Bruce Stuart, PhD

 To evaluate the impact of the star rating bonus payment policy on annual influenza vaccination rates before and after the policy was adopted for Medicare Advantage (MA) plans in 2012.

 Observational study using data from the Medicare Current Beneficiary Survey from 2007 to 2015 to test whether the bonus payment policy led to higher flu vaccination rates in MA prescription drug (MAPD) plans vs fee-for-service prescription drug plans (PDPs), which were ineligible for bonus payments.

 Mean preperiod (2007-2011) and postperiod (2012-2015) influenza vaccination rates were compared for enrollees in both types of plans using descriptive and multivariate difference-in-difference (DID) equations. The experimental effect of the MA bonus payment policy was estimated as the interaction between plan type (MAPD plan vs PDP) and period (pre- vs post period) controlling for the main effects of plan type (MAPD vs PDP), timing of the observation (pre- vs post period), and other potential confounders.

 The study sample included 40,369 person-years of data in the preperiod and 27,703 person-years of data in the post period. Vaccination rates increased by 3.8% in MAPD plans compared with 2.7% in PDPs, leading to a relative MAPD-favored difference that was nonsignificant (

 = .31). However, the effect was statistically significant (odds ratio [OR], 1.12; 

 = .03) in the main multivariate DID model. A larger relative difference was observed among beneficiaries 75 years and older (OR, 1.18; 

The Medicare bonus payment policy led to a small increase in beneficiaries’ flu vaccination rates, suggesting that expanding the star measure set could be an effective way to increase uptake for other recommended adult vaccines.

This study evaluated the impact of the Medicare Advantage (MA) star rating bonus payment policy on annual influenza vaccination rates before and after the policy was adopted in 2012.

Recommended adult vaccinations are generally cost-effective, yet uptake tends to be low.

One option to encourage uptake is leveraging the pay-for-performance policy under which MA plans with high star ratings earn bonus payments.

A small but statistically significant increase in flu vaccination rates in MA plans suggests that expanding the star measure set to include other recommended adult vaccines could be an effective way to increase vaccine uptake.

A recent review of cost-effectiveness studies of adult vaccines concluded that most report favorable profiles.

 Nonetheless, uptake of recommended vaccines remains low.

 This is of particular concern in older patients, given the greater incidence and severity of infection among older adults. Approximately 90% of influenza-related deaths and 50% to 70% of influenza-related hospitalizations occur among patients 65 years and older.

 Although older patients typically have higher rates of vaccination than younger age groups, a substantial proportion of older adults still fail to receive recommended vaccines.

CMS has promoted the uptake of preventive vaccines in Medicare. Some vaccines (eg, pneumococcal vaccine, hepatitis B vaccine, influenza vaccine) are covered for free under the Part B Supplemental Medical Insurance Benefit. Most other commercially available vaccines are covered under the Part D Prescription Drug Benefit. However, vaccines covered by Part D are generally subject to cost sharing, which in 2017 averaged $39 to $47 per dose in Medicare Advantage prescription drug (MAPD) plans and $27 to $75 per dose in stand-alone prescription drug plans (PDPs).

 CMS guidance encourages plans to create zero-dollar cost-share vaccine tiers under Part D, but the agency has offered little incentive for plans to comply. As of 2017, no PDPs offered zero–co-pay vaccine tiers and fewer than 5% of MAPD plans did so.

One potential way to increase vaccination rates is through pay-for-performance programs wherein health plans are eligible for bonus payments if they meet specified quality targets.

 One such policy involves the MA Star Ratings Program initiated by CMS in 2007. Under the program, CMS publishes a 1- to 5-star rating measure designed to aid consumers choosing MAPD plans. The star rating metrics include various measures of health services quality and member experience with drug availability. Influenza vaccination is the only vaccine currently included in the star rating metrics. Starting in 2012, MAPD plans with 4 or more stars became eligible to receive quality bonus payments. MAPD plans received an estimated $3.1 billion in bonuses in 2012, with about one-third of bonus money given to 74 contracts with ratings of 4 stars or higher.

 By 2019, 77% of MA enrollees were in plans receiving bonus payments at a total estimated cost of $6 billion.

Evidence suggests that MAPD plans consider their star ratings and associated bonus payments to be important. Li and Doshi

 found that the availability of bonus payments led to a significant boost in both star ratings and enrollment among MAPD plans compared with PDPs. Other studies have shown a positive association between high star ratings and MAPD enrollment growth

 Furthermore, it appears that highly rated MAPD plans are able to charge higher premiums compared with plans with lower star ratings.

 Higher premium income is desirable in its own right and also provides revenue to support additional service offerings to enrollees, which in turn can drive higher enrollment.

Few studies have evaluated the impact of star ratings on patient experiences and clinical outcomes, but what has been published generally supports the idea that quality ratings matter. Elliott et al

 reported that MAPD enrollees express more positive experiences with their care compared with PDP enrollees and that, among MAPD enrollees, the quality of their experiences is positively correlated to star ratings. Timbie et al

 confirmed higher satisfaction with high star-rated plans and also showed that MAPD plans outperform PDPs on 16 clinical measures included in the star ratings. Likewise, a study by Sussell et al

 analyzed the impact of value-based MAPD bonus payments on quality of care for type 2 diabetes and found small but statistically significantly better glycated hemoglobin A

 control among patients in high star-rated plans. No study has yet examined the impact of the bonus payment policy on vaccination uptake.

The objective of this study was to evaluate the impact of the MA star rating bonus payment policy on annual influenza vaccination rates before and after the policy was adopted in 2012. We hypothesized that potential bonus payments increased influenza vaccine uptake in MAPD plans relative to PDPs, which were ineligible for such payments.

This observational study used data from the Medicare Current Beneficiary Survey (MCBS) from 2007 to 2015, excluding 2014, for which MCBS files were not released. Three characteristics of the MCBS make it an appropriate data set for the current study: (1) it is nationally representative of all Medicare beneficiaries, (2) it contains annual survey questions regarding various adult vaccinations, and (3) it collects information on a large number of sociodemographic and health characteristics that can serve as control variables in the statistical analysis. A recent study validated the accuracy of self-reported influenza vaccination.

The study sample was a pooled cross-section of 8 years of MCBS data representing community-dwelling residents enrolled in Part D plans. Long-term care residents were excluded because facility protocols usually dictate vaccination status.

 Also excluded were individuals who had Part D coverage for only part of the year or who switched coverage during the year. Finally, persons with missing/unknown responses to the influenza vaccination question were excluded.

The primary outcome of interest was the change in annual influenza vaccination rates for noninstitutionalized Medicare beneficiaries between 2007 and 2011 (the preperiod) and 2012-2015 (the post period) in MAPD plans vs PDPs, based on the survey question, “Did you have a flu shot for last winter?” We also evaluated 3 enrollee subpopulations who are at high risk of serious influenza complications and are therefore expected to be targets for plan vaccination efforts: those 75 years and older, those with diabetes, and those with heart disease/stroke.

The MA bonus payment policy introduced in 2012 was treated as a natural experiment, the effects of which were analyzed using a pre-post comparison group design also known as difference-in-difference (DID) analysis. This approach represents a much stronger analytic method than simple pre-post comparisons in that it controls for contemporaneous changes in a control group (PDP enrollees) when computing pre-post changes in the experimental group (MAPD enrollees).

The study entailed both descriptive and multivariate analyses. We first examined characteristics of the PDP and MAPD samples in the pre- and post periods. Next, we calculated mean pre- and postperiod influenza vaccination rates for enrollees in both plan types. Finally, we subtracted preperiod from postperiod values for MAPD and PDP enrollees (this represents the first difference) and then subtracted the first difference for PDP enrollees from the first difference for MAPD enrollees to obtain the final DID estimates.

Of course, many factors aside from the availability of bonus payments could influence the observed DID values. We estimated multivariate models to control for confounding. The effect of the MA bonus payment policy was estimated as the interaction between being in an MAPD plan with an observation in the post period while controlling for the main effects of plan type, observation period, and other potential confounders.

The unit of observation was the person-year weighted to be nationally representative of the community-dwelling Medicare population. All multivariate analyses were estimated using logistic regression with full sample weights and replicate weights using the balanced repeated replication (BRR) method for variance estimation. The BRR method accounts for clustering of individuals within the MCBS primary and secondary sampling units as well as repeated observations among the same individuals over time.

We conducted sensitivity tests to assess the impact of alternative assumptions regarding preperiod/postperiod assignments. These tests used the same analytic framework as in the main study. We examined 2 alternative pre-/post assignments, one including 2011 in the post period rather than the preperiod and one in which 2011 data were excluded from the study altogether. The reason for considering 2011 in the post period is that 2012 star-related bonus payments were based on 2011 data. The analyses excluding 2011 data were based on the assumption that MAPD plans may not have had time to adjust to the new policy until 2012. Only descriptive DID analyses were conducted for the 2 alternative pre-/post periods.

As a final sensitivity test, we replicated the analysis for pneumococcal vaccination status (“Have you ever had a pneumonia shot?”) to test whether there were differentially higher rates of vaccination among MAPD enrollees for a vaccine 

included in the MA star rating metrics. This test used the same descriptive and multivariate analytic approach as the main study except that a dummy variable for influenza vaccination was included as a covariate in the regression model to account for the possibility of spillover effects whereby a visit to receive an annual influenza vaccination could itself lead to increased pneumococcal vaccination. We hypothesized that the DID values for pneumococcal vaccination would be close to zero and not statistically significant.

The main study sample included 40,369 person-years of data in the preperiod (2007-2011) and 27,703 person-years of data in the post period (2012-2015). Once weighted to be nationally representative of the entire Medicare population, this sample represented 20.2 million beneficiaries in 2007 and 35.6 million beneficiaries in 2015. The increase over time is attributable both to growth in overall Medicare enrollment and to a higher proportion of beneficiaries enrolling in Part D. Notably, MAPD enrollment increased from 31.1% of total Part D coverage in 2007 to 41.5% in 2015. Further details on sample construction are shown in 

 presents sample characteristics by plan type and study period. The samples differ in important respects in both periods. MAPD enrollees were older, on average, with fewer disabled beneficiaries compared with PDP enrollees. The MAPD sample also had higher proportions of beneficiaries who were Hispanic, married, higher income, metro residents, and residing in the West. The biggest relative changes over time were in education and income. Between the pre- and post periods, the share of enrollees with some post–high school education increased by 5.1 percentage points in MAPD plans but double that (10.4 points) in PDPs. Likewise, the increase in share of enrollees with incomes greater than $25,000 grew more in PDPs (11.8 points) compared with MAPD plans (7.2 points).

 shows vaccination rates across the 2 plan types in the pre- and post periods. Overall, vaccination rates were highest among those 75 years and older, non-Hispanic White individuals, individuals with incomes greater than $25,000, and those with diabetes, heart disease/stroke, or 3 or more chronic conditions (other than diabetes and heart disease/stroke). The lowest rates were observed among beneficiaries receiving Social Security Disability Insurance, Black beneficiaries, and beneficiaries who had never married. There were also marked regional differences, with vaccination rates highest in the Midwest and West.

 presents descriptive DID results showing changes in annual influenza vaccination rates for MAPD and PDP enrollees for the full sample and 3 subgroups. For the full sample (panel A), vaccination uptake was similar across the 2 settings during the preperiod (69.3% in MAPD plans and 69.1% in PDPs). However, in the post period, vaccination rates in MAPD plans had increased by 3.8 percentage points compared with 2.7 percentage points in PDPs, leading to a relative MAPD-favored difference (DID, 1.1 points), which was not statistically significant (

For persons 75 years and older (panel B), preperiod influenza vaccination rates were higher in PDPs compared with MAPD plans (78.9% vs 76.2%), but as in the full sample, the postperiod increase in vaccination rates in MAPD plans (2.8 percentage points) exceeded that in PDPs (0.4 percentage points), leading to a DID of 2.4 percentage points in favor of MAPD plans (

 = .06). Preperiod vaccination rates among plan enrollees with diabetes (panel C) and heart disease/stroke (panel D) were similar in both PDPs and MAPD plans (73.1% to 73.9%), with higher increases observed among MAPD plans in the post period. The DID estimates for individuals with diabetes and heart disease/stroke were numerically higher among those enrolled in MAPD plans by 2.0 points (

 presents adjusted DID results for the full sample and 3 subgroups. Detailed regression results are available in the online supplementary materials (

). As in the descriptive findings, the direction and magnitude of the DID estimates favored MAPD plans, but the conditional estimates were more precise. Estimates for both the full sample (odds ratio [OR], 1.12; 

 = .03) and the sample 75 years or older (OR, 1.18; 

 = .03) were statistically significant. The DID estimates for the diabetes and heart disease/stroke subsamples were identical (ORs, 1.15), and although they were not statistically significant (

 = .10 in both cases), they too showed reduced variance from the descriptive results.

Sensitivity analysis tests to determine whether alternative pre-/postperiod assignment affected the study results were uniformly negative (

). Reassigning 2011 data to the post period and eliminating 2011 data altogether each changed the DID estimates by just ±0.1 percentage points. The pneumococcal vaccination analysis yielded nonsignificant DID values in both descriptive and multivariate models (

Between 2012 and 2015, annual rates of influenza vaccination among Medicare beneficiaries enrolled in both MAPD plans and PDPs exceeded the Healthy People 2020 target of 70%. The rates were marginally higher in MAPD plans, which in and of itself is surprising given that MAPD plans had higher minority enrollment and much slower growth in income and educational levels over the study period compared with PDPs. Our results showed that mean vaccination rates among minorities varied between 6% and 19% below that of White beneficiaries, and higher income and education were both consistently associated with higher vaccination levels.

We attribute the relative improvement in influenza vaccination rates among beneficiaries enrolled in MAPD plans compared with PDPs to the MA bonus payment policy. The fact that we observed no significant difference in uptake of pneumococcal vaccination, which is not included in the star rating measures, between MAPD and PDP enrollees further supports this conclusion. The net gain in influenza vaccination uptake was small but statistically significant, with an adjusted OR of just 1.12 (

 = .03) for the MAPD population as a whole, and somewhat higher for beneficiaries 75 years and older (OR, 1.18; 

A particular strength of our study is the fact that weights in the MCBS data set permit us to generalize these results to the entire Medicare population over the study period. Another strength is that our multivariate DID model adjusted for covariates including those that changed over time, which proved to be a key factor in the higher precision of the multivariate coefficients relative to unadjusted estimates. Finally, the modest size of the impact was not unexpected given that the study population represented Medicare beneficiaries enrolled in all MAPD plans, including some plans that could reasonably expect to gain bonus payments by increasing vaccination rates whereas others could not. Another possible reason for the small gain is that influenza vaccination rates were very close to the Healthy People 2020 target prior to the advent of the bonus payment policy.

Extrapolating from these findings, one might expect to see much larger impacts should the MA bonus payment policy be applied to other recommended vaccines—those with lower uptake rates and/or newly developed vaccines with no prior history. CMS has made COVID-19 vaccines available free of any cost sharing, but there are already signs that free shots may not be enough to ensure widespread uptake.

 Incentivizing payers through bonus payments tied to vaccine uptake could have an important public health impact under these circumstances. Such a policy could align the incentives for private plans to increase vaccination uptake, which otherwise represents a short-term expense (vaccination and administration costs) with a delayed benefit (prevention of disease) that may not generate future savings should enrollees switch plans. It is also important to look beyond COVID-19 to other adult vaccines currently under development for which pay-for-performance policies could prove an effective way of increasing vaccine uptake and maximizing public health impacts. This is especially important for Part D covered vaccines, for which plans currently have no incentives to reduce cost sharing despite CMS guidance to create zero-dollar cost-share vaccine tiers.

Future research is needed to better understand how to maximize the impact of Medicare star ratings. Given the large number of quality measures included in the Star Ratings Program, policy makers need to understand how weighting of individual measures affects potential outcomes and whether higher weighting for vaccines might spur uptake as well as possibly reduce future health care utilization and costs among Medicare beneficiaries. Research should also investigate whether bonus payments could serve as a possible policy lever to address health disparities, especially because the vaccination rate for ethnic minorities is far below that of White beneficiaries as seen in our study. Finally, given that our study was conducted before the COVID-19 pandemic, research is needed to tell whether polarization of opinion around COVID-19 vaccines may affect vaccination efforts for other diseases.

As with all observational studies, the methods used in this analysis cannot prove causality. Other study limitations warrant mention. The analysis was designed to measure the average effect of potential bonus payments across a diverse group of MAPD sponsors, some of which may have attempted to maximize enrollee vaccination rates before the availability of bonuses whereas others may have had such low star ratings that receipt of any bonus payment was out of reach. In neither case would we expect to observe higher vaccination rates in the post period relative to the preperiod attributable to the bonus payment policy. Put another way, had it been possible to isolate only those MAPD plans most likely to benefit from the bonus payment policy, we would have expected to see a greater relative increase in vaccination rates than observed in the present study. Unfortunately, we had no way to assess which plans could reasonably be assured of earning bonuses because identification of plan attributes including star ratings is not possible with MCBS data. Future studies linking data on vaccination rates with plan characteristics and vaccination promotional efforts would provide policy makers with more granular details regarding which interventions are most successful and cost efficient. It would be of particular interest to determine whether and how plans implemented the well-known CDC immunization strategy known as the AFIX (Assessment, Feedback, Incentives, eXchange) Program (replaced in 2019 with the IQIP [Immunization Quality Improvement for Providers] Program).

The Medicare policy of awarding bonus payments to highly rated MAPD plans had a small but statistically significant positive impact on uptake of influenza vaccines among MAPD enrollees following implementation of the policy in 2012.

 School of Pharmacy, University of Maryland, Baltimore (BS), Baltimore, MD; Touro College of Pharmacy, Touro University (FEL), New York, NY; COVIA Health Solutions (SK-B, JP), Lansdale, PA; Pfizer Inc (JV, DB), Collegeville, PA; Perelman School of Medicine and Leonard Davis Institute of Health Economics, University of Pennsylvania (JAD), Philadelphia, PA.

 This study was funded by Pfizer Inc, which also provided funds to COVIA Health Solutions for manuscript development.

 Dr Kamal-Bahl is president and founder of COVIA Health Solutions, which provides consulting services to various pharmaceutical/biotechnology clients; reports funding from Pfizer Inc to COVIA Health Solutions for conducting this study and preparation of the manuscript; and owns stock in Merck and Pfizer. Dr Vietri and Mr Bakken are employed by and own stock in Pfizer Inc, which develops and markets vaccines but does not currently market influenza vaccines in the United States (the focus of this manuscript). Mr Puckett is employed by COVIA Health Solutions, which received funding from Pfizer for conducting this study and manuscript development. Drs Stuart, Doshi, and Loh served as consultants on this study. Dr Doshi also reports serving as an advisory board member or consultant for Acadia, Allergan, Boehringer Ingelheim, Catabasis, Ironwood Pharmaceuticals, Janssen, Kite Pharma, MeiraGTx, Merck, Otsuka, Regeneron, Sarepta, Sage Therapeutics, Sanofi, Takeda, The Medicines Company, and Vertex; she has also received research funding from AbbVie, Biogen, Humana, Janssen, Merck, Novartis, Pfizer, PhRMA, Regeneron, Sanofi, and Valeant, all unrelated to this study.

 Concept and design (BS, SK-B, JV, DB, JAD); acquisition of data (SK-B); analysis and interpretation of data (BS, FEL, SK-B, JV, DB, JAD); drafting of the manuscript (BS, JP); critical revision of the manuscript for important intellectual content (BS, FEL, SK-B, JV, DB, JAD); statistical analysis (BS, FEL); obtaining funding (SK-B, DB); administrative, technical, or logistic support (JP); data visualization (JP); and supervision (SK-B, JV, DB).

 Sachin Kamal-Bahl, PhD, COVIA Health Solutions, 115 Meadow View Ln, Lansdale, PA 19446. Email: sachin@coviahealthsolutions.com.

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Articles

Influenza vaccine uptake improved among Medicare Advantage enrollees when influenza vaccination was introduced as a performance metric in Medicare star ratings and accompanying bonus payments.

Financial Incentives Tied to Medicare Star Ratings: Impact on Influenza Vaccination Uptake in Medicare Beneficiaries

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.

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.

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.

Bruce Stuart, PhD

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