Geographical Access to Preferred Pharmacies in Medicare Part D

ABSTRACT

Objective: To examine geographic access to preferred pharmacies in Medicare Part D prescription drug plans (PDPs) and Medicare Advantage PDPs (MAPDs).

Study Design: We conducted a nationwide, retrospective analysis using Part D preferred pharmacies’ data, Zip Code Tabulation Areas (ZCTAs) from the US census, and socioeconomic characteristics from the American Community Survey and the Area Health Resources Files from 2010 to 2024.

Methods: We computed mean distance from the centroid of each ZCTA to the nearest preferred pharmacy in stand-alone PDPs and in MAPDs. We compared mean distance as well as additional distance to a preferred vs a nonpreferred pharmacy, across rural and urban ZCTAs, and across geographic areas with relatively high densities of different racial/ethnic groups. We employed regression models to evaluate the association between ZCTA-level characteristics and additional distance to a preferred pharmacy.

Results: Rural areas in the West North Central, Mountain, and East South Central divisions of the US tended to lack preferred pharmacies in PDPs and MAPDs, as did some urban areas. Among ZCTAs with preferred pharmacies, a rural ZCTA was associated with a 0.734-mile longer additional distance (P < .01) among PDPs, which was more than double the mean additional distance. For MAPD plans, the additional distance for rural ZCTAs was 0.320 miles longer (P < .01), an 80% increase relative to the mean.

Conclusions: Many rural and urban areas in the US lack preferred pharmacies. In areas with preferred pharmacies, distances were generally minimal relative to the cost savings, but rural-urban disparities persist.

Am J Manag Care. 2026;32(7):-e0.

https://doi.org/10.37765/ajmc.2026.89989

Takeaway Points

• Rural areas in the West North Central, Mountain, and East South Central divisions of the US tend to lack preferred pharmacies in Medicare Part D prescription drug plans (PDPs) and Medicare Advantage PDPs (MAPDs), as do some urban areas.
• In areas with preferred pharmacies, travel distances were generally minimal relative to the cost savings, but rural-urban disparities persisted.
• Access to preferred pharmacies in PDPs and MAPDs improved in both rural and urban areas and for nearly all racial and ethnic groups between 2010 and 2024.

Medicare Part D is a voluntary prescription drug insurance program available to all Medicare beneficiaries. Part D beneficiaries obtain drug coverage from private plans, either (1) stand-alone prescription drug plans (PDPs) that supplement traditional Medicare or (2) Medicare Advantage PDPs (MAPDs), which are operated by health maintenance organizations and preferred provider organizations and cover all Medicare benefits including drugs.¹ In recent years, both PDPs and MAPDs have increased their use of in-network preferred pharmacies to encourage beneficiaries to use lower-cost pharmacies. In-network preferred pharmacies are groups of pharmacies and pharmacy chains that contract with Part D plans, offering lower drug prices in exchange for a higher volume of customers.² Between 2010 and 2024, the percentage of Medicare Part D stand-alone PDPs with in-network preferred pharmacies increased from less than 10% to more than 95%, and the percentage of MAPD plans with in-network preferred pharmacies increased from less than 5% to approximately 55%, before dropping to 40% in 2024 (Figure 1).^3,4

CMS regulates in-network pharmacies to ensure that most Medicare beneficiaries have nearby access to in-network retail pharmacies in rural, suburban, and urban areas.⁵ Within networks, however, there may be both nonpreferred and preferred pharmacies, with the latter sometimes offering substantial cost savings for Medicare beneficiaries.^6,7 Nearby access to these in-network preferred pharmacies (also called preferred pharmacies in this paper) is not regulated by CMS. As a result, there may be uneven access to the most affordable pharmacies across and within CMS markets. We know little about how accessibility of preferred pharmacies may vary by the rurality and racial/ethnic compositions of communities. Geographic differences in access may have implications for older individuals’ out-of-pocket drug spending and adherence to medication as well as for disparities in these outcomes.

To illustrate the potential cost savings for beneficiaries using preferred vs nonpreferred pharmacies, Figure 2 shows mean co-payments during the period 2010-2024 for preferred and nonpreferred pharmacies in PDPs and MAPDs, focusing on a 30-day supply of a drug in the initial coverage phase in tiers 1 to 3 (this figure is similar to Figure 1 in Starc and Swanson,⁶ with updated years and with the inclusion of MAPD plans; a version of this figure showing coinsurance is in eAppendix A [eAppendices available at ajmc.com]).⁶ Note that for Part D beneficiaries in the low-income subsidy (LIS) program, the distinction between preferred and nonpreferred is less relevant because in most cases these beneficiaries pay the same co-payment at preferred and nonpreferred pharmacies. From 2010 to 2024, mean co-payments ranged from $2.10 for tier 1 drugs to $45 for tier 3 drugs in PDPs and from $2.70 for tier 1 drugs to $47 for tier 3 drugs in MAPDs. The data in Figure 2 show that generally co-payments are lower in preferred vs nonpreferred pharmacies and that in some cases the gap has increased over time, especially for tier 1 and tier 2 drugs in PDPs.

A few prior studies have examined preferred pharmacies in the PDP market. Xu et al³ found that non-LIS Part D beneficiaries would have $147 lower out-of-pocket costs if they were to fill all prescriptions at preferred pharmacies, whereas LIS beneficiaries were generally insulated from these spending differences. These cost savings led to some degree of switching from nonpreferred to preferred pharmacies among non-LIS beneficiaries.⁴ Starc and Swanson⁶ found that non-LIS beneficiaries in the PDP market obtain substantial savings by using preferred pharmacies for generic tier 1 drugs, with minimal increases in distance to a preferred vs a nonpreferred pharmacy. However, to our knowledge, no study has evaluated access to preferred pharmacies in MAPDs and compared access in MAPDs vs in PDPs. Given the increasing enrollment in MAPDs in recent years,⁸ it is important to understand access and disparities in access to preferred pharmacies in MAPDs as well as in PDPs. Thus, we conducted a nationwide analysis to examine geographical access as well as rural/urban and racial/ethnic disparities in access to preferred pharmacies in stand-alone PDPs and MAPDs over the period 2010-2024.

METHODS

Data and Study Sample

Our sample included preferred and nonpreferred pharmacies located in plans’ service areas in stand-alone PDPs and MAPDs for the even-numbered years between 2010 and 2024. In PDP plans, there were approximately 43,000 to 58,000 preferred pharmacies and 58,000 to 67,000 nonpreferred pharmacies in each year; MAPD plans had approximately 3800 (there were significantly fewer MAPD plans in 2010 and 2012) to 59,000 preferred pharmacies and 58,000 to 68,000 nonpreferred pharmacies in each year. One pharmacy could be identified as a preferred or nonpreferred pharmacy at the same year, same Zip Code Tabulation Area (ZCTA), and same plan. Pharmacy-specific information, including the National Provider Identifier (NPI), service area (CMS market), location (zip code), and type (preferred pharmacy or not), was sourced from the CMS Prescription Drug Plan Formulary, Pharmacy Network, and Pricing Information Files. We also utilized data from the 2010 Rural-Urban Commuting Area (RUCA) codes, the zip code to ZCTA crosswalk (US census, 2022), the centroid of each ZCTA (US census, 2022), the population-weighted centroid of each zip code, the 5-year American Community Survey, and the Area Health Resources Files. Our analysis included ZCTAs in the 50 contiguous states and in the District of Columbia.

We collected pharmacy addresses as of July 2025 from publicly available data in the National Plan and Provider Enumeration System NPI registry. We were able to match 91% of pharmacies in PDPs and 94% of pharmacies in MAPDs to their corresponding address and geographic coordinate information. Using the zip code to ZCTA crosswalk, we then mapped these addresses to their corresponding ZCTAs. For each ZCTA, we identified whether it was rural or urban, and as explained in the next paragraph, we selected all the preferred and nonpreferred pharmacies located in each ZCTA and in the plan’s service area.

Outcome Measures

We considered 4 distance outcomes: (1) mean distance to the nearest preferred pharmacy in the PDP market, (2) mean distance to the nearest preferred pharmacy in the MAPD market, (3) additional distance to the nearest preferred pharmacy in the PDP market, and (4) additional distance to the nearest preferred pharmacy in the MAPD market. Our method of creating these outcomes involved several steps. First, we calculated the distance from each pharmacy to each ZCTA’s geometric centroid, based on the great circle distance; we limited these calculations to pharmacies within a certain number of miles of each centroid (5 miles for urban ZCTAs; 15 miles for rural ZCTAs). Second, for each ZCTA, we included only plans in the ZCTA’s service area that included at least 1 of the pharmacies identified. Third, for each plan, we found the pharmacy with the shortest distance to the ZCTA centroid, and then we calculated the enrollment-weighted mean (at the plan-state-year level) of the shortest distances across plans in each ZCTA’s service area. We created this measure separately for preferred and nonpreferred pharmacies and used the measure ofmean distance to the nearest preferred pharmacy to provide information on overall access to preferred pharmacies across rural and urban ZCTAs in the PDP and MAPD markets. To measure accessibility of preferred pharmacies relative to nonpreferred pharmacies, we also created another measure—additional distance to the nearest preferred pharmacy—by subtracting the average distance to the nearest nonpreferred pharmacy from the average distance to the nearest preferred pharmacy.

Our other outcome measure was preferred pharmacy cost savings. First, we compiled data on co-payments for preferred and nonpreferred pharmacies in PDPs and MAPDs, focusing on a 30-day drug supply in the initial coverage phase for tiers 1 to 3. Next, at the ZCTA level, we identified all plans that served the ZCTA (ie, plans with at least 1 in-network pharmacy in the area). For each plan and tier, we calculated the difference between the enrollment-weighted (at the plan-state-year level) co-payment at nonpreferred pharmacies and the enrollment-weighted (at the plan-state-year level) co-payment at preferred pharmacies. We then took the mean of these differences across all plans serving the ZCTA for each tier. This mean difference constituted our cost savings measure. Calculations were performed separately for PDPs and MAPDs.

Independent Variables of Interest

To classify rurality, we used the 2010 RUCA codes based on zip code approximations and applied the zip code to ZCTA crosswalk to map zip codes to ZCTAs. RUCA codes 1 to 3 were categorized as urban (metropolitan areas), and codes 4 to 10 were classified as rural (micropolitan, small town, and rural areas) based on methodology from the US Department of Agriculture Economic Research Service.⁹ We also classified ZCTAs as American Indian/Alaska Native (AI/AN), Asian, Black, Hispanic, and White when the proportion of corresponding race or ethnicity in that ZCTA was above the 95th percentile in the distribution. For each racial and ethnic group, we computed separate 95th percentile cutoff levels for rural and urban ZCTAs.

Covariates

We created a set of ZCTA-level measures for our multivariate analysis, including the percentage of ZCTA residents of each racial/ethnic group, educational attainment for the population 25 years and older, the uninsured rate, and whether the area was designated as a Health Professional Shortage Area (HPSA) for dental health, mental health, or primary care.

Spatial and Statistical Analysis

We showed the percentage of ZCTAs without preferred pharmacies within 5 miles for urban ZCTAs and within 15 miles for rural ZCTAs of the ZCTA centroid as well as the mean distance to the nearest preferred pharmacy, additional distance to the nearest preferred pharmacy, and cost savings in preferred pharmacies for PDPs and MAPDs overall and in urban vs rural ZCTAs. Using ArcGIS Pro 3.3 (Esri), rural-urban disparities in access to preferred pharmacies were represented by maps showing the mean distance to the nearest preferred pharmacy and additional distance to the nearest preferred pharmacy among (1) stand-alone PDPs and (2) MAPDs. We also redid the analysis using population-weighted centroids, calculated from the spatial distribution of the ZCTA’s population.

In addition to the rural-urban distinction, we extended our investigation to examine the racial and ethnic composition of ZCTAs, identifying whether rural-urban disparities in preferred pharmacy access were disproportionately relevant for areas with relatively high proportions of different racial and ethnic groups. Finally, to explore the effects of rurality while controlling for effects of other ZCTA characteristics on the additional distance to the nearest preferred pharmacy, we employed linear regression models with ZCTA-level observations from 2010 to 2024. The models controlled for a set of ZCTA characteristics, including race/ethnicity, educational attainment, uninsured rate, and HPSA status.

This study was exempt from institutional review board review.

RESULTS

As of 2024, in the PDP market, approximately 31.8% of ZCTAs lacked preferred pharmacies within 5 or 15 miles of their centroids. Among ZCTAs with preferred pharmacies, the mean distance to a preferred pharmacy was 4.63 miles, the additional distance to a preferred pharmacy was 0.64 miles, and the cost savings was $5.09 (Table, panel A). These statistics were similar among MAPDs in 2024 (Table, panel B). The Table suggests that approximately one-third of ZCTAs lacked preferred pharmacies and that among both PDPs and MAPDs, in ZCTAs with preferred pharmacies, the additional distance to a preferred pharmacy was minor relative to the substantial cost savings. This was true in both rural and urban areas, despite mean distances being longer in rural vs urban areas (Table). Means may not tell the whole story, but the 75th and 90th percentiles of the statistics in the Table (shown in eAppendix B1-B2) also support the idea that additional distances are minor relative to the cost savings.

Comparing 2024 (Table) to 2010 data (eAppendix B3-B5), the most notable trends were that the cost savings from using preferred pharmacies increased between 2010 and 2024 and that the percentage of ZCTAs without preferred pharmacies declined substantially over time in the MAPD market. In eAppendix B and eAppendix C, we considered additional distance and cost savings, respectively, of preferred pharmacies in ZCTAs classified by urban and rural status as well as by having a relatively high proportion of a particular racial/ethnic group residing in that area. In general, the percentage of ZCTAs with preferred pharmacies increased between 2010 and 2024, particularly in rural areas and particularly in the MAPD market. Even so, ZCTAs with relatively high proportions of AI/AN and White populations still had low rates of preferred pharmacies in the PDP and MAPD markets as of 2024 (eAppendix C1). Among ZCTAs with preferred pharmacies, the additional distance to a preferred pharmacy was small for all the ZCTA categories defined by race/ethnicity and urban/rural status in the PDP and MAPD markets (eAppendix C1). There was an overall increase in cost savings from using preferred pharmacies between 2010 and 2024; this increase primarily affected ZCTAs with relatively high proportions of minority groups (eAppendix C2).

Figure 3 (A) shows that in the 2024 PDP market, many areas in the West North Central, Mountain, and East South Central Census divisions lacked preferred pharmacies. Among ZCTAs with preferred pharmacies in the PDP market, hot spots, or areas with mean distances at the 90th percentile or higher, were spread across the country. The map of the mean distance to a preferred pharmacy in MAPD plans in 2024 (Figure 3 [B]) depicted a generally consistent picture with that of PDPs (Figure 3 [A]), with hot spots not clustered geographically, but very large areas in the Midwest, in the Mountain states, and in the Southeast had no MAPD preferred pharmacies at all. Figure 4 shows the additional distance to a preferred pharmacy in the PDP (Figure 4 [A]) and MAPD (Figure 4 [B]) markets. The same maps generated using population-weighted centroids are in eAppendix D. Generally, those maps are consistent with those shown in the article.

Finally, the multivariate analysis (eAppendix E) revealed that, even after controlling for socioeconomic characteristics, rural ZCTAs faced significantly longer additional distances to preferred pharmacies than urban ZCTAs. Specifically, for PDPs, a rural ZCTA was associated with a 0.734-mile longer distance (coefficient = 0.734; 95% CI, 0.712-0.757; P < .01), which more than doubled the mean additional distance, 0.60 miles. For MAPD plans, the additional distance was 0.320 miles longer (coefficient = 0.320; 95% CI, 0.282-0.359; P < .01), representing an 80% increase relative to the mean additional distance, 0.40 miles.

DISCUSSION

These findings show that (1) many rural areas in the West North Central, Mountain, and East South Central divisions, as well as some urban areas, lacked preferred pharmacies; (2) among ZCTAs with preferred pharmacies, distances to preferred pharmacies were generally minimal relative to cost savings; (3) cost savings associated with preferred pharmacies increased, and preferred pharmacy networks in the MAPD market expanded across all kinds of ZCTAs between 2010 and 2024; and (4) urban-rural disparities in additional distance to a preferred pharmacy persisted, even after adjusting for other potentially confounding factors.

Our results have several policy implications. First, MAPD plans typically enroll relatively high numbers of Black and Hispanic beneficiaries, individuals with lower incomes, individuals with less education, and people who live in urban areas.¹⁰ Enrollees in MAPD plans are more likely to have chronic conditions, which require regular access to prescription drugs,¹⁰ and poor access to lower-cost pharmacies can be associated with lower medication adherence.¹¹ It is important to note that PDPs’ pharmacy networks tend to be broader than those of MAPDs, which are more localized. This difference also may affect access to preferred pharmacies. Our findings suggest that although some groups had greater distances to a preferred pharmacy, the situation in the MAPD market improved between 2010 and 2024, which is encouraging. Policy makers should monitor access to in-network preferred pharmacies for MAPDs available to less advantaged communities. Compared with beneficiaries in PDPs, individuals in MAPDs may find it more burdensome to change plans to access a preferred pharmacy because MAPDs offer integrated coverage for drugs and medical services. Changing MAPDs may affect access to other kinds of health care.

In addition, a CMS study showed that in-network preferred pharmacies are somewhat less accessible to Medicare beneficiaries than in-network nonpreferred pharmacies.¹² A recent study by the Federal Trade Commission suggested that pharmacy benefit managers steer patients to their own preferred pharmacies and away from smaller, unaffiliated pharmacies in rural areas, but that such patient steering may increase the distance to in-network preferred pharmacies.¹³ Our results highlight that policy makers should ensure that rural populations have sufficient access to in-network preferred pharmacies in Medicare Part D.

Limitations

This study had several limitations. First, we considered only great circle distance, which is a single measure of potential access to preferred pharmacies. Also, we did not have data on actual distances to preferred pharmacies in beneficiaries’ own plans. Second, the study did not consider other dimensions of access to in-network preferred pharmacies, such as language, cultural, and transportation barriers. These nongeographic factors, in addition to geographic access, can affect whether beneficiaries seek preferred pharmacies. Third, this study did not employ prescription drug event claims data. Thus, it could not analyze the financial incentives that beneficiaries with stand-alone PDPs or MAPDs would receive if they switched from a regular pharmacy to a preferred pharmacy.^3,4 Finally, this study excluded Puerto Rico, which has a high MA penetration rate,¹⁴ from the analysis because we did not include US territories.

CONCLUSIONS

Access to in-network preferred pharmacies is an important way for Medicare beneficiaries to lower their drug costs. Rural populations, however, have less access to in-network preferred pharmacies regardless of whether they enroll in stand-alone PDPs or MAPDs. Further research is needed on how access to in-network preferred pharmacies affects drug adherence and health outcomes among Medicare beneficiaries. n

Acknowledgments

The authors gratefully acknowledge funding by the National Institute on Aging–funded Center for Aging and Policy Studies, project No. 1185491.

Author Affiliations: Department of Economics, University at Albany, State University of New York (PC, YH, CYH), Albany, NY.

Source of Funding: This study was funded by National Institute on Aging–funded Center for Aging and Policy Studies, project No. 1185491.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (PC, YH, CYH); acquisition of data (YH); analysis and interpretation of data (PC, YH, CYH); drafting of the manuscript (PC, CYH); critical revision of the manuscript for important intellectual content (PC, YH, CYH); statistical analysis (PC, YH); obtaining funding (PC, CYH); and supervision (PC, CYH).

Address Correspondence to: Pinka Chatterji, PhD, Department of Economics, University at Albany, State University of New York, 1400 Washington Ave, Albany, NY 12222. Email: pchatterji@albany.edu.

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