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Pharmacy shortage areas across the United States: a visual representation by a web mapping application

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Pharmacy shortage areas across the United States: a visual representation by a web mapping application

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Pharmacy Shortage Areas Across the United States: A Visual Representation by a Web Mapping
Application

by

Taylor Corrin Robinson

A Thesis Presented to the
FACULTY OF THE USC DORNSIFE COLLEGE OF LETTERS, ARTS AND SCIENCES
University of Southern California
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(GEOGRAPHIC INFORMATION SCIENCE AND TECHNOLOGY)

December 2022

Copyright © 2022 Taylor Corrin Robinson
ii

To My Parents

iii
Acknowledgements
I am grateful to my thesis committee advisor Dr. Elisabeth Sedano and committee members Drs.
Robert Vos and Leilei Duan. Without your help and encouragement, this manuscript would not
have been accomplished. I am grateful for the ability to serve on and be a part of the Pharmacy
Access Initiative group. I am grateful to Dr. Dima Qato for her graciousness in asking me to
serve as part of the PAI. I am grateful to Andrew Shooshtari and Dr. Jenny Guadamuz for their
continued support and production of results throughout this project.
To Drs. Laura Loyola and Andrew Marx, my first two professors in my first semester at
USC, thank you for creating the foundation I needed to feel secure in this program and for
creating a comfortable space for me to grow my education in.
To my first GIS professor, Dr. Christopher Labosier, I am thankful for your continued
support, advice, letters of recommendation, and for your belief in my abilities from my first time
taking one of your classes. I am grateful to have been your advisee and to have completed and
published research under your leadership, which I believe helped me with my acceptance into
this program. Thank you!
To my friends and family, thank you for your continued support. Thank you for listening
to me explain what GIST is, my excitement for my program and my thesis topic, even when only
some of you understood what I was saying. Thank you for listening to my rants when I got
frustrated and for celebrating me in my accomplishments. I would not have been able to get to
this point without all of you. Thank you!
To my parents and siblings specifically, thank you for your continued encouragement,
support, and love given not only from day one of this program but from birth. From the minute I
found out I was accepted into this program it was evident how proud you were of me and I could
iv
not be more appreciative of that feeling. I am beyond grateful for everything you all have given
me. Your support and celebrations could not be topped by anything. Thank you!

v
Table of Contents
Acknowledgements ........................................................................................................................ iii
List of Tables ................................................................................................................................ vii
List of Figures .............................................................................................................................. viii
Abbreviations .................................................................................................................................. x
Abstract .......................................................................................................................................... xi
Chapter 1 Introduction .................................................................................................................... 1
1.1. Pharmacy Importance .........................................................................................................2
1.2. Pharmacy Deserts................................................................................................................3
1.3. Study Area ..........................................................................................................................6
1.4. Federal Policies ...................................................................................................................7
1.5. Thesis Structure ..................................................................................................................9
Chapter 2 Related Work................................................................................................................ 10
2.1. Pharmacy Access ..............................................................................................................10
2.1.1. Pharmacy Desert Definitions ...................................................................................11
2.1.2. Pharmacy Closure ....................................................................................................13
2.1.3. Insurance Access ......................................................................................................13
2.1.4. Medication Access ...................................................................................................14
2.2. Spatial Accessibility to Healthcare Services and Facilities ..............................................15
2.2.1. Methods....................................................................................................................15
2.3. Urbanicity .........................................................................................................................18
2.4. MUAs and CMS ...............................................................................................................20
2.5. Web Maps and Applications .............................................................................................22
Chapter 3 Data Sources and Methods ........................................................................................... 25
3.1. Overview of Methods .......................................................................................................25
vi
3.2. Data ...................................................................................................................................27
3.3. Data Preparation................................................................................................................30
3.4. Analysis of Shortage Areas ...............................................................................................34
3.5. Web Map Creation ............................................................................................................37
Chapter 4 Results .......................................................................................................................... 41
4.1. Shortage Area Results .......................................................................................................41
4.2. Web Mapping Application ................................................................................................48
4.2.1. Web Map Dynamics ................................................................................................48
Chapter 5 Conclusions .................................................................................................................. 61
5.1. Discussion .........................................................................................................................61
5.2. Limitations and Improvements .........................................................................................64
5.3. Future research ..................................................................................................................65
References ..................................................................................................................................... 68

vii
List of Tables
Table 1: Primary and Secondary RUCA codes from the US Department of Agriculture. (U.S.
Department of Agriculture 2020).................................................................................................. 18
Table 2: Population Density examples for a few selected census tracts ....................................... 20
Table 3: Data types and sources that are used in this project ....................................................... 28
Table 4: Attributes used from the ACS 2015-2019 estimates in this project ............................... 29
Table 5: GEOID Structure for Geographic Areas. (Source: Census.gov) .................................... 33
Table 6: Shortage Area Designation Distances for Census Tracts ............................................... 41
Table 7: Racial and Ethnic Composition of Census Tracts .......................................................... 42

viii
List of Figures
Figure 1:Workflow diagram of the full methodology process of analysis ................................... 26
Figure 2: Workflow used to remove geographic errors from pharmacy locations (Source: Data
Analyst, Andrew Shooshtari). ....................................................................................................... 31
Figure 3: Workflow to Create Web Map and Web Mapping Application ................................... 38
Figure 4:Web app showing the shortage area distribution nationally........................................... 43
Figure 5: Shortage areas using CMS distances ............................................................................. 44
Figure 6: Distribution of shortage areas by majority race ............................................................ 46
Figure 7: Pharmacy distribution by status .................................................................................... 47
Figure 8: Distribution of pharmacy locations by type .................................................................. 48
Figure 9: Web mapping application showing the layer list .......................................................... 49
Figure 10: Web mapping application showing the legend and the home view of the study area . 50
Figure 11: View after clicking the home button .......................................................................... 51
Figure 12: Web mapping application showing the pop up on a state. .......................................... 52
Figure 13: Web mapping application showing the county level and pop-up ............................... 53
Figure 14: Web mapping tool showing tract layer and an urban shortage area pop-up. .............. 54
Figure 15: Web mapping tool showing tract layer and an urban non-shortage area pop-up. ....... 54
Figure 16: Web mapping application showing the block group pop-up ....................................... 55
Figure 17: Web mapping application showing a newly opened pharmacy pop-up ...................... 56
Figure 18: Web mapping application showing a closed pharmacy location ................................ 57
Figure 19: Web mapping application showing an active pharmacy location. .............................. 57
Figure 20: Swipe tool showing comparison of two layers. ........................................................... 58
Figure 21: Showing how swipe tool works and can be used. ....................................................... 59
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Figure 22: Showing how swipe tool can be used to compare layers. ........................................... 60

x
Abbreviations
ACS American Community Survey
AMPAC Access to Medicines and Pharmacies Advisory and Advocacy Committee
BG Block Group(s)
CMS Centers for Medicare and Medicaid Services
DEA Drug Enforcement Agency
DHHS Department of Health and Human Services
FDA Food and Drug Administration
FPL Federal Poverty Level
GEOID Geographic Identifier
GIS Geographic Information Systems/Science
HPSA Health Professional Shortage Area
MUA Medically Underserved Area(s)
NAD North American Datum
NCPA National Community Pharmacist Association
NCPDP National Council for Prescription Drug Programs
PAI Pharmacy Access Initiative
RUCA Rural-Urban Commuting Area
USC University of Southern California
2SFCA Two-Step Floating Catchment Area

xi
Abstract
Pharmacies are one of the most used healthcare facilities across the nation due to of the number
of services provided within a retail setting. The services provided by a retail pharmacy include
dispensing of medications, vaccines, diagnostic testing, counseling, and more, all of which are
pertinent to creating and maintaining a healthy community. A pharmacy shortage area is a
location or area with an inadequate number of pharmacies available to a community leading to
residents traveling far distances to their nearest pharmacy. Determining states or parts of the
country that have a large number of these shortage areas provides a blueprint of where
pharmacies are greatly needed. This project provides a national distribution of the accessibility of
pharmacy locations by census tract for 2018 and 2020. These results are presented in a web
mapping application through ArcGIS Pro. By presenting pharmacy shortage areas by census tract
and over time, policymakers have highly relevant information. Given this information, there is a
better chance for federal, state, and local policy changes that may improve pharmacy
accessibility overall.

1

Chapter 1 Introduction
Pharmacies are a healthcare facility which provide access to medications, treatments, and other
primary care services. Compared to other types of healthcare facilities such as emergency care,
primary care, etc., pharmacies provide their amenities with more convenience, such as with
drive-through access, longer hours and weekends, and sometimes more locations. Better access
to pharmacy locations is important to medical equity across the nation. Especially for low-
income neighborhoods and neighborhoods with low-vehicle ownership, the need for adequate
access is extremely important because these services are sometimes an alternative to higher
priced healthcare facilities.
This study aims to analyze what types of populations are affected by adequate or
inadequate pharmacy access across the United States. The analysis is done using a calculation
based on the nearest pharmacy to the centroid of a census block group. The pharmacy data years
include 2018 and 2020 pharmacies with indicators of which pharmacies have closed between the
two years and which were newly opened by 2020. Using population density, to determine
urbanicity at the census block group level, all block groups within an urban, suburban, or rural
census tract are considered a shortage area based on distance thresholds. For each year, using the
distance to nearest pharmacy from each census block group, a census tract is considered a
shortage area if an area of over 500 residents or over 33% of the tract’s population is further than
a given distance from the nearest pharmacy. Following existing literature on pharmacy deserts,
the distance thresholds are set based on whether tracts are urban, suburban, or rural and for
characteristics of the populations in each tract (Guadamuz, et al. 2021).
Figuring out the locations of these pharmacy shortage areas can provide statistics of who
is being affected by a lack of access to pharmacies and their services. Applying this across a
2

large study area, such as the United States, shows at the national level just how many people and
places are affected. This project shares these areas in an interactive map, which can serve as a
blueprint for policy makers and pharmacy executives to make decisions on where pharmacy
locations are needed.
This project was completed by the author working with the Pharmacy Access Initiative
group which includes four other members within the University of Southern California (USC)
Spatial Sciences Institute and School of Pharmacy. In addition to the author, the group’s
members included in this project were the Director Dima Qato, PharmD, MPH, PhD; Robert
Vos, PhD, GISP; Post-Doctoral Research Fellow Jenny Guadamuz, PhD; and Research Project
Specialist/Data Analyst Andrew Shooshtari, MS. The Pharmacy Access Initiative is community-
based public health research, and this project was partnered with the National Community
Pharmacists Association (NCPA). In addition, feedback was solicited from an Advisory and
Advocacy Committee (AMPAC) made up of clinicians, elected officials, and federal and state
policymakers.
1.1. Pharmacy Importance
Pharmacies play a vital role in providing healthcare services to the general public. The
standard services provided by pharmacies include prescription, over the counter and behind-the-
counter medications, patient consultations, and some vaccines, which are also available within
primary health care facilities. These services provided by pharmacies are convenient for patients
because of the ability to receive these services, usually without having to wait for weeks for an
appointment, as compared to some primary care facilities. Along with the convenience of
pharmacy hours, the vaccines available also can be given at reduced prices with coupons that
might not be offered in primary care facilities. Also, pharmacist consultation includes advice on
3

what medications to take based on symptoms and help with compliance and use of medications
which are offered for free in a pharmacy setting. These services being offered at pharmacies, that
would otherwise cost at other health care facilities, is why adequate pharmacy access is
necessary to all.
Management of common health conditions in the US, such as heart disease and diabetes,
require regular access to the types of services provided by pharmacies. Medications, including
insulin for diabetes, are one of the main factors for a patient to remain compliant in maintaining
their health condition. Heart disease and diabetes account for two of the top ten leading causes of
death by the Centers for Disease Control in the United States as of 2020, with heart disease
ranking highest in mortality rates (Centers for Disease Control and Prevention 2022). Both heart
disease and diabetes cause those diagnosed to rely heavily on pharmacies to receive most of their
medications and treatments and to remain compliant. Conditions which lead to heart disease
include high cholesterol which is a condition that can cause the arteries leading to the heart to be
clogged and can be treated with various medications and changes in diet. Failure to consistently
treat high cholesterol, for example, can cause heart disease and may cause heart attacks as well.
Being compliant with receiving and taking medications to reduce cholesterol is very important to
the reduction of a heart disease diagnosis. Having adequate access to pharmacies can result in
better compliance amongst a community.
1.2. Pharmacy Deserts
The term “pharmacy desert” has become popular for describing a location with poor
access to pharmacies and their services. The addition of the word “desert” comes from the term
“food desert” which describes a location with scarce access to healthy food options or any food
locations in general (Alkon and Agyeman 2011). The definition of food desert locations is
4

similar to pharmacy desert locations in that they do not provide adequate availability of what is
needed for the community. Healthy food options and pharmacy services should be available and
accessible to all.
Pharmacy deserts can be defined as locations of populations with little to no access to
pharmacies and their services based on many factors, one being distance traveled by a person to
their pharmacy. Another definition includes areas of populations that have pharmacy locations
available in their neighborhoods, but these pharmacies are not in their insurance network,
making it nearly impossible to receive certain available care (Kelling 2015). Insurance
companies often have multiple plans from which beneficiaries can choose what will benefit them
best. Insurance companies decide for their pharmacy plans which locations and companies to
create contracts with, which reduces the amount of freedom patients have to receive care from
pharmacies. Previous small-area studies that focused on the spatial accessibility of pharmacies or
healthcare facilities within certain distances from populations or neighborhoods, (Wisseh, et al.
2020; Pednekar and Peterson 2018; Bonner 2015) provide information on the social inequities
within certain locations. Overall, mostly minority populations and lower income populations are
affected the most by inaccessibility to healthcare facilities and pharmacies. Basic healthcare
services should be accessible to all and determining where there are locations that lack this
access is a way of showing social inequities within the healthcare system.
The income of a location or neighborhood has a great influence on whether or not a
pharmacy desert might exist. Low-income census tracts or neighborhoods might not be able to
provide the necessary financial resources needed to keep community pharmacies in business
(Gebhart 2019). Independent pharmacies are privately owned, often pharmacist-owned,
businesses which are single store operations (Riemschneider 2010). A chain pharmacy is an
5

organization in which more than two pharmacies are operating under the same name (Miller and
Goodman 2017). Independent pharmacies, which generally do not have a budget as large as their
chain company counterparts, receive the same reimbursement amounts for government
insurances such as Medicare and Medicaid. Medicare and Medicaid reimbursements, which are
the amounts that the federal government is willing to pay a pharmacy for the medications of
beneficiaries of Medicare or Medicaid insurances, are beneficial to pharmacies which have other
sources of income for their business. For independent pharmacies, many of which are in
neighborhoods with populations that have a large amount of Medicaid or Medicare beneficiaries,
the reimbursement rates alone are not enough to keep the business running, hence the need for
better and higher reimbursement rates. For an independent pharmacy to thrive as well as the
chain pharmacies, the community around the pharmacy is needed to do their part in keeping the
business running monetarily, even with products other than pharmaceuticals. There is a
correlation between low-income populations and populations with majority government
insurance, therefore, these pharmacy locations would not receive the monetary income from their
residents that they would in higher income locations (Keisler-Starkey and Bunch 2021).
Therefore, determining where these pharmacy desert locations are can provide a better
understanding of the needs and potential improvements of the neighborhood.
Identifying what qualifies a neighborhood to be a pharmacy desert has been studied and
grouped by a plethora of topics. Previous studies on pharmacy deserts focused on social
determinants and inequities of accessible pharmacies (Guadamuz, et al. 2021; Wisseh, et al.
2020; Ikram, Hu and Wang 2015), spatial accessibility of pharmacies based on travel time
(Ouma, et al. 2021; Weiss, et al. 2018), and distribution of pharmacy types (i.e., community,
retail, etc.) (Jagadeesan and Wirtz 2021; Kelling 2015). The inequities of pharmacy access are
6

most prevalent in areas where there are low-income populations and low rates of vehicle
ownership. Both of these factors contribute to the fact that pharmacy shortage areas can occur
from the lack of financial support from the community. Communities which have a higher
percentage of the population enrolled in government insurances affect the monetary intake of
pharmacies, especially independent pharmacies, because of the Medicare and Medicaid
reimbursement rates. Not only will having a lack of vehicle ownership within a census tract
affect the accessibility of pharmacies for the population but also the extended travel times and
distances to reach the closest pharmacy has an effect on the accessibility of those living in these
neighborhoods or census tract as well.
In this work, the term “pharmacy shortage area” will be used rather than “pharmacy
desert”. Using the term shortage area allows for locations to be considered in this category if the
distance to the nearest pharmacy is larger than a certain threshold for the type of tract it is (i.e.,
urban, rural, or suburban). Rather than considering a tract as a desert, the term shortage area
allows for the presence of pharmacies within or near the shortage areas but an inadequate access
to these locations based on the distance traveled by residents of these tracts.
1.3. Study Area
To present the data on pharmacy shortage areas, where they are, what they mean, and
who are affected, the study area needed to be large and include a wide range of locations.
Starting from the tract level and increasing in size to the national level can provide locations of
shortage areas which is of importance to deciding where there should be increased pharmacy
locations. Having the study area reach the entire nation informs all those living in the United
States of their status of whether they are living in a shortage area or not and can give insight to
those who depend on pharmacies of whether their neighborhood is medically beneficial to them
7

or not. The study area of this project includes the contiguous United States, Alaska, Hawaii, and
the District of Columbia. The US territories are not included in this project’s study area.
1.4. Federal Policies
There are federal policies that apply to pharmacies and pharmaceutical services or
products which can affect pharmacy patients and how they receive or interact with these
products. The United States Food and Drug Administration (FDA) and the Drug Enforcement
Agency (DEA) are both federal administrations and agencies which monitor the distribution of
pharmaceutical products in order to provide safety and reduce harm for those receiving such
products. Many of the policies created within this administration and agency are still active
within pharmacies and affect how drugs are sold and what services are provided within
pharmacies. Consultation with a pharmacist is a free pharmacy service that is useful to those who
do not have insurance or would have to pay copays and large payments to a primary healthcare
facility for such service. Federal policies allow for patient consultation by a pharmacy to be
accessible and free to the public.
The Food and Drug Administration (FDA) regulates policies which pertain to pharmacies
because pharmacies sell of pharmaceutical drugs. Specifically, the Food, Drug and Cosmetic Act
was put into place to replace and strengthen past legislation on product branding and misleading
food and drug labels (Leelamanthep and Sergent 2021). The Food, Drug, and Cosmetic Act
allows for the criminalization of false claims and creates a requirement of ingredients,
indications, and warnings to be listed on the product’s label. Other policies including the
Durham-Humphrey Amendment separate drugs into two categories: over the counter and
prescription drugs. The Kefauver-Harris Amendment establishes a framework process in order to
get a drug approved to be consumed. This framework includes multiple phases of trials and
8

lengthy research done by the FDA. The Comprehensive Drug Abuse Prevention and Control Act,
which initiated the creation of the Drug Enforcement Administration (DEA), separates all drugs
into six classes. Beginning with schedule (class) one drugs which have a high potential for abuse
and are illegal in the United States (i.e., Marijuana, Heroin, and LSD), reducing in abuse
potential to schedule five drugs which include limited levels of narcotics (i.e., cough
suppressants with <200mg of Codeine per 100mL) (Diversion Control Division - Drug
Enforcement Agency, n.d.).
Amendments and Acts which apply directly to the pharmacies include the Omnibus
Reconciliation Act which allows individual states to create local standards on pharmacists
providing patient consultation on medications. The goal of this amendment is to increase
pharmacy-patient interactions through algorithms which detect drug interactions, allergies, dose,
duration, and duplications in the medications given to a patient (Leelamanthep and Sergent
2021). The pharmacist, through this act, is allowed the final say in determining what is best for
each patient, whether that be a change in dosing, therapeutic substitution, etc. Another act which
directly affects pharmacies is the Medicare Modernization Act which, funded by the government,
provides an opt-in option for patients who are “economically struggling” to pay reduced costs for
medications, subject to annual check-ups (Leelamanthep and Sergent 2021).
All of the aforementioned laws and policies have shaped the way medications are legally
distributed in the US. The process in which medication compliance is conducted, the goal of
which is to prevent patients from being harmed by medications, is a strategy that is currently
used in pharmacies, hospitals, and doctors’ offices and is used to prioritize the patient’s overall
wellbeing.
9

1.5. Thesis Structure
This thesis consists of five chapters including this Introduction, Related Work, Data and
Methods, Results, and Conclusions, all of which provide background information, analysis, and
explanation of results of the web application presenting pharmacy shortage areas.. Chapter 2
provides description and analysis of previous studies which on pharmacy deserts and inadequate
access to pharmacies and healthcare and how they compare to this project. Chapter 3 explains the
data preparation, geospatial analyses, and creation of the web mapping application. Chapter 4
presents the results of the analysis that are shared via the web mapping application which include
the national distribution of pharmacy locations which closed from 2018 to 2020, the newly
opened locations nationally, the pharmacies that stayed active both years nationally, along with
the tracts that are considered shortage areas and the demographics of those areas. Chapter 5
includes discussion of results, potential future work that could be done using the information
found in this study, and limitations of the project.
10

Chapter 2 Related Work
This chapter presents a literature review of past articles which studied pharmacy deserts and how
the studies relate to and differ from the pharmacy shortage areas studied in this project. The
analyses differ from study to study allowing for a well-rounded understanding of the different
types of pharmacy deserts and how the definition of a pharmacy desert can be applied to the
classification of a pharmacy shortage area. Along with the comparison of past articles on
pharmacy desert studies, this chapter describes how urbanicity is categorized in the US and
within this project, the definition of areas that are medically underserved, and the travel distances
to the nearest pharmacy that are considered acceptable by Centers for Medicaid and Medicare
Services (CMS) which is being compared to the travel distances found in this project. This
chapter also discusses methods and issues relating to the creation of web maps and web mapping
applications.
2.1. Pharmacy Access
A “desert” in the context of accessibility refers to a location with little or no accessibility
to a particular social service, due to either limited availability of the service or limitations in the
ability of nearby populations to actually receive the benefit of that service. The different types of
deserts, when applied to pharmacy access, include spatial and physical access, location
availability, and insurance or benefit access. Each of these definitions of a social service “desert”
is distinguished by different service thresholds and methodologies for measuring where the
pharmacy locations are able to be accessed adequately and where they are not. Many previous
research projects done on pharmacy deserts present their own definition of how they determine a
pharmacy desert and their methods to find them.
11

2.1.1. Pharmacy Desert Definitions
Determining the distance to a pharmacy can be found using a nearest within a radius, a
radius which can be set to see what the closest location of a pharmacy is, which can give a
neighborhood an idea of what the general nearest pharmacy location is to them. Wisseh, et al.
(2021), define a pharmacy desert as a location in which the distance to the nearest community
pharmacy is longer than one mile. A community pharmacy can be any pharmacy that is able to
serve a community as opposed to those locations that might not be publicly available such as a
hospital pharmacy. The radius chosen for analysis includes not only distance taken by car but
also by foot. This radius can be changed by different residential categories (i.e., rural, urban,
suburban, etc.), which may be appropriate since there are different typical expectations of travel
distances across rural, urban, and suburban places.
Wisseh et al. (2021) define a pharmacy desert as a location with a distance longer than
one mile to the nearest pharmacy. This comes from the concept of a food desert and has been
altered to work for pharmacy access. Wisseh et al. (2021) also determine pharmacy desert
subtypes which consisted of type one having denser communities than type two. Type one also
includes more Hispanic and non-Hispanic Black residents than type two communities. Not only
was this study the first of their knowledge to present pharmacy deserts within Los Angeles
County based on their definition, but it also was the first to provide analysis on the composition
of these communities.
A one-mile radius can be utilized to define a reasonable distance to travel to a pharmacy.
(Wisseh et al. 2021; Pednekar and Peterson 2018). Pednekar and Peterson (2018) utilize the same
definition of a longer than one mile radius while also adding in that if more than 33% of
residents within a census tract have to travel the full distance of one mile, that census tract is
considered a pharmacy desert. The authors consider a pharmacy desert a location where
12

geographic access to a pharmacy or pharmacy services are lacking or scarce. In addition to using
a one-mile radius, Pednekar and Peterson (2018) assess pharmacy deserts using hot spot analysis.
A hot spot analysis identifies areas of clustering of what is being analyzed, in this instance
pharmacy deserts. Using hot spot analysis and their definition of a pharmacy desert, they find
that every county in Pennsylvania includes at least one census tract that meets their definition of
a pharmacy desert.
Qato et al. (2014) study pharmacy access in Chicago, along with characteristics of
populations with poor access to pharmacies. They define a pharmacy desert using population
income levels and physical access to the nearest pharmacy. Their two-part define requires that
first, at least 20% of a census tract’s residents have a household income below federal poverty
level, and second, that more than 33% of residents within a census tract are more than a mile
from the nearest pharmacy or more than 33% of residents are considered “low vehicle access”
(Qato et al. 2014). Finding a percentage range to determine whether a tract is considered to have
low vehicle ownership or to be considered low income is important to the terminology of a
pharmacy desert. Not only do the residents within pharmacy deserts have a lack of access to the
pharmacy locations and their services, but these locations also have to provide for more residents
than those within locations of adequate pharmacy availability. Qato et al. (2014) find that
segregated Black communities, low-income communities, and federally designated Medically
Underserved Areas (MUAs) disproportionately include more pharmacy deserts in their
communities. They suggest that policies regarding prescription access should not only focus on
insurance or prescription prices but also incorporate pharmacies more in locations of pharmacy
deserts and MUAs.
13

2.1.2. Pharmacy Closure
All pharmacy locations, including independent, grocery, chain, or even hospital
pharmacies, can open and close locations and change their services over periods of time similar
to other retail type stores and chain locations. Loss of pharmacy locations could turn a
community or location which already had poor availability to pharmacy locations and services to
a location or community with no availability. Guadamuz et al. (2020) assess the closing of
pharmacy locations in the US from 2009 to 2015. They find that the risk of closures in urban
areas is significantly higher than in suburban or rural areas. The risk of closure for urban and
non-urban locations is higher for independent pharmacies. Low income, uninsured, and publicly
insured residents and their communities are at high risk for closures as well. These risks are signs
that show the community could become a pharmacy desert if the trends of pharmacy closures
continue.
2.1.3. Insurance Access
Pharmacy closures can be caused by a variety of reasons including funding,
reimbursement costs for Medicare, and sales. Klepser et al. (2011) study whether closures in
pharmacy locations are related to the implementation of Medicare Part D. Implementation of
Medicare Part D allows for the lowering of reimbursement rates for pharmacies which affected
the sales of medications and profits of pharmacies. These lowered reimbursement rates affect
independent pharmacies the most and show that before Medicare Part D, there was a steady
increase of locations, after the implementation of this Medicare plan, there was a significant
decrease in the number of independent locations.
14

2.1.4. Medication Access
Pharmacies in some states within the US provide behind-the-counter medications which
are open to the public and do not require a prescription for purchase. Some of these medications
are emergency contraceptives, Narcan, insulin, Pseudoephedrine, syringes, and Schedule V
cough syrups. Each of these drugs may be purchased without a prescription because each is used
in an emergency and the availability of pharmacist consultation minimizes the risk in taking
these drugs incorrectly (Pray and Pray 2011). In some states, non-prescription behind-the-
counter drugs including Pseudoephedrine products and syringes require the patient to present a
government-issued ID before sale.
Foster et al. (2006) survey 426 women via 25 independent pharmacies in California.
California was chosen because it was one of only eight states at the time which provided
emergency contraceptives without a prescription. The goal was to tell if there were any
boundaries to accessing emergency contraceptives and how the women found out about
receiving these emergency contraceptives from their community pharmacies (Foster et al. 2006).
The study finds that over 80% of those surveyed thought it was very important to be able to have
access to these contraceptives and that speaking to a pharmacist was also very helpful. Being
able to access these behind the counter medications without a prescription could decrease the
time between the patient receiving the medication. Some drugs are life threatening if taken
improperly, and others have short efficacy periods, such as emergency contraceptives which are
only effective within 72 hours after unprotected intercourse or failure of birth control, hence the
need for these medications without requirement of prescription (Weismiller 2004). Pharmacies
providing these medications are also more convenient for those who need them with the
amenities provided such as longer hours and drive thru access.
15

2.2. Spatial Accessibility to Healthcare Services and Facilities
Geographic information-based analysis methods (i.e., cluster/hot spot, provider to
population, etc.) are useful ways of determining pharmacy access and pharmacy deserts when
analyzing the pharmacy locations spatially. Some of these analyses, which can be used to
determine and present pharmacy deserts, include Euclidean distance, two-step floating catchment
area, three-step floating catchment area, and more. Each of these different analysis methods
could be more beneficial depending on what outcome is needed.
2.2.1. Methods
When determining spatial accessibility to healthcare facilities and resources, geographic
information analysis methods are prominent in providing this information. Provider-to-
population ratio provides a ratio which details whether an area is considered a shortage of
healthcare providers or resources based on the population size of the area and the number of
physicians in the area. This ratio does not, however, account for the actual interaction between
specific providers and individuals in the population, making them a rough estimate of shortage
areas. Ouma et al. (2021) applies the provider-to-population ratio to accessibility to Ugandan
healthcare facilities and presents the disparities between the areas with lack of provider service
and the already drawn administrative boundaries. For the purposes of the study herein, applying
a provider-to-population ratio across the study area of the entire US would not be beneficial.
With patients crossing health districts at a small scale, as in Ouma et al. (2021) the ratio may be
minimally skewed, however, with a large-scale study area, this method would not be best for
producing accurate results. Some patients who suffer from severe diseases are willing to travel to
other states or across the country to receive treatment. Using provider-to-population ratio across
the nation would affect the outcome of the ratio being that populations as a whole might not use
16

the providers in their areas due to various circumstances, including health insurance network
definitions.
A gravity model is a model used to predict behaviors in an analysis such as volume of the
flow of goods, for example provider to population flow. A two-step floating catchment area
(2SFCA) analysis is a version of a gravity model which measures spatial accessibility. The
2SFCA first assesses physician availability to its population, then added in are the population
values, supplies and the area in which the physician would be providing their services. The ratios
of these interactions are summed while travel time and physician demand (Luo and Wang 2003).
While the provider-to-population ratio does not account for a patient potentially going to a
provider outside of their health district, utilizing this method within a 2SFCA analysis can
increase the trustworthiness of the resulted values. Rekha et al. (2017) assign a spatial
accessibility value when combining the provider-to-population ratio into the 2SFCA.
The various different versions of the floating catchment area methods are best for smaller
study areas and projects with extended time and resources. This project incorporates a national-
level study area and applying these methods to this large study area would require more time and
resources than were available during the time of this project.
Using a Euclidean distance within a method finds the straight-line distance between two
points. Euclidean distances can be used to find spatial accessibility to healthcare facilities by
finding the nearest location to a neighborhood or household. This method does not, however,
account for the road network nor any travel barriers (Ouma et al. 2021). This method also does
not account for the curvature of the earth which is best when the study area is smaller and does
not reach a wide distance. Applying a Euclidean distance measure to an analysis applied to very
long distance is not accurate because it does not account for how the earth curves and assumes a
17

flat surface. There may be errors in results of using this method because the distance may be
shorter or longer depending on geographic barriers within the area (Jagadeesan and Wirtz 2021).
However, Euclidean distance is a relatively faster measurement in a GIS than a network analysis
using a road network. For this project, which covers the entire nation but measures many
relatively short distances, using the Euclidean distance measurement rather than distance along a
road network is much more feasible and offers adequate accuracy for the estimates required
herein.
A fourth method to determine spatial accessibility to healthcare facilities involves finding
clusters of healthcare locations, socioeconomic factors which affect accessibility to these
locations, or even clusters of areas with minimal or adequate access to these healthcare facilities.
Hot spot analysis is used to define areas of high or low values for a given topic, for example
pharmacy access. Pednekar and Peterson (2018) use hot spot analysis to determine where the
areas of high concentrations of pharmacy deserts are and the racial/ethnic groups present at these
clustered locations along with what type of pharmacy, chain or independent, are most prevalent
as well. They found that rural areas of predominately White populations are more likely to be
pharmacy deserts. This hot spot analysis method is useful to show where there is a lack of
pharmacy locations and who are affected, however, to determine pharmacy access across the
entire nation, this method would not be best. Spatial access includes variables other than
pharmacy placement and the population surrounding these locations, not only distance to these
healthcare locations but also the socioeconomic factors present (i.e., vehicle availability, income,
etc.) that could affect a patient’s access to their local healthcare facility.
Oliveira, Singletary, and Lu (2021) study pharmacy location distribution in Georgia by
using cluster analyses. Their hot spot analysis showed a cluster of pharmacy locations in and
18

surrounding the state capital, Atlanta. This method provides an accurate representation of where
pharmacy locations may be needed and where there are already enough to service a community.
However, because as mentioned previously, the other factors of accessibility are not
implemented in these methods which would not be best for a spatial access study such as this
one.
2.3. Urbanicity
Rural-Urban Commuting Area (RUCA) Codes are used to define metropolitan and
micropolitan areas by urbanization, population density, and commuting. RUCA codes consist of
primary and secondary codes as shown in Table 1. Primary codes refer to the primary
commuting share of the location meaning, in a metropolitan area, when divided into smaller
sections, these smaller sections have a majority of one of the primary code designations.
Secondary codes are subdivisions of the primary codes which is where the classifications may
overlap for the areas and the second largest commuting share. The designation of the
categorizing of a census tract is done using these codes by US Department of Agriculture. Code
one of the primary RUCA codes relate roughly to metropolitan core areas, or urban areas, codes
two through nine consist of locations where there were high commuting areas within a small-
town core or “nonmetropolitan adjacent” codes, or suburban areas, and code 10 being the rural
areas (U.S. Department of Agriculture 2020). After studying the location of these designations
overlayed on a base map of the US, many locations which fell into codes two through nine were
found to be more rural than suburban. This led to the determination of what this project would
use to categorize the census tracts into urban, suburban, and rural areas.
Table 1: Primary and Secondary RUCA codes from the US Department of Agriculture. (U.S.
Department of Agriculture 2020)
19

Code Classification
1 Metropolitan, primary urbanized area
1.0
1.1
No additional code
Secondary flow (30%-50%) to larger urban area
2 Metropolitan, high commuting (>30%) to an urbanized area
2.0
2.1
No additional code
secondary flow (30%-50%) to larger urban area
3 Metropolitan, low commuting (10%-30%) to an urbanized area
3.0 No additional code
4 Micropolitan area, primary flow within large urban cluster
4.0
4.1
No additional code
Secondary flow (30%-50%) to an urbanized area
5 Micropolitan area, high commuting (>30%) to large urban cluster
5.0
5.1
No additional code
Secondary code (30%-50%) to an urbanized area
6 Micropolitan area, low commuting (10%-30%) to large urban cluster
6.0 No additional code
7 Small town, primary flow within small urban cluster
7.0
7.1
7.2
No additional code
Secondary flow (30%-50%) to an urbanized area
Secondary flow (30%-50%) to a large urban cluster
8 Small town, high commuting (>39%) to small urban cluster
8.0
8.1
8.2
No additional code
Secondary flow (30%-50%) to an urbanized area
Secondary flow (30%-50%) to a large urban cluster
9 Small town, low commuting (10%-30%) to small urban cluster
9.0 No additional code
10 Rural areas, primary flow to tracts outside urban area or urban cluster
10.0
10.1
10.2
10.3
No additional code
Secondary flow (30%-50%) to an urbanized area
Secondary flow (30%-50%) to a large urban cluster
Secondary flow (30%-50%) to a small urban cluster
99 Not coded: zero population, no rural-urban identifier

The shortage area designation within this project is done on the census tract level which
caused the need to determine which tracts are considered urban, suburban, and rural. For the use
of this project, it was determined that population density measures were best to apply
classifications of urbanicity more correctly to the census tracts for shortage area designation
using the formula:
20

𝑇𝑜𝑡𝑎𝑙 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝐵𝑙𝑜𝑐𝑘 𝐺𝑟𝑜𝑢𝑝 𝑇𝑜𝑡𝑎𝑙 𝑆𝑞𝑢𝑎𝑟𝑒 𝑀𝑖𝑙𝑒𝑠 𝑜𝑓 𝐵𝑙𝑜𝑐𝑘 𝐺𝑟𝑜𝑢𝑝 = 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝐷𝑒𝑛𝑠𝑖𝑡𝑦

These population density measures, as shown in Table 2, used the total population and
total square miles of each census block group. With this new measure of urbanicity, a population
of over 3000 per square mile were considered urban, block groups with populations from 1000-
3000 per square mile were considered suburban, and block groups with a population less than
1000 per square mile were considered rural. The census tracts urbanicity is then decided based
on the majority urbanicity at the block group level.
Table 2: Population Density examples for a few selected census tracts
FIPS Total
Population
Population Density (Per Sq.
Mile)
Area (Sq.
Mile)
1001020100 1993 525.7633 3.790678953
1001020200 1959 1525.639 1.284052281
1001020300 3507 1698.004 2.06536594
1001020400 3878 1573.236 2.464982849
1001020500 10596 2405.455 4.404987977
1001020600 3668 1181.366 3.104880795
1001020700 3586 414.4332 8.652782175
1001020801 3196 66.60838 47.98194586
1001020802 11142 151.2318 73.6750039
1001020900 6143 54.34644 113.0340866
1001021000 2527 16.91777 149.369526

2.4. MUAs and CMS
MUAs are government-recognized areas which have a shortage of medical professionals.
Medical professionals can include doctor, nurses, and pharmacists. Hospitals or health clinics in
21

these locations can apply to be a Federally Qualified Health Center which can receive increased
Medicare reimbursements (Health Professional Shortage Area - HPSA Acumen, n.d.). MUAs
can include areas as small as a group of counties to an area as large as an entire country (Heath
Resources and Services Administration 2021). Within this study, MUAs are compared to the
locations of shortage areas to determine if there is a correlation between higher travel distances
to pharmacies and locations of shortage of medical professionals.
CMS have distance thresholds that Medicare Part D sponsors must adhere by
establishing pharmacy networks with enough conveniently located retail pharmacies to ensure
sufficient access to its beneficiaries. A pharmacy network is a group of pharmacies which have a
contract with an insurance plan. Those who are enrolled in that insurance plan can receive
reduced prices for their prescriptions at those pharmacies which are included in the contract.
Medicare part D is an insurance plan in which the beneficiary pays an insurance carrier a
premium to receive reduced copays on prescriptions. Medicare Part D, like other insurance plans,
requires a deductible and initial coverage copay until a certain amount is reached. Unfortunately,
within the Part D plan there is a coverage gap which is implemented once the initial coverage is
reached. This coverage gap requires the patient to pay 25% of the retail price of all medications
until the out-of-pocket limit is reached (Boomer Benefits 2011). Medicare Part D beneficiaries
living in the sponsor’s service area should live certain distances from a retail pharmacy that
participates in the sponsor’s network depending on urbanicity of the area. For urban areas, on
average, 90% of the beneficiaries in the sponsor’s service areas should live within two miles
from a retail pharmacy that participates in the sponsor’s network. For suburban areas, on average
90% of the beneficiaries should live within five miles from a retail pharmacy that participates in
the sponsor’s network. For Rural areas, on average, 70% of the beneficiaries should live within
22

15 miles from a retail pharmacy that participates in the sponsor’s network (Department of Health
and Human Services and CMS 2008).
The distances that CMS considers reasonable distances for their Medicare part d
beneficiaries to travel to the nearest pharmacy are what are being applied to create CMS shortage
areas. In this project, the distances of 2 miles for urban, 5 miles for suburban and 15 miles for
rural tracts are what are considered the thresholds for what is considered a census tract a shortage
area or not for CMS. This is not to be confused with the shortage areas designations that CMS
has created within their own projects and analyses.
2.5. Web Maps and Applications
Web mapping was introduced in the early 1990s with a website that provided an
interactive map for the user with characteristics much like the current web maps but with much
slower speeds and less manipulations. The Xerox Palo Alto Research Center initiated a web
mapping movement with their Map Viewer which had minimal manipulation by the user other
than zooming and adjusting the visibility of rivers and borders (Haklay, Singleton, and Parker
2008). With the era of technology in 1993, when this Map Viewer was implemented, navigating
through the map included delays for the user. The speed of the Map Viewer tool would be
prevalent for the next decade with web mapping technologies. With the development of web
mapping increasing, locations and directions were brought to the forefront with the
implementation of Multimap.com, MapQuest, StreetMap, Yahoo Maps, and others. At the time
of implementation, these websites incurred limitations for the user because the sites could only
provide pre-loaded information. Bt 2005, internet GIS had become more refined, however the
limitations prevented the expansion and growth of these maps (Haklay, Singleton, and Parker
2008). The complexity of using these web maps was still present because when these
23

technologies were introduced, internet services were not as advanced as they are now. These web
mapping sites and creators would become increasingly more advanced and intricate as time went
on.
Web mapping provides different user manipulation depending on the type of service
indicated for the client (Adnan, Singleton, and Longley 2010). With thin client architecture,
majority of the processing is done on the side of the processor which allows the client to take
requests of the mapping tool and receive HTML responces (Agrawal and Gupta 2017). This type
of architecture does not utilize the client as much as it could and results in a limitation in
functionality of the server being that any request made by the client, including zooming, is done
through the server. With thick client architecture, the client has more ability to processes with
plug-ins and can reduce the utilization of the processors side. With the client having more
abilities, this could be beneficial if there are connection issues with the server. A hybrid
architecture of these two include data related tasks done by the processor and user interaction
tasks done by the client (Agrawal and Gupta 2017).
A benefit of web maps includes information being updated in real time and no need to
create a paper or stagnant map. An example of this is being able to show real time changes in an
event like an election. With the advantage of the ‘refresh’ button, users are able to receive
information as it is uploaded (Neumann 2017). Digital or web mapping allows an unexperienced
user to create a map using something as simple as an excel worksheet into an Esri online map
(Totman 2012). Another advantage of web mapping is having the ability to alter the privacy of
the map. These maps, through Esri’s online mapping software, can be closed off to only allow
certain members of your organization or be made open to the public. Lastly, an aspect of web
mapping that may not be available on paper maps is the ability to place oneself in the first person
24

view of the map. If zoomed in far enough on a web map, the user can reach the street level, such
as in Google Maps, you can travel down a street at eye level.
With web mapping, creation involves multiple layers of data along with the availability
of zooming in and out to different extents. Designing a web map allows for a more flexible focus
area because of the ability to update the map and allows the user to decide where to zoom in or
out. The ability to turn on and off certain data layers can allow the user to adjust the hierarchy of
the map and benefit their specific goals of the map. With designing a static or paper map, there is
usually one goal in mind and it is portrayed on the map. One aspect of the map, whether that be
the streets, localities, businesses, or other attractions, is the focus. Visual hierarchy is also
important in static maps because showing streets rather than attractions can move the user’s
attention in the wrong way depending on the focus of the map (Code 2020).
Web mapping has advanced significantly since its implementation and has become an
application for purposes other than just providing a visualization of an area. Interactive web
maps allow the user to manipulate a map to benefit their needs. Different from a static web map,
interactive maps allow for the changing of features on the map, scales, base maps, and adjust
features. This type of mapping is beneficial to this study by allowing the user to navigate the map
to any location within the country to visualize the local pharmacy shortage areas.

25

Chapter 3 Data Sources and Methods
This chapter provides the step-by-step methodology of the analysis used to find pharmacy
shortage areas across the study area if the US and the creation of the web mapping application.
The data’s origin, preparation, and use are provided here as well. An explanation of the approach
used is described here as well as how a shortage area is defined.
3.1. Overview of Methods
The methodology approach used in this study follows the workflow as shown in Figure 1.
The process starts at the census block group level and expands to the national level. Each census
block group is given a centroid point which is then used to find the nearest pharmacy location for
both 2018 and 2020. After determining the urbanicity of each tract (i.e., urban, rural, or
suburban) by using a population density measure, each category of urbanicity is given a
threshold of what considers the tracts a shortage area or not based on the majority at the block
group level.
26

Figure 1:Workflow diagram of the full methodology process of analysis
Once each tract is given an urbanicity category, based on the distance to nearest
pharmacy of the block group centroid, if more than 500 residents or more than 33% of the tract’s
population has a travel distance to the nearest pharmacy over a certain threshold, the tract is
considered a pharmacy shortage area. For urban tracts, over 500 residents or over 33% of the
population traveling a distance of over 1 mile, for suburban a distance over 2 miles, and for rural
a distance of over 10 miles considers the tract a shortage area for pharmacies. Disregarding the
urbanicity, if a tract is considered low vehicle (i.e., over 100 households within a tract do not
own a vehicle) or low income (i.e., over 20% of the population lives under the poverty line
(<100% FPL)) it is considered a shortage area if the distance for over 500 residents or over 33%
27

of the population has a travel distance farther than ½ mile. Data from the Health Resources and
Services Administration identify areas designated as Medically Underserved Areas (MUAs).
MUA is a federal designation used to identify a shortage of primary health care services for
residents within a geographic area. These include areas that have too few primary care providers,
high infant mortality, high poverty or high elderly population.
Once each tract is given a shortage area or a non-shortage area designation, statistics are
then produced at the block group, tract, county, and state levels to provide analysis of what
groups of demographics are affected by a shortage of pharmacies. Finally, a web map is created
using all data layers including pharmacy point data, block group, tract, county, and state level
boundaries.
3.2. Data
The different data layers used in this project included table data, polygon data and point
data as shown in Table 3. The pharmacy data used in this study came from the National Council
for Prescription Drug Programs dataQ
®
. This data was received with attributes including
pharmacy name, type (independent or chain), address, amenities (drive thru access, walk in
clinic, etc.), year opened, and provider ID. This data was received for the years 2018 and 2020.
The data within dataQ

is updated weekly and monthly for up-to-date changes with ownership or
closings. The pharmacy data used was from July of both years 2018 and 2020.
28

Table 3: Data types and sources that are used in this project
Dataset Type Source
Pharmacy Locations Table
National Council for
Prescription Drug Programs
dataQ
®

Census Block Group Boundary
Layer
Polygon
U.S. Census Bureau
TIGER/LINE Shapefiles
Census Tracts Polygon
U.S. Census Bureau
TIGER/LINE

Shapefiles
County Boundary Layer Polygon
U.S. Census Bureau
TIGER/LINE

Shapefiles
State Boundary Layer Polygon
U.S. Census Bureau
TIGER/LINE

Shapefiles
American Community Survey
(ACS) demographic and
economic characteristics
Table
American Community Survey
(2015-2019)
MUAs Table
Health Resources and Services
Administration

The boundary layers for the census block groups, tracts, counties and states are shapefiles
which were retrieved from TIGER/Line

shapefiles from the Census Bureau for 2019. The
TIGER/Line

data boundaries do not include demographic information, but information of the
size and contents of the boundaries such as amount of land or water within the boundaries. The
census boundaries are chosen every 10 years, however, the TIGER/Line

files through the
census bureau are updated yearly to adjust for legal boundaries as of January 1, 2019
(TIGER/Line

Shapefiles). This data was downloaded in shapefile form.
The data used to populate the boundary layers came from the American Community
Survey (ACS) 5-year estimates from 2015-2019. These estimates were used to provide
population data and demographic information at all levels. Some of the attributes in these layers
included number of and percentage of population with Medicare, Medicaid or public insurance,
29

no insurance, percent Black, White, or Latinx, and other demographic and population data. The
attributes used in this project are shown in Table 4 below.
Table 4: Attributes used from the ACS 2015-2019 estimates in this project
Attribute Type Use
Population Number Population of the block
group, tract, county or
state
100% FPL Percentage Percent of population
making an income under
100% of the federal
poverty level
150% FPL Percentage Percent of population
making an income under
150% of the federal
poverty level
Percent White, Black, Latinx Percentage Percent of the population
that is White, Black, or
Latinx
Percent no vehicle Percentage Percent of population
without a vehicle
Percent no insurance Percentage Percent of population
without insurance
Median Household Income Number Median household income
at the tract level
Percent Medicare, Medicaid Percentage Percent of population with
Medicare or Medicaid
Medically Underserved Areas Number Number of tracts that re
considered MUAs
Population Over 65 Number Population of persons 65
years and older
The NAD 1983 (2011) datum and geographic coordinate system are used throughout this
study for all data layers. This choice provides reasonably accurate geodesic distances over a
larger study area, as opposed to a projected coordinate system which would be beneficial for
30

smaller-sized study areas. A North American datum and coordinate system accounts for the
curvature of the earth in this region.
3.3. Data Preparation
The data used within this project required data wrangling and management before being
used to run analysis. The pharmacy data required the most manipulation with its geographic
location with 68,590 locations. Once the addresses of the pharmacy data were provided from the
NCPDP, the file of these pharmacy locations for both 2018 and 2020 were downloaded into a
new project in ArcGIS Pro. The locations were then geocoded using the geocoder tool in ArcGIS
Pro and a coordinate was provided for each of these locations. Through the geocoding process,
102 locations in the 2018 and 105 in the 2020 datasets were found unmatched. The locations
which were not found automatically using the geocoding tool and were placed manually by
typing in the address into Google Maps and comparing the base map from Google Maps to the
base map in ArcGIS Pro.
Some locations of pharmacies may have closed in one location and reopened in another
location with the same provider ID and were counted as one pharmacy. Some pharmacy
locations contained an address spelling error, for example one year the street address being
spelled “moffett” and the next “mofat”. Lastly, some pharmacy locations had the same address
but different coordinates for both years. To account for these errors in the data, Data Analyst
Andrew Shooshtari applied a workflow to combat the errors in the changes over the years for the
pharmacy locations as shown below in Figure 2.
31

Figure 2: Workflow used to remove geographic errors from pharmacy locations (Source: Data
Analyst, Andrew Shooshtari).
Once the pharmacy locations were run through the error removal workflow, the file was
returned and placed back into ArcGIS Pro. The pharmacy location data layer was then
downloaded and displayed in the project using the Display XY Data tool for each year.
The block group, tract, county, and state layers were then downloaded from the
TIGER/Line

Shapefile website into the ArcGIS project with the pharmacy data. Centroids of
the block group layer were created by adding two fields to the attribute table labeled ‘x’ and ‘y’
and were made to be a ‘double’ field type. Both the ‘x’ and ‘y’ fields were used in the Create
Geometry tool as the x-coordinate of centroid and y-coordinate of centroid respectively. The
coordinate system was set to NAD 1983 (2011). Once the coordinates were placed for the
32

centroid of each block group, the attribute table of the block groups was then exported as a table
in the same project content pane. Once exported, the XY data was displayed into the NAD 1983
(2011) coordinate system to place point data of the centroid points.
The centroid points of the block group were used in the near tool to find the nearest
geodesic pharmacy point location. Using this tool added a field to the attribute table which
contained the distance in meters to the nearest pharmacy location. This field was then converted
to miles by adding an additional field and calculating the field in meters by dividing the original
distance field by 1,609. This was applied to both the 2018 and 2020 pharmacy point data
locations.
The centroid point data layer, now with additional fields of the distance to nearest
pharmacy for 2018 and 2020, was run through the Spatial Join tool to join the pharmacy
characteristics including name, amenities, status, etc. This was done twice, separately, to join the
GEOID of the block group to its nearest pharmacy information and the results were separate. The
results of each of the spatial join layers were then joined back to the original block group
centroid layer using the Join Field tool. This tool allowed for the joining of specific fields to
reduce duplication of fields and add all pharmacy characteristics of each location nearest to each
block group by its GEOID.
The Geographic Identifiers or GEOIDs are numbers that range from two to 12 digits that
are assigned to a specific block group, tract, county, or state. As shown in Table 5, at the block
group level there are 12 digits which includes two digits for the state, five digits for the county,
six digits for the tracts, and one for the block group. In the United States, every state has their
own specific GEOID or FIPS code. Within each state the counties have an assigned three-digit
33

number which when paired with the state digits leads to that specific county. The same is done at
the tract and block group level adding six and one digit respectfully (US Census Bureau 2021).
Table 5: GEOID Structure for Geographic Areas. (Source: Census.gov)
Area Type Geoid Structure Number of Digits Example
Geographic Area
Example GEOID
State State 2 Texas 48
County State+County 2+3=5 Harris County, TX 48201
County
Subdivision
State+County+Cousub 2+3+5=10 Pasadena CCD,
Harris County, TX
4820192975
Places State+Place 2+5=7 Houston, TX 4835000
Census Tract State+County+Tract 2+3+6=11 Census Tract 2231
in Harris County,
TX
48201223100
Block Group State+County+Tract+Block
Group
2+3+6+1=12 Block Group 1 in
Census Tract 2231
in Harris County,
TX
482012231001
Block* State+County+Tract+Block 2+3+6+4=15 (Note –
some blocks also
contain a one
character suffix (A, B,
C, etc.)
Block 1050 in
Census Tract 2231
in Harris County,
TX
482012231001050
Congressional
District (113th
Congress)
State+Cd 2+2=4 Connecticut
District 2
0902
State Legislative
District (Upper
Chamber)
State+Sldu 2+3=5 Connecticut State
Senate District 33
09033
State Legislative
District (Lower
Chamber)
State+Sldl 2+3=5 Connecticut State
House District 147
09147
ZCTA ** Zcta 5 Suitland, MD
ZCTA
20746
* The block group code is not included in the census block GEOID code because the first digit of
a census block code represents the block group code.
** ZIP Code Tabulation Areas (ZCTAs) are generalized areal representations of United States
Postal Service (USPS) ZIP Code service areas.

34

The file of information with each block groups nearest pharmacy and all of the
characteristics of that pharmacy for years 2018 and 2020 were then sent to Data Analyst Andrew
Shooshtari. An API pull of the census data were done using an R package called ‘tidycensus’
with R Studio by Post-Doctoral Research Fellow, Jenny Guadamuz. The summary statistics
provided from the demographic and population information were done through SAS 9.4 by Data
Analyst Andrew Shooshtari.
3.4. Analysis of Shortage Areas
For this project, the definition of a ‘pharmacy shortage area’ is a census tract in which
more than 500 residents or over 33% of the tract’s population have to travel a distance farther
than one mile for urban classified census tracts, two miles for suburban classified tracts, and ten
miles for rural classified tracts. The urbanicity of the tracts is based off of the population density
found at the block group level. Tracts with a population density of over 3000 persons per square
mile are considered urban, 1000-3000 persons per square mile are considered suburban and less
than 1000 persons per square mile are considered rural tracts. Tracts which are defined as low
income and low vehicle ownership are considered shortage areas if the distance to the nearest
pharmacy is longer than one-half mile. A tract is considered low vehicle if more than 100
households in the tract do not have vehicles. A tract is considered low income if 20% of the
population within the tract lives below the poverty line (<100%FPL). It should be noted that
although this data used ACS data, not full decennial census information, margins of error for the
population and social attributes within the ACS data were not put into consideration.
Only retail pharmacies were used within this study. Of those retail pharmacies, only
pharmacies with a provider type of 1 and a dispenser class of 1, 2 or 5 were used. Pharmacies
with a provider type of 1 are community or retail pharmacies, and pharmacies with a dispenser
35

class of 1 are independent pharmacies defined as one to three pharmacies with common
ownership, dispenser class 2 are chain pharmacies defined as a group of four or more pharmacies
under common ownership, dispenser class 5 are franchise pharmacies defined as independently
owned pharmacies with a single franchise agreement with a franchisor where the franchisee
receives training and support from the franchisor. (Research Data Assistance Center (ResDAC)
2013)
To create a centroid for the census block group layer, two fields labeled ‘x’ and ‘y’ were
added to the attribute table of the block group layer within ArcGIS pro. The Create Geometry
tool was used to populate the ‘x’ and ‘y’ fields with coordinates of the centroid point of each
block group. For each block group and census tract, Data Analyst Andrew Shooshtari joined
together ACS population and demographic information. Using SAS 9.4, Data Analyst Andrew
Shooshtari produced the statistical results and merged to the GEOID of the tract and block group
layers. This analysis was then taken back into ArcGIS Pro to build the web application.
Once all points were present for both years of pharmacy data, pharmacies with a provider
type of 1 and a dispenser class of 1, 2, and 5 were separated and to be used for the rest of the
analysis. These pharmacies were chosen to be separated to remove those pharmacies that are not
open to the public. Removing those pharmacies which are not accessible to the public eliminates
the possibility of a block groups nearest pharmacy not actually being able to be accessed by its
residents. The centroid point of the block group was used in the “Near” tool to find the geodesic
distance to the nearest pharmacy location for both 2018 and 2020 pharmacy locations.
The three levels of urbanicity used in this study are urban, suburban, and rural, with
population density levels described above. With each tract having an urbanicity classification,
thresholds are applied for an acceptable distance traveled by residents to the nearest pharmacy. If
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the closest distance to a pharmacy was greater than this distance threshold, the tract was
considered to be a pharmacy shortage area for those who lived in these areas. For urban census
tracts, any distance traveled to the nearest pharmacy farther than one mile is considered a
shortage area, farther than two miles for suburban tracts and farther than 10 miles for rural tracts.
For tracts considered low vehicle or low income, no matter the urbanicity, having to travel
farther than a half mile to the nearest pharmacy labels that tract as a pharmacy shortage area. A
tract is considered a shortage area if over 500 residents, or over 33% of a tract’s residents, are
within a shortage area based on the distances to nearest pharmacy at the block group level. This
means that at the block group level, which is smaller regions than the census tracts, if over 33%
of the populations within the block groups that have to travel farther than one mile for urban
block groups, two for suburban and 10 for rural, at the tract level, the tract is considered a
shortage area. An indicator for each tract was added to the attributes of the tract GEOID with a
text of ‘yes’ or ‘no’ for tracts determined as pharmacy shortage areas or not, respectively.
Once the attributes for each layer, including statistical measures, were joined, and applied
to the GEOID of the block group, tract, county, and state layers, these data were then transferred
into an Excel file. The excel files were then placed within an ArcGIS Pro map project to join to
the 2019 Census boundaries. Joining ACS population and demographic information to the census
boundaries, multiple polygon files, allows for the geographic location of these GEOIDs to be
placed amongst the study area. With each block group, tract, county, and state, once the ACS
information is joined to the polygon boundary layers, these layers are then saved as web layers to
be used in the web tool in ArcGIS Online.
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3.5. Web Map Creation
Creating a web mapping application through ArcGIS online begins with creating an
online map or a web map. The workflow of this creation is shown in Figure 3. This map includes
the layers that can be visible within the application. The data layers which were managed and
edited within ArcGIS Pro and shared to a web layer were added to the web map. These layers
included block group, tract, county, and state boundaries. Within each boundary layer, each
individual county, state, tract, or block group included its own data and statistical information.
The tract layer was duplicated twice to represent the shortage areas in both 2018 and 2020 and to
present the CMS thresholds for 2018 and 2020 as well. Once these data layers were added to the
web map, the title of the fields which include the data were manipulated to provide a “plain
word” description of each field. This step was one to make sure the pop-up window at each layer
was understandable to the viewers of the application. For the state, county and tract levels, the
statistical information includes some percentage results which require a percentage output. Some
fields within these layers were presented as a decimal instead of a percentage. To fix this, the
individual web layers were opened in the contents pane of the ArcGIS and each field presented
as a decimal was multiplied by 100 using the “calculate field” tool. For the pop-up views, in the
field editor, commas to separate thousands were turned on for all fields which weren’t
percentages. All fields with percentages were allowed two decimal places.
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Figure 3: Workflow to Create Web Map and Web Mapping Application
To create the pop up for each data layer, the “enable pop ups” was turned on for all
layers. The title was changed for each data layer depending on the data present. For the county
layer was coded to provide the county name followed by “county”. The state layer was given the
state name as the title. For the tract layers, two were labeled as “Shortage Areas – 2020” and
“Shortage Areas – 2018” to differentiate between the two separate years. The other two tract
layers were labeled as “CMS Shortage Areas - 2018” and CMS Shortage Areas - 2020” to
present what would be considered a shortage area for both years under the CMS thresholds
applied to this project’s methods for shortage area designation. The pharmacy layer was given
the title of the type of pharmacy (chain or independent) to show what each point was. After the
titles were chosen, each layer’s pop-up fields were chosen for what was needed to be presented.
The chosen fields were then organized from top to bottom of in the order of what should be
shown first to last.
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To present the data layers on the map, colors and styles were chosen. The tract layers
were color coded by the attribute (or field) which provided the urbanicity and shortage status of
each tract. Three colors were chosen for the six possible outputs of the urbanicity and shortage
status. The shortage areas for each urbanicity type (urban, suburban, and rural) were all fully
opaque for the color chosen. For the non-shortage areas of the three urbanicity types, the colors
were put at 70% transparent to differentiate between shortage areas and non-shortage areas while
still being able to tell the urbanicity of each tract. For the state layer, a single symbol type was
used in order to have all states the same color. The labels for the states were enabled and the
name field was used for the labels. This allowed for each state to have its name presented within
its boundaries. The same was applied to the county layer as was done for the states layer. The
block group layer was presented using a single symbol style without labels. A visible range was
applied to the block group, state and county layers, this visible range allowed for the
disappearance of one layer when zoomed in to a certain extent. This was done to reduce
confusion of what layer was being presented and to allow for an easier flow of data visualization.
For the pharmacy points, the layer was presented using unique symbols and the colors were
chosen by attribute. The attribute (or field) chosen for the pharmacy layer was the status of the
pharmacy, whether the pharmacy was active for 2018 and 2020, closed by 2020 or newly opened
by 2020. This was done to provide a visual representations of which pharmacy locations have
been created, closed or always active; red for closed, orange for newly opened, and blue for
active throughout both years.
The next and final step in building the web mapping application is to create the actual
application. Using the Create Web Map tool in ArcGIS online, the theme of Launch Pad was
chosen because of the layout which seemed most appropriate for the data being presented. This
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theme provides the widgets to sit along the bottom, a search bar at the top, and the zoom near the
top left. This layout allows for the data presented to be the main focus. after the theme was
chosen, the map which was edited previously was added to the map. The widgets were chosen
and the ones included were the legend, the layer list, and the swipe tool. Once these were all
added, the application was saved and a run through was done to make sure everything was
shown as it was supposed to.
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Chapter 4 Results
This chapter presents the results of the analyses of the distribution of shortage areas nationally
and the web mapping application in which they are displayed. It also shows how the application
works and is used for the goal of this project.
4.1. Shortage Area Results
Census tracts were considered shortage areas if the distance to the nearest pharmacy for
an urban tract is farther than one mile, for suburban tracts farther than two miles, for rural tracts
farther than 10 miles, for low income and low vehicle tracts farther than one-half mile no matter
the urbanicity of the tract. These distances as compared to CMSs reasonable travel distances,
applied as shortage area designation distances, to the nearest pharmacy are shown in Table 6.
Table 6: Shortage Area Designation Distances for Census Tracts

Urban Suburban Rural
Low Income
and
Low Vehicle
Current Project
>1 mile >2 miles >10 miles >1/2 mile
CMS
>2 miles >5 miles >15 miles N/A

In order to show the distribution of shortage areas and provide analysis on where these
are mostly located, the web mapping application was manipulated to show many different
analyses. The distribution of majority Black, White and Latinx populations across the nation and
those that are living in a shortage area are also shown within the web map. A view of shortage
area distribution by race is shown at the national level to show what populations are being
affected most and where. This map also shows the comparison in the shortage area distribution in
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the project compared to the CMS distance thresholds. A census tract is considered to be
predominately one race or ethnicity when one race or ethnicity represents over 50% of the total
tract population, as shown in Table 7. For a tract to be considered diverse, not one race or
ethnicity constitutes a majority. The percentages used in the categorization of majority White,
Black or Latinx populations do not account for the margins of error in the ACS demographics
data.
Table 7: Racial and Ethnic Composition of Census Tracts
Majority Black >50% of the tract population is considered non-Hispanic Black
Majority White >50% of the tract population is considered non-Hispanic White
Majority Latinx >50% of the tract population is considered Hispanic
Diverse No single race/ethnicity holds majority of the tract’s population

On the home view of the web map, the distribution of pharmacy shortage areas is seen
nationally by urbanicity. As shown in Figure 4, the web map shows the urban shortage areas in a
darker pink, and the urban non-shortage areas in a lighter more transparent pink, the suburban
shortage areas in a brown, and the suburban non-shortage areas a lighter more transparent brown,
the rural shortage areas in green, and the rural non-shortage areas in a lighter more transparent
green. The transparency is used to show where the different tracts are by urbanicity while also
showing where the shortage areas are.
The pharmacy shortage areas cover a large portion of the United States when the distance
thresholds are applied. With a large portion of the western half of the nation being considered
rural areas, the shortage areas are very prominent. There are also spots within the large cities
across the nation (Houston, Dallas, Chicago, Los Angeles), where the urban and suburban
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shortage areas are visible at the national level. It is easier to see the rural shortage areas being
that the rural tracts are usually much larger than the urban tracts. Being able to see the urban and
suburban shortage areas at the national level shows there is an abundance at those locations.

Figure 4:Web app showing the shortage area distribution nationally
Compared to the CMS distance thresholds, if applied as pharmacy shortage area
designations, the presentation of shortage areas is significantly less. Within this project, as shown
in Table 4, the distances to be considered a shortage area are a few miles less than compared to
CMS distances for what is a reasonable distance to travel to a pharmacy. The CMS thresholds do
not catch nearly as many shortage areas with what they consider a reasonable distance, as seen in
Figure 5.
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Figure 5: Shortage areas using CMS distances
The web map showing the CMS shortage areas show the distribution of rural shortage
areas prominently, however, the suburban and urban shortage areas are not visible at the national
view. There are significantly fewer shortage areas captured using CMS thresholds which shows
that across the nation, majority of the population should not have an issue finding a pharmacy
location near them. The main issue with the CMS thresholds is the assumption that urban,
suburban, and rural populations are able to travel these distances without putting into account the
fact that some of the population might not have transportation able to travel these distances. With
the travel distance thresholds chosen for this project, low income and low vehicle tracts are
considered and a shorter distance of one-half mile is applied.
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The distribution of White, Black, and Latinx populations across the nation are shown in
Figure 6 with the dark opaque colors portraying the shortage areas and light and transparent
colors showing the non-shortage areas. Like the rural shortage areas in the home view of the web
map, the distribution of White populations is very prominent as well. The rural areas in the
western portion of the nation show predominately White populations and diverse populations
that are considered shortage areas. Black populations living in a shortage area are more prevalent
in the southeastern regions while the Latinx populations living in a shortage area are most
prominent in the southwestern and western regions. The Black and Latinx populations as seen at
the national view are more prominent and visible where there are shortage areas, as compared to
the White populations where the visibility of shortage and non-shortage majority White areas are
almost equally visible. The visual analysis shows that where there are Black and Latinx
populations, these two groups are more likely to live in a shortage area than White populations.
The difference between the White population living in a shortage area compared to the Black
population living in a shortage area is much more prevalent than between the Black and Latinx
populations.
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Figure 6: Distribution of shortage areas by majority race
The pharmacy locations as shown by status, opened by 2020, closed by 2020, and active
both years are distributed in Figure 7. The layer below the pharmacy points show the shortage
areas by urbanicity, as in Figure 4. The closed pharmacy locations are not all surrounded by
newy opened locations and vice versa. This proves that some pharmacy shortage areas were due
to having lost a pharmacy, while others were due to the pharmacy locations staying the same but
the area maintained a distance longer than the distance threshold for its urbanicity. The
distribution of pharmacy locations are shown to be heavily prominent in more populted areas
causing the rural areas in the western areas of the United States to be considered shortage areas
even with the creation of new pharmacies. The large city areas (Houston, Dallas, Chicago, Los
Angeles, etc.) are the areas with the most closed and opened pharmacies. Plenty of the pharmacy
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locations which stayed active both 2018 nd 2020 were located in the areas that were considered
non-shortage areas.

Figure 7: Pharmacy distribution by status
The pharmacy distribution by type, chain or independent, show the chain pharmacies
heavily placed in the more urban areas, as shown in Figure 8. Throughout the rural areas in the
western section of the United States contain more independent pharmacies than chain. When
looking at the non shortage areas, the majority shown are independent pharmacies. Comparing
the independent pharmacies most prevalent in the non-shortage areas, and the active pharmacies
from figure 10being active, the assumption can be made that when an independent pharmacy is
in a location where its residents can reach their locations reasonably, the pharmacy remains able
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to serve its customers for longer. This visualization means that a main factor of pharmacy
longevity depends on its customer access.

Figure 8: Distribution of pharmacy locations by type
4.2. Web Mapping Application
The web mapping application was created in ArcGIS online using data manipulated in
ArcGIS Pro. This section provides an explanation of how the application works, the benefits of
the tool and the data that is provided.
4.2.1. Web Map Dynamics
Opening the web mapping application, the user is able to visualize the study area. This
web mapping application allows for the user to manipulate what layers are visible within the
map, as shown in Figure 9 below, by checking and unchecking the different data layers. The
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layer that is the highest in the list is what is shown above all other layers when clicked on. If all
layers are turned on at the same time, there may be only one or two layers visible to the layers
because of a hierarchy within the layers. With the top layer being placed on top of the others and
the lowest layer in the list underneath those before it.

Figure 9: Web mapping application showing the layer list
By having the ability to turn on and off the data layers, the map can show exactly what
the user wants to see and alter the goal of the map for each user. The widgets on the bottom of
the map allow for the user to show the legend, change the base map, select a location to focus on,
or use the swipe tool. The legend shows the different colors or shaped of each of the layers in
order to differentiate what is being shown on the map. As seen in Figure 10, the legend shows
the difference in colors between the shortage and non-shortage areas for each urbanicity type.
With the pharmacy data layer, the legend will show the three different classes for a pharmacy
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and the assigned colors: active both 2018 and 2020 (blue), closed by 2020 (red), and newly
opened by 2020 (orange).

Figure 10: Web mapping application showing the legend and the home view of the study area
The zoom buttons on the top left of the map along with the home button allow the user to
zoom to the specified level. The home button will allow the user to see the original view of the
study area which is seen in Figure 11. At the home view the layers that would be viewable
include the shortage area layers for 2018 and 2020, the CMS shortage area layers for 2018 and
2020, and the state layer.
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Figure 11: View after clicking the home button
4.2.1.1. State Level
At the state level, once a state is clicked, a pop up appears providing statistical
information for that specific state. Some analyses shown include number of tracts, state
population, number of shortage tracts for both 2018 and 2020, and other Medicare, Medicaid,
and demographic statistics. A way of finding or zooming into a state could be done by typing in
the state in the search bar at the top of the mapping application. This will allow you to click on
the state, county, city, or even an address and zoom into the specific location. The chosen
location will be outlined in a thin blue line to show the location chosen. Once clicking on the
location, if there are layers turned on, the pop up will show an arrow in the top right corner to
toggle between the different pop-up information for each layer at that location. Figure 12 below
shows an example of the pop-up that would be shown at the state level in Illinois.
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Figure 12: Web mapping application showing the pop up on a state.
4.2.1.2. County Level
At the county level, once zoomed in the state layer turns off and the county layer turns
on, as shown in Figure 13. This is due to the visible range which was applied to these layers to
provide a visual hierarchy within the map and to prevent over crowdedness within the map. The
county layer pop-up provides a similar structure to the state pop-up. Once zoomed into the
county layer, clicking on a county allows for the pop up to show. The statistical analysis
provided in the county pop-ups include the name of the county, population, number of tracts,
percentage of tracts in a shortage area for 2018 and 2020, the distribution of shortage areas by
urbanicity, and pharmacy closing and opening percentages for the county.

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Figure 13: Web mapping application showing the county level and pop-up
4.2.1.3. Tract Level
When zoomed in to the tract level, the county layer is no longer visible. The tract layer is
able to be seen for a wider visible range because some tracts are very small and require
additional zooming. As shown in Figures 14 and 15, the pop-ups for the tract layers include the
type of tract it is, whether or not it is a shortage area and its urbanicity, the majority race of the
tract, Medicaid and Medicare percentages, percent with no insurance, population living in a
shortage area and percent over the age of 65. The pop up also shows what the shortage status is
for CMS thresholds for the selected tract. The tract level differentiates its urbanicity and shortage
area status by color and transparency respectively. The darker or more vibrant the color shows a
shortage area, the lighter and more transparent tracts are non-shortage areas with the pink color
representing urban tracts, brown representing suburban tracts and green representing rural tracts.
The difference in transparency is done to be able to see where the urban, suburban, and rural
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tracts are whether they are considered shortage areas or not. The thin blue line shows what tract
is selected and what tract the pop-up is shown for.

Figure 14: Web mapping tool showing tract layer and an urban shortage area pop-up.

Figure 15: Web mapping tool showing tract layer and an urban non-shortage area pop-up.
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4.2.1.4. Block Group Level
The block group level is visible once zoomed in past the tract level, shown in Figure 16.
The block groups are smaller census designated sections within a census tract. Each of these
block groups provides a pop-up as well when clicked on. These pop-ups provide the population,
majority race, distance to nearest pharmacy for 2018 and 2020, and the type of pharmacy that is
the nearest for each block group. The block group layer is not labeled but visualized by a solid
color with a black outline.

Figure 16: Web mapping application showing the block group pop-up
4.2.1.5. Pharmacy Layer
The pharmacy layer provides point data of the pharmacy locations across the United
States. Each pharmacy location or point provides its own pop up as well as shown in Figures 17,
18, and 29. The pop-ups for the pharmacy layer include the type of pharmacy, its status, the
county it is in, and the amenities of the pharmacy. The amenities included in each pop up for the
pharmacies are drive up access, vaccine access, and if a walk-in clinic is available. The
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pharmacy points are color coded by its status, closed by 2020, newly opened by 2020, or active
for both 2018 and 2020. The pop-ups for this level are activated when clicking on the individual
points for each pharmacy. There is the ability to toggle between the pharmacy point layer pop-
ups and other data layers if there are multiple layers turned on at once. If a block group
boundary, for example, is clicked with the pharmacy layer on, the pop-up will provide the option
to toggle between the block group’s pop-up and the different pharmacy pop-ups which reside
within the chosen block group.

Figure 17: Web mapping application showing a newly opened pharmacy pop-up

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Figure 18: Web mapping application showing a closed pharmacy location

Figure 19: Web mapping application showing an active pharmacy location.
4.2.1.6. Swipe Tool
The swipe tool within the mapping application is used to show differences between two
data layers. When the tool is clicked on, you can choose between any data layers that are turned
on to swipe between. The tool allows a straight line with a left and right arrow showing it can be
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swiped in either direction. This is useful for showing comparisons between the 2018 and 2020
shortage area data layers. It can also be useful to show the difference in how this project defines
a shortage area compared to the CMS thresholds.
Throughout Figures 20, 21 and 22, there is a visual representation of how the swipe tool
works. Starting with Figure 20, the mapping application can be seen showing the swipe tool
clicked on, the swiping line in the middle of the screen along with the option to choose which
layer to swipe. When first clicking on the swipe tool, Figure 20 is what is shown. The selected
layer is the forward most layer that has been clicked on. The tool can be used at any zoom level
to see the changes at even the block group level.

Figure 20: Swipe tool showing comparison of two layers.
In Figure 21, the swipe tool was swiped to the left side of the screen to show the CMS
shortage areas for 2020. The swiping layer, shortage areas by this projects definition for 2020 is
swiped away in this image. This tool allows for the base map to stay the same while adjusting the
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data layers. The only layer that is affected by the swipe tool is the layer most forward on the
map, which is selected in the black outlined bot on the right side of the screen. This layer can be
changed by clicking the drop-down button to select which layer will be used to compare to the
layer below it.

Figure 21: Showing how swipe tool works and can be used.
In Figure 22, the swiping tool is swiped to the right to show the shortage areas for 2020
by this project’s definition, completely covering the CMS shortage areas layer. The layer being
swiped is the one chosen in the black “Swipe Layer” box at the bottom right of the image. While
using the swipe tool, to tell what is being shown, the legend tool can be turned on as well to
prevent any confusion between the layers. The search bar at the top of the screen is disabled
while using the swipe tool and in order to use the search tool again, clicking the swipe tool a
second time will stop the swipe tool and enable the search tool to be used again.

60

Figure 22: Showing how swipe tool can be used to compare layers.

61

Chapter 5 Conclusions
This chapter discusses the real-world meaning of the statistical results described in the prior
chapter and the utility of the web mapping application which displays these results in an
interactive and intuitive format. The chapter next describes some limitations of the project and
then describes possibilities for future extensions of this project.
5.1. Discussion
The visual analysis supported by the web mapping application leads the viewer to
understand that that Black and Latinx communities are most affected most by farther distances to
pharmacies. Across the US, Black populations appear to have more areas of population living in
a shortage area. This general visual finding holds true in urban, suburban, or rural census tract.
The visual analysis also supports the finding that Latinx populations living in urban, rural, and
suburban tracts have more areas of populations living in shortage areas as compared to the White
populations . Nationally, Black and low-income communities have the most shortage areas,
according to the visual results of the web mapping application.
The thresholds of one mile for urban tracts, two miles for suburban tracts, and ten miles
for rural tracts caught significantly more shortage areas than those of the CMS thresholds. This
shows how the CMS thresholds should be redefined knowing that populations that live in a
shortage area show significantly less areas with inadequate pharmacy access by applying what is
a reasonable distance for their Medicare Part D beneficiaries to travel to their nearest pharmacy.
The distances chosen for this project were chosen as reasonable distances to travel for each type
of tract based on urbanicity. For urban tracts, the thought process for choosing one mile came
from the ability to access a pharmacy within a city area where many people walk or take public
transportation for their main forms of transportation. For suburban areas, being that the houses
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are usually in deeper neighborhoods, the distance is somewhat further with also being reasonable
for those traveling out of their neighborhoods to their nearest pharmacy. For the rural areas, ten
miles was considered to be a reasonable distance being that most rural populations have to travel
far distances to reach many amenities of their communities or nearby cities. The ten-mile
distance by car is a distance that would be sound for the rural populations. For low income and
low vehicle tracts, applying a half mile distance was considered reasonable being that if no
vehicle is able to be used by the population, the primary form of transportation would be walking
or public transportation without the option to drive. With the results showing that low-income
areas are one of the groups of populations most affected by farther distances to pharmacies, this
provides insights to CMS to reconsider their “reasonable” distance measures for their Medicare
Part D beneficiaries but also provides a blueprint of where there should be more pharmacy
access for these communities. With the CMS thresholds being applied as distances for
determining a shortage area, there seems to be a disregard for reasonable access for those who
are minorities or those who live in low-income neighborhoods.
Alaska contains the most tracts considered shortage areas because of the large amounts of
uninhabited open space. Many tracts in Alaska are used for science research or extraction of
natural resources and do not actually have a population. This causes the number of pharmacies in
these areas to be scares or nonexistent which raises the distance to the nearest pharmacy and
increasing the number of tracts considered a shortage area.
Populations living in shortage areas alters by state as does the number of tracts. The states
with the most population living in a shortage area are Texas and California. These two states
have the highest populations in general, therefore having the highest number of people living in a
shortage area is understandable. However, the areas of tracts considered pharmacy shortage areas
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in Texas and California cover less than half of their states . This means the tracts that are
considered shortage areas for these states have high populations. Alaska contains the highest
areas of tracts considered a shortage area and a very low population living in a shortage area.
This confirms that the tracts that are considered shortage areas have very small populations or
none at all.
There were significantly more independent pharmacies that opened by 2020 that were not
open in 2018. This result shows that there could have been a push in the creation of independent
pharmacies. Another way this result could be interpreted is that some independent pharmacies
closed and moved locations from 2019 to 2020. More independent pharmacies closed by 2020,
however more independent pharmacies were created by 2020 also. This fluctuation in the
creation and closing of independent pharmacies shows that chain pharmacies have what could be
better resources to stay active for longer than independent pharmacies can.
This tool was shown to the members of NCPA and AMPAC during an August 2022
meeting of the USC-NCPA Pharmacy Access Initiative group. The National Community
Pharmacist Association is a national association that represents independent community
pharmacies. The goal of the NCPA is to promote and protect the independent pharmacists and
their patients and communities. The Access to Medicines and Pharmacies Advisory and
Advocacy Committee is a committee including state representatives, government employees and
educators. This committee is dedicated to finding ways to create better pharmacy access to
patients across the United States.
The dynamics of this web mapping application and the amenities, widgets, and tools
within this map were presented. A general consensus of this tool was found to be useful.
Members of both the NCPA and AMPAC groups found the tool to provide information in a way
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that is easy to understand and knowledgeable. The visual representation of the view of the United
States and the shortage areas across the nation was found to be the best view of the tool. It was
said to really show the impact of how pharmacy locations can affect its neighboring populations.
Another consensus of this web mapping application was that it could be used for many other uses
with pharmacy locations in the future. The members of both groups also agreed this web
mapping application would be beneficial when providing statistics to state and federal
government representatives. This web map seems to be a useful tool in many different pathways
in the field of pharmacy.
5.2. Limitations and Improvements
A limitation of the web mapping application is the speed in which it takes to load the data
layers. Having multiple data layers on at one time reduces the loading speed of the application.
This could provide some issues for the user depending on their needs. Another limitation which
occurred during the data management portion of this project was keeping adequate storage.
Because the license of ArcGIS Pro is through USC, each student is given a certain amount of
storage. Multiple times throughout this project there was a need for more storage. This did not
affect the end product of this project, however, running out of storage at certain times reduced
the efficiency of the data management timeline.
Due to the contract written between the Programs on Medicine and Public Health USC
group and NCPA, the data is not yet public. this limits the reach this data could go to for the time
being, however, the goal is to present this to policy holders and representatives to make changes
related to pharmacy access.
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When presenting the shortage areas by race and ethnicity, only White non-Hispanic,
Black non-Hispanic and Latinx communities were provided. This study did not consider majority
Asian populations when depicting shortage areas by race/ethnicity.
Lasty, margins of error when presenting majority Black, White, or Latinx populations
within census tracts were not incorporated. The margins of error when being applied to the
statistical analysis used when presenting the web application to the NCPA and AMPAC groups
were not included here either.
5.3. Future research
Building upon this research, this web mapping application can be used to provide an in
depth understanding of what exactly a resident can expect from their nearest pharmacy.
Providing the types of services offered at each pharmacy, such as what medications are available,
hours, and other lifesaving services, can be critical to a patient. With drug overdose being
prevalent and rates rising in most states, knowing that naloxone (buprenorphine) or Narcan is
available at your nearest pharmacy or what pharmacy carries them is very important. Also,
knowing if a pharmacist can prescribe Narcan to you without a prescription and actively keeps it
in stock is useful information. Along with naloxone and Narcan, availability of emergency
contraceptives is another resource that patients should know their nearest pharmacy does or does
not have. Another drug which can be prescribed by pharmacists, depending on the location, is an
EpiPen which is another lifesaving drug which its availability could be presented in the web map
as well.
A second addition which could be applied in future works is the distribution of insurance
networks. Having each pharmacy be able to provide the insurance plans they accept would be
monumentally helpful to the determination of pharmacy shortage areas. Being able to know that
66

you have multiple pharmacies within a mile of you is good to know, however, if your insurance
is not accepted at these locations, this should be another factor which would consider your
neighborhood or census tract a shortage area. Also, being able to provide information on what
the preferred pharmacies are within each network (i.e., each pharm could say “Aetna preferred
pharm” etc.) would be beneficial to the resident or patient as well.
Expanding this project to include years of pharmacy data before 2018 and after 2020
would be a great continuation of this mapping application. With the web application being able
to show changes over time, having multiple years of data would be interesting to show where
pharmacy locations have closed or opened throughout the years and what tracts have become
shortage areas or not because of the pharmacies.
The current study area of this project only includes the contiguous United States, Alaska,
Hawaii and the District of Columbia. In future works, expanding the study area to include the
united stated territories could be the next step in order to provide a complete US analysis.
Expanding to other countries would also be a great next step to show pharmacy shortage areas in
other nations.
The data used within this project are not public information at this time and are only to be
seen by the members of the AMPAC and NCPA groups. With the data not being public, this
requires the web mapping application to not be public as well. Having this shown to the public
eventually is the goal of this project to ensure government and state representatives have an
accurate visualization and understanding of the problem of pharmacy shortage areas.
Lastly, expanding this study to include not only all pharmacies, including non-retail
pharmacies, but also primary healthcare facilities, hospitals and emergency care and shortage
areas of these facilities would also be useful to show in the web map. Including more than just
67

pharmacies into the web map along with multiple years of data could present a time lapse of how
healthcare changes over the years and who is affected by these changes. It could also show if we
are moving in the correct direction or if we are staying stagnant with the healthcare coverage
currently being provided.
68

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Abstract (if available)

Abstract Pharmacies are one of the most used healthcare facilities across the nation due to of the number of services provided within a retail setting. The services provided by a retail pharmacy include dispensing of medications, vaccines, diagnostic testing, counseling, and more, all of which are pertinent to creating and maintaining a healthy community. A pharmacy shortage area is a location or area with an inadequate number of pharmacies available to a community leading to residents traveling far distances to their nearest pharmacy. Determining states or parts of the country that have a large number of these shortage areas provides a blueprint of where pharmacies are greatly needed. This project provides a national distribution of the accessibility of pharmacy locations by census tract for 2018 and 2020. These results are presented in a web mapping application through ArcGIS Pro. By presenting pharmacy shortage areas by census tract and over time, policymakers have highly relevant information. Given this information, there is a better chance for federal, state, and local policy changes that may improve pharmacy accessibility overall.

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Asset Metadata

Creator Robinson, Taylor Corrin (author)

Core Title Pharmacy shortage areas across the United States: a visual representation by a web mapping application

School College of Letters, Arts and Sciences

Degree Master of Science

Degree Program Geographic Information Science and Technology

Degree Conferral Date 2022-12

Publication Date 09/08/2022

Defense Date 08/25/2022

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag ArcGIS Pro,OAI-PMH Harvest,Pharmacy,pharmacy shortage areas,web mapping application

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Sedano, Elisabeth (committee chair), Duan, Leilei (committee member), Vos, Robert (committee member)

Creator Email taylorrobinsonx@outlook.com,tcrobins@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC111939646

Unique identifier UC111939646

Legacy Identifier etd-RobinsonTa-11192

Document Type Thesis

Format application/pdf (imt)

Rights Robinson, Taylor Corrin

Type texts

Source 20220909-usctheses-batch-979 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

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Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email cisadmin@lib.usc.edu