Essential Retail Pharmacies Under the Consolidated Appropriations Act of 2026

Abstract

This cross-sectional study investigates the distribution and characteristics of essential retail pharmacies (ERPs) designated by the Consolidated Appropriations Act of 2026 and considers policy amendments to ensure patient access to ERPs is protected.

Introduction

In February 2026, the Consolidated Appropriations Act of 2026 ushered in pharmacy benefit manager (PBM) reform, established “reasonable and relevant” plan contract terms for Medicare Part D–participating pharmacies, and designated essential retail pharmacies (ERPs).¹ Given mass pharmacy closures in the US,² ERP designation intends to provide insight into factors influencing the closure of pharmacies that are critical to patient access, including reimbursement, transaction volume, and Part D preferred pharmacy network (PPN) participation.^1,3 Despite discussion around their role, there is no information on ERP distribution or characteristics.

Methods

This cross-sectional study was reviewed by the University of Southern California Institutional Review Board and determined to be exempt because of the use of deidentified data. We followed STROBE reporting guidelines. The study linked Part D pharmacy claims (100% sample; 2023) and network files, National Council for Prescription Drug Programs data (2023), and American Community Survey data (2018-2022) at the pharmacy level. Variable operationalizations are in the eTable in Supplement 1. Pharmacies were eligible for inclusion if they were operational in Part D during 2023. Like the Consolidated Appropriations Act of 2026, we defined ERPs as pharmacies that are not affiliated with a PBM and that have no other pharmacy within 10 miles, 2 miles, or 1 mile in rural, suburban, and urban areas, respectively.^1,4 Risk or mean ratios were used to compare independent and chain ERP characteristics, and χ² and t tests were used to assess differences. SAS Studio, version 2025.06 (SAS Institute Inc) and R version 4.5.2 (R Foundation) were used to conduct the analysis, and P < .05 (2-sided) defined statistical significance.

Results

In 2023, there were 62 072 operational pharmacies, 4.11% of which were ERPs (Table). Among 2552 ERPs, 56.03% were independent, 48.20% were in rural neighborhoods, and 82.79% served predominantly White Part D beneficiaries. Regarding PPN status, ERPs were preferred by a lower percentage of Part D plans than non-ERPs (27.00% vs 39.26%; Table), with chain ERPs preferred over independent ERPs (49.47% vs 10.53%; P < .001). Independent ERPs filled fewer Part D prescriptions per month than chain ERPs (mean [SD] 1226.85 [1290.06] vs 1743.88 [1603.50]; P < .001). Overall, a higher proportion of ERPs served pharmacy shortage areas (PSAs) compared with pharmacies overall (44.98% vs 20.15%). ERPs serving PSAs were more than twice as likely to be independent than chains (13.61% vs 6.28%; P < .001).

Table. Proportion of Pharmacies That Are Essential Retail Pharmacies (ERPs) and Differences in ERP Status for Chain vs Independent Pharmacies in the US.

Characteristic	Overall pharmacies, %			Chain, %		Independent, %		Unadjusted ratio, independent vs chain ERPa
	Total (n = 62 072)	ERP (n = 2552)	Non-ERP (n = 59 520)	Total (n = 38 177)	ERP (n = 1082)	Total (n = 23 215)	ERP (n = 1430)
Total	100.00	4.11	95.89	100.00	2.83	100.00	6.16	2.18b
Pharmacy and neighborhood characteristics
Pharmacy type
Chain	61.54	42.40	32.36	100.00	100.00	NA	NA	NA
Independent	37.42	56.03	36.62	NA	NA	100.00	100.00	NA
Clinic	1.04	1.57	1.02	NA	NA	NA	NA	NA
Neighborhood urbanicity
Urban	42.95	41.14	43.03	41.66	4.56	45.22	2.99	0.66b
Suburban	28.26	10.66	29.01	32.37	1.29	21.57	2.20	1.71b
Rural	28.79	48.20	27.96	25.97	1.98	33.21	13.05	6.59b
Located in an MUAc
Yes	29.28	45.89	28.57	23.77	2.74	37.98	10.16	3.70b
No	70.72	54.11	71.43	76.23	2.86	62.02	3.71	1.30b
Serves a PSAd
Yes	20.15	44.98	19.09	19.73	6.28	20.51	13.61	2.17b
No	79.85	55.02	80.91	80.27	1.99	79.49	4.24	2.13b
Located in an MUA and serves a PSA
Yes	8.43	23.59	7.78	6.51	5.47	11.32	16.93	3.10b
No	91.57	76.41	92.22	93.49	2.65	88.68	4.78	1.80b
Medicare Part D beneficiary characteristics
Predominant beneficiary race and ethnicitye
American Indian, Alaskan Native, or Pacific Islander	0.15	0.13	0.52	0.00	0.00	0.19	16.67	Not estimablef
Asian	1.38	1.43	0.08	0.03	0.00	3.71	0.25	Not estimablef
Black	6.56	6.10	6.58	5.16	3.03	8.73	4.49	1.48g
Latinx	5.36	3.81	5.42	3.64	2.98	8.27	2.92	0.98
Multiracial	8.92	6.70	9.01	8.20	3.55	10.07	2.44	0.69g
White	77.64	82.79	77.42	82.97	2.75	69.02	7.79	2.83b
Preferred pharmacy status, mean (SD)h	38.76 (28.68)	27.00 (25.87)	39.26 (28.69)	56.04 (21.76)	49.47 (22.91)	11.23 (11.23)	10.53 (11.47)	0.21b
Medicare Part D transaction characteristics
Monthly transactions, mean (SD), No.	1739.02 (4049.70)	1441.66 (1453.31)	1751.85 (4124.58)	1997.74 (4449.81)	1743.88 (1603.50)	1308.65 (3246.18)	1226.85 (1290.06)	0.70b

Abbreviations: MUA, medically underserved area; NA, not applicable; PSA, pharmacy shortage area.

^a Risk ratio is reported for categorical variables, and mean ratio is reported for continuous variables. χ² Test was used for risk ratio differences and t test was used for continuous variable differences. Chain ERPs were used as the reference category.

^b P < .001.

^c Defined by the Health Resources and Services Administration.

^d Defined by whether the pharmacy is the nearest to a pharmacy shortage area, which is defined as a census tract where the population-weighted distance to the nearest pharmacy is greater than the following thresholds: 1 mile for urban areas, 2 miles for suburban areas, and 10 miles for rural areas. For tracts with low vehicle ownership and low income, the threshold is 0.5 miles, regardless of urbanicity.

^e Predominant racial and ethnic makeup of the pharmacy’s beneficiaries in 2023, categorized as follows: American Indian, Alaskan Native, or Pacific Islander (≥50% non-Latinx American Indian, Alaskan Native, or Pacific Islander); Asian (≥50% non-Latinx Asian); Black (≥50% non-Latinx Black); Latinx (≥50% Latinx); multiracial (no single racial and ethnic group exceeded 50%); and White (≥50% non-Latinx White). Race and ethnicity data were available in the Medicare data in the Master Beneficiary Supplement File and aggregated at the pharmacy level. These data report both a combination of reported beneficiary race and algorithm-derived race to identify racial categorization.

^f Mean ratio is not estimable because the reference group value was 0.

^g P < .05.

^h Percentage of preferred pharmacy network plans in the zip code tabulation area for which the pharmacy is preferred.

Geographic distribution of ERPs varied (Figure). Among the 3008 counties containing a pharmacy, 54.79% had no ERPs, whereas in 14.30% of counties all pharmacies were ERPs. New York’s Manhattan, Kings, and Bronx counties had no ERPs, and less than 5% of pharmacies were ERPs across several large counties, including Miami-Dade, Florida (0.87%); Los Angeles, California (1.35%); Harris, Texas (2.61%); and Cook, Illinois (3.35%). ERPs were concentrated in the central US, with 54.55% of counties in North Dakota, 53.70% in South Dakota, 48.04% in Kansas, and 46.06% in Nebraska containing only ERPs. These 137 ERP-only counties contained 167 pharmacies. All were in rural neighborhoods (100.00%), 86.23% were independent, and 99.39% served predominantly White beneficiaries. Although many were located in medically underserved areas (89.22%), 50.30% were nearest to PSAs.

Figure. Map Showing County-Level Percentages of Pharmacies Qualifying as Essential Retail Pharmacies in the US, 2023.

These data are from 2023 Medicare prescription claims and the National Council for Prescription Drug Programs. The denominator is the number of pharmacies in the county, and the numerator is the number of ERPs in the county. Percentages of ERPs at the county level ranged from 0.00% in 1648 counties, 2.18% (n = 36) of which had only 1 pharmacy in the county, to 100.0% in 430 counties, 78.98% (n = 331) of which only had 1 pharmacy in the county. ERP indicates essential retail pharmacy; sample size, number of counties.

Discussion

The Consolidated Appropriation Act of 2026 designated less than 5% of pharmacies as ERPs and disproportionately included rural neighborhoods, independent pharmacies, and pharmacies serving mostly White beneficiaries. Many counties with high pharmacy closure rates had low shares of pharmacies identified as ERPs.² For example, Miami-Dade county experienced a net loss of 16% of pharmacies from 2010 to 2021,² and 0.87% of pharmacies qualified as ERPs in 2023. Notably, our findings indicate that nearly half of the pharmacies nearest to PSAs were defined as ERPs, suggesting ERP designation differentiates pharmacies disproportionately serving low-access neighborhoods. ERP designation also captured pharmacies preferred by PPNs less often than pharmacies overall, and independent ERPs were preferred by PPNs significantly less often than chain ERPs. Because exclusion from PPNs is associated with pharmacy closure,³ this finding highlights the need for ERP protections, particularly for independent ERPs, to protect access to pharmacies for Part D beneficiaries.

Current ERP designation excludes many pharmacies at higher risk of closure and is not consistent with currently used access measures.⁵ Before enacting ERP protections, policymakers should explore alternative definitions, including relaxation of geographical thresholds⁵ and consideration of additional designation criteria (such as neighborhood sociodemographic characteristics) to expand ERP eligiblity.⁶ Given poorer Part D PPN preferred status, policy solutions could include strengthening reasonable and relevant network protections¹ for pharmacies by mandating that ERPs are included in Part D PPNs. Notably, this was included in early ERP legislation⁷ but was not passed into law.

Study limitations include lack of PPN participation information outside of Part D, which may impact the observed extent to which pharmacies are preferred by PPNs. Future studies should examine the association between ERP status and pharmacy closure and identify ERP characteristics that predict closure.

Supplement 1.

eTable. Study Terms and Definitions

Supplement 2.

Data Sharing Statement