.2%-98.6%) for MA-PDPs and 60.2% to 62.6% (range, 55.1%-66.9%) for stand-alone PDPs. In 2017, the IQRs were 40.4% to 55.8% (range, 27.5%-74.3%) for MA-PDPs and 15.5% to 19.3% (range, 12.1%-22.8%) for stand-alone PDPs.

Across all plans, the differences among the proportions of plans requiring co-payments greater than the GDDP cash price for a 30-day supply were not significantly different by therapeutic class in 2009 (60.2%, 61.9%, and 58.8% for the cardiovascular, mental health, and endocrine classes, respectively; pairwise 

In contrast, in 2017, there were differences by therapeutic class (20.6%, 52.7%, and 28.3% for cardiovascular, mental health, and endocrine classes, respectively; 

 <.017 for cardiovascular compared with mental health class; 

 >.017 for all other pairwise comparisons).

GDDP-Available Medications in 2009 Versus 2017

Twenty generic drugs were available through GDDPs in 2009 that were not available in 2017; the median out-of-pocket costs for these drugs, and the proportion of plans offering them for more than the GDDP cash price, were consistently higher when compared with the 43 generic drugs available through GDDPs only in 2017 (

The trends in median out-of-pocket costs for a 90-day supply of GDDP-available medications were similar to those of a 30-day supply for both outcomes (eAppendix Table 4).

In the decade following the creation of the Medicare PDP benefit and GDDPs, there was a significant decrease in beneficiary out-of-pocket costs for generic medications treating common chronic conditions that were available through Walmart’s GDDP. Similarly, a smaller proportion of plans required beneficiary out-of-pocket costs greater than the GDDP cash price for both a 30-day and a 90-day medication supply in 2017 than in 2009. Not only were these reductions more pronounced for stand-alone PDPs than for MA-PDPs, stand-alone PDPs also had less geographic variation across states compared with their MA-PDP counterparts. Despite reductions across all medication classes from 2009 to 2017, changes were uneven by therapeutic class: For example, in 2017, out-of-pocket costs for CVD medications were significantly less than those for mental health medications. Nevertheless, median beneficiary out-of-pocket costs and the proportion of plans requiring co-payments greater than the GDDP cash price decreased for all plan type and tier stratifications.

The availability of cheaper medications through GDDPs may have led plans to lower out-of-pocket costs for these drugs for beneficiaries, because a cheaper nonplan option is available. Despite this progress, it is still necessary to find opportunities to lower costs further, particularly given concerns about the ability of older adults to afford medications and the number of beneficiaries taking GDDP-available medications.

 In 2017, $3.8 billion was spent by Medicare on the 43 medications included in our drug sample and available through the Walmart GDDP.

 Although the proportion of plans requiring out-of-pocket co-payments greater than GDDP cash prices decreased from three-fifths of plans in 2009 to one-third in 2017, alternative options, such as utilization of GDDPs, may be crucial for saving costs month after month if insurance plans remain more expensive for these medications.

A 2014 study estimated that only one-third of adults 65 years or older utilized GDDPs, showing that significant room for potential savings remains.

 One reason that plans may continue to require higher out-of-pocket costs is the increasing use of mechanisms that steer consumers toward particular drugs. The proportion of PDPs listing generic drugs in tiers other than tier 1 or a select care tier grew over time from 2009 to 2017, a result consistent with others’ findings that Part D plans have placed an increasing number of generic drugs in nonpreferred drug tiers over time.

 This means patients are incentivized to choose certain medications from preferred tiers (ie, tier 1 and select care tier); this limits patient choice and is contingent on clinicians prescribing a generic medication listed in a preferred tier for patients’ respective plans.

 However, at the same time, Walmart’s GDDP similarly narrowed the selection of drugs made available, from 62 to 43 medications for chronic diseases from 2009 to 2017.

Our finding of cost differences to beneficiaries, depending on whether the prescription is obtained using their pharmacy benefit plan or a GDDP, even if obtained from the same pharmacy, is a concern. Inconsistencies may result in instances when a drug’s co-payment is higher than the cost to the insurance or pharmacy benefit manager (ie, “clawbacks”). These instances have garnered increasing national attention and are in part due to gag clauses, which are contractual terms that prevent pharmacists from disclosing list prices and informing patients about other potentially cheaper options for the same medication.

 Although recent legislation has formally banned these clauses,

 the enforcement and effects of these bans remain unknown. Ideally, all stakeholders would have readily available access to data and would be able to inform patients of the best way to lower out-of-pocket costs for individual medications and simultaneously minimize the inconvenience for patients who may achieve the lowest total cost for their medications by filling their generic medications using different modalities (eg, using a combination of insurance coverage and accessing GDDPs). Opportunities include informing clinicians at the time of ordering prescriptions, patients when choosing the pharmacy with the most affordable option(s), and pharmacists at the time of filling prescriptions.

Articles

Out-of-pocket costs required by Medicare prescription drug plans for drugs available through Walmart’s generic drug discount program have decreased from 2009 to 2017.

Medicare Beneficiaries’ Out-of-Pocket Costs for Commonly Used Generic Drugs, 2009-2017

To assess whether Medicare formularies restrict access to drugs receiving new FDA black box warnings for which safer drug alternatives are available.

A retrospective analysis using Medicare Prescription Drug Plan Formulary files to determine formulary changes for drugs receiving FDA black box warnings between 2007 and 2013.

We identified all FDA-approved medications available in tablet or capsule formulation that received a black box warning between 2007 and 2013 related to death and/or cardiovascular risk. We then determined formulary coverage of these drugs pre-black box warning, 1 year after, and 2 years after. For each formulary, we identified formulary restrictiveness, defined as: unrestrictive coverage (no prior authorization or step therapy), restrictive coverage (prior authorization or step therapy required), or no coverage.

Nine drugs with at least 1 FDA-approved safer drug alternative received 10 new black box warnings for death and/or cardiovascular risk between 2007 and 2013. In response to FDA black box warnings, overall formulary restrictiveness increased for 40% (n = 4) of drugs at 1 year, and for 50% (n = 5) at 2 years. However, for the majority of drugs (n = 7),  most formularies remained unrestrictive 2 years after a new black box warning.

Medicare formularies became more restrictive for half of the drugs that recently received new FDA black box warnings for death and/or cardiovascular risk and for which safer drug alternatives are available. However, a substantial proportion of formularies remained unrestrictive, suggesting inconsistent responses to new safety information to curtail the use of these medications.

The impact of FDA black box warnings on formulary restrictiveness is not well known. This study investigated whether Medicare formulary coverage changed after oral drugs with safe available drug alternatives received new FDA black box warnings related to death and/or cardiovascular risk. 

Some formularies either dropped coverage or began requiring prior authorization to restrict the prescribing of unsafe drugs. 

A substantial proportion of formularies made no changes to restrict coverage. 

There are opportunities to improve consistency in formulary responses to FDA black box warnings to curtail utilization of drugs with known safety risks and for which safer drug alternatives are available.

The FDA requires that drugs with serious or life-threatening risks have a boxed warning (commonly referred to as a “black box warning” [BBW]) on their label. The BBW is one of the FDA’s strongest actions short of drug recall or withdrawal, and is intended to inform patients and prescribers about serious safety concerns that must be considered in assessing the risks and benefits of a drug or that the FDA allows use of the drug only with restrictions to ensure safe use. However, BBWs have not been consistently associated with reduced prescribing

 for several possible reasons. First, prescribers may be unaware that the FDA had issued the warnings.

Second, prescribers may think that, despite BBWs, the drugs have a superior benefit/risk ratio to alternative therapies or that the safety concern is not as severe as suggested, and opt to continue prescribing while using strategies to mitigate risk, such as closer patient monitoring.

 Third, BBWs may apply only to limited patient populations, and prescribers may not believe the risk applies more generally.

Another possibility is that BBWs are not taken into account within insurance plan drug formularies. Formularies limit prescribing of unsafe drugs through 2 strategies: formulary exclusion (ie, no coverage) or utilization management (eg, prior authorization or step therapy). One study found no change to state Medicaid plans’ prior authorization requirements following the 2005 BBW for atypical antipsychotic use among the elderly,

 and another found that only 2 state Medicaid programs restricted use after the 2007 BBW for rosiglitazone.

 To better understand the potential role that insurance plan drug formularies may play in restricting the use of potentially unsafe drugs that have safer available alternatives, we characterized changes to Medicare formularies for oral drugs that received new BBWs between 2007 and 2013 for death and/or cardiovascular risk. As these are among the most severe adverse events, we hypothesized that they would have the greatest likelihood of resulting in formulary changes to restrict use. Because each BBW is specific to the drug’s indications and risk, we focused on a limited sample to provide an illustrative summary of formulary changes.

We conducted a retrospective analysis of Medicare formularies using data from the CMS Prescription Drug Plan Formulary Files. We obtained data on both standalone prescription drug plans and Medicare Advantage prescription drug plan formularies for Medicare Part D, as of each June and December from 2006 through 2015.

We used the FDA’s MedWatch website to identify all FDA-approved tablet or capsule formulation medications used in an outpatient setting that received BBWs between 2007 and 2013 related to death and/or cardiovascular risk. MedWatch is the agency’s safety information and adverse event reporting program. We examined all BBWs and first excluded those for drugs used in-hospital only or not relevant to Medicare beneficiaries. We also excluded BBWs for combination drugs, since the primary drug would already be included in our sample. Next, we excluded BBWs for cancer, HIV, or immunosuppressants as these are protected classes treating diseases that require specialized pathways of care. We also excluded warnings for a narrow subset of patients and warnings related to drug-drug interactions. Lastly, we limited our study to drugs with at least 1 drug alternative without a BBW approved for use for the same primary condition, based on the US Pharmacopeial Convention Category & Class versions 5.0 and 6.0. When a drug had multiple BBWs, each warning was considered a unique event.

Our main outcome was formulary restrictiveness based on formulary exclusion, step therapy, or prior authorization. For each drug’s lowest available dose, we classified all Medicare formularies as unrestrictive, restrictive, or no coverage at 3 time points: immediately preceding, at least 1 year after, and at least 2 years after the BBW. An unrestrictive formulary covered the drug without prior authorization or step therapy requirements. A restrictive formulary covered the drug but required either prior authorization and/or step therapy. A formulary that did not explicitly provide coverage did not include the drug on its formulary, which means patients are responsible for the entire drug cost except in very rare circumstances.

We used descriptive statistics to characterize the proportion of formularies that were unrestrictive, restrictive, and did not provide coverage for each drug at all 3 time points. We used χ

 or Fischer’s Exact tests to examine differences in formulary restrictiveness for each drug, comparing restrictiveness pre-BBW to restrictiveness at both 1 year and 2 years afterward. All statistical tests were 2-tailed with a type 1 error rate of 0.025 to account for multiple comparisons for each drug.

Nine medications in a tablet or capsule formulation received a total of 10 new BBWs related to death and/or cardiovascular risk between 2007 and 2013 and had at least 1 drug alternative without a BBW. Four of the 9 drugs (44%) had existing BBWs before the FDA issued BBWs between 2007 and 2013. Seven (70%) of the 10 BBWs were related to death and 5 (50%) to cardiovascular risk (2 related to both cardiovascular risk and death). Two BBWs, those for propylthiouracil and ketoconazole, specifically advised against use of the given medication except in very unique circumstances.

Two years after each of the 10 new BBWs, for 5 (50%) drugs, Medicare formularies became more restrictive, on average, by removing coverage or adding prior authorization requirements. There was no overall increase in step therapy requirements. For 1 (10%) drug, formularies became less restrictive on average and for 4 (40%) drugs there was no overall change in average formulary restrictiveness. When restricting to the 4 drugs with prior BBWs, results were overall similar: Medicare formularies became more restrictive for 2 (50%) drugs at both 1 and 2 years.

Rosiglitazone is approved to improve glycemic control for type 2 diabetes. In August 2007, thiazolidinediones, a medication class that includes rosiglitazone and pioglitazone, received a BBW for congestive heart failure. Prior to this BBW, 78% (n = 255) of formularies provided unrestrictive coverage, 19% (n = 62) provided restrictive coverage, and 3% (n = 10) provided no coverage. There was no significant change in formulary restrictiveness at 1 or 2 years (Figure).

Prior to its strengthened February 2011 BBW for a significantly increased risk of myocardial infarction, 57% (n = 148) of formularies provided unrestrictive coverage, 36% (n = 94) provided restrictive coverage, and 7% (n = 18) provided no coverage. At 1 year, rates were 32%, 36%, and 32%, respectively (

 <.001) (Figure), suggesting that formularies had become significantly more restrictive. Changes in restrictiveness were due to fewer formularies providing coverage and covering formularies requiring prior authorization (Table).

Pioglitazone is approved to improve glycemic control for type 2 diabetes. Prior to its August 2007 BBW for congestive heart failure, 67% (n = 220) of formularies provided unrestrictive coverage, 19% (n = 62) provided restrictive coverage, and 14% (n = 45) provided no coverage. At 1 year, rates were 73%, 27%, and 0%, respectively (

<.001) (Figure), suggesting that formularies had become significantly less restrictive. Changes in restrictiveness were predominantly due to more formularies providing coverage.

Zyban is approved for smoking cessation. Prior to its July 2009 BBW for serious neuropsychiatric events, including suicide, 24% (n = 75) of formularies provided unrestrictive coverage, 5% (n = 11) provided restrictive coverage, and 73% (n = 232) provided no coverage. There was no significant change in formulary restrictiveness at 1 year (Figure). At 2 years, rates were 19%, 0%, and 80%, respectively (

 = .006), suggesting that formularies had become significantly more restrictive. Changes in restrictiveness were predominantly due to fewer formularies providing coverage.

Varenicline is approved for smoking cessation. Prior to its July 2009 BBW for serious neuropsychiatric events including suicide, 60% (n = 191) of formularies provided unrestrictive coverage, 21% (n = 68) provided restrictive coverage, and 19% (n = 59) provided no coverage. At 1 year, rates were 51%, 49%, and 0%, respectively (

 <.001) (Figure). Changes in restrictiveness were predominantly due to more formularies requiring prior authorization (Table).

Quinine is approved for malaria treatment, although it was widely used for nocturnal leg cramps. Prior to its November 2009 BBW explaining that use for nocturnal leg cramps could result in life-threatening hematological reactions, 60% (n = 159) of formularies provided unrestrictive coverage, 18% (n = 56) provided restrictive coverage, and 32% (n = 103) provided no coverage. There was no significant change in formulary restrictiveness at 1 year (Figure). At 2 years, rates were 41%, 27%, and 31%, respectively (

 = .012), suggesting that formularies had become significantly more restrictive. Changes in restrictiveness were predominantly due to more formularies requiring prior authorization (Table).

Propylthiouracil is approved for treatment of hyperthyroidism. Prior to its April 2010 BBW for potentially fatal liver failure, stating propylthiouracil was only indicated in patients intolerant of methimazole, 100% (n = 292) of formularies provided unrestrictive coverage. There was no significant change in formulary restrictiveness at 1 or 2 years (Figure).

Dronedarone is approved for treatment of paroxysmal or persistent atrial fibrillation or atrial flutter but included a BBW contraindicating its use in patients with advanced heart failure. Prior to its additional December 2011 BBW contraindicating its use in patients with permanent atrial fibrillation because of cardiovascular risk, 82% (n = 199) of formularies provided unrestrictive coverage, 9% (n = 22) provided restrictive coverage, and 9% (n = 23) provided no coverage. At 1 year, rates were 72%, 10%, and 18%, respectively (

 <.001) (Figure), suggesting that formularies had become significantly more restrictive. Changes in restrictiveness were predominantly due to fewer formularies providing coverage. At 2 years, rates were 80%, 8%, and 12%, respectively (

 = .63), suggesting that changes in formulary restrictiveness had reversed, as more formularies began covering the drug again.

Dantrolene is approved for treatment of chronic spasticity. In July 2012, its prior BBW on fatal and nonfatal hepatotoxicity was updated to note that a higher proportion of hepatic events had fatal outcomes in elderly patients, although the majority of these cases were complicated by confounding factors. Prior to the addition to this BBW, 88% (n = 242) of formularies provided unrestrictive coverage, 0% (n = 11) provided restrictive coverage, and 12% (n = 34) provided no coverage. There was no significant change in formulary restrictiveness at 1 or 2 years (Figure).

Ketoconazole is approved as an antifungal therapy. Previous BBWs had been issued for QT prolongation and hepatotoxicity. Prior to the July 2013 BBW stating that the drug should only be used when other effective antifungal therapy was not available or tolerated, 100% (n = 314) of formularies provided unrestrictive coverage. At 1 year, 84% of formularies provided unrestrictive coverage, 16% provided restrictive coverage, and 1% provided no coverage (

 <.001) (Figure), suggesting that formularies had become significantly more restrictive. Changes in restrictiveness were predominantly due to more formularies requiring prior authorization (Table).

After 10 new BBWs were issued by FDA between 2007 and 2013 for death and/or cardiovascular risk among 9 tablet or capsule drugs with safer available drug alternatives, we observed that Medicare formularies became somewhat, but not universally, more restrictive at 1 and 2 years afterward. Formularies sometimes removed coverage and/or implemented prior authorization requirements to deter the use of drugs with new safety warnings. However, a substantial number of formularies continued to provide unrestrictive coverage for these drugs after the FDA’s strong safety warning. Providing unrestrictive coverage when other effective but safer drug alternatives are available suggests that formularies are not taking advantage of existing utilization management strategies to promote the use of safer available alternative medications, thus unnecessarily exposing some Medicare beneficiaries to risky medications. There were no consistent patterns in the drugs for which BBWs were not associated with increased formulary restrictiveness.

CMS instructs its contracting Part D sponsor formularies to consider drug safety and efficacy when making decisions about which drugs to include on formularies.

 Further, although changes are generally constrained once the coverage year begins, CMS specifically allows formularies to add utilization management tools at any time after a new FDA BBW and allows these unsafe drugs to be removed without meeting advance notice requirements.

First, our conclusions are limited to BBWs related to death and/or cardiovascular risk. Second, our analysis excluded drugs in a non-tablet or capsule formulation and those without safer available drug alternatives. Third, we did not examine changes in Medicare formulary tier, which is a strategy most likely to be used to impose cost-sharing requirements and limit the use of expensive, as opposed to unsafe, therapies. Fourth, our data may not be generalizable to formularies of insurers other than Medicare. Nevertheless, our findings provide important insights into the role of formulary policy because Medicare beneficiaries represent the largest proportion of insured patients using prescription medications. Finally, we did not examine the impact of formulary changes at the plan level or on prescriptions dispensed. Ultimately, linking formulary restrictiveness after BBWs to patient outcomes would help inform future formulary management decisions.

Medicare formularies increased restrictions to some drugs with new FDA BBWs for death and/or cardiovascular risk. Although this indicates important action, a substantial portion of formularies provided unrestrictive drug coverage 1 and 2 years afterward. Increasing the use of formulary exclusion or restrictiveness for drugs that receive BBWs could help curtail prescriptions of unsafe medications when safer drug alternatives exist.

Medicare formularies were inconsistent in increasing restrictiveness to drugs that received FDA black box warnings for death and/or cardiovascular risk with safer available drug alternatives.

Association Between FDA Black Box Warnings and Medicare Formulary Coverage Changes

With the advent of healthcare payment reform, identifying high-risk populations has become more important to providers. Existing risk-prediction models often focus on chronic conditions. This study sought to better understand other factors to improve identification of the highest risk population. 

A retrospective cohort study of a paneled primary care population utilizing 2010 data to calibrate a risk prediction model of hospital and emergency department (ED) use in 2011. 

Data were randomly split into development and validation data sets. We compared the enhanced model containing the additional risk predictors with the Minnesota medical tiering model. The study was conducted in the primary care practice of an integrated delivery system at an academic medical center in Rochester, Minnesota. The study focus was primary care medical home patients in 2010 and 2011 (n = 84,752), with the primary outcome of subsequent hospitalization or ED visit. A total of 42,384 individuals derived the enhanced risk-prediction model and 42,368 individuals validated the model. Predictors included Adjusted Clinical Groups—based Minnesota medical tiering, patient demographics, insurance status, and prior year healthcare utilization. Additional variables included specific mental and medical conditions, use of high-risk medications, and body mass index. 

The area under the curve in the enhanced model was 0.705 (95% CI, 0.698-0.712) compared with 0.662 (95% CI, 0.656-0.669) in the Minnesota medical tiering-only model. New high-risk patients in the enhanced model were more likely to have lack of health insurance, presence of Medicaid, diagnosed depression, and prior ED utilization. 

An enhanced model including additional healthcare-related factors improved the prediction of risk of hospitalization or ED visit.

		Many risk models that predict hospitalization or emergency department (ED) use are based on comorbid health burden. 

		We investigated other potential predictors, including previous utilization, and found an association of higher hospitalization and ED use with the following: Minnesota medical tiering, 2 prior ED visits, warfarin use, no insurance or Medicaid, being aged over 70 years, and obesity stage 3.

		These findings help to develop new models for determining patients at risk for adverse health outcomes. They focus on potential future areas of clinical practice and research on social determinants of health.

Changing the payment incentives to improve the organization of care delivery, including accountable care organizations, has led to a reorganization of delivery of care focusing on patient-centered medical homes.

 Many patient-centered medical homes stratify their populations and try to align the level of care with the needs of their patients. Providers manage the most complex patients with increased resources to avoid adverse outcomes requiring high-cost care in acute settings.

Two key questions need to be answered: Who is likely to have utilization, and what can be done for these patients? These medically complex patients could be better managed through care coordination programs that are integrated within the delivery system, such as Guided Care, which incorporates specially trained nurses to supplement primary care in a comprehensive care model that provides coordinated, patient-centered primary care for patients with complex needs.

 Different methods for identifying these patients have been developed, including provider identification and referral.

 A hybrid system that utilizes provider selection and an automated risk stratification model may be ideal. Most of the currently available public and proprietary risk models and instruments to identify the population for inclusion in more intensive management programs are based on number and combination of comorbidities. These instruments include self-administered tools such as the Predicting Risk of Admission (PRA) survey, which incorporates self-rated health and comorbid conditions as well as prior healthcare utilization.

 Another model utilizing medical record data is the Elder Risk Assessment (ERA) index, which determines risk of hospitalization based upon increasing age and previous hospitalization as well as comorbid health conditions.

A third risk model, Adjusted Clinical Groups (ACG), uses medical care claims data and stratifies patients based on age and existing chronic conditions.

 The state of Minnesota pays medical homes a care management fee based on Minnesota medical tiering. Minnesota medical tiering is a system of classifying and counting comorbidities originally based on collapsing chronic diseases from ACGs into a body system count.

 This system can also be manually applied without the proprietary software. Our clinical practice uses Minnesota medical tiering to identify appropriate patients for care management. Our prior work comparing these instruments, including Minnesota medical tiering, indicates that these instruments are similar in terms of predicting the risk of hospitalization or emergency department (ED) visit; however, the ACG performed slightly better while the Charlson Comorbidity Index performed worse.

 Although the administrative data-based risk identification methods have been validated and can predict hospitalization with C statistics of 0.67-0.73,

 other potential predictors may improve current risk prediction models and could provide information useful in developing tailored interventions to manage a patient’s condition.

Our clinical experience indicates that coordinating the management of multiple chronic conditions is important in reducing rehospitalizations among geriatric patients with multimorbidity, but other factors may also be important in predicting and managing an individual’s risk.

 In our practice, many patients predicted to be high risk are excluded from care management interventions because they have concurrent mental health or social conditions that care coordinators are unprepared to address. Body mass index (BMI), medication use,

 living environment, English as a second language, previous healthcare use including specialty visits, insurance, and mental health status

 might all be important factors. Patients with those issues may need a different approach, including more support from social services, pharmacy, and psychology.

Obesity is known to influence utilization, but it is widely underdiagnosed and so may be missed in many comorbidity counts.

 The use of anticoagulation therapy, pain, and diabetic medications can lead to an increased need for unplanned acute care and may be amenable to pharmacy consultation.

 Patients living on their own or in dysfunctional families may not be able to cope with increased treatment burdens and may benefit from social services.

 Similarly, those who need language interpretation services may need extra assistance. Patients with a high number of specialty visits may have uncoordinated care and may need help in coordinating their care. Lack of insurance and lower socioeconomic status are associated with increased rates of hospitalization.

 Patients experiencing mental health issues such as depression can also have higher readmission rates.

 Providers and systems with established comorbid health models should understand the potential roles of these additional predictors for hospitalization. 

It is unclear how well these additional factors would predict hospitalization when added to an already established clinical model based on administrative data. To answer this question, we performed a retrospective cohort study comparing a standard risk stratification model of Minnesota medical tiering with an enhanced model containing additional predictors of demographics, selected use of medications, mental health conditions, other specific chronic medical conditions, prior healthcare utilization, and insurance status. We sought to understand how well the enhanced model predicts hospitalization and ED use in a subsequent year.

We constructed a retrospective cohort study to develop and validate a clinical prediction model for hospitalization. The study was approved by the Mayo Clinic Institutional Review Board.

The study was conducted at an academic medical center in Rochester, Minnesota. The population came from the primary care medical practice, Employee and Community Health (ECH), which includes 4 primary care clinics in Dodge and Olmsted Counties, Minnesota. Primary care is provided by family medicine, primary care internal medicine, and community pediatric and adolescent medicine.

Patients 18 years or older who were assigned to a primary care provider within any of the primary care clinics for all 12 months in 2010 (base year) and throughout 12 months or until death in 2011 (assessment year) were included in the analysis. Subjects were excluded if they refused consent for medical record review in accordance with Minnesota state law. Of the 90,411 patients within the ECH program, as of December 31, 2010, 84,752 (93.7%) gave consent for medical record review.

All information for enrolled ECH patients was electronically abstracted from the electronic medical record (EMR) and administrative databases for 2010 and 2011.

We modeled a combined binary outcome of hospitalization and/or ED visit (hereafter: hosp/ED) at St. Mary’s Hospital or Rochester Methodist Hospital in the assessment year. Hospitalization was defined as any elective or emergent overnight. To avoid double counting, ED visits exclude those resulting in a hospitalization.

Demographic variables—age, gender, marital status, and socioeconomic status&mdash;were collected at the end of the base year. Age was grouped into 6 categories: 18 to 29 years, 30 to 39 years, 40 to 49 years, 50 to 59 years, 60 to 69 years, and 70 years or older. Socioeconomic status was determined by insurance type (Medicaid or no insurance versus other insurance type).

As Minnesota medical tiering is the basis for care coordination payments for patient-centered medical homes in Minnesota, we used it as the basis for our risk model development. Medical conditions are based on diagnosis codes billed by clinical providers.

 Minnesota medical tiering has 5 categories from 0 with no conditions to tier 4 with more than 10 conditions. In our care management program, high-risk patients possess Minnesota medical-tiering scores of 3 or 4. 

Candidate predictors for identifying high-risk patients included primary speaking language not being English; high-risk medications (warfarin, insulin, narcotics); and previous healthcare utilization in specific settings (ED visits, hospitalization, specialty provider visits). Medications were based on the presence of outpatient pharmacy orders in 2010 written by any healthcare providers within the system. Medication use was determined from the EMR, not from filled prescriptions. The medications included warfarin, narcotics and insulin. Non—English speaking status was assigned to those patients needing an interpreter, which was documented within the electronic health record. We included all diagnosis codes (

International Classification of Diseases, Ninth Revision, Clinical Modification

]) from hospitalizations, ED visits, and primary and specialty care visits to identify comorbid mental health and medical conditions, based on a published method.

BMI—recorded as weight (kilograms) / height (meters)

—was also included as a potential predictor. BMI was assessed as being the most recent value in the record within 2 years prior to January 1, 2011. Specific BMI categories followed standard World Health Organization criteria: underweight (BMI <18.5); normal weight (BMI 18.5-24.9); overweight (BMI 25-29.9); stage 1 obesity (BMI 30-34.9); stage 2 obesity (BMI 35-39.9); and stage 3 obesity (BMI >40).

We developed the enhanced model to identify those patients at high risk of hosp/ED within the next year. The 2 models compared in the analysis consisted of Minnesota medical tiering and the enhanced model, which incorporated additional candidate factors. Data were randomly divided into development (50%) and validation cohorts.

We derived the final enhanced model from a stepwise logistic regression followed by a sensitivity analysis seeking stability in the predictors determined by the severity of the outcome variable. Predictors in the saturated model consisted of 72 main factors as well as all possible 1-way and 2-way interaction terms. We determined stability of the variables by changing the number of ED visits or inpatient (IP) visits in the outcome variable. We performed the saturated stepwise model while incrementally increasing the number of ED visits or IP visits. The final set of variables for each change in outcome variable was captured. We then considered a count of how many times a variable had a 

 value ≤.05 in the model. Finally, we included any variable that remained significant in 4 or more of the 11 models considered.

The stepwise regression was repeated 100 times and each repetition considered the variables in random order to determine if order affected the results of the stepwise algorithm. Each iteration produced the same model. Effects were entered into and removed from the model so that each forward selection step could be followed by a backward elimination step. The algorithm stopped when no further effects could be added to the model or if the current model was identical to a previous model.

Comparisons between prediction models focused on predictability and precision. We used C statistics (area under the receiver operating characteristic [ROC] curve [AUC]) as our measure of predictability. For assessment of precision, we compared the sets of high-risk patients found within the tier 3 and 4 groups in Minnesota medical tiering with an equal number of high-scoring patients from the enhanced model. Statistical analysis was performed using SAS version 9.3 (SAS Institute, Cary, North Carolina).

A total of 42,384 and 42,368 patients were included in the development and validation samples, respectively. The development and validation data sets were similar. There were no differences in sex, age decile, BMI, marital status, insurance status, prior healthcare utilization, comorbid health status, or Minnesota medical tiering (

). In addition, the 2 samples had similar rates of hospitalization and ED visits with 18.2% of the development sample and 17.6% of the validation sample having a hospitalization or ED visit in the subsequent year.

Those patients who belonged to the highest category of Minnesota medical tiering (4) had the greatest odds of incurring any hospitalization or ED visit with an odds ratio of 18.2 (95% CI, 15.6-21.24) compared with the lowest tier category (0). Next most important was prior utilization of 2 or more ED visits with an odds ratio of 7.13 (95% CI, 6.34-8.01) for predicting hosp/ED. Similarly, 2 or more previous hospital stays had an odds ratio of 4.28 (95% CI, 3.90-4.69) for predicting hosp/ED. Mental health conditions, including substance abuse, personality disorder or dementia, each had odds near 3 for predicting hosp/ED. Specifically, depression had an odds ratio of 2.92 (95% CI, 2.71-3.15). Among other medical conditions, cardiac disease with heart failure and pulmonary heart disease had odds ratios over 5. All specific conditions with odds ratios over 2 are reported in Table 1. All specific conditions are listed in 

. Patients in each of the medication categories had odds ratios greater than 2, as did those utilizing 3 or more specialist visits in the prior year. Patients with a language interpreter did not have elevated risk of hosp/ED (Table 2).

The multivariable enhanced tiering model is presented in 

. The final model included 30 main effects (representing 16 factors) and 4 2-way interaction terms (more detailed results are available from the authors).

Of notable mention, being in the highest medical tier remains the strongest predictor for hosp/ED after the addition of other factors with an odds ratio of 3.50 (95% CI, 2.78-4.42). More than 1 previous ED visit also resulted in an increased odds ratio of 3.17 (95% CI, 2.78-3.60). Several significant interaction terms are noted in Table 3. In the validation cohort, the C statistic for the enhanced multivariable model was 0.711 (95% CI, 0.704-0.717) compared with a C statistic for the Minnesota medical tiering alone model of 0.667 (95% CI 0.660-0.673). These C statistics were actually slightly higher in the validation cohort than in the development cohort (enhanced model: 0.705 [95% CI, 0.698-0.712]; Minnesota medical tiering: 0.662 [95% CI, 0.656-0.669]). The differences between the ability of the enhanced model to predict hospitalization and ED use and Minnesota medical tiering are displayed in the ROC curves found in the 

Many different patients were identified as being high-risk in the enhanced model as compared with those in the original Minnesota medical tiering model (tier 3 or 4). 

 presents the characteristics of high-risk patients identified with both models as well as those unique to the 2 approaches. We found that patients identified as high-risk in the enhanced model were much more likely to experience hospital utilization in 2011 than those in the Minnesota medical tiering-only model (

 <.001). Patients identified as high-risk based only on the enhanced model, compared with those identified only through Minnesota medical tiering, were of younger age, had higher previous healthcare utilization, and had more frequent mental health conditions. Many of the high-risk enhanced model patients were aged less than 40 years compared with only 11% of patients being aged less than 40 years in the Minnesota medical tiering model.

In this retrospective cohort of patients receiving care within the ECH, several variables were important in predicting hosp/ED in addition to the number of chronic comorbidities currently used in Minnesota medical tiering and our care coordination identification. We found that age, BMI, previous healthcare utilization, mental health conditions, anemia, heart failure, epilepsy, hyperlipidemia, warfarin use, and narcotic use all simultaneously contributed to the prediction of hospitalization and ED visits in the next year. This enhanced model had an AUC of 0.711 (95% CI, 0.704-0.717) for predicting hosp/ED, as compared with 0.667 (95% CI, 0.660-0.673) found in the model utilizing only Minnesota medical tiering. Previous work comparing different risk stratification methods for hospitalization determined slight superiority of ACG compared with the ERA, Hierarchical Condition Categories, Minnesota medical tiering, and chronic care counting instruments.

 The enhanced model takes into consideration previous hospitalization, specific high-risk illnesses, mental health conditions, and high-risk medication use (eg, warfarin, narcotics) that are not universally accounted for in other models. In comparing high-risk groups of the same size from both models and excluding patients identified in both models, 47% of the patients in the enhanced model experienced hosp/ED compared with 32.5% of patients from the Minnesota medical tiering model. The enhanced model accounts for factors in a younger population (eg, mental health and previous utilization) that are not detected in the Minnesota medical tiering model.

Previous hospital and ED utilization served as strong predictors for hosp/ED in the following year. Specifically, 2 or more prior ED visits presented an odds ratio (OR) close to 3 while 1 prior hospital stay resulted in an OR of 1.79 (95% CI, 1.66-1.93). Together, hospitalization and ED visits often indicate a medically complex individual or an individual with severe illness. Many risk stratification models have utilized previous healthcare usage as a predictor for future healthcare use. The PRA incorporates previous hospitalization within the past year as a factor predictive of future hospitalization.

 Similarly, the ERA also includes previous hospital days to calculate a risk score for future hospitalization.

 Previous ED visits as well as previous hospitalization have been associated with increased hospital admission in a multi-centered VA study of 1378 patients designed to identify factors of hospitalization.

An enhanced risk model incorporating medication use, prior healthcare utilization, and mental health with comorbid health conditions predicts healthcare utilization better than health conditions alone.  

Enhanced Risk Prediction Model for Emergency Department Use and Hospitalizations in Patients in a Primary Care Medical Home

 A number of new hepatitis C virus (HCV) medications have become available in the United States, but little is known about how these treatments have been adopted into practice and their financial burden on patients. The aim of this study was to examine whether the introduction of new HCV medications was associated with changes in treatment rates and out-of-pocket (OOP) costs. 

Retrospective analysis of administrative claims data from Optum Labs Data Warehouse.

We performed a retrospective analysis using a large, US commercial insurance database to identify 56,116 adults with chronic HCV between January 1, 2010, and December 31, 2014. Logistic regression was performed to calculate patients’ predicted probability of being treated before and after the new medications became available. 

A total of 5436 (9.7%) of patients with HCV received treatment during an average of 1.8 years of follow-up. In the last quarter of 2014, 0.1% of patients with HCV received interferon/ribavirin as the primary treatment; no one received boceprevir or telaprevir, 1.1% received sofosbuvir combined with simeprevir, 1.4% received sofosbuvir or simeprevir alone, and 2.0% received ledipasvir/sofosbuvir. The introduction of new medications was significantly associated with an increased treatment rate, from 5.4% to 6.8% (

 <.001). The increase was high among elderly patients and patients with liver transplant, liver cancer, and liver disease or cirrhosis. The median OOP costs of patients receiving new regimens were relatively low ($112-$340), but great variations existed.

 At the end of 2014, patients were almost exclusively using new therapies, which was associated with increased treatment rate, especially among patients who may need urgent treatment but are intolerant or ineligible for interferon-based regimens.

		At the end of 2014, patients were almost exclusively using the new hepatitis C medications that were made available since 2013. 

		New therapies increased treatment rate, especially among patients who may need urgent treatment but are intolerant or ineligible for previous interferon-based regimens. However, the treatment rate among young and relatively healthy patients decreased.

		The median out-of-pocket costs of patients receiving new regimens were relatively low ($112-$340), but there were great variations and the mean costs were high ($1982-$2127).

Chronic hepatitis C virus (HCV) is an infection that affects nearly 3.2 million individuals in the United States.

 HCV is associated with increased risk of developing liver cirrhosis, hepatocellular carcinoma, liver failure, and death.

 Over the past 2 decades, the mainstay of treatment of HCV has been interferon alfa and ribavirin, but the treatment causes numerous adverse events, including influenza-like symptoms, depression, cytopenia, hemolytic anemia, fatigue, pruritus, and rash.

 It also requires long treatment duration (typically 48 weeks), and results in an average low cure rate—defined by sustained viral response (SVR)&mdash;of less than 50%.

 Additionally, many patients are not eligible for, or cannot tolerate, interferon-based treatment.

 As a result of these challenges, until recently, only 10% to 30% of adults with chronic HCV received treatment.

New hepatitis C medications have been quickly adopted into practice and increased treatment rate. The median out-of-pocket costs of new medications were relatively low.

Nilay D. Shah, PhD

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