Pharmacy switching in response to preferred pharmacy networks in Medicare Part D

Abstract

Objective

To evaluate the effects of preferred pharmacy networks—a tool that Medicare Part D plans have recently adopted to steer patients to lower cost pharmacies—on the use of preferred pharmacies and factors underlying beneficiaries' decisions on whether to switch to preferred pharmacies.

Data Sources

Medicare claims data were collected for a nationally representative 20% sample of beneficiaries during 2010–2016 and merged with annual Part D pharmacy network files.

Study Design

We examined preferred networks' impact on pharmacy choice by estimating a difference‐in‐differences model comparing preferred pharmacies' claim share before and after implementation among unsubsidized and subsidized beneficiaries. Additionally, we evaluated the factors affecting whether a beneficiary switched from mainly using nonpreferred to preferred pharmacies.

Data Collection/Extraction Methods

We examined stand‐alone drug plans that adopted a preferred network during 2011–2016. Our main sample included beneficiaries 65 years and older who stayed in their plan in both the first year of implementation and the year before and whose cost‐sharing subsidy status and ZIP code remained unchanged during the 2‐year period.

Principal Findings

Unsubsidized Part D beneficiaries faced an average difference of $129 per year in out‐of‐pocket spending between using nonpreferred and preferred pharmacies, while subsidized beneficiaries were insulated from these cost differences. The implementation of preferred networks resulted in a 3.7‐percentage point (95% CI: 3.3, 4.2) increase in preferred pharmacies' claim share in the first year among the unsubsidized. Existing relationships with preferred pharmacies, the size of financial incentives, proximity to preferred pharmacies, and urban residence were positively associated with beneficiaries' decisions to switch to these pharmacies.

Conclusions

Preferred pharmacy networks caused a moderate shift on average towards preferred pharmacies among unsubsidized beneficiaries, although stronger financial incentives correlated with more switching. Researchers and policymakers should better understand plans' cost‐sharing strategies and assess whether communities have equitable access to preferred pharmacies.

What is known on this topic

• Historically, patients faced the same copay for a certain drug across all retail pharmacies in their plan's network, and thus had little incentive to choose the pharmacy with the lowest price.

• Between 2011 and 2021, the percentage of Medicare stand‐alone prescription drug plans with a preferred pharmacy network has increased from below 9% to over 98%.

• Preferred pharmacy networks use lower cost‐sharing to steer patients to the subset of preferred pharmacies.

What this study adds

• The subset of preferred pharmacies in a preferred network was generally narrow.

• Unsubsidized Part D beneficiaries faced substantial financial incentives to use preferred pharmacies and moderately switched to those pharmacies, while subsidized beneficiaries faced minimal incentives and demonstrated little switching.

• Existing relationships with preferred pharmacies, the size of financial incentives, convenient access to preferred pharmacies, and urban residence were positively associated with beneficiaries' shift towards preferred pharmacies.

1. INTRODUCTION

Health plans have long been using preferred provider networks to steer patients to lower cost providers. ¹ These networks contain costs by financially incentivizing patients to choose lower cost, higher value physicians and hospitals, ² , ³ , ⁴ , ⁵ and giving plans more leverage in their negotiations with providers. ⁶ , ⁷ , ⁸ In addition, recent value‐based insurance design efforts have attempted to promote the use of high‐value therapeutic classes and generic drugs with tools such as cost‐sharing, tier placement, and formulary coverage. ⁹ , ¹⁰ , ¹¹ , ¹² However, at the same time, pharmacies offer different values, and there is considerable price variation across pharmacies for the same drug. ¹³ , ¹⁴ , ¹⁵ However, because patients typically face the same copay for that drug across all retail pharmacies in the network, they have little incentive to choose the one with the lowest price.

In recent years, plans have been adopting preferred pharmacy networks to encourage the use of low‐cost pharmacies. Between 2011 and 2021, the percentage of Medicare stand‐alone prescription drug plans (PDPs) with a preferred pharmacy network has increased from less than 9% to over 98% (authors' calculation). The use of preferred networks has also been spreading in Medicare Advantage (MA) drug plans (authors' calculation) and commercial markets. ¹⁶ In these networks, plans divide their retail pharmacies into two tiers: nonpreferred (or “standard”) pharmacies and preferred cost‐sharing pharmacies (PCSPs). They establish lower cost‐sharing at preferred pharmacies as a price signal to steer patients to these pharmacies.

Despite the importance of their potential impact, existing literature is thin on patient choice related to Part D preferred networks. One form of preferred physician and hospital networks, namely tiered provider networks, bear particular resemblance to preferred pharmacy networks. Research on tiered provider networks has generally shown patients' preference for providers with whom they have existing relationships and a moderate response to financial incentives. ² , ⁵ However, there are reasons to think that patients may respond differently to preferred pharmacy networks. On one hand, the money at stake for them is generally less in a pharmacy visit, compared with a physician or hospital visit. They also visit pharmacies more frequently than physicians, ¹⁷ and thus inconvenience of switching pharmacies may add up over time. On the other hand, pharmacies are generally viewed as more substitutable than physicians and hospitals. Yet, to our knowledge, Starc and Swanson's was the only empirical study so far of Part D preferred networks. ¹⁸ They found that preferred networks were associated with lower point‐of‐sale prices, and results from their pharmacy demand model revealed a small shift towards preferred pharmacies and moderate cost savings as a result of these networks. However, the model was at an aggregate level and estimated average effects.

In this study, we add to the literature on preferred pharmacy networks by evaluating network restrictiveness and financial incentives, estimating the effects on beneficiaries' pharmacy choices, and, more importantly, focusing on patients most likely to be affected by the policy—unsubsidized nonpreferred pharmacy users—to assess factors underlying their heterogeneous responses, such as existing relationships with preferred pharmacies, size of financial incentives, and access to preferred pharmacies.

2. DATA AND METHODS

2.1. Preferred pharmacy networks in PDPs

More than 47 million beneficiaries obtained prescription drug coverage through a Medicare Part D plan in 2020. ¹⁹ The majority of Part D beneficiaries enroll in PDPs, although MA plans (including prescription drug benefits) are becoming more popular over time.

In recent years, preferred pharmacy networks have emerged as a tool for plans to curb rising prescription drug costs. Such networks use financial incentives created by differential cost‐sharing to steer patients to their preferred pharmacies. Table S1 illustrates cost‐sharing before and after implementation for major plans—plans with an enrollment of 25,000 or more in our 20% Medicare beneficiary sample—that implemented a preferred network during our study period of 2011–2016. Differential cost‐sharing (often copays) generally applied to nonspecialty tiers, which often were Tiers 1–4 in a five‐tier formulary, and could be large in relative terms for Tiers 1 and 2 generic drugs. Copay strategies varied across plans: some plans—such as AARP MedicareRx Preferred, Humana Enhanced, and First Health Part D Essentials—established moderate copay differentials, while some—such as Blue MedicareRx Plus/Premier, WellCare Classic, and WellCare Extra—instituted large differentials between preferred and nonpreferred pharmacies. For instance, the differential was $3 for a 30‐day prescription of a Tier 1, preferred generic drug in the AARP plan in 2013, while that could be as large as $8 in WellCare Classic in 2014. However, as discussed below, the size of financial incentives for a particular patient would also depend on some other factors.

Plans enter preferred pharmacy arrangements with pharmacy chains or individual or groups of independent pharmacies. ²⁰ An insurer may establish different networks across its plan offerings. For example, in 2021, Walgreens was the only preferred chain in UnitedHealthcare's AARP Walgreens plan's network, whereas UnitedHealthcare's other Part D plans also included Walmart, Kroger, and Publix in their networks. ²¹ The breadth of the subset of preferred pharmacies is not subject to Part D's network adequacy standards, which apply to a pharmacy network as a whole. Access to preferred pharmacies could thus be restrictive. This has prompted the Centers for Medicare and Medicaid Services (CMS) to monitor and publish plans' access levels and to require those with extremely restrictive preferred networks to disclose that information in their marketing materials. ²² Additionally, the composition of a preferred network may change over time. For instance, Cigna Secure Rx dropped Walgreens and added CVS as their preferred pharmacies in 2021. ²¹

In a preferred network, beneficiaries face a tradeoff between convenience and cost savings: those using nonpreferred pharmacies can switch pharmacies, or they can opt for sticking with their pharmacies and incur more out‐of‐pocket (OOP) spending. The financial incentives are substantially weaker for beneficiaries who receive Medicare's low‐income cost‐sharing subsidies (LICS), that is, LIS beneficiaries. Because their cost‐sharing is capped statutorily (see Table S4 for LIS cost‐sharing in 2016 by eligibility group), ²³ differential copays at preferred and nonpreferred pharmacies affect LIS beneficiaries' OOP payment only when the preferred copay is lower than their maximum cost‐sharing for a particular drug, and their potential savings are limited to that maximum for choosing a preferred pharmacy over a nonpreferred one for that prescription.

2.2. Data and sample

In this study, we used beneficiary summary, Part D plan characteristics and tier cost‐sharing files, and prescription drug event claims from a nationally representative 20% sample of Medicare beneficiaries from 2010 to 2016. To facilitate before‐and‐after comparisons, we examined PDPs that transitioned to a preferred network during 2011–2016—a period during which preferred network penetration increased from less than 9% to 85%—and included the year 2010 as the pre‐period for plans in 2011. We focused on plans' first year of preferred networks, because we observed considerable year‐to‐year changes in pharmacies' preferred status within a plan. We referred to the year of implementation for a given plan as Year t hereinafter, and the year prior to that as Year t − 1.

We restricted our main sample to include beneficiaries 65 years and older who enrolled in the same plan in Year t − 1 and Year t and whose LIS status and ZIP code remained unchanged during the 2‐year period. We defined LIS beneficiaries as those who received Medicare low‐income subsidy for premium and cost‐sharing, which included dually eligible beneficiaries and full and partial LIS beneficiaries. We included all beneficiary‐plans for a small fraction of beneficiaries who happened to enroll more than once (in different years) during our study period in a plan transitioning to a preferred network. The sample comprised 1,230,532 beneficiary‐plans, among which 28% were LIS and 0.1% represented beneficiaries in the sample more than once.

We complemented these data with the First Databank drug database and pharmacy network files (2011–2016) obtained from CMS. The drug database provided information on whether a National Drug Code (NDC) is generic or branded and its active ingredient. For each Part D plan in a given year, the pharmacy network files contained the National Provider Identifier of every pharmacy included in the network, its ZIP code, and indicators for whether the pharmacy was a retail or mail‐order pharmacy and whether it was preferred. In addition, we obtained the 2010–2014 American Community Survey 5‐year estimates data for beneficiaries' ZIP code‐level socioeconomic conditions, such as educational attainment, income level, and vehicle ownership. Following Medicare Payment Advisory Commission, ²⁴ we also used the urban influence code developed by the US Department of Agriculture to categorize each beneficiary's county of residence as a metropolitan, rural micropolitan, rural adjacent, or rural nonadjacent county.

2.3. The design of preferred networks

We first evaluated the tradeoff faced by beneficiaries by examining the restrictiveness of preferred networks and the financial incentives to use preferred pharmacies as a result of the differential copays. For network restrictiveness, the analysis focused on the nine major plans, which accounted for three quarters of our sample population. We calculated the percentage of network pharmacies designated as preferred in each PDP region for each plan, both unweighted and weighted by the number of claims submitted by each pharmacy in Year t − 1.

We assessed financial incentives by simulating beneficiaries' annual OOP spending differentials between using nonpreferred and preferred pharmacies for all their prescriptions. The differentials depend on differential copays, as well as other plan characteristics such as deductible and gap coverage, their utilization of drugs, and their LIS status. To account for all these factors, we computed beneficiaries' total OOP spending over the course of a year if all their retail claims had been at nonpreferred pharmacies and if all had been at preferred. Given that mail‐order pharmacies' overall market share was low and increased only slightly during the study period, we assumed that substitution caused by preferred networks occurred only among retail pharmacies and the use of mail‐order pharmacies was unaffected.

Taking point‐of‐sale prices and tier cost‐sharing as exogenous to beneficiaries' pharmacy choices, we first constructed empirical plan‐NDC level formularies containing cost‐sharing and average prices at preferred, nonpreferred, and mail‐order pharmacies. We then ran each beneficiary's claims through the formulary of the specific plan and plan characteristics including deductible, initial coverage limit, and catastrophic coverage threshold, and calculated total claim costs and patient OOP spending for using a preferred and a nonpreferred pharmacy for each retail claim. For LIS beneficiaries, we applied cost‐sharing rules specific to each eligibility category. See the Data S1 for more detail about the simulation.

2.4. Average effects on the use of preferred pharmacies

We then examined the average effects of preferred networks on beneficiaries' switching towards preferred pharmacies, with difference‐in‐differences models comparing the use of preferred pharmacies before and after implementation among non‐LIS relative to LIS beneficiaries. The latter served as the control group since they were largely insulated from differential copays. The unit of observation was beneficiary‐plan‐year, and we observed each beneficiary plan twice—in the year before the implementation and the first year of implementation. For each pre‐ and post‐implementation year pairs, we mapped each pharmacy's preferred status in a given plan in Year t to Year t − 1, and for the sake of simplicity, hereinafter we refer to pharmacies that would go on to become preferred in Year t as preferred pharmacies, even though by definition there was no preferred/nonpreferred distinction in t − 1. We computed two outcome measures for each year: preferred pharmacies' claim share and whether a beneficiary used preferred pharmacies for most claims. We estimated the following model:

$$Y_{\mathit{iy}}=\alpha +\beta \mathit{\text{NONLIS}}\_{\mathit{\text{PCSP}}}_{\mathit{iy}}+{\gamma }_i+{\theta }_y+{\epsilon }_{\mathit{iy}},$$

where i indexes the beneficiary plan and y indexes the year. The key independent variable, NONLIS_PCSP, indicates treatment status, taking the value of 1 if the beneficiary was non‐LIS and their plan implemented a preferred network in Year y. The model also includes beneficiary‐plan‐fixed effects ${\gamma }_i$ and year‐fixed effects ${\theta }_y$. Standard errors were clustered at the plan‐PDP region level.

The identifying assumption was that, in the absence of preferred networks, the use of preferred pharmacies among non‐LIS and LIS beneficiaries who stayed in their plans would have followed a common trend. While we observed only one pre‐period, and thus were unable to test the pre‐trends directly, we compared the percentages of beneficiaries changing their top pharmacy from year to year for non‐LIS and LIS in plans transitioning to preferred networks and plans without a preferred network (Figure S1). The rate of switching remained flat for LIS beneficiaries in both types of plans and non‐LIS in plans without a preferred network, and it fluctuated for non‐LIS in plans implementing a preferred network, which lends support to the common trend assumption. In addition, as a sensitivity test, we restricted the control group to LIS beneficiaries with zero simulated OOP spending differentials.

2.5. Factors associated with beneficiaries' pharmacy switching

Next, we evaluated factors underlying non‐LIS beneficiaries' pharmacy switching in response to the implementation of preferred networks. This set of analyses focused on those who did not fill most of their prescriptions in Year t − 1 at preferred pharmacies. We estimated a beneficiary‐plan‐level logistic regression model, where the outcome variable was whether a beneficiary switched to using preferred pharmacies for most claims in Year t.

The model includes four key explanatory variables. First, a set of dummy variables characterize the beneficiary's preferred claim share in Year t − 1 (0%, 0%–25%, 25%–50%), as existing relationships with pharmacies may affect their choice in Year t. Second, simulated OOP spending differential between using nonpreferred and using preferred pharmacies for all of the beneficiary's retail claims in Year t, which is in hundreds of dollars, captures the financial incentives to use preferred pharmacies. Third, we measured proximity to preferred pharmacies with standardized preferred pharmacies' claim share in the beneficiary's three‐digit ZIP area within the same plan in Year t − 1, assuming that without differential copays, beneficiaries choose the pharmacies most convenient for them. Fourth, a set of dummies indicate whether the beneficiary's county of residence was metropolitan, rural micropolitan, rural adjacent, or rural nonadjacent. The model also controls for beneficiary demographics, including age, gender, and race/ethnicity (non‐Hispanic White, Black, Hispanic, Asian/Pacific Islander, or other), total prescription drug costs in Year t − 1, the count of chronic conditions in the summary file as of the start of Year t, and a vector of socioeconomic characteristics measured at the beneficiary's ZIP code level (educational attainment, income, and car ownership levels). In addition, it includes the year of implementation and plan fixed effects, and standard errors are clustered at the beneficiary level.

3. RESULTS

3.1. Preferred network restrictiveness and financial incentives

Plans generally designated a very restrictive subset of pharmacies as preferred in the first year, and the restrictiveness varied across regions. Overall during our study period, preferred pharmacies accounted for just 26% of the claims in Year t − 1, the year prior to the implementation of preferred networks. Figure 1 illustrates the percentages of preferred pharmacies across PDP regions in the year of implementation for the major plans, with each dot representing a region and each box showing the 25th percentile, the median, and the 75th percentile. In the left panel, the dots generally cluster between 10% and 30%, suggesting that depending on the region, 1 in 10 to 3 in 10 network pharmacies obtained preferred status when plans adopted a preferred network. The variation across regions within a plan was likely the result of the geographic distribution of chain pharmacy locations. In addition, the weighted restrictiveness in the right panel was similar to the unweighted except for EnvisionRxPlus Silver, whose preferred pharmacies had lower than average market shares in the previous year. The overall narrowness of preferred networks suggests that a large number of beneficiaries faced a tradeoff between sticking with their old pharmacies and more OOP spendings, and that beneficiaries in regions at the more restrictive end likely experienced limited access to preferred pharmacies.

FIGURE 1.

The percentages of preferred pharmacies across PDP regions for major plans that transitioned to a preferred network. This figure includes plans with 25,000 or more beneficiaries. Each label on the vertical axis includes the name of the plan and the year in which it implemented a preferred network. Year t − 1 is the year prior to the implementation. For a certain plan, each dot represents a Prescription Drug Plan (PDP) region, and each box shows 25th, 50th, and 75th percentiles

Beneficiaries faced heterogeneous financial incentives to use preferred pharmacies. Table 1 shows the distribution of OOP spending differentials between filling all prescriptions at nonpreferred and preferred pharmacies for non‐LIS and LIS beneficiaries. We excluded beneficiary years with a differential in the top or bottom 0.5% of the distribution. The non‐LIS saw an average differential of $129 (18% of their mean OOP spending). The median differential was $93, and the 75th percentile was $185. By contrast, because LIS beneficiaries' cost‐sharing was set with statutorily capped amounts, preferred networks' differential copays had minimal consequences for them, with 47% of them having zero OOP spending differential.

TABLE 1.

Simulated OOP spending differentials over the course of a year between filling all prescriptions at nonpreferred pharmacies and preferred pharmacies

	Distribution of differentials ($)					Mean actual OOP spend ($)	N (beneficiary‐plans)
	Mean	P25	P50	P75	P90
Non‐LIS	129	31	93	185	299	716	950,100
LIS	8	0	0	8	29	89	238,749

Note: Low‐income subsidy (LIS) beneficiaries comprise those receiving both partial and full benefits. The sample includes beneficiaries who were 65 years and older, who remained in their plan when it implemented a preferred network, whose LIS status and ZIP code remained unchanged during and prior to the implementation year, and who filled at least one prescription in both years. The table pools all beneficiary plans from 2011 to 2016. OOP spending differentials are based on current year's claims and are reported in nominal amounts. The table excludes those with a simulated OOP differential in the top or bottom 0.5% of the distribution.

Abbreviation: OOP, out‐of‐pocket.

The size of financial incentives did seem to affect beneficiaries' pharmacy switching. For the major plans, while the sample was dominated by the two largest plans with moderate incentives and moderate to little pharmacy switching—Humana Enhanced and AARP MedicareRx Preferred—those with a larger mean OOP spending differential tended to experience more switching towards preferred pharmacies among the non‐LIS (Table 2). Notably, WellCare Extra beneficiaries faced the largest incentives on average ($632) and switched to preferred pharmacies at the highest rate, with preferred pharmacies' share of claims increasing by 33.8 percentage points. In comparison, LIS beneficiaries, facing minimal incentives, hardly increased their use of preferred pharmacies (Table 3).

TABLE 2.

Changes of preferred claim share and simulated OOP spending differentials for non‐LIS beneficiaries in major plans

Plan	Change of preferred claim share between t − 1 and t (p.p.)	Mean simulated OOP spending differential ($)	N
Humana Enhanced, 2013	−0.2	68	192,722
First Health Part D Essentials, 2014	0.2	101	48,343
Express Scripts Medicare ‐ Value, 2015	9.4	102	12,517
AARP MedicareRx Preferred, 2013	4.0	117	470,921
Cigna Medicare Rx Secure, 2014	4.0	178	16,614
Blue MedicareRx Plus/Premier, 2014	4.6	209	35,549
WellCare Classic, 2014	8.9	215	70,043
EnvisionRxPlus Silver, 2015	1.2	279	3,067
WellCare Extra, 2014	33.8	632	20,659

Note: This table includes plans with 25,000 or more beneficiaries. The sample includes non‐LIS beneficiaries who were 65 years and older, who remained in their plan when it implemented a preferred network, whose LIS status and ZIP code remained unchanged during the year prior and the implementation year, and who filled at least one prescription in both years. OOP spending differentials are based on current year's claims and are reported in nominal amounts. The table excludes those with a simulated OOP differential in the top or bottom 0.5% of the distribution.

Abbreviations: OOP, out‐of‐pocket; LIS, low‐income subsidy.

TABLE 3.

The average effects of preferred network implementation on preferred pharmacies' claim share and beneficiaries' likelihood of using preferred pharmacies for the majority of their claims

Simple means	Preferred claim share		Being majority preferred
	Non‐LIS	LIS	Non‐LIS	LIS
Pre (%)	28.5	22.5	27.5	26.2
Post (%)	31.7	22.0	31.0	26.4
Change (p.p.)	3.2	−0.6	3.6	0.2
DID estimates
	0.037***		0.038***
	[0.033, 0.042]		[0.033, 0.043]
N (beneficiary‐plan‐years)	2,362,286		2,362,286

Note: *p < 0.05, **p < 0.01, ***p < 0.001. Low‐income subsidy (LIS) beneficiaries comprise those receiving both partial and full benefits. The sample includes beneficiaries who were 65 years and older, who remained in their plan when it implemented a preferred network, whose LIS status and ZIP code remained unchanged during the year prior and the implementation year, and who filled at least one prescription in both years. Preferred pharmacies in the pre‐period are those that would go on to be preferred in the subsequent year—after the preferred pharmacy network was first introduced. Standard errors are clustered at the Plan‐Prescription Drug Plan (PDP) region level, and 95% confidence intervals are in brackets.

Abbreviation: DID, difference‐in‐differences.

3.2. Average effects on the use of preferred pharmacies

Table 3 presents unadjusted and adjusted difference in differences for the use of preferred pharmacies. The top panel shows that preferred pharmacies' claim share among the non‐LIS increased from 28.5% to 31.7% between Years t − 1 and t, while that among the LIS remained roughly unchanged. The difference between changes among the two groups was thus 3.8 percentage points. Similarly, for the percentage of beneficiaries filling the majority of prescriptions at preferred pharmacies, the difference between changes was 3.4 points.

The difference‐in‐differences estimates indicate that during the study period, the implementation of preferred networks caused a 3.7 percentage point increase in preferred pharmacies' claim share in the first year among non‐LIS beneficiaries. It also resulted in a 3.8 point increase in the likelihood of using preferred pharmacies for the majority of one's claims. Estimates were similar when the control group consisted of only LIS beneficiaries with zero simulated OOP spending differentials. Thus, there was a modest but significant shift among non‐LIS beneficiaries in the use of preferred pharmacies after plans adopted preferred pharmacy networks.

3.3. Factors associated with beneficiaries' pharmacy switching

Table S2 compares the characteristics of non‐LIS beneficiaries who switched to filling the majority of prescriptions at preferred pharmacies and those who did not. In Table 4, we report logistic regression results as marginal effects when holding other covariates at their means. Compared with those who never used preferred pharmacies in the previous year, beneficiaries who did were 14.7 percentage points more likely to become “majority preferred” in Year t. A $100 increase in OOP spending differential was associated with a 1.4 point increase in the likelihood of switching to “majority preferred.” In addition, a 1 SD (19 point) increase in the preferred claim share in a beneficiary's three‐digit ZIP area in Year t − 1 was associated with a 0.9 point increase in their likelihood of switching. The rurality of a beneficiary's residence also played a role. Compared with those in metropolitan (urban) counties, beneficiaries in rural micropolitan, rural adjacent, and rural nonadjacent counties were 0.7, 1.9, and 2.5 points less likely to switch, respectively.

TABLE 4.

Factors associated with pharmacy switching

	Marginal effect	95% CI
Ever used preferred pharmacies in the previous year	0.147***	[0.144, 0.150]
Simulated OOP spending differential between preferred and nonpreferred in t (in 100s)	0.014***	[0.013, 0.014]
Standardized preferred claim share in three‐digit ZIP area in t – 1	0.009***	[0.009, 0.010]
Rurality of county of residence (ref. = Metropolitan)
Rural micropolitan	−0.007***	[−0.008, −0.005]
Rural adjacent	−0.019***	[−0.021, −0.017]
Rural nonadjacent	−0.025***	[−0.027, −0.023]
N (beneficiary‐plans)	648,334

Note: *p < 0.05, **p < 0.01, ***p < 0.001. The table reports marginal effects when holding other variables at their means. The sample includes non‐LIS beneficiaries who were 65 years and older, who remained in their plan when it implemented a preferred network, whose LIS status and ZIP code remained unchanged during the year prior and the implementation year, and who filled at least one prescription in both years but did not use preferred pharmacies for most of their claims in Year t − 1. We exclude those with a simulated OOP differential in the top or bottom 0.5% of the distribution. Preferred pharmacies in the previous year are those that would go on to be preferred in the subsequent year—after the preferred pharmacy network was first introduced.

Abbreviations: CI, confidence interval; LIS, low‐income subsidy; OOP, out‐of‐pocket.

4. DISCUSSION

The dramatic rise of preferred pharmacy networks in recent years is an important yet understudied development in Medicare Part D. Such networks use lower patient cost‐sharing to encourage the use of lower cost pharmacies. Beneficiaries maintain the option of using any pharmacy in the network, but face higher cost‐sharing (typically a copayment) at a nonpreferred pharmacy. This study is one of the first to examine beneficiaries' pharmacy choices in the context of preferred networks.

In examining the tradeoff presented by preferred networks, we found that the subset of preferred pharmacies was generally very narrow in the first year, and thus a large number of beneficiaries faced a tradeoff between switching pharmacies and making more OOP payment. Non‐LIS beneficiaries on average faced a financial incentive of $129 to use preferred pharmacies, while LIS had minimal incentive to do so. Consequently, we estimated that during 2011–2016, the implementation of preferred networks on average caused a 3.7 percentage point increase in preferred pharmacies' claim share in the first year among the non‐LIS. The effect was in line with the estimate by Starc and Swanson. ¹⁸

Moreover, we evaluated factors affecting beneficiaries' decisions of whether to switch to preferred pharmacies, since the moderate average effect might mask the impact of plans' heterogeneous preferred network designs. Consistent with the research on tiered physician and hospital networks, ² , ⁴ , ⁵ our results suggest that patients with existing relationships with preferred pharmacies were more likely to switch their prescriptions there and that beneficiaries responded to stronger financial incentives.

Given patients' response to financial incentives and that plans with larger incentives seemed to see more switching, it is unclear why several of the major plans in our sample established moderate copay differentials between preferred and nonpreferred pharmacies. Notably, they were the predominant type when weighted by enrollment. One explanation may be that plans need to balance cost savings and beneficiary satisfaction. Plans with more leverage may be able to extract the same price concessions from pharmacies with smaller copay differentials, which would cause less distaste among their beneficiaries. Another nuance in plans' strategies is that the drugs placed on a certain tier affect the extent to which the copay differential of that tier translates into differences in patient OOP spending. For example, the differences between preferred and nonpreferred pharmacies may be muted for low‐cost generic drugs, because a Part D patient's OOP payment does not exceed the point‐of‐sale price of a drug. That is, if the point‐of‐sale price of a drug at a nonpreferred pharmacy is less than the plan's cost‐sharing at preferred pharmacies, the beneficiary will face little or no incentive to use the preferred pharmacy. Thus, future research may examine plans' copay and drug placement strategies in tandem through the lens of plan–pharmacy bargaining.

In 2022, 98% of PDPs and 66% of MA plans have a preferred pharmacy network. ²⁵ The vast majority of beneficiaries will need to select a plan based on their pharmacy preference or switch to their plan's preferred pharmacies, or they could face higher OOP spending. Therefore, policy responses are necessary to help beneficiaries make informed plan and pharmacy choices and reduce barriers to accessing preferred pharmacies. Preferred networks, coupled with network variation across an insurer's plan offerings and year‐to‐year changes in a pharmacy's preferred status, have greatly added to the complexity of Part D beneficiaries' decision making. ²⁶ Given the large number of plans an average beneficiary needs to compare, ²⁷ the Medicare Plan Finder should show with the list of plans each plan's preferred pharmacies and whether the pharmacy the beneficiary picks is preferred. In addition, our analyses revealed that beneficiaries with greater access to preferred pharmacies and those in urban areas were more likely to switch to preferred pharmacies. CMS should thus continue to monitor network restrictiveness and ensure adequate access to preferred pharmacies, especially in rural areas and disadvantaged communities where access to pharmacies is already lacking. ²⁸

This study has several limitations. First, we lacked information on how prominently plans communicated/advertised the adoption of preferred networks to beneficiaries. Plans send out Annual Notice of Change to beneficiaries about coverage and costs, but there is no evidence on the effectiveness of such letters. It is thus possible that beneficiaries' lack of awareness of the existence of a preferred network and any potential cost savings contributed to the relatively low rate of pharmacy switching.

Second, our data did not cover years after 2016. Since preferred pharmacy networks are a new development in Part D, it is important that future research continues to assess the trends of network restrictiveness, financial incentives, and beneficiaries' use of preferred pharmacies. Another consequence of our study period was that our analyses excluded MA beneficiaries, because by 2016 only 30% of MA plans (enrolling 21% of MA enrollees) had a preferred pharmacy network. Given the increase in preferred network adoption in MA recently, future research should study MA plans with more recent data, because having self‐selected into plans that feature network restrictions, MA beneficiaries may be more responsive to preferred networks, and MA is an increasingly large part of Medicare.

Third, we covered only the first year of preferred networks in our analyses because of substantial year‐to‐year changes in a pharmacy's preferred status within a certain plan. We were thus unable to examine the role of inattention in beneficiaries' pharmacy switching. It is also possible that more patients would switch to preferred pharmacies over time as their exposure and experience grow, ⁴ , ²⁹ although preferred status changes may cause confusion and increase errors.

Fourth, our simulated OOP spending differentials were based on Year t claims and did not account for the impact of cost‐sharing on utilization. While the demand for prescription drugs is relatively inelastic, ³⁰ we simulated with prior‐year claims as a sensitivity analysis, and the results were similar.

Fifth, our analyses excluded beneficiaries who switched plans between Years t − 1 and t. They were less likely than those who stayed to have used preferred pharmacies for most of their claims in Year t − 1 (Table S3). Excluding them might produce a small upward bias in our estimates of the average effect of preferred networks, to the extent that exiting a plan was negatively associated with the willingness to switch pharmacies.

Sixth, in order to facilitate before‐and‐after comparisons, our sample of plans did not include new plans that entered the PDP market with a preferred network. Because of the inertia in Part D plan enrollment, their combined enrollment in the first year was only one‐fifth of the size of our sample. However, further research may evaluate these plans, since beneficiaries may pay more attention to the pharmacy network when they are new to a plan, or they may pick a plan based on preferences for pharmacies.

Finally, our measure of access to preferred pharmacies in a patient's neighborhood—their claim share in the year prior to preferred networks—did not capture individual patients' travel time to preferred pharmacies, nor did it distinguished between pharmacy chains and independent pharmacies. We thus did not control for pharmacy brand in the analyses.

In summary, our results show that PDP preferred pharmacy networks led to moderate switching towards the narrow subset of preferred pharmacies among unsubsidized, non‐LIS beneficiaries, and that existing relationships with preferred pharmacies, the size of financial incentives, proximity to a preferred pharmacy, and urban residence were positively associated with their decisions to switch. Given the rise of preferred networks in both Part D and commercial drug plans, researchers and policymakers should seek to better understand plans' strategies and to assess whether communities have equitable access to preferred pharmacies.

Supporting information

Data S1 Supporting information.

Xu J, Trish E, Joyce G. Pharmacy switching in response to preferred pharmacy networks in Medicare Part D. Health Serv Res. 2022;57(5):1112‐1120. doi: 10.1111/1475-6773.13973