Trends in the number of pharmacies in operation and pharmacy openings and closures are statistically significant at P < .01 using Poisson regression. A total of 77 510 pharmacies were in operation between 2009 and 2015, of which 9568 pharmacies closed and 14 614 were newly opened. Aggregate closure rates were estimated among all pharmacies that were in operation at some point during 2009 through 2015, excluding pharmacies newly opened in 2015 (n = 74 883 pharmacies). Closure rates are defined as the number of pharmacies that closed divided by the number of pharmacies in operation during 2009 through 2015. Annualized mean closure rates were 2.5% (95% CI, 2.1%-3.0%).
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Guadamuz JS, Alexander GC, Zenk SN, Qato DM. Assessment of Pharmacy Closures in the United States From 2009 Through 2015. JAMA Intern Med. 2020;180(1):157–160. doi:10.1001/jamainternmed.2019.4588
Despite the critical role of pharmacies in the pharmaceutical supply chain and evidence that pharmacy closures contribute to nonadherence of prescription medications,1 there is limited information on the prevalence and risk factors for pharmacy closure. In this study, we examined trends in pharmacy closures in the United States between 2009 and 2015, and analyzed pharmacy, community, and market factors that might be associated with such closures. We hypothesized that pharmacies disproportionately serving publicly insured populations were at increased risk for closure owing to lower pharmacy reimbursement rates from Medicaid and Medicare.2 We also hypothesized that independent pharmacies were more likely to close because they often do not participate in preferred pharmacy networks.3
We used several data sources to conduct these analyses. National Council for Prescription Drug Programs data was used to determine the number and type of pharmacies (ie, chain, independent, mass merchandise, grocery, government, and/or clinic based) in operation that closed in the United States from 2009 through 2015.4 Pharmacy addresses were geocoded using ArcGIS, version 10.4 and linked to the American Community Survey 5-year data (2011-2015), Health Resources and Service Administration (HRSA) data, and US Census data to derive community (ie, urbanity, percentage minority, percentage living in poverty, medically underserved area status) and market (ie, ratio of public vs privately insured individuals, percentage uninsured, number of pharmacies per 10 000 persons) characteristics for each pharmacy at the county level.
This analysis included pharmacies in operation at any point during the study period except those that newly opened in 2015. First, we quantified the prevalence of our primary outcome of interest, pharmacy closure, over time at the national level. Second, we used Kaplan-Meier survival curves and discrete-time proportional hazard models to identify risk factors for pharmacy closure stratified by urbanity. We stratified by urbanity because pharmacies located in rural areas may operate under different financial incentives, including tiered pharmacy reimbursement rates for Medicaid prescriptions.5 Pharmacies that opened prior to 2009 entered the models that year; pharmacies that newly opened between 2009 and 2014 entered the models in the year of the opening. This study design provided six 1-year intervals during which a pharmacy closure could occur. We used a significance value of 5% in all testing; P values reported are 2-sided. Statistical analyses were performed with Stata, version 14. The institutional review board at the University of Illinois at Chicago determined that this study did not need approval because it was not considered human-subject research.
From 2009 to 2015, the total number of US pharmacies increased by 7.8% from 62 815 to 67 721. Of the 74 883 pharmacies in operation at any point during this period, 9564 (12.8%) had closed by 2015 (Figure). The risk of closure was significantly greater in urban compared with nonurban areas (cumulative hazard rate from bivariate analyses: 16.2% vs 13.2%; multivariate hazard ratio [HR], 1.10; 95% CI, 1.04-1.17) (Table). In both urban (27.2%; HR, 3.15; 95% CI, 2.89-3.43) and nonurban (23%; HR, 2.90; 95% CI, 2.72-3.08) areas, independent pharmacies were more likely to close than their counterparts. In urban areas, pharmacies serving disproportionately low-income (HR, 1.9; 95% CI, 1.13-1.47), uninsured (HR, 2.11; 95% CI, 1.90-2.33), and publicly insured (HR, 2.29; 95% CI, 1.82-2.88) populations were at increased risk of closure. These factors were not associated with closure in nonurban areas.
Despite the growing number of pharmacies in the United States, findings from this study indicate that 1 in 8 pharmacies had closed between 2009 and 2015, which disproportionately affected independent pharmacies and low-income neighborhoods. Although efforts to promote pharmacy access have focused on addressing pharmacy closures in rural areas,6 we found that pharmacies located in low-income, urban areas are at greater risk of closing. These findings suggest that policies aimed at reducing pharmacy closures should consider payment reforms, including increases in pharmacy reimbursement rates for Medicaid and Medicare prescriptions. The findings also suggest the importance of understanding the influence of preferred pharmacy networks in order to protect independent pharmacies most at risk for closure, especially in urban areas. Such efforts are important because pharmacy closures are associated with nonadherence to prescription medications, and declines in adherence are worse in patients using independent pharmacies that subsequently closed.1
Accepted for Publication: August 20, 2019.
Corresponding Author: Dima M. Qato, PharmD, MPH, PhD, Department of Pharmacy Systems, Outcomes and Policy, College of Pharmacy, University of Illinois at Chicago, 833 S Wood St, Ste 266, Chicago, IL 60612 (firstname.lastname@example.org).
Published Online: October 21, 2019. doi:10.1001/jamainternmed.2019.4588
Author Contributions: Dr Qato had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: All authors.
Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: Guadamuz, Qato.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Guadamuz, Qato.
Obtained funding: Qato.
Administrative, technical, or material support: Qato.
Study supervision: Qato.
Conflict of Interest Disclosures: Ms Guadamuz reports receiving grants from the Robert Wood Johnson Foundation Health Policy Research Scholar program and grants from the National Heart, Lung, and Blood Institute (T32-HL125294). Dr Alexander reports being the former chair of the US Food and Drug Administration’s Peripheral and Central Nervous System Advisory Committee; serving as a paid advisor to IQVIA; holding equity in Monument Analytics, a health care consultancy whose clients include the life sciences industry and plaintiffs in opioid litigation; and being a member of OptumRx’s National P&T Committee. This arrangement has been reviewed and approved by Johns Hopkins University in accordance with its conflict of interest policies. Dr Qato reports being a paid consultant for Public Citizen’s Health Research Group, has received funding from Blue Cross Blue Shield and Cardinal Health, is supported in part by grants from the Robert Wood Johnson Foundation as part of the Clinical Scholars Leadership program, and is a fellow of the National Academy of Medicine. No other disclosures are reported.
Funding/Support: This work was supported by the National Institutes of Health’s National Institute on Aging (R21AG04923).
Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
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