Bruce Stuart, PhD

To test if offering zero generic co-pays for oral antidiabetic drugs (OADs) and statins increases generic dispensing for low-income subsidy (LIS) recipients with diabetes enrolled in Medicare Part D.

 We analyzed a natural experiment in which LIS recipients were randomized to Part D plans in 2008. Some plans placed selected generic OADs and statins on zero co-pay tiers whereas others did not. Randomization eliminated selection effects which could bias the study findings.

 We analyzed a 5% random sample of Medicare beneficiaries with diabetes from the Chronic Condition Data Warehouse using Part D claims, formulary provisions, and co-pay tiers together with a special file prepared by CMS that identified all randomly assigned LIS recipients in 2008. We calculated proportions using generic drugs in the 2 classes and annual days’ supply among users in plans with and without zero co-pay tiers for the country as a whole and California (where zero co-pay plans were particularly popular).

We found that the demand for generic OADs was not significantly different in plans with and without zero co-pay tiers. By contrast, a large difference was observed in the percent of LIS recipients using generic statins in plans with zero co-pay tiers (61.4% vs 54.6%; P <.01). However, the difference disappeared once we controlled for formulary restrictions on the most popular brand statin at the time (Lipitor).

This cautionary tale suggests that policy makers should give greater consideration to formulary provisions when evaluating the effects of free generics in value-based insurance designs.

An increasingly popular strategy in value-based health insurance designs, including some Part D plans, is to have a preferred generic tier with no co-pays for selected medications offered on the presumption this will shift demand away from expensive brand name drugs and produce savings for insurers and customers alike. 

We tested whether low versus zero generic co-pays in Part D plans had any significant impact on generic dispensing of antidiabetic drugs and statins and found none. 

Plans used both the pull of free drugs and the push of formulary restrictions to achieve higher rates of generic use, and it was the push rather than the pull that proved effective in the case of statins. 

An increasingly popular strategy in value-based insurance design (VBID) is a preferred generic co-pay tier in which certain medications are available free of charge on the presumption this will shift demand away from expensive brand name drugs and produce savings for insurers and customers alike. Behavioral economists find that the demand for free goods tends to be much higher than for the same goods offered at very low prices. According to Anderson: “Free has the effect of bending the demand curve—demand shoots up in a very nonlinear fashion.”

 Shampan’er and Ariely argue that this occurs because moving to free not only reduces cost, but also confers special benefits to consumers.

 But does this phenomenon hold for prescription drugs? The answer to this question has obvious relevance to private payers using VBID precepts to set drug co-payment levels, and it is also important for public payers including Medicare, as evidenced by Medicare Payment Advisory Commission’s recommendation that co-payments for beneficiaries receiving low-income subsidies (LIS) be raised for brands but reduced to zero for generics.

 would appear to support the wisdom of that policy based on analyses of Medicare beneficiaries’ use of statins, antidepressants, and antidiabetic drugs in Part D plans with and without zero co-pay tiers. However, neither study could account for formulary exclusions and both used cross-sectional designs that are potentially sensitive to selection bias (ie, if beneficiaries with a preference for generic drugs are drawn to plans offering them at no cost, this unobserved behavior will confound the true impact of zero co-pays on generic utilization). We designed the current study to be free of both sources of bias.

In brief, our approach exploited a natural experiment in which Part D enrollees receiving the LIS were randomized to benchmark plans in their region. Some of these plans offered zero generic co-pays and others did not. Randomization ensures equivalent beneficiary characteristics among all plans within a given region and eliminates selection bias. Any observed differences in generic utilization across plans must thus be due to plan policies, including free generics and differences in formulary restrictions. We focused the analyses on Medicare beneficiaries with diabetes who used oral hypoglycemic agents (ie, OADs) and/or statins in 2008.

The study used 2008 data from a 5% random sample of the Medicare population from the CMS Chronic Condition Data Warehouse (CCW). The files included basic enrollment information by service type (Parts A, B, C, and D); Part D LIS status, including plan assignment method (beneficiary selection or CMS random assignment); paid claims records for all Medicare-covered services; and a special file provided us by CMS that contained complete formulary information on covered drugs and restrictions (prior authorization [PA], step therapy, and quantity limits) for every Part D plan.

We selected the study population as a subset of a sample of Medicare beneficiaries with diabetes drawn from a previously published study.

 The inclusion criteria for the original study cohorts were: 1) Medicare beneficiaries enrolled throughout 2008 with continuous Part A, B, and D coverage; 2) dual-eligible LIS recipients with incomes less than or equal to the federal poverty level and continuously enrolled in CMS-assigned benchmark prescription drug plans (PDPs); 3) diagnosed with diabetes based on 

International Classification of Diseases, Ninth Revision, Clinical Modification

 codes 250.xx, 357.2, 362.01, 362.02, or 366.41 on hospital inpatient and medical claims prior to 2008; and 4) filled at least 1 OAD and/or statin prescription in 2008.

Beneficiaries enrolled in Medicare Advantage prescription drug plans (MAPDs) were excluded because CMS does not randomly assign LIS recipients to MAPDs. LIS enrollees in long-term care nursing facilities were excluded because medications are centrally managed and available with no cost sharing. Finally, beneficiaries residing in American territories and Puerto Rico were excluded as we did not have information regarding generic substitution laws in these areas, which might affect generic utilization rates.

For the current study, we created 2 overlapping cohorts of randomized LIS recipients who used OADs and/or statins in 2008. Each cohort was subdivided into 2 groups based on whether their assigned Part D plan offered any generic OADs or statins free of charge. Plans with zero generic co-pay tiers were identified using the following criteria. First, we limited the analysis to plans with at least 30 drug users in each cohort to assure there were multiple beneficiaries taking the most common generic drugs in each class. Next, we identified plans in which non-LIS enrollees had zero co-pays for all prescriptions filled for at least 1 generic OAD or statin over the year. We then checked to determine whether all LIS recipients enrolled in these same plans also paid zero co-pays for the same generic medications. Plans meeting both criteria were considered zero co-pay plans. Nonzero co-pay tier plans were identified based on evidence that no generic OAD or statin was filled at zero co-pay tiers except for prescriptions filled by dual-eligible LIS recipients during the catastrophic phase of the Part D benefit. Individuals assigned to plans that did not offer free generics paid statutory co-pays of $1.05 for generics and $3.10 for brands.

Exploratory analyses showed that among all PDP regions, 49% of LIS enrollees in plans offering zero co-pays for some generic OADs and 59% of enrollees in plans offering zero co-pays for generic statins were California residents. Given the high popularity of zero co-pay tiers in California PDPs, we decided to estimate models both at the national level and for the state.

 presents a flowchart showing how the study samples were selected, beginning with the entire CCW 5% sample of about 2.6 million beneficiaries and ending with the subsamples of LIS recipients randomized to California PDPs with and without zero co-pay provisions for OADs and statins.

Although random plan assignment would ensure the balance of enrollee characteristics within each Part D region, it would not ensure balance across regions. To address that issue, we identified a large number of personal factors that could plausibly influence rates of generic prescribing, including demographic characteristics, diabetes duration and severity, diabetes management activities, comorbidities, and utilization of Medicare services other than drugs. Individual variables in each of these domains are shown in the left-hand column of 

Randomization also does not control for plan policies, other than zero co-pay tiers, that might influence utilization of generic OADs and statins. The most obvious candidate here is plan formulary design given that Part D sponsors have been shown to use these drug lists to drive market demand.

 We used the First DataBank drug dictionary to identify all drugs within our 2 classes of interest and then matched that against the CMS formulary file.

 This allowed us to identify all OADs and statins that were included and excluded from each plan formulary by name and brand/generic status. The formulary file also permitted us to identify all products with significant access restrictions requiring either PA or step therapy.

The 2 most common operational measures of formulary restrictions in the literature are the percentage of individuals subject to specific types of restrictions

 and a dummy variable indicating whether at least 1 drug in the class is subject to restriction.

 Neither approach captures the full impact of formulary designs on individual behavior because restrictions on popular drugs can have a much bigger effect than those placed on seldomly used products. Although our study was not focused primarily on the impact of formulary design, we were interested in whether formulary restrictiveness was correlated with plan policies for zero co-pays. We tested this association for all individual OADs and statins and discovered a strong positive correlation coefficient (r = 0.5) between restrictions on Lipitor (primarily formulary exclusions) and the offer of zero co-pays for generic statins. No other restrictions on commonly used drugs had correlation coefficients greater than 0.25. Based on these findings, we added a Lipitor restriction variable to our multivariate model together with count variables indicating the total number of OAD and statin drugs subject to formulary exclusion, PA, or step therapy.

Finally, because some Part D regions include several states, we controlled for state-level differences in generic substitution laws. There were 3 broad types of laws: 1) allows for generic substitution by pharmacists if “brand only” not indicated by physician, allows for brand if requested by patient, and mandates brand only if indicated by physician; 2) mandates generic substitution by pharmacists if “brand only” not indicated by physician, allows for brand if requested by patient, and mandates brand only if indicated by physician; and 3) mandates generic substitution if “brand only” not indicated by physician, and mandates brand if indicated by physician.

We tested the relationship between zero co-pay tiers and generic utilization rates using a standard 2-part model design. The first equation in each set of models estimated the relationship between the availability of zero generic co-pays and the proportion of all drug users filling any generic prescription. The second equation estimated the impact of zero co-pays on annual days’ supply of generics filled by generic users. All models were estimated using ordinary least squares (OLS) regression, with the full set of covariates listed in Table 1. In the national models, we controlled for regional differences with 33 region dummies, with 1 region (32 for California) as the reference.

The study was approved by the University of Maryland Institutional Review Board.

Table 1 profiles the characteristics of our study cohorts of LIS recipients using OADs. At the national level, we identified 43 plans offering zero co-pays for generic OADs with a combined 3682 LIS enrollees meeting study inclusion/exclusion criteria. The most common free OADs in these plans were glipizide, glipizide XL, glyburide, and glyburide-metformin (

). The comparison cohort included 11,007 LIS recipients enrolled in 242 plans that offered no free OADs. The 2 national cohorts exhibited somewhat different demographic characteristics, but had similar rates for diabetes severity, comorbidities, and Medicare utilization. The biggest difference between the 2 plan types was in the number of OADs subject to formulary exclusion or major restriction (12 in zero co-pay plans vs 9 in nonzero co-pay plans).

By contrast, the 2 California cohorts had virtually identical characteristics, as would be expected given random assignment (California is a single PDP region). We identified 3 California PDPs that offered free OADs (n = 1790) and 8 that did not (n = 1511). Unlike plans in other regions, the California PDPs offering free OADs actually placed major formulary restrictions on fewer individual products compared with other plans (9.5 vs 12.4).

 presents similar data for our statin user cohorts. Nationally, 30 PDPs with 3334 LIS enrollees offered enrollees free generic statins in 2008 compared with 223 (n = 11,132) that did not. Over half of the zero co-pay plans offered either pravastatin and/or lovastatin free and just under a quarter offered free simvastatin (Table 2). Both types of plans placed formulary restrictions on many statin products. Lipitor was subject to major formulary restrictions for two-thirds of LIS recipients enrolled in zero co-pay plans nationally compared with just 21% in nonzero co-pay plans. In California, about half of the LIS recipients in zero co-pay plans faced restrictions on Lipitor, whereas only 1.8% of enrollees in nonzero co-pay plans did so.

We found that very high percentages of LIS recipients used generic OADs (>86%) in the national-level analyses (

). There was a small, nonsignificant 1.2% difference in the percentage of OAD users who filled any prescription for a generic OAD between plans with and without zero co-pay tiers. Zero co-pay status also had a small 1.7-day nonsignificant effect on the annual days’ supply of OADs among generic users. Adjustment for all covariates, including counts of OADs subject to major formulary restrictions, had no effect on these results (complete results from all regressions are available in the eAppendix [

]). Findings were similar in the OAD analyses restricted to California PDPs.

Our statin analyses produced very different results. Much smaller percentages of the study population used generic statins compared with generic OADs, and there was a marked difference in unadjusted generic statin user rates between plans with and without zero co-pay tiers (61.4% vs 54.6% in the national-level analysis and 53.4% vs 41.1% in the California analyses, respectively; both significant at 

<.01). In the unadjusted comparisons, annual days’ supply of statins was 8.4 days lower in zero co-pay PDPs (

<.01) nationally and 3.4 days (nonsignificant) lower in California.

After adjustment, all significant differences in generic statin use disappeared. To see if Lipitor restrictions were responsible for the dramatic difference in the proportions of generic statin users in zero versus nonzero co-pay plans, we estimated otherwise identical regressions excluding that variable. Without Lipitor restrictions in the models, the adjusted effects associated with zero co-pays were almost as high as in the unadjusted models (results not shown). Formulary restrictions—and Lipitor restrictions in particular&mdash;explain why generic statin use was much higher in PDPs with zero co-pays.

Using a study design with high internal validity based on random assignment, we found little evidence of any bounce in demand for generic OADs or statins among LIS recipients enrolled in plans offering zero co-pays for selected generics in these 2 drug classes. This lack of response is consistent with recent study results showing that low-income individuals are generally not sensitive to small changes in prescription drug prices.

 However, it is not what we expected to find based on other experimental studies of demand for free goods, the results of which have shown a marked spike in demand at zero prices.

 One plausible reason for this null finding is that, unlike other consumer goods, prescription drugs depend on physician orders, and physicians may either be reluctant to change their recommended medications in response to small patient savings or they may be unaware that the drugs are available free.

Our results are also inconsistent with findings published by Hoadley et al

 For example, Hoadley et al found that in 2008 (the same year as in our study), the probability of using any generic statin was 13% higher in plans with zero co-pays compared with plans in which the mean generic co-pay was $5.15. Their analysis excluded LIS recipients, so the price differential between those enrolled in plans with and without a zero co-pay tier was somewhat larger than in our study, but the unadjusted effect size was virtually the same (12.5% difference in our national-level analysis). We determined this difference was attributable solely to formulary restrictions on Lipitor, not zero co-pays.

The CMS Part D formulary file was not available to researchers until 2010, but we had access to an earlier beta version of this file for 2008. Researchers using standard CCW files prior to 2010 had access to flags for PA, step therapy, and quantity limits from the CCW plan characteristics file for drugs filled by at least 1 Part D enrollee included in the researcher’s study sample. However, these flags systematically underestimate the true extent of formulary restrictions, because formulary exclusions are not accounted for and other restrictions are undercounted in cases where no enrollee used drugs subject to utilization management. These effects can be quite large: based on the CMS beta version of the 2008 formulary file, we found that 16.2% of all benchmark plans excluded Lipitor as a covered drug, 7.5% placed it on PA, and 7.1% required step therapy. By contrast, Hoadley et al, using the 2008 plan characteristics file flags, determined that no Part D plan required either PA or step therapy for Lipitor.

Our focus on LIS recipients with diabetes means that we cannot generalize our results to other segments of the Medicare Part D population. However, it is worth noting that LIS recipients account for 40% of the entire Part D enrollment and about 55% of program costs.

 Diabetes is one of the most prevalent chronic conditions in the Medicare population, affecting 28% of all beneficiaries in 2014.

Examining zero co-pay effects on individuals who are otherwise subject to nominal cost sharing represents perhaps the strongest test of the hypothesis that “free” confers special benefits to users of chronic care medications. Based on the economics literature, we had expected a spike in demand at zero price even though the absolute reduction in out-of-pocket cost was minimal; that turned out not to be the case. Moreover, one might have expected that LIS recipients would be sensitive to even small price differences given their low incomes, but that too was not confirmed.

Second, we examined just 2 drug classes and further research is necessary to determine whether the effects we found for OADs and statins also apply to other classes. Both classes included many different products, which should make generic substitution relatively easy. Zero co-pay tiers could have a different impact in classes with fewer medications.

Third, although we were able to control for confounding associated with formulary exclusion and restrictions, we did not have access to other plan policies that could conceivably bias our results. For example, one common aspect of medication therapy management programs is for pharmacists to provide patients with information about possible generic substitution. Disease management and wellness programs might have a similar effect.

Lastly, our data are for 2008 when VBID pricing precepts applied by Part D plans were less developed than today. The fact that zero generic co-pay tiers are much more commonly used today only increases the relevance of our study findings. Part D plan formularies are also much more restrictive than they were in 2008. How these 2 trends interact is an important unanswered question that warrants future research.

Some Part D plans use both the pull of free drugs and the push of formulary restrictions to achieve higher rates of generic use. In this study, the push, rather than the pull, proved effective. This cautionary tale suggests that policy makers should give greater consideration to formulary provisions when evaluating the effects of free generics in VBIDs.

 University of Maryland, Baltimore (BS, FH, JX), Baltimore, MD; PhRMA (JSD), Washington DC.

 Dr Dougherty is employed by PhRMA, a trade organization representing drug manufacturers. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (BS, FH, JSD); acquisition of data (BS); analysis and interpretation of data (BS, FH, JSD, JX); drafting of the manuscript (BS, FH); critical revision of the manuscript for important intellectual content (BS, JSD, JX); statistical analysis (BS, JX); obtaining funding (BS); and supervision (BS).

 Bruce Stuart, PhD, University of Maryland, Baltimore, 220 Arch St, Baltimore, MD 21201. E-mail: bstuart@rx.umaryland.edu. 

2. Shampan’er K, Ariely D. How small is zero price? the true value of free products. Federal Reserve Bank of Boston website. http://www.bostonfed.org/economic/wp/wp2006/wp0616.pdf. Published October 2006. Accessed October 12, 2015.

3. Report to the Congress: Medicare and the health care delivery system. Medicare Payment Advisory Commission website. http://www.medpac.gov/docs/default-source/reports/jun12_entirereport.pdf?sfvrsn=0. Published June 2012. Accessed October 12, 2015.

4. Hoadley JF, Merrell K, Hargrave E, Summer L. In Medicare Part D plans, low or zero copays and other features to encourage the use of generic statins work, could save billions. 

. 2012;31(10):2266-2275. doi: 10.1377/hlthaff.2012.0019.

Articles

The offer of free medications to low-income Medicare beneficiaries with diabetes enrolled in Part D plans has no impact on generic prescribing rates.

Does the Offer of Free Prescriptions Increase Generic Prescribing?

To compare the rates of mammographic screening and reasons for not receiving mammography between Medicare Advantage (MA) and Medicare fee-for-service (FFS) beneficiaries.

 Unadjusted rates of mammographic screening and reasons for not receiving mammography were compared between MA and FFS beneficiaries in total and within subgroups. Probabilities of mammographic screening were also compared between the MA and FFS beneficiaries after adjusting for age, education, race/ethnicity, health status, marriage status, and region of residence using modified Poisson regression models.

We found significantly higher unadjusted rates of mammographic screening in the MA beneficiary population compared with the FFS population (50.3% vs 44%, respectively; 

 <.0001). MA beneficiaries were more likely to receive a mammogram than FFS beneficiaries (unadjusted relative risk [RR], 1.13; 95% CI, 1.07-1.19; and adjusted RR, 1.06; 95% CI, 1.01-1.12). Furthermore, the top 6 reasons why MA and FFS beneficiaries did not receive mammograms were: “not needed,” “doctor did not recommend,” “forgot,” “not recommended annually,” “did not like mammograms,” and “appointment due soon.” Among non-Hispanic blacks and Hispanics specifically, FFS beneficiaries were more likely to report doctors not recommending mammograms than MA beneficiaries.

MA beneficiaries were 1.06 times more likely to receive mammography screening compared with the FFS population. Additionally, providers were less likely to recommend mammographic screening to non-Hispanic black and Hispanic FFS beneficiaries compared with non-Hispanic black and Hispanic MA beneficiaries.

		Medicare Advantage (MA) beneficiaries were more likely to receive mammography screening compared with fee-for-service (FFS) beneficiaries. 

		Three of the top 6 reasons for not receiving mammography involved whether mammography was needed. Although discrepancies in clinical guidelines remain, minimal mammographic screening recommendations should be disseminated to both providers and patients.

		While clinical guidelines were being debated, providers were less likely to recommend mammographic screening to non-Hispanic black and Hispanic FFS beneficiaries compared with non-Hispanic black and Hispanic MA beneficiaries.

Breast cancer is the most common type of cancer among women and is the second-leading cause of cancer-related death among women.

 The decline in breast cancer mortality has been attributed, in part, to the early detection of breast cancer with mammographic screening.

Although the value of mammographic screening has been demonstrated, the frequency of screening and age of the target populations remains a topic of debate. In 2009, the US Preventive Services Task Force (USPSTF) recommended that women aged 50 to 74 years should receive screening mammography every 2 years.

 The American Cancer Society (ACS) disagreed and continued to support the longstanding recommendation that women 40 years or older should receive screening mammography every year.

One important determinant of the use of mammographic screening is health insurance status.

 The 2 types of insurance compared in this study are Medicare Advantage (MA) and fee-for-service (FFS). MA plans are private health plans that receive a capitated amount per beneficiary from Medicare to provide all Part A and B benefits. MA prescription drug plans receive a separate payment from Medicare to provide Part D benefits.

MA plans started as Medicare managed care in the 1970s, and then were called “Medicare + Choice” after the 1997 Balanced Budget Act. It was not until the Medicare Modernization Act of 2003 that MA plans were called “Medicare Advantage.”

 When Medicare managed care plans first began in the 1970s, they were distinguished from traditional Medicare (or FFS) not only because they were private health plans operating under capitated payments, but also because they were mostly health maintenance organizations (HMOs).

MA enrollment has increased from 13% of all Medicare beneficiaries in 2004 to 31%, or 16.8 million beneficiaries, today.

 Sixty-four percent of MA beneficiaries are enrolled in HMOs and 31% are enrolled in preferred provider organizations.

Another key difference between MA and FFS is that MA plans are incentivized by star ratings whereas traditional FFS is not. More specifically, MA plans are given a 1 to 5 star rating annually, which is shared with the public

; consumers can then use this information when selecting plans. Additionally, MA plans with higher star ratings receive bonus payments. Providing preventive services, such as breast cancer screenings, is one of many measures used to calculate star ratings, and thus, MA plans have more of an incentive to ensure that their beneficiaries are receiving mammography.

Previous studies have found that women with Medicare HMO insurance had an earlier-stage breast cancer diagnosis than women with Medicare FFS insurance, which may lead to increased survival.

 Among breast cancer survivors, another study found that mammography was more often reported by those with Medicare plus private than those with only public insurance.

 Regarding the Medicare population as a whole, a study using 1996 data showed that Medicare HMO beneficiaries were more likely to receive mammograms than FFS beneficiaries.

 Updating these figures and better understanding why patients decline mammograms is needed. This study has 2 aims: 1) compare mammographic screening rates between Medicare FFS and MA beneficiaries in 2009, and 2) compare reasons for not receiving mammographic screening between Medicare FFS and MA beneficiaries in 2009.

This study used 2009 Medicare Current Beneficiary Survey (MCBS) data, collected via computer-assisted personal interviews.

 The MCBS is a longitudinal panel survey of a representative sample of the Medicare population

 that captures many beneficiary characteristics, including age, highest degree of education, race/ethnicity, self-reported health status, marriage status, region of residence, and MA enrollment. Additionally, the MCBS asks beneficiaries about mammography utilization and their reasons for not receiving mammography.

This study was approved by the University of Maryland Baltimore Institutional Review Board.

 shows how the sample was selected. Only females without a history of breast cancer were included. Since mammogram type (diagnostic or screening) was not specified, females with a history of breast cancer were excluded so calculated mammogram receipt rates would be focused on screening mammograms.

Of 14,695 Medicare beneficiaries, 7460 (51%) were females with no history of breast cancer. Of those, 5568 (75%) were FFS beneficiaries and 1892 (25%) were MA beneficiaries. Of the FFS and MA beneficiaries, 5417 (97%) and 1875 (99%) responded to whether they received a mammogram, respectively.

Next, 3033 (54%) and 932 (49%) FFS and MA beneficiaries did not receive mammograms, respectively. Of the FFS and MA beneficiaries who did not receive mammograms, 2175 (72%) and 755 (81%) reported why they did not receive a mammogram, respectively (not shown in the Figure).

All measures except age were self-reported from the survey. Age came from administrative records maintained by CMS. Receipt of screening mammogram was based on females with no history of breast cancer reporting whether they had received a mammogram in the last year. MA enrollment was based on whether patients reported current coverage by a Medicare HMO. However, this Medicare HMO was more similar to an MA plan because it included not only traditional risk HMOs, but also cost HMOs and other healthcare prepayment plans.

For preliminary analyses, the FFS and MA beneficiary populations were compared using χ

 tests of homogeneity to determine if differences in characteristics between the 2 populations were statistically significant.

For the first aim, unadjusted mammographic screening rates were calculated and compared between the FFS and MA beneficiary populations. Next, mammographic screening rates were compared between the 2 populations within strata of potential confounders, including age, education, race/ethnicity, health status, marriage status, and region of residence.

Lastly, a series of modified Poisson regression models were constructed to assess the effect of MA enrollment on the receipt of screening mammography. Poisson regression with robust error variance has been previously used to estimate relative risk for binary outcomes.

 Relative risks were reported instead of odds ratios to avoid overestimation of risk if events are common, due to misinterpretation of odds ratios as a measure of risk.

 The final adjusted model contained all 6 potential confounders mentioned above: age, education, race/ethnicity, health status, marriage status, and region of residence. The previous study using data from 1996 also controlled for age, education, race/ethnicity, and health status.

 Marriage status and region of residence were also controlled for in the study because these characteristics were statistically significantly different between the FFS and MA populations in preliminary analyses and were considered potential confounders of the relationship between health plan type and mammogram use.

For the second aim, unadjusted reported rates for reasons for not receiving a mammogram were calculated and compared between the FFS and MA beneficiary populations. Next, these rates were compared within strata of the same 6 potential confounders. Chi-squared and Fisher’s exact tests were used throughout to determine if differences were statistically significant at 

shows the characteristics of FFS versus MA beneficiaries. In general, these results indicate that there are greater proportions of MA beneficiaries, compared with FFS beneficiaries, who are older, racial minorities, married, healthier, and living in western regions, mountainous regions, or Puerto Rico.

 compares unadjusted mammographic screening rates in Medicare FFS versus MA beneficiaries in the entire sample and then within strata of the 6 potential confounders: age, race/ethnicity, education, self-reported health status, marriage status, and region of residence.

A statistically significantly higher proportion of MA beneficiaries than FFS beneficiaries received a mammogram in 2009 (Table 2) (50.3% vs 44%, respectively; 

 <.0001). Also, statistically significantly higher proportions of MA beneficiaries received a mammogram in 2009 compared with FFS beneficiaries within specific strata: aged under 65 years, non-Hispanic white race/ethnicity, non-Hispanic black race/ethnicity, other races/ethnicities, high school graduate or lower education, good or fair health status, single marriage status, northern regions, eastern regions, and Puerto Rico.

MA enrollees were more likely to receive a mammogram compared with Medicare FFS enrollees before and after adjusting for confounders (Table 2) (unadjusted relative risk [RR], 1.13; 95% CI, 1.07-1.19; and adjusted RR, 1.06; 95% CI, 1.01-1.12).

 shows the top 6 reasons why beneficiaries did not receive mammograms in 2009. These reasons, from most to least reported, were that the beneficiary did not think it was needed, the doctor did not recommend it, the beneficiary had forgotten, the beneficiary did not think the mammogram was recommended annually, the beneficiary did not like mammograms, and the beneficiary had an appointment due soon. The proportions of beneficiaries reporting these reasons in the FFS and MA beneficiary populations were not statistically significantly different.

Eleven other reasons for not undergoing mammography were asked in the survey. These were: “cost,” “test useless,” “beneficiary not at risk for breast cancer,” “doctor recommending against it,” “inconvenience,” “beneficiary afraid of results,” “radiation causing cancer,” “beneficiary had a mastectomy,” “beneficiary never heard of a mammogram,” “beneficiary too ill,” and “other.” These reasons were not included in Table 3 because they were reported by less than 5% of both the FFS and MA beneficiaries. Additionally, only 8 differences were found to be statistically significant when comparing proportions of FFS and MA beneficiaries’ reporting reasons for not receiving mammography within strata of age, education, race/ethnicity, health status, marriage status, and region (see 

 Among beneficiaries aged under 65 years, a higher proportion of FFS versus MA beneficiaries reported not receiving mammograms because the doctor did not recommend it (Table 4) (30.11% vs 12.70%, respectively; 

 <.05). Among beneficiaries 85 years or older, a higher proportion of MA versus FFS beneficiaries reported not receiving mammograms because it was not recommended annually (Table 4) (21.54% vs 14.98%, respectively) or they had forgotten (Table 4) (14.36% vs 6.42%, respectively; 

 Among beneficiaries who graduated high school at the most, a higher proportion of MA versus FFS beneficiaries reported not receiving mammograms because the appointment was due soon (Table 4) (12.94% vs 8.28%, respectively; 

 Among non-Hispanic blacks, a higher proportion of FFS versus MA beneficiaries reported not receiving mammograms because the doctor did not recommend it (Table 4) (30.29% vs 16.87%, respectively; 

 <.05). In the same group, a higher proportion of MA vs FFS beneficiaries reported not receiving mammograms because they did not like mammograms (Table 4) (15.66% vs 6.22%, respectively; 

 <.05). Among Hispanics, a higher proportion of FFS versus MA beneficiaries also reported not receiving mammograms because the doctor did not recommend it (Table 4) (34.15% vs 9.09%, respectively; 

 Among beneficiaries reporting excellent or very good health status, a higher proportion of MA versus FFS beneficiaries reported not receiving mammograms because they were not recommended annually (Table 4) (18.45% vs 12.98%, respectively; 

MA beneficiaries are slightly more likely to receive mammographic screening compared with FFS beneficiaries. Reasons for not receiving mammography differ between MA and FFS beneficiaries within specific subgroups of age, race/ethnicity, education level, and health status.

Carrasquillo et al (2001) used 1996 data and found that Medicare HMO beneficiaries tended to be slightly younger minorities with similar education and perceived health as FFS beneficiaries.

 This study similarly finds that MA beneficiaries tend to be racial minorities and have similar education compared with FFS beneficiaries. However, this study also finds that compared with FFS beneficiaries, more MA beneficiaries are older, healthier, married, and living in western regions, mountainous regions, or Puerto Rico. The increased MA enrollment over the past decade may have contributed to these changes in demographic characteristics between the MA and FFS populations.

Medicare Advantage beneficiaries were 1.06 times more likely to receive mammography compared with fee-for-service (FFS) beneficiaries. FFS providers were less likely to recommend mammograms to racial/ethnic groups.

The Effect of Medicare Advantage Enrollment on Mammographic Screening

To determine the extent to which Medicare Part D enrollees fill unadjudicated prescriptions for diabetes, hypertension, and hyperlipidemia medications outside of the Part D benefit.

Retrospective analysis of prescriptions filled by community-dwelling beneficiaries continually enrolled in a Medicare Part D plan in 2009.

We used the Medicare Current Beneficiary Survey to compare self-reported prescription fills for oral antidiabetes medications, renin-angiotensin-aldosterone system inhibitors, and statins to adjudicated prescription drug event data recorded by Part D plan sponsors. For unadjudicated prescriptions with no evidence of Part D payment, we determined whether the fills were paid for in cash, filled through VA pharmacies or discount generic programs, or had other reported sources of coverage.

A total of 6.2% of all prescriptions filled by Part D beneficiaries were unadjudicated, ranging from 5.3% of all oral antidiabetes medications to 6.8% of statins. Cash prescriptions accounted for more than half of all out-of-plan use, but we found little evidence of unadjudicated out-of-plan use of discount generics. Prescriptions filled at VA pharmacies and those with other reported sources of coverage each accounted for about 1% of total fills.

Out-of-plan medication use accounts for a small share of total prescriptions filled by Part D beneficiaries. Nevertheless, CMS should continue to work with plan sponsors to develop initiatives that facilitate the collection of beneficiaries’ complete utilization data, as a more complete reporting could improve the quality of care delivered to Part D enrollees.

Out-of-plan use of oral antidiabetic medications, renin-angiotensin-aldosterone system (RAAS) inhibitors, and statins accounted for a small share of total utilization by Medicare Part D beneficiaries.

	• 6.2% of prescriptions were unadjudicated, ranging from 5.3% of oral antidiabetes medications to 6.8% of statins.

	• Cash prescriptions accounted for more than half of all out-of-plan use, but we found little evidence of unadjudicated prescriptions filled through discount generic programs.

	• Prescriptions filled at Veterans Affairs (VA) pharmacies accounted for 1.1% of total fills.

	• CMS should continue to work with plan sponsors to collect complete and accurate utilization data, including cash, VA, other third-party, and discount generic prescriptions.

The Medicare prescription drug program, known as Part D, provides an optional drug benefit for Medicare beneficiaries administered by CMS through contracts with private health plan sponsors. Each time a beneficiary fills a prescription for a medication covered by Part D, the plan sponsor is responsible for electronically submitting documentation of the transaction, called a prescription drug event (PDE) record, to CMS. Each PDE record contains detailed information about the prescription fill, including the name of the drug, the quantity and number of days’ supply of medication dispensed, payment made by the plan sponsor, and beneficiary cost-sharing. While CMS relies on the submission of PDE records to accurately administer the benefit and make payments to plans for drugs covered by Part D, there are several reasons why these data may provide an incomplete picture of a beneficiary’s entire medication history. For example, over-thecounter (OTC) drugs, medications excluded from Part D coverage by law, off-formulary products, and medication samples obtained from physicians typically do not generate PDE records.

 In addition, documentation of prescription fills for medications otherwise covered by the Part D program might also be missing if they are not adjudicated at the pharmacy and are therefore unknown to the plan sponsor. 

 Important sources of such out-of-plan use may include unadjudicated prescriptions paid in cash by the beneficiary, prescriptions filled at non-network pharmacies, prescriptions obtained through the Department of Veterans Affairs (VA), generic drugs purchased through pharmacy discount programs, and products obtained through other prescription assistance programs that operate outside of the Part D benefit.

Out-of-plan medication use by Part D enrollees raises a number of concerns. First, spending on drugs covered by Part D that occurs outside of the plan adjudication process may not be counted toward beneficiaries’ true out-of-pocket spending, which can reduce program benefits for beneficiaries with high drug expenditures. Second, for payers, clinicians, and researchers who rely upon accurate administrative data, missing claims may lead to biased or unreliable estimates of drug adherence, spending, and outcomes. Third, incomplete information on beneficiaries’ drug use can inhibit plan sponsors’ ability to target adherence programs, such as medication therapy management (MTM), or may interfere with other quality and safety assurance processes, including drug utilization review (DUR). While these challenges are not unique to the Part D program, Part D plan sponsors face the additional concern that incomplete utilization records may negatively impact the performance measures that determine their Star Ratings.

The Part D Star Ratings program evaluates the quality and performance of prescription drug plans (PDPs) and Medicare Advantage prescription drug plans (MAPDs) offered to Medicare beneficiaries. Based on an assessment of their performance on 18 quality measures across 4 domains of care, plan sponsors receive an overall annual score ranging from 1 to 5 stars, in half-star increments, with a higher number of stars indicating better performance. While CMS initially designed the Star Rating system to help enrollees choose high-quality Part D plans, the Affordable Care Act and a subsequent CMS demonstration project expanded the scope of the program beginning in 2012, allowing high-performing MAPDs to qualify for quality-based bonus payments. PDPs are not eligible for additional quality payments based on their Star Ratings, but 5-star PDPs and MAPDs are allowed to enroll beneficiaries year-round, giving them a competitive advantage in the market. Meanwhile, plan sponsors with consistently low quality ratings may be denied permission to expand their service areas, and in some cases, prohibited from marketing to, enrolling, or providing coverage to beneficiaries.

Recent research on MAPDs suggests that plans’ Star Ratings significantly influence the enrollment decisions of both first-time enrollees and current Part D beneficiaries who are looking to switch coverage.

 Plan sponsors wishing to maximize their Star Ratings have a specific incentive to focus on improving enrollees’ adherence with oral antidiabetic medications, renin-angiotensin-aldosterone system (RAAS) inhibitors, and statins. CMS evaluates plans on the share of enrollees that are adherent to these medications and triple-weights the results relative to other quality indicators, accounting for over 27% of the overall Part D Star Rating. Data published by CMS indicate that on average, both MAPDs and PDPs score more poorly on these adherence measures than on measures across the other 3 domains.

Anecdotal reports from plan sponsors suggest that plans are unable to capture enrollees’ complete utilization data, and are therefore unable to accurately measure adherence, due to beneficiaries filling prescriptions through the VA and discount generic programs.

 According to CMS guidance, PDE data reported by plan sponsors may only include prescriptions processed by network pharmacies or prescription reimbursement requests received directly from beneficiaries. Due to data validation and beneficiaryprivacy concerns, CMS currently prohibits plan sponsors from augmenting their utilization files with additional data obtained from other sources, such as third-party vendors.

The extent of out-of-plan use among Medicare Part D beneficiaries and its impact on plans’ Star Ratings have not been well documented in the literature. This study addresses several knowledge gaps related to out-of-plan use. First, to address plan sponsors’ concerns that the adherence measures included in their Star Ratings may be negatively impacted by prescriptions filled outside of the Part D benefit, we examine the extent to which enrollees filled unadjudicated prescriptions for oral antidiabetic medications, RAAS inhibitors, and statins. Second, we estimate the potential scope of out-of-plan use due to unadjudicated cash prescriptions, medications filled at VA pharmacies, and discount generic drugs that might not have been electronically transmitted to Part D plan sponsors. Third, we track the growth in beneficiaries’ use of discount generic programs in 2009, relative to published estimates for 2007 and 2008. Finally, we discuss the policy implications of our findings for CMS and Part D plan sponsors.

We used data from the 2009 Medicare Current Beneficiary Survey (MCBS) to conduct the research. The MCBS is an ideal data source for this analysis because it reports adjudicated PDEs from Part D plan sponsors together with self-reported prescription medication events (PMEs) that may or may not have been adjudicated. Self-reported PMEs are collected from survey respondents 3 times during the year and include data on the drug name, quantity dispensed, and source(s) of payment. PDE records are extracted directly from CMS. Each medication event in the MCBS identifies whether the prescription was reported solely as a PME (ie, with no matching PDE record), recorded solely as a PDE (ie, with no self-reported PME), or whether CMS was able to identify and match both a PME and PDE for that fill. We restricted our analysis to prescriptions filled by community-dwelling beneficiaries continually enrolled in an MAPD or PDP in 2009, excluding beneficiaries who died during the year. The study was approved by the institutional review board at the University of Maryland, Baltimore.

We limited the drugs in the analysis to those included in the Pharmacy Quality Alliance (PQA) specifications for the Star Ratings adherence measures. In 2009, these included 4 classes of oral antidiabetes drugs (biguanides, dipeptidyl peptidase-4 inhibitors, sulfonylureas, and thiazolidnediones), RAAS inhibitors, and statins.

 To estimate the extent to which Part D beneficiaries filled outof- plan prescriptions for these medications, we identified self-reported PMEs and calculated the share without plan sponsor adjudication, as evidenced by the absence of a matching PDE record. We restricted our analysis of unmatched PMEs to those for which Medicare Part D was not reported as a payment source (eg, prescriptions paid for in cash, by the VA, or by other payers). To determine the share of unmatched PMEs potentially attributable to generic discount program use, we calculated the share of generic cash PMEs with reported out-of-pocket payments of either $4 or $10, which correspond to amounts commonly charged by pharmacies with discount programs.

We then determined whether or not those fills had any evidence of adjudication, as indicated by matching PDE records. All analyses were conducted using SAS 9.2 (SAS Institute, Cary, North Carolina).

 presents characteristics of MCBS respondents continuously enrolled in a Medicare Part D plan throughout 2009, weighted to reflect the entire Medicare population. Half of all beneficiaries (50.2%) filled 1 or more prescriptions for a RAAS inhibitor, 50.2% filled 1 or more prescriptions for a statin, and 19.9% filled 1 or more prescriptions for an oral antidiabetic medicine. About 38% of the sample filled prescriptions for 2 of these medications, and 11.3% filled prescriptions for all 3. Roughly 60% of beneficiaries were enrolled in a PDP for all of 2009, and a small share—less than 2%&mdash;switched between PDP and MAPD coverage during the year. A third of the sample received the low-income subsidy. Reflective of the distribution of the broader Part D population, the majority of the sample was female and more than 70% of the subjects were white; however, survey respondents   were older than Part D enrollees in general.

 Geographically, more than a third of respondents lived in the South, and about 18% of the sample reported having ever served in the military.

Analysis of the PME and PDE data showed that Part D enrollees in our MCBS sample filled a total of 45,322 prescriptions for oral antidiabetics, RAAS inhibitors, and statins in 2009 (

). The vast majority of these fills (93.8%) had evidence of Part D adjudication and there was little variation across drug classes (93.2% for statins to 94.7% for oral antidiabetic agents). Altogether, 82.5% of prescriptions in the sample had an associated Part D payment. A total of 14.4% of fills were reported as cashonly with a ratio of about 3 to 1 being Part D adjudicated (10.7% versus 3.7%). Prescriptions filled through the VA accounted for a very small share of total fills, ranging from just 0.8% of RAAS inhibitors to 1.6% of statins. Equally small shares of prescriptions for these drug classes had other reported sources of payment.

The results of our analysis of cash-only $4 and $10 fills indicate that only a small share of unadjudicated generic oral antidiabetic agents, RAAS inhibitors, and statins met these criteria: fewer than 1% had an out-of-pocket payment of $4 and 4.5% had a payment of $10. As shown in 

, 90% to 98% of all generic $4 and $10 fills had a matching PDE record, indicating they had been adjudicated by a Part D plan sponsor. Statins had the largest share of unadjudicated $4, and $10 generic fills; however, many of these were prescriptions for simvastatin, which was not commonly included in pharmacy discount programs at the time of the analysis.

 As a result, the share of unadjudicated $4 and $10 statin fills attributed to discount program use in Table 3 may be overestimated.

Part D plan sponsors are growing increasingly concerned that PDE data may not provide a complete assessment of beneficiaries’ medication utilization. Unadjudicated prescriptions filled outside of the Part D benefit can inhibit plan sponsors’ ability to target medication therapy management programs and can interfere with other quality and safety assurance processes. Missing prescriptions for oral antidiabetics, RAAS inhibitors, and statins may also negatively impact the performance measures that determine plan sponsors’ Star Ratings. This study quantifies the extent of out-of-plan use for these 3 types of medications in 2009. Our analysis of all prescriptions adjudicated by plan sponsors, as well as unadjudicated prescriptions self-reported by beneficiaries, indicates that missing data unlikely had any significant impact on plan sponsors’ Star Ratings, because only a small share—about 6%&mdash;of oral antidiabetics, RAAS-inhibitors, and statins were filled outside of the Part D benefit. Cash prescriptions accounted for more than half of total out-of-plan use. Prescriptions filled at VA pharmacies accounted for about 1%, and there was little evidence of unadjudicated out-of-plan use of discount generics.

One reason beneficiaries may have filled cash prescriptions out of plan is that the drugs were excluded from plan formularies. In support of this hypothesis, we found that about 44% of unadjudicated cash fills were for brand name medicines. In the case of oral antidiabetics, which had a higher share of unadjudicated cash prescriptions than either RAAS inhibitors or statins, more than half of all unadjudicated prescriptions paid for out-of-pocket were for drugs commonly excluded from Part D formulary coverage. A second possibility—and the one that has received the most attention&mdash;is the use of discount generic programs. Prior research has shown that pharmacy practices regarding the adjudication of discount generic prescriptions vary, with some pharmacies routinely submitting claims to plan sponsors and others stipulating that the programs cannot be used in conjunction with patients’ insurance.

 A handful of studies have shown low take-up of discount programs, particularly among Part D enrollees. Stuart and Loh report that about 3% of Part D beneficiaries filled a prescription for a discount programeligible generic drug in 2007.

 Similarly, research by Zhang and colleagues suggests that among all Part D enrollees taking an eligible $4 generic drug in 2007, only 16.3% of beneficiaries obtained them through discount generic programs. 

 While our findings indicate little growth in the use of discount generic programs among Part D beneficiaries between 2007 and 2009, a recent survey suggests that the share of the general population utilizing these programs increased from 4.7% in 2008 to 31.9% in 2010.

According to current CMS guidance, Part D plan sponsors may only report PDE data for claims adjudicated by network pharmacies or for prescription reimbursement requests received directly from beneficiaries; the agency does not permit plan sponsors to supplement their PDE files with utilization data collected from third-party vendors. To improve the accuracy and completeness of the PDE data, CMS encourages plan sponsors to incentivize network pharmacies to adjudicate claims for discount generic and other promotional drugs directly at the point of sale. While improved pharmacy reporting would alleviate some of the unintended consequences of out-of-plan use due to discount generic programs, it would not affect prescriptions filled through the VA. The VA provides separate and distinct coverage from Medicare and there is no coordination of benefits or data sharing between VA pharmacies and Part D plan sponsors.

Our findings indicate that VA coverage accounted for a small share of oral antidiabetics, RAAS inhibitors, and statins filled outside of the Part D benefit; however, it is possible that unadjudicated VA prescriptions for these medications could systematically distort the performance ratings of certain plan sponsors in regions with VA hospital systems. With this possible exception in mind, missing utilization data due to out-of-plan use that is randomly distributed should not distort plan Star Ratings because CMS uses relative rankings among sponsors when it assigns performance scores. Based on our findings, we believe that the share of unadjudicated prescriptions is currently too small to significantly affect plans’ Star Ratings; however, future research is needed to determine whether regional variation in the use of VA pharmacies and discount generic programs exists and whether this variation may systematically bias the Star Ratings of plan sponsors across regions.

The results of this study should be considered in light of several limitations. First, self-reported prescription medication events reported in the MCBS are subject to recall bias, and may over- or under-report total beneficiary utilization. Second, although MCBS survey administrators are instructed not to record utilization of physician samples, inclusion of these medications in the self-reported data would lead to an overreporting of out-of-plan use. Third, we lack data to determine whether unadjudicated prescriptions resulted from formulary exclusions or use of pharmaceutical manufacturer or other charitable assistance programs. Fourth, we did not examine use of generic discount or other pharmacy promotional programs with a per prescription cost of other than $4 or $10. Finally, although we found that virtually all $4 and $10 cash prescriptions for generic drugs were adjudicated by plan sponsors, we cannot determine if this was the result of claims being adjudicated by pharmacies or claims submitted directly to plan sponsors by beneficiaries.

Despite these limitations, our findings indicate that out-of-plan prescriptions, particularly those filled through discount generic programs or the VA, account for a small share of prescriptions filled by Part D enrollees and are unlikely to impact plan sponsors’ Star Ratings. Nevertheless, efforts to ensure a more complete reporting of beneficiaries’ medication histories could improve quality of care by allowing plan sponsors to conduct more accurate drug utilization and medication therapy management reviews. CMS should continue to work with plan sponsors to develop initiatives that facilitate the collection of complete and accurate utilization data, including records of cash prescriptions and those filled through VA pharmacies, discount generic programs, and other coverage sources.

 Department of Pharmaceutical Health Services Research, University of Maryland, Baltimore; Policy and Research Department, Pharmaceutical Research and Manufacturers of America (PhRMA); Peter Lamy Center for Drug Therapy and Aging, Department of Pharmaceutical Health Services Research, University of Maryland, Baltimore (BS).

 This work was funded by the Pharmaceutical Research and Manufacturers of America (PhRMA). The sponsoring organization had no role in the analysis or interpretation of data or the decision to approve or disapprove publication of the final manuscript. Any views expressed here are solely those of the authors and do not necessarily reflect the views of PhRMA.

 Ms Roberto is employed on a part-time basis by PhRMA, but was not compensated for her work on this project, which was completed for course credit. The Institutional Review Board at the University of Maryland, Baltimore, was aware of the lead author’s employment arrangement with PhRMA, and a conflict of interest exemption was granted by the president of the university.

 Concept and design (PNR, BS); acquisition of data (BS); analysis and interpretation of data (PNR, BS); drafting of the manuscript (PNR); and critical revision of the manuscript for important intellectual content (PNR, BS).

 Pamela N. Roberto, MPP, 220 Arch St, Baltimore, MD 21201. E-mail: 

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. Published April 2013. Accessed July 1, 2013.

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http://www.cms.gov/Medicare/Prescription-Drug-Coverage/PrescriptionDrugCovGenIn/PerformanceData.html

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7. PQA measures used by CMS in the star ratings. 

. Springfield, VA: Pharmacy Quality Alliance. Accessed June 12, 2013.

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Out-of-plan medication use accounted for a small share of diabetes, hypertension, and hyperlipidemia prescriptions filled by Medicare Part D beneficiaries.

Out-of-Plan Medication in Medicare Part D

To (1) measure utilization of and adherence to heart failure medications and (2) assess whether better adherence is associated with lower Medicare spending.

 Pooled cross-sectional design using six 3-year cohorts of Medicare beneficiaries with congestive heart failure (CHF) from 1997 through 2005 (N = 2204).

Adherence to treatment was measured using average daily pill counts. Bivariate and multivariate methods were used to examine the relationship between medication adherence and Medicare spending. Multivariate analyses included extensive variables to control for confounding, including healthy adherer bias.

 Approximately 58% of the cohort were taking an angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB), 72% a diuretic, 37% a beta-blocker, and 34% a cardiac glycoside. Unadjusted results showed that a 10% increase in average daily pill count for ACE inhibitors or ARBs, beta-blockers, diuretics, or cardiac glycosides was associated with reductions in Medicare spending of $508 (not significant [NS]), $608 (NS), $250 (NS), and $1244 (

 <.05), respectively. Estimated adjusted marginal effects of a 10% increase in daily pill counts for beta-blockers and cardiac glycosides were reductions in cumulative 3-year Medicare spending of $510 to $561 and $750 to $923, respectively (

 Higher levels of medication adherence among Medicare beneficiaries with CHF were associated with lower cumulative Medicare spending over 3 years, with savings generally exceeding the costs of the drugs in question.

Modest increases in adherence to medication regimens among Medicare patients with heart failure were associated with lower Medicare spending in 3 major drug classes.

Bruce Stuart, PhD

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