State Telepharmacy Policies and Pharmacy Deserts

Key Points

Question

What is the association between telepharmacy policy implementation and pharmacy deserts?

Findings

In this cohort study of 12 states and telepharmacy policy changes between 2016 and 2019, states that adopted less restrictive policies through legislation or regulation had a statistically significant reduction in pharmacy deserts and the population living within them compared with controls.

Meaning

These results suggest that states looking to expand pharmacy access may benefit from adopting less restrictive telepharmacy policies.

This cohort study investigates whether US state-level telepharmacy policy is associated with pharmacy deserts and access to pharmacy services.

Abstract

Importance

Pharmacy deserts have increased, potentially affecting patient access and care. Historically, telepharmacies have been used to reduce pharmacy deserts to restore access, but states frequently restrict their operation.

Objective

To analyze whether telepharmacy policy is associated with pharmacy deserts and access to pharmacy services.

Design, Setting, and Participants

This cohort study analyzed pharmacy location and census data from 2016 through 2019 for US states with new telepharmacy policies. Nearby control states were used for comparison in a pretest-posttest nonequivalent group design. Statistical analysis was performed from January 2022 to July 2023.

Exposure

Intervention states were selected if a change in telepharmacy policy was adopted in 2017 or 2018.

Main Outcomes and Measures

Pharmacy deserts were defined as any geographic area located at least 10 miles from the nearest pharmacy. Primary outcomes included the change in number of telepharmacies, pharmacy deserts, and population in pharmacy deserts. Secondary outcomes included the percentage of telepharmacies located in medically underserved areas or populations (MUA/Ps), and the association between a telepharmacy opening nearby and the transition of a pharmacy desert into a nonpharmacy desert.

Results

Twelve US states were included in the study (8 intervention states, 4 control states). Intervention states experienced an increase in the mean number of telepharmacies to 7.25 with a range of 4 (Arizona, Indiana) to 14 (Iowa), but control states remained at a mean of 0.25 telepharmacies with a range of 0 to 1 (Kansas). Compared with controls, intervention states experienced a 4.5% (95% CI, 1.6% to 7.4%) decrease in the percentage of places defined as pharmacy deserts (P = .001) and an 11.1% (95% CI, 2.4% to 22.6%) decrease in the population in a pharmacy desert (P = .03). Telepharmacies were more likely to be located in a MUA/P than traditional pharmacies (preperiod in MUA/P: 63.2% of telepharmacies [12 of 19] vs 33.9% of traditional pharmacies [5984 of 17 511]; P = .01; postperiod in MUA/P: 62.7% of telepharmacies [37 of 59] vs 33.7% of traditional pharmacies [5998 of 17 800]; P < .001). When a telepharmacy was established in pharmacy deserts, 37.5% (30 of 80) no longer met the study’s definition of a pharmacy desert the following year. In contrast, only 1.8% of places (68 of 3892) where a nearby telepharmacy did not open experienced this change (χ²₁=416.4; P < .001).

Conclusions and Relevance

In this cohort study, intervention states experienced a reduced population in pharmacy deserts, suggesting an association with new telepharmacy openings. States aiming to improve pharmacy access might consider less restrictive telepharmacy policies to potentially elicit greater patient outcomes.

Introduction

Ninety percent of US residents live within 5 miles of a community pharmacy, and 85% of patients receive medications from a local brick-and-mortar pharmacy.^1,2 Despite the reported availability of pharmacies, patients across the US experience difficulty accessing pharmacy services in urban and rural settings.³

Multiple factors influence the variability and accessibility of pharmacy services for patients, including nationwide pharmacy closures, transportation, disability, economic challenges, and cultural or linguistic barriers.^4,5 These obstacles create pharmacy deserts, a term derived from the US Department of Agriculture’s concept of a food desert, a low access area where healthy food is hard to acquire. Similarly, a pharmacy desert refers to any geographic area where patients have difficulty obtaining medications.^6,7 Evidence has shown barriers to adequate care can lead to decreased medication adherence and reduced outcomes, adding costs to the already overwhelmed health care system.^5,8,9

Currently, patients living in pharmacy deserts have few options for pharmacy services that combine medication dispensing and pharmacist interaction: mail order, pharmacy delivery, or physician dispensing. With these options, patients may experience a delay in receiving medications and have limited or no interaction with a pharmacist.^9,10,11,12 Studies show when patients have timely access to medication and a pharmacist, outcomes and adherence improve.^13,14 Patients can benefit from the implementation of telepharmacy, which provides both of these vital pharmacy services they may otherwise have difficulty receiving.^9,15

The National Association of Boards of Pharmacy defines the practice of telepharmacy as “the practice of pharmacy by registered pharmacists located within US jurisdictions through the use of telepharmacy technologies [secure electronic communications, information exchange, or other methods that meet applicable state and federal requirements] between a licensee and patients or their agents at distances that are located within US jurisdictions.”¹⁶ While the definition varies by state, in this study, telepharmacy refers to a brick-and-mortar pharmacy location staffed with 1 or more pharmacy technicians supervised remotely by a pharmacist at a different location who verifies prescriptions and provides counseling.¹⁷

To date, telepharmacy is permitted in 28 states, with varying statutes and regulations based on the needs of each state.^18,19 Although telepharmacy has a long history and broad adoption globally, receives support from nationally recognized pharmacy organizations, and has evidence indicating its ability to safely dispense medication and enhance medication adherence, states impose burdensome restrictions on the practice through policy.^{9,15,16,20,21,22,23,24,25,26,27} These varied restrictions, including those limiting the geographic location of a telepharmacy, can be arbitrary and capricious, placing an undue burden on Boards of Pharmacy to enforce and pharmacy owners to comply, the consequences of which transfer to the patient. Although pharmacy deserts can occur in rural and urban areas, this study explores the extent to which adopting telepharmacy statutes and regulations may be associated with improved access to medications by reducing the number of pharmacy deserts, as defined in accordance with definitions in other published literature, and the population within them.^5,6

Methods

Institutional review board approval and informed consent were not needed for this cohort study as no human participants were involved in this research and all data used for this study are available in the public domain, in accordance with 45 CFR §46. Overall, this study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cohort studies.²⁸

Objectives

This study examined the number of telepharmacies in states that passed statutory and regulatory policies and the number and proportion of the population living in pharmacy deserts during the study period from 2016 to 2019. Additionally, this study sought to determine the extent to which telepharmacies were located in medically underserved areas or populations (MUA/Ps) and whether pharmacy deserts transitioned to nonpharmacy deserts with the addition of a nearby telepharmacy.

Study Design

To explore the association between policy change and access, a pretest-posttest nonequivalent group design was used, with nearby states that did not pass telepharmacy policies during the study period serving as natural controls. This design is common in policy analyses and robust to many of the threats to validity common among pretest-posttest studies lacking controls.²⁹ Intervention states were defined as states that formally implemented or updated telepharmacy statutes or regulations between 2017 and 2018. These 2 years were chosen due to the substantial amount of telepharmacy-related policy changes that occurred, as well as a limitation on the historic availability of pharmacy data prior to 2016. Control states were defined as geographically or culturally similar states with no telepharmacy-related statutory or regulatory changes during either year. The list of intervention states with details related to telepharmacy regulations and their matched control states is included in Table 1.^{30,31,32,33,34,35,36,37,38,39,40,41}

Table 1. States With New Telepharmacy Policies in 2017 and 2018 and Their Control States.

Intervention state	Distance to the nearest pharmacy, mi	Pharmacist-to-telepharmacy ratio	Maximum prescription count	Technician ratio at a telepharmacy	Technician training specific to telepharmacy	Intervention year	Control state
Arizona	0	1:1 or 1:2a	NA	NA	Certified + 1000 h +  2-h CE	2018	Oklahoma
Idaho	0	NA	NA	NA	Registered + certified	2017	Utah
Indiana	10 or waiver	1:1	NA	1:6b	Certified +2000 h	2017	Ohio
Iowa	10 or waiver	NA	150/d	NA	Certified + registered  + 2000 h	2017	Ohio
Nebraska	10	NA	NA	1:3	Certified	2018	Kansas
Wyoming	10	NA	NA	NA	NA	2017	Utah
New Mexico	20 (can be waived)	1:4	200/d	Determined by pharmacistc	Certified + registered  + 2000 h	2017	Oklahoma
Texas	22, 10 for clinics or 0 FQHC	1:2	125/d	1:3c	Registered	2018	Kansas

Abbreviations: CE, continuing education; FQHC, Federally Qualified Health Center; NA, not applicable.

^a A pharmacist can supervise 1 site, but if not simultaneously supervising and dispensing can supervise 2 sites (can request a waiver for more).

^b Pharmacy technicians are included in the count toward supervising pharmacy ratio.

^c Not addressed in the telepharmacy language.

Data Collection

Pharmacy location data for this study came from the National Council of Prescription Drug Plans (NCPDP) DataQ database which contains locations of every dispensing pharmacy in the US. This data was used to identify all community and/or retail pharmacies (NCPDP pharmacy service type code = 01) operating in each intervention and control state for the year prior to the policy change (preperiod), the intervention year when the policy changed, and the year after the policy change (postperiod).⁴² For each year, NCPDP data was collected for January 1 of the following year to address any delays in data collection by NCPDP and capture any pharmacies active during the year. Telepharmacies were identified by petitioning Boards of Pharmacy in each intervention and control state for a list of current and previously operating telepharmacies for each study year and then, for accuracy, including known telepharmacies operating on a telepharmacy vendor’s platform.^{43,44,45,46,47,48,49,50,51,52,53,54,55}

Data from NCPDP and the US Census Bureau on all geographic areas was used to identify pharmacy deserts, their populations, and if a pharmacy were located in a MUA/P. Each pharmacy address, as identified by their NCPDP record, was geocoded to identify its longitude and latitude. These geographic coordinates were used to identify the census tract, county subdivision, and county in which the pharmacy was located, as well as the status of nearby places as pharmacy deserts or nonpharmacy deserts.^56,57 In accordance with definitions in other published literature, a community was defined as a pharmacy desert if the straight-line distance between the US Census Bureau–designated centroid for a given community and the nearest pharmacy was 10 miles or more.⁶ Census tract, county subdivision, and county Federal Information Processing Standard codes for pharmacies were used to identify whether a pharmacy was located in a MUA/P using files maintained by the Health Resources and Services Administration (HRSA).^58,59

Statistical Analysis

Descriptive statistics were used to quantify the trend in telepharmacy openings in intervention and control states across time. Fisher exact tests were used to explore the extent to which telepharmacies were located in MUA/Ps compared with traditional pharmacies. To assess the trend in pharmacy deserts across the study period between cohorts, a repeated measures regression approach was used to compare differences in the percentage of communities meeting the definition of a pharmacy desert and the proportion of the population in a pharmacy desert across the 3 study years. A generalized estimating equations approach was used for the primary statistical models. These models included fixed effects for cohort and a linear trend for time, with an interaction between cohort and time as the primary variable of interest. Time was defined as a 3-year period including the preperiod year, intervention year, and postperiod year. The intervention year was included as part of the time trend to capture the opening of any new telepharmacies and track state policy changes at any point throughout the year. The outcome for the 2 primary models was the percentage of places defined as pharmacy deserts and the percentage of the population in a pharmacy desert. These outcomes were assumed to be normally distributed and an identity link was used. The models also included a random effect for state to account for the correlation of observations within states over time, which assumed an independent correlation structure. Additional subanalyses were conducted using a preperiod and postperiod only and alternative model specifications to control for clustering of observations within states over time (eMethods in Supplement 1). Finally, χ² tests were used to explore the association between new telepharmacy openings and the transition of pharmacy deserts to non-pharmacy deserts, and prespecified α was .05 for all statistical tests. Statistical analysis was performed using SAS version 9.4 (SAS Institute) from January 2022 to July 2023.

Results

Among 12 US states included in the study, 8 were intervention states (Arizona, Idaho, Indiana, Iowa, Nebraska, New Mexico, Texas, and Wyoming) and 4 were control states (Kansas, Ohio, Oklahoma, and Utah). In the preperiod (before the intervention year), the intervention states had a mean of 2.25 telepharmacies with a range of 0 (Arizona, Indiana, Nebraska) to 8 (Iowa). Among control states, the mean number of telepharmacies was 0.25 with a range of 0 to 1 (only Kansas had a telepharmacy in the referenced years with a telepharmacy count of 1). The presence of telepharmacies in intervention states before policy passage is likely the result of variances or waivers approved by the Board of Pharmacy. For example, the Iowa Board of Pharmacy approved several telepharmacies on a case-by-case basis as pilot projects before passing legislation in 2016 and adopting formal rules in 2017. In the postperiod (after the intervention year), the mean number of telepharmacies in intervention states experienced an increase to 7.25 with a range of 4 (Arizona, Indiana) to 14 (Iowa); the mean number of telepharmacies in control states remained at 0.25 with a range of 0 to 1 (Kansas, having a count of 1, remained the only control state with a telepharmacy). The complete description of the number of telepharmacies by state over time is listed in eTable 1 in Supplement 1.

Across the study periods, the percentage of geographic areas defined as pharmacy deserts and the percentage of the state’s population living in a pharmacy desert decreased faster in states that adopted protelepharmacy policy (Figures 1 and 2). In intervention states, the observed mean absolute percentage of places defined as pharmacy deserts declined from 26.7% (95% CI, 15.1%-38.3%) to 25.5% (95% CI, 14.4%-36.7%), whereas rates in control states remained unchanged at 19.0% across all periods (ie, preperiod: 19.0% [95% CI, 9.1%-28.8%]; postperiod: 19.0% [95% CI, 9.2%-28.8%]). This 1.2 percentage point decrease among intervention states translates to a 4.5% (1.2 percentage points of 26.7 percentage points) relative decrease in the percentage of places defined as pharmacy deserts. Trends in observed mean percentage of the population living in a pharmacy desert were similar, with decreases from 2.34% (95% CI, 0.95%-3.73%) to 2.05% (95% CI, 0.87%-3.22%) for intervention states and essentially no change in control states (1.45% [95% CI, 0.63%-2.27%] to 1.42% [95% CI, 0.61%-2.23%]). Subtracting out the absolute difference in the percentage of the population living in a pharmacy desert among the control group (0.03%), the absolute difference of the difference among the intervention group is 0.26%, which translates to a relative 11.1% (0.26% of 2.34%) decrease (11.1% [95% CI, 2.4%-22.6%]; P = .03).

Figure 1. Percentage of Places Defined as Pharmacy Desert by Period.

Projected values derived from a linear regression model with fixed effects for time, cohort, and time × cohort interaction with a repeated measure for state.

Figure 2. Percentage of Population Residing in a Pharmacy Desert by Period.

Projected values derived from a linear regression model with fixed effects for time, cohort, and time × cohort interaction with a repeated measure for state.

Estimated means from the statistical model assessing the difference in trend over time between cohorts were not meaningfully different from the observed means, indicating a generally constant trend in outcomes with respect to time within the 2 cohorts (Figures 1 and 2). As indicated by the time × cohort interaction term in the statistical model, the percentage of places defined as pharmacy deserts decreased 0.63% (95% CI, −1.01% to −0.24%) per year for intervention states compared with controls (P = .001), and the percentage of the population residing in pharmacy deserts decreased by 0.13% (95% CI, −0.25% to −0.01%) (P = .03) (Table 2). Translating these absolute percentage point reductions to relative percentage change, a 0.63% annual reduction over 2 years from a baseline percentage of 26.7% results in a relative 4.5% reduction (95% CI, 1.6% to 7.4%) (P = .001). Applying the same method to the percentage of population residing in a pharmacy desert results in a relative percentage reduction of 12.5% (95% CI, 2.4% to 22.6%). Compared with control states, intervention states in the preperiod did not have statistically significant differences in number of pharmacy deserts or percentage of their populations residing in pharmacy deserts. Sensitivity analyses using 2-period pretest-posttest models and models with alternative specifications to account for clustering of observations found nearly identical β coefficients and confidence intervals for associations between the time × cohort interaction and the outcome across models and no differences in statistical significance (eTables 2, 3, 4, 5, 6, 7, and 8 in Supplement 1).

Table 2. Statistical Estimates for the Percentage of Places and Population Defined as or Residing in Pharmacy Deserts.

Model parameter	Percentage of places defined as pharmacy desert		Percentage of population residing in pharmacy desert
	Estimate, % (95% CI)	P value	Estimate, % (95% CI)	P value
Intercept	18.99 (8.15 to 29.83)	<.001	1.45 (0.54 to 2.36)	.002
Cohort (intervention vs control)	7.72 (−6.38 to 21.82)	.28	0.88 (−0.52 to 2.27)	.22
Time	0.025 (−0.222 to 0.271)	.84	−0.015 (−0.048 to 0.019)	.39
Time × cohort interaction	−0.63 (−1.01 to −0.24)	.001	−0.13 (−0.25 to −0.01)	.03

Across the study years, higher percentages of telepharmacies were located in a MUA/P compared with traditional pharmacies located in a MUA/P (preperiod in MUA/P: 63.2% of telepharmacies [12 of 19] vs 33.9% of traditional pharmacies [5984 of 17 511]; P = .01; intervention year in MUA/P: 59.0% of telepharmacies [23 of 39] vs 33.7% of traditional pharmacies [5948 of 17 638] ; P = .002; postperiod in MUA/P: 62.7% of telepharmacies [37 of 59] vs 33.7% of traditional pharmacies [5998 of 17 800]; P < .001) (Table 3). Finally, there were 3972 places defined as a pharmacy desert either in the year prior to or the year of the policy change and had a traditional pharmacy as their nearest pharmacy, 98 (2.5%) transitioned to a nonpharmacy desert the following year, and 80 (2.0%) had a telepharmacy open as the nearest pharmacy (eTable 9 in Supplement 1). A χ² test assessing the association between pharmacy desert closure and telepharmacy opening found that 37.5% of telepharmacy openings (30 of 80) associated with pharmacy desert closures compared with only 1.8% (68 of 3892) when the closest pharmacy remained a traditional pharmacy (χ²₁ = 416.4; P < .001).

Table 3. Location of Telepharmacies in MUA/P^a.

Pharmacy type	Preperiod		Intervention year		Postperiod
	Pharmacy located in MUA/P, No. (%)	P value	Pharmacy located in MUA/P, No. (%)	P value	Pharmacy located in MUA/P, No. (%)	P value
Traditional pharmacy	5934 (33.9)	.01	5948 (33.7)	.002	5998 (33.7)	<.001
Telepharmacy	12 (63.2)		23 (59.0)		37 (62.7)

Abbreviation: MUA/P, medically underserved areas or populations.

^a P values calculated with Fisher exact test.

Discussion

Within a year of policy implementation, states that adopted or updated telepharmacy statutes and regulations experienced a 2-fold benefit: a decrease in the number of pharmacy deserts and the percentage of patients in them. A secondary, unexpected outcome was an increase in the number of telepharmacies in MUA/Ps, which often include urban communities.⁶⁰

While not exploring the direct association between telepharmacy and patients’ outcomes, this study found that telepharmacies were more than twice as likely than traditional pharmacies to be located in areas of high medical need. Policy adoption was associated with a statistically significant 4.5% (95% CI, 1.6%-7.4%) relative decrease in the number of places defined as pharmacy deserts and 11.1% (95% CI, 2.4%-22.6%) relative decrease in the percentage of the population in a pharmacy desert. Additionally, the opening of telepharmacies was associated with the reduction of pharmacy deserts, with 1 in 5 pharmacy deserts no longer identifying as such when a telepharmacy opens nearby.

As pharmacy closures and socioeconomic factors persist, pharmacy deserts are likely to expand unless policies are implemented to ensure continued access to pharmacy services.⁵ Telepharmacies are a viable alternative to traditional pharmacies, and policies expanding telepharmacy practice have already been established by states that have used the model for years.^{30,32,61,62,63,64,65} However, many Boards of Pharmacy do not permit the practice or place severe restrictions in policy which limit the utilization, potentially affecting patients regardless of their location. Such limitations include restrictions on distance or mileage to the nearest pharmacy, maximum prescription count, and the number of telepharmacy locations a pharmacist or pharmacy may oversee. Other regulations often pursued by policy makers, such as technician ratios and training, often result in an additional burden that may deter pharmacy owners from opening a telepharmacy.^{19,30,31,32,33,34,35,36,37,38,39,40,41,62} Additional studies need to be conducted to fully understand the association between these policies and patient safety.

Other policy alternatives that have been implemented to increase patient access, such as mail order, physician dispensing, and pharmacy delivery, are often associated with delays in therapy, reduced interaction time with a health care professional, and additional costs.^{10,11,12,13,14,} These policy alternatives are permitted in almost every state, but patients still overwhelmingly prefer to fill medications at a brick-and-mortar location. This is particularly true for patients with lower income or from minoritized ethnic and racial groups.^2,66 Telepharmacy not only maintains access to this in-person method but also establishes, restores, and enhances access for patients in underserved areas.⁶⁷

The North Dakota Telepharmacy Project, created to combat the increase in rural community pharmacy closures, showed similar results. By the end of the 6-year study, the project successfully provided pharmacy services to approximately 80 000 rural citizens. As a result, North Dakota established permanent rules in 2003 enabling telepharmacy to operate on a broader scale throughout the state. Additional benefits of telepharmacy were uncovered by researchers: $26.5 million in economic growth for the surrounding communities and the creation of 80 to 100 local jobs.^20,61 In contrast, mail-order pharmacies have little to no connection to the local community and often send earnings out of state.⁶⁸ Additionally, because patients do not interact with a pharmacist as seen in an in-person pharmacy setting, such as telepharmacy, they may be at higher risk for the dangers of polypharmacy, furthering hospitalizations, worsening outcomes, and increasing health care costs.^10,69

Limitations

This study has several limitations. First, results may not truly represent the nationwide access problem since data focused solely on pharmacy deserts located 10 miles or more from the nearest pharmacy. Thus, this study did not fully examine the benefit of telepharmacy to other medically underserved areas located inside that radius, such as urban communities. Qato et al⁵ found that patients living within these mileage areas can experience limited access to pharmacy services, suggesting that various factors affect a patient’s ability to access a pharmacy.⁵ Patients in these mileage areas are often from minoritized racial or ethnic groups with lower economic status and may also experience linguistic and cultural differences. These factors can provide substantial barriers to care, raising concerns for medication nonadherence, a problem already prevalent in many communities.^3,5 Medication adherence plays a role in improving health outcomes for chronic conditions, and reduced access to medications further contributes to disparities in care and increases health care costs.⁷⁰ Second, this study applies several key design features to minimize bias, primarily the use of control states and preperiods. However, there are limitations. This study assesses the outcomes of pro-telepharmacy laws and regulations among 8 states that changed policies over a 2-year period; these findings may not apply to additional states in other years. While the intervention year indicates a period in which a state adopted new policies, many states, such as Iowa, had preexisting pathways through which telepharmacies could open before the policies were implemented. Additionally, while a pretest-posttest nonequivalent group design is among the strongest observational study designs, the ability to infer causality in nonrandomized experiments is naturally limited, and all sources of bias cannot be eliminated.

Conclusion

States that adopted telepharmacy policies have experienced a decrease in pharmacy deserts, and telepharmacies are more likely than traditional pharmacies to be located in areas of high medical need. Adopting less restrictive statutes and regulations for telepharmacy appears to be a solution to restoring pharmacy access and can support improvements in public health for underserved patients in rural and urban areas.

Supplement 1.

eMethods. Alternative Model Specifications for Primary Analyses

eTable 1. Telepharmacy Frequency by State and Time Period

eTable 2. Three-period GEE Model for with Exchangeable Correlation Structure

eTable 3. Three-period GEE Model with AR(1) Correlation Structure

eTable 4. Three-period Fixed Effects Model

eTable 5. Two-period GEE Model with Independent Correlation Structure

eTable 6. Two-period GEE Model with Exchangeable Correlation Structure

eTable 7. Two-period GEE Model with AR(1) Correlation Structure

eTable 8. Two-period Fixed Effects Model

eTable 9. Association Between Telepharmacy Opening and Pharmacy Desert Closure

Supplement 2.

Data Sharing Statement