Barriers and Facilitators That Influence HIV Pre- exposure Prophylaxis (PrEP)-Prescribing Behaviors Among Primary Care Providers in the Southern United States

Abstract

The Southern United States (US) bears the highest burden of HIV prevalence in the country, disproportionately affecting African American communities. Despite the proven efficacy of pre-exposure prophylaxis (PrEP) in reducing HIV transmission, its uptake remains suboptimal in this region. This study aimed to identify factors influencing PrEP-prescribing behaviors among primary care providers (PCPs) in the Southern US through the application of the transtheoretical model of behavior change. A cross-sectional survey was conducted among PCPs in 10 Southern states to assess their PrEP-prescribing practices, barriers, and facilitators. The results indicate that non-White PCPs and those practicing in urban and suburban settings are more likely to prescribe PrEP. Key barriers include lack of training, perceived stigma, and systemic issues such as health insurance coverage and time constraints. Significant facilitators are access to prescribing resources, streamlined insurance procedures, and patient motivation. Targeted educational programs and policy changes to address these barriers can enhance PrEP uptake, thereby reducing HIV transmission in high-risk populations. The findings underscore the need for tailored interventions to support PCPs in integrating PrEP into routine care, ultimately contributing to better public health outcomes in the Southern US.

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DOI: 10.7759/cureus.66868
Barriers and Facilitators That Influence HIV Pre-
exposure Prophylaxis (PrEP)-Prescribing
Behaviors Among Primary Care Providers in the
Southern United States
Daryl O. Traylor , Maithe Enriquez , Melva Thompson-Robinson , Mansoo Yu , Tina Bloom ,
Linda Bullock
1. Public Health, A.T. Still University College of Graduate Health Sciences, Mesa, USA 2. Basic Sciences, University of
the Incarnate Word School of Osteopathic Medicine, San Antonio, USA 3. Infectious Diseases, Sinclair School of
Nursing, University of Missouri, Columbia, USA 4. Public Health, School of Nursing, University of Nevada, Las Vegas,
Las Vegas, USA 5. Social Work and Public Health, University of Missouri, Columbia, USA 6. School of Nursing, Notre
Dame of Maryland University, Baltimore, USA 7. Research, Sinclair School of Nursing, University of Missouri,
Columbia, USA
Corresponding author: Daryl O. Traylor, dtraylor1@ewu.edu
Abstract
The Southern United States (US) bears the highest burden of HIV prevalence in the country,
disproportionately affecting African American communities. Despite the proven efficacy of pre-exposure
prophylaxis (PrEP) in reducing HIV transmission, its uptake remains suboptimal in this region. This study
aimed to identify factors influencing PrEP-prescribing behaviors among primary care providers (PCPs) in the
Southern US through the application of the transtheoretical model of behavior change. A cross-sectional
survey was conducted among PCPs in 10 Southern states to assess their PrEP-prescribing practices, barriers,
and facilitators. The results indicate that non-White PCPs and those practicing in urban and suburban
settings are more likely to prescribe PrEP. Key barriers include lack of training, perceived stigma, and
systemic issues such as health insurance coverage and time constraints. Significant facilitators are access to
prescribing resources, streamlined insurance procedures, and patient motivation. Targeted educational
programs and policy changes to address these barriers can enhance PrEP uptake, thereby reducing HIV
transmission in high-risk populations. The findings underscore the need for tailored interventions to
support PCPs in integrating PrEP into routine care, ultimately contributing to better public health outcomes
in the Southern US.
Categories: Public Health, HIV/AIDS, Infectious Disease
Keywords: southern united states, pre-exposure prophylaxis, transtheoretical model, primary care providers, hiv
prevention
Introduction
The Southern United States (US) bears the highest burden of HIV prevalence in the country, with African
American communities experiencing a disproportionate impact [1]. According to epidemiological data, the
Southern US accounts for over half of all new HIV diagnoses in the US, despite comprising only 38% of the
national population [2,3]. Furthermore, the Southern US not only faces the highest HIV diagnosis rate but
also the highest HIV-related mortality in the nation [3]. This region faces unique challenges, such as cultural
barriers to receiving HIV care, lack of healthcare access, lower funding, stigma, and structural racism [3,4] .
These alarming statistics underscore the urgent need for effective HIV prevention strategies in this region.
Building on this understanding, it is important to recognize that HIV has an affect on not just African
American men [3-7]. The epidemic is primarily concentrated among African Americans, women, and rural
residents in the Deep South [8]. Notably, Texas, Louisiana, Alabama, Mississippi, Florida, Tennessee,
Arkansas, South Carolina, and North Carolina have experienced the highest rates of HIV diagnosis,
mortality, and lowest survival rates as compared to the national data [5,6,8]. Factors contributing to this
disparity include poverty, transportation access issues, stigma, lack of healthcare access, and the social
determinants of health [9].
To address these disparities, pre-exposure prophylaxis (PrEP) has emerged as a highly effective strategy for
preventing HIV transmission [10]. Clinical trials have demonstrated that when taken consistently, PrEP can
reduce the risk of HIV infection by up to 99% in individuals at high risk [10]. Despite its proven efficacy, the
PrEP uptake remains suboptimal, particularly in the Southern US where it is most critically needed [11].
Barriers to PrEP adoption include lack of awareness, stigma, and limited access to healthcare providers who
are knowledgeable about PrEP [11-13].
Transitioning from efficacy to actual use, it is evident that despite the effectiveness of PrEP, access among
1, 2 3 4 5 6
7
Open Access Original Article
How to cite this article
Traylor D O, Enriquez M, Thompson-Robinson M, et al. (August 14, 2024) Barriers and Facilitators That Influence HIV Pre-exposure Prophylaxis
(PrEP)-Prescribing Behaviors Among Primary Care Providers in the Southern United States. Cureus 16(8): e66868. DOI 10.7759/cureus.66868

African Americans in the Southern US remains low. A limited uptake is particularly observed among African
American men who have sex with men (MSM) [14]. Barriers to PrEP use in this population include stigma,
healthcare mistrust, poverty, and misinformation [15]. Geographic access to PrEP clinics is limited,
especially in areas with higher poverty and historically underserved populations [16,17]. Moreover,
awareness of PrEP is notably low among low-income African American cis/het women and transgender
individuals [18].
Despite these barriers, there are promising interventions aimed at improving PrEP initiation and persistence
among African American MSM [19]. Additionally, studies have shown that collaborations between public
health departments and federally qualified health centers can significantly enhance access [20]. These
collaborative efforts highlight the potential for systemic changes to bridge the gap in the PrEP uptake and
effectively address the HIV epidemic in the Southern US.
Lack of primary care provider engagement in HIV screening and PrEP
prescribing
A significant barrier to effective HIV prevention in the Southern US is the lack of engagement by primary
care providers (PCPs) in HIV screening and PrEP prescribing. Despite the critical role that PCPs play in the
healthcare system, many do not routinely offer HIV testing or discuss PrEP with their patients, particularly
those from high-risk populations such as African Americans [21]. This lack of engagement can be attributed
to several factors, including insufficient training, perceived stigma, and time constraints during patient
consultations [22].
One major reason for the low engagement of PCPs in PrEP prescribing is a lack of adequate training and
education on HIV prevention strategies. Many PCPs report feeling unprepared to discuss PrEP with their
patients due to gaps in their knowledge and understanding of the medication's efficacy and usage guidelines
[21]. This gap in education is further exacerbated by the limited inclusion of HIV prevention in medical
school curricula and continuing medical education (CME) programs, leaving many PCPs without the
necessary tools to effectively advocate for PrEP [23,24].
Additionally, perceived stigma and personal biases among healthcare providers can significantly hinder the
adoption of PrEP-prescribing practices [22]. Some PCPs may hold stigmatizing views towards individuals at
high risk of HIV, such as African American MSM and transgender individuals, which can lead to reluctance in
offering PrEP as a preventative measure [25]. This stigma not only affects the willingness of PCPs to
prescribe PrEP but also impacts the trust and communication between patients and providers, further
discouraging patients from seeking PrEP [22].
Time constraints during patient consultations also pose a significant barrier to PrEP prescribing. Many PCPs
operate under heavy workloads with limited time to spend on preventive health discussions. As a result,
discussions about PrEP, which require a detailed conversation about HIV risk behaviors, medication
adherence, and potential side effects, are often deprioritized or overlooked [22]. This issue is particularly
pronounced in under-resourced areas of the Southern US, where healthcare providers may be stretched thin
by high patient volumes and limited healthcare infrastructure [21].
Researching the phenomena of low PCP engagement in HIV screening and PrEP prescribing is critical for
several reasons [26]. Understanding the barriers faced by PCPs can inform targeted interventions and
educational programs designed to equip these providers with the knowledge and skills needed to effectively
prescribe PrEP. Additionally, addressing these barriers can lead to improved patient-provider relationships,
fostering a more supportive environment for discussing HIV prevention. Increasing the engagement of PCPs
in PrEP prescribing may significantly enhance the overall uptake of PrEP, thereby reducing HIV transmission
rates and improving public health outcomes in the Southern US [26].
Transtheoretical stages of change model and its application to PrEP-
prescribing behavior
The transtheoretical model (TTM), developed by Prochaska and DiClemente in the late 1970s, is a widely
utilized theoretical framework in health behavior change. The model posits that individuals move through a
series of stages when modifying behavior: precontemplation, contemplation, preparation, action, and
maintenance [25]. Each stage represents a different level of readiness to change, and understanding these
stages can provide insights into how to effectively encourage behavior modification.
Stages of Change
In the precontemplation stage, individuals are not considering change and may be unaware of the need for
change. Moving to the contemplation stage, individuals recognize the need for change and begin to consider
it, but have not yet committed to taking action. In the preparation stage, individuals are planning to act soon
and may start making small changes. During the action stage, individuals have recently begun to implement
the behavior change. Finally, in the maintenance stage, individuals sustain the behavior change over time
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and work to prevent relapse [25].
Application to PrEP-Prescribing Behavior
The TTM may be particularly useful as a predictor for the PrEP-prescribing behavior among PCPs due to its
focus on the readiness to change [25]. In the context of HIV prevention, the model can help identify where
PCPs are in terms of their willingness and ability to prescribe PrEP.
In the precontemplation stage, PCPs may not consider PrEP prescribing relevant or necessary and may lack
awareness about the efficacy of PrEP or have misconceptions about its use. In the contemplation stage, PCPs
start to acknowledge the importance of PrEP but have not yet committed to integrating it into their practice,
and they may seek more information or begin to consider how it could benefit their patients. During the
preparation stage, PCPs plan to start prescribing PrEP and may attend training sessions or seek out
resources to understand the prescribing process better. In the action stage, PCPs actively prescribe PrEP and
discuss it with eligible patients, integrating PrEP discussions into routine care and building confidence in
managing PrEP patients. Finally, in the maintenance stage, PCPs have incorporated PrEP prescribing into
their regular practice and continue to update their knowledge and skills while addressing any barriers that
could prevent sustained PrEP use among patients.
Decisional balance
Understanding Decisional Balance
Decisional balance, a core construct of the TTM of behavior change, refers to the process of weighing the
pros and cons of changing a behavior [26]. This concept is essential for understanding how individuals make
decisions about adopting new behaviors, particularly in the context of health interventions. Decisional
balance involves evaluating the perceived benefits (pros) and costs (cons) associated with a particular
action, which influences an individual's readiness to change [27].
In the TTM, decisional balance shifts as individuals progress through the stages of change.
Application to PCP PrEP Prescribing
In the context of PCP prescribing of PrEP for HIV prevention, decisional balance can be a crucial factor
influencing their prescribing behavior. The following aspects illustrate how decisional balance may be
applied.
Pros of prescribing PrEP include its efficacy in preventing HIV. PCPs recognize the high efficacy of PrEP in
reducing HIV transmission, especially in high-risk populations. Additionally, prescribing PrEP contributes to
broader public health goals by reducing the overall incidence of HIV. Providers also understand that offering
PrEP can significantly improve the health and safety of their patients at risk of HIV.
On the other hand, some PCPs may perceive cons to prescribing PrEP. Many PCPs may feel unprepared to
prescribe PrEP due to insufficient training or gaps in their knowledge about its use and guidelines. Concerns
about stigma associated with HIV and PrEP may deter PCPs from initiating discussions about PrEP with
their patients. Additionally, adherence issues can be a concern, as PrEP requires strict compliance to be
effective, and inconsistent use may lead to drug resistance. The need for regular monitoring, such as renal
function tests, can strain limited resources and time. There is also apprehension about unintended
behavioral consequences, where patients may engage in riskier behaviors under the belief that PrEP offers
complete protection, potentially increasing other sexually transmitted infection (STI) rates. Furthermore,
cultural and ethical considerations, patient selection challenges, and potential language barriers can make
discussions about PrEP difficult. PCPs may also have concerns about the long-term effects of PrEP,
regulatory and legal implications, and limited patient awareness of or demand for PrEP, making it
challenging to justify the investment of time and resources. These factors, coupled with issues such as a lack
of health insurance coverage and time constraints during consultations, may discourage PCPs from
prescribing PrEP.
Shifting the Balance
To enhance PrEP prescribing among PCPs, interventions can be designed to shift the decisional balance by
increasing the perceived benefits and reducing the perceived costs. This can be achieved through targeted
education and training, providing comprehensive training programs that equip PCPs with the necessary
knowledge and skills to prescribe PrEP confidently. Addressing stigma is another critical aspect, which can
be tackled by implementing initiatives to reduce stigma associated with HIV and PrEP through awareness
campaigns and fostering a supportive clinical environment. Streamlining processes is also essential;
insurance companies should develop simplified health insurance prior authorization procedures, and
providing resources such as PrEP-prescribing guidelines may reduce the systemic barriers faced by PCPs.
Finally, emphasizing the benefits, including highlighting the public health impact and patient benefits of
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PrEP, can reinforce the pros of prescribing it, encouraging PCPs to integrate PrEP into routine care.
By understanding and leveraging the concept of decisional balance, healthcare systems can design effective
strategies to promote PrEP prescribing among PCPs, ultimately contributing to better HIV prevention
outcomes in high-risk populations.
Benefits of using the TTM for PrEP prescribing
Utilizing the TTM to understand and predict PrEP-prescribing behavior offers several advantages. Tailored
interventions can be designed to target PCPs at different stages of readiness, making them more effective.
For instance, educational materials and training sessions can be customized to address the specific needs
and concerns of PCPs at each stage. Additionally, the model recognizes that behavior change is a gradual
process. By acknowledging small steps and incremental progress, it can help maintain motivation and
reduce the likelihood of relapse. Furthermore, the TTM provides a comprehensive framework that considers
both individual and systemic factors influencing behavior change. This holistic approach can lead to more
sustainable improvements in PrEP-prescribing practices.
By identifying the stage at which a PCP is operating, targeted interventions can be designed to move them
through the stages towards the action and maintenance phases. For instance, those in the precontemplation
stage might benefit from educational campaigns that highlight the high HIV burden in the Southern US and
the effectiveness of PrEP. PCPs in the contemplation stage might need more detailed information on how to
discuss PrEP with patients and address common concerns. Those in the preparation stage could benefit from
practical training sessions that build their confidence and competence in prescribing PrEP.
Need for the study
The need for this study is critical due to the persistently low rates of PrEP prescribing by PCPs in the
Southern US, despite the region's high HIV prevalence and significant risk among African American
communities. The current literature inadequately addresses the specific barriers and facilitators that
influence PCPs' prescribing practices in this context. By identifying these factors, the study aims to inform
targeted interventions and educational programs that can enhance the PrEP uptake and ultimately reduce
HIV transmission in this high-risk population. Understanding and addressing these gaps is essential for
improving health equity and achieving more effective HIV prevention strategies.
Materials And Methods
Research design
This cross-sectional study utilized a convenience sample to examine factors influencing the PrEP-
prescribing behaviors of primary care providers in the Southern United States. The study aimed to provide a
quantitative assessment of PCPs' personal and professional variables aligned with the transtheoretical
stages of change model to predict PrEP-prescribing practices. The outcome variables of interest were the
stage of adoption of PrEP prescribing and whether or not PrEP was prescribed.
Research questions
The research questions for the study were as follows: (1) What are the relationships between PCP personal
and practice variables and the stage of adoption, using the TTM, of PrEP prescribing? (2) What PCP personal
and practice characteristics predict prescribing or not prescribing PrEP? (3) What is the relationship between
the TTM decisional balance construct and the TTM stages of change for PrEP prescribing? (4) Does PCPs’
TTM decisional balance predict prescribing or not prescribing PrEP?
Participant recruitment
Participants were recruited using social media and email due to the COVID-19 lockdowns, between March 7,
2021, and May 8, 2021, following approval from the University of Missouri, Columbia's Institutional Review
Board (no. 2056192). Invitations were extended to PCPs practicing in the Southern US, encouraging those
interested to participate in an online survey hosted on Qualtrics® (Provo, UT, USA). To maintain provider
confidentiality, no identifying information or internet protocol addresses were collected.
Eligibility Criteria
To be eligible for the study, participants had to provide consent to participate, be 18 years of age or older, be
a licensed physician, advanced practice registered nurse, or physician assistant, be currently practicing
primary care medicine, and practicing in one of the following states: Arkansas, Alabama, Georgia, Florida,
Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, or Texas. Participants who did not meet
these criteria were excluded from the study.
Data collection procedure
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Upon accessing the survey link, participants were directed to an information page detailing the study's
purpose and informed consent. After consenting, they were assessed for eligibility through a series of
screening questions. Eligible participants then completed the survey, which included questions about their
personal demographics, practice characteristics, and PrEP-prescribing behaviors.
Survey Instrument
The survey was adapted from the HIV PrEP survey used by Terndrup et al. [27], incorporating the TTM
staging algorithm. Permission was sought and granted from Terndrup et al. to use the survey, though the
validity and reliability of the instrument were not established. The survey included five screening and
consent questions, one TTM staging question, two TTM decisional balance questions regarding barriers and
facilitators to PrEP prescribing, and 21 questions on provider demographics and practice variables. The
survey assessed factors such as the geographic location of practice, years of practice, and whether African
American patients were seen and screened for PrEP (see the Appendices).
Sample size and power analysis
A power analysis using G*Power 3.1.9.4 (Heinrich Heine University Düsseldorf, Germany) was conducted to
determine the appropriate sample size for logistic regression analysis. Based on an odds ratio of 2 for a
medium effect size, an alpha of .05, and a power of .80, the desired sample size was 219. To account for
potential incomplete surveys, 300 participants were targeted.
Data analysis
Data was analyzed using IBM SPSS Statistics, version 26 (IBM Corp., Armonk, NY). The dataset was screened
for missing responses, patterns, and inconsistencies. Frequency distributions and summary statistics were
calculated to detect outliers. Ordinal logistic regression was used to analyze the relationships between
multiple independent variables (e.g., age, race/ethnicity, gender, sexual orientation) and the ordinal
dependent variable (stage of PrEP prescribing). Missing data was handled using pairwise exclusion.
Results
A total of 330 responses were submitted to the online survey. One hundred and seven responses were
removed because the participants did not finish all sections of the survey, leaving a total of 223 participants
included in the final dataset. All remaining missing responses to individual questions were handled using
pairwise exclusion.
Table 1 outlines the demographic profile of the study participants. The participants had an average age of
44.04 years (SD = 10.81). A majority were men (n = 129, 57.8%), with most identifying as heterosexual (n =
180, 80.7%). The predominant racial identity was White (n = 113, 50.7%), and the majority indicated they
were not Hispanic (n = 182, 81.6%). The largest group of participants practiced in Texas (n = 118, 52.9%) and
in urban settings (n = 151, 67.7%). The most common profession among the participants was that of
physicians (n = 149, 66.8%), with the highest educational attainment being having a medical doctor (n = 100,
44.8%). On average, participants had 13.65 years of experience in primary care (SD = 9.77).
Variable Frequency Percentage
Practicing state
Arkansas 11 4.9
Alabama 7 3.1
Georgia 30 13.5
Florida 10 4.5
Louisiana 6 2.7
Mississippi 9 4.0
North Carolina 8 3.6
South Carolina 14 6.3
Tennessee 6 2.7
Texas 118 52.9
Multiple 3 1.3
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No response 1 0.4
Geographic area
Urban 151 67.7
Suburban 31 13.9
Rural 35 15.7
No response 6 2.7
Gender
Male 129 57.8
Female 85 38.1
Genderqueer/non-conforming 4 1.8
Transgender 2 0.9
Prefer not to answer 2 0.9
No response 1 0.4
Sexual orientation
Heterosexual 180 80.7
Gay 17 7.6
Lesbian 12 5.4
Bisexual 4 1.8
Other 4 1.8
Prefer not to answer 4 1.8
No response 2 0.9
Race
American Indian or Alaskan Native 4 1.8
Asian 15 6.7
Black or African American 48 21.5
Native Hawaiian or Pacific Islander 2 0.9
White 113 50.7
Multiracial or multiple selections 18 8.1
Other 4 1.8
Prefer not to answer 19 8.5
Hispanic
Yes 38 17.0
No 182 81.6
No response 3 1.3
Type of practitioner
Physician 149 66.8
Advanced Practice Registered Nurse 51 22.9
Physician Assistant 22 9.9
No response 1 0.4
Highest degree completed
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Doctor of Osteopathic Medicine (DO) 49 22.0
Medical Doctor (MD) 100 44.8
Master of Science - Nursing (MSN) or equivalent 30 13.5
Doctor of Nursing Practice (DNP) 20 9
Master of Science - Physician Assistant (MSPA) or equivalent 19 8.5
Doctor of Medical Science - Physician Assistant (DMSPA) or equivalent 3 1.3
No response 2 0.9
TABLE 1: Sample demographics
PrEP-prescribing practices and patient demographics: insights through
the TTM
Table 2 presents descriptive statistics concerning participants' PrEP practices and their patients. The data
indicates that participants were primarily at either the lowest (n = 72, 32.3%) or highest (n = 69, 30.9%)
stages of PrEP prescribing. A significant portion of participants did not regularly screen African American
patients for HIV (n = 130, 58.3%), assess their need for PrEP (n = 149, 66.8%), prescribe PrEP (n = 182,
82.1%), or refer them for PrEP prescriptions (n = 185, 83.0%). Over half of the respondents reported that they
had never written a PrEP prescription for an African American patient (n = 128, 57.4%), and 70.4% had not
received PrEP training (n = 157). Despite this, most participants were familiar with PrEP prior to the survey
(n = 181, 81.2%). Additionally, most participants indicated that 50% or fewer of their patients were African
American (n = 178, 79.8%).
Variable Frequency Percentage
Stage of PrEP prescribing
Does not prescribe and does not intend to within 6 months 72 32.3
Does not prescribe but intends to within 6 months 37 16.6
Does not prescribe but intends to within 30 days 18 8.1
Has prescribed for less than 6 months 27 12.1
Has prescribed for more than 6 months 69 30.9
Do you routinely screen your African American patients for HIV?
Yes 92 41.3
No 130 58.3
No response 1 0.4
Do you routinely screen your African American patients for PrEP need?
Yes 74 33.2
No 149 66.8
Have you ever written a prescription for PrEP for an African American patient?
Yes 95 42.6
No 128 57.4
Do you routinely write PrEP prescriptions for your African American patients?
Yes 40 17.9
No 183 82.1
Have you ever referred an African American patient for a PrEP prescription?
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Yes 92 41.3
No 130 58.3
No response 1 0.4
Do you routinely refer African American patients for PrEP prescriptions?
Yes 36 16.1
No 185 83.0
No response 2 0.9
Have you received specific training on PrEP?
Yes 65 29.1
No 157 70.4
No response 1 0.4
Before this survey, had you ever heard of PrEP?
Yes 181 81.2
No 41 18.4
No response 1 0.4
% of patients who are African American
0% 4 1.8
1%-25% 97 43.5
26%-50% 81 36.3
51%-75% 21 9.4
76%-100% 16 7.2
No response 4 1.8
% of patients who are White
0% 3 1.3
1%-25% 51 22.9
26%-50% 76 34.1
51%-75% 53 23.8
76%-100% 34 15.2
No response 6 2.7
% of patients who are American Indian or Alaskan Native
0% 68 30.5
1%-25% 101 45.3
26%-50% 2 0.9
TABLE 2: PrEP practices and patient characteristics
PrEP, pre-exposure prophylaxis
Chi-square tests of independence were performed to assess whether the stage of change regarding PrEP
prescribing and the act of prescribing PrEP varied based on the participants' self-reported proportion of
African American patients. The participants were categorized into two groups: those with “50% or less”
African American patients and those with “more than 50%” African American patients. Tables 3, 4 present
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the cross-tabulations of these variables. The chi-square test for the PrEP stage of change was significant,
χ2(4) = 17.27, p = .002, suggesting that participants with a higher proportion of African American patients
were more likely to be in an advanced stage of change. Similarly, the chi-square test for PrEP prescription
was significant, χ2(1) = 16.41, p = .001, indicating that participants with a greater proportion of African
American patients were more likely to have prescribed PrEP.
Percentage of African American patients Stage 1, n (%) Stage 2, n (%) Stage 3, n (%) Stage 4, n (%) Stage 5, n (%)
50% or less 66 (36%) 33 (18%) 15 (8%) 10 (11%) 48 (26%)
More than 50% 4 (11%) 3 (8%) 3 (8%) 7 (19%) 20 (54%)
TABLE 3: Cross-tabulation of percentage of African American patients versus pre-exposure
prophylaxis (PrEP) stage of change
Percentage of African American patients Prescribed PrEP, n (%) Never prescribed PrEP, n (%)
50% or less 67 (37%) 115 (63%)
More than 50% 27 (73%) 10 (27%)
TABLE 4: Cross-tabulation of percentage of African American patients versus pre-exposure
prophylaxis (PrEP) prescription
Facilitators and barriers to PrEP prescribing
The study assessed the factors influencing decisions to prescribe PrEP among participants, using a Likert
scale ranging from 1 (not important) to 5 (extremely important). Table 5 presents the mean ratings and
standard deviations for both facilitators and barriers identified. The highest-rated factor, averaging a score
of 4.70 (SD = 0.80), was “lack of insurance coverage”. Conversely, “clinic in-service PrEP training” received
the lowest average rating of 1.83 (SD = 1.30).
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Variable M SD
PrEP training during residency 2.35 1.57
Staff or providers who are knowledgeable about PrEP provision 3.27 1.45
Access to resources such as PrEP prescription guidelines and protocols 3.63 1.53
On-site support 2.67 1.47
Clinic in-service PrEP training 1.83 1.30
Knowledge of PrEP's efficacy 2.85 1.70
Patient motivation or "buy in" 4.25 1.19
Peers who prescribe PrEP 3.18 1.66
Patient access to financial incentives that would lower the cost of PrEP 2.77 1.51
Streamlined insurance prior authorization procedures 4.32 1.25
Lack of provider training/education regarding PrEP 4.27 1.20
Lack of clinic leadership support regarding PrEP 3.10 1.42
Lack of clinical guidelines/protocols for prescribing/monitoring PrEP 3.87 1.38
Clinic and lab monitoring requirements 2.73 1.41
Staffing time constraints related to risk reduction and PrEP adherence counseling 4.29 1.07
Lack of insurance coverage and out-of-pocket patient costs for PrEP 4.70 0.80
Likelihood of low adherence to PrEP 3.32 1.51
Likelihood of developing HIV resistance 2.27 1.49
Patients may engage in riskier behavior while on PrEP 3.25 1.55
Insufficient evidence of PrEP's effectiveness 1.98 1.37
TABLE 5: Descriptive statistics for facilitators and barriers to PrEP prescribing
PrEP, pre-exposure prophylaxis
Determinants of PrEP-prescribing practices and stages of change
among PCPs
Research Question 1: What Is the Relationship Between Personal and Practice Variables and the Stage of Change
of PrEP Prescribing?
To address Research Question 1, an ordinal logistic regression analysis was performed. The analysis included
age, race/ethnicity, gender, and sexual orientation as personal variables, and type of primary care
practitioner (physician, nurse practitioner, physician assistant), years of practice, practice setting (urban,
suburban, rural), and receipt of PrEP training as practice variables. The dependent variable was the stage of
change for PrEP prescribing, categorized into five ordinal levels indicating increasing adoption stages.
Multicollinearity was assessed using variance inflation factors (VIFs), all of which were below 10, suggesting
no severe multicollinearity.
The overall model was statistically significant, χ2(17) = 170.77, p < .001, indicating that the combined
predictors significantly predicted the stage of change. Table 6 presents the individual regression coefficients.
Race emerged as a significant predictor; non-White participants were 3.97 times more likely to be in a higher
stage of change (p < .001). Practice setting also significantly influenced the stage of change, with urban (OR
= 8.65, p < .001) and suburban settings (OR = 6.14, p = .004) associated with higher stages of change.
Additionally, having received PrEP training significantly predicted a higher stage of change (OR = 18.41, p <
.001).
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Variable Estimate Standard error Wald's statistic Significance Odds ratio
Age -0.04 0.03 1.97 .160 0.96
Years of practicing 0.01 0.03 0.18 .670 1.01
Male 0.61 2.10 0.09 .771 1.84
Female 0.92 2.09 0.19 .659 2.51
Genderqueer 1.04 2.16 0.23 .629 2.83
Transgender -2.30 2.29 1.01 .316 0.10
Non-White 1.38 0.35 15.40 < .001 3.97
Hispanic -0.06 0.41 0.02 .890 0.94
Heterosexual -0.08 1.54 0.00 .956 0.92
Gay 2.27 1.70 1.79 .181 9.69
Lesbian 0.45 1.60 0.08 .777 1.57
Bisexual 0.95 1.87 0.26 .611 2.59
Physician 0.06 0.51 0.01 .912 1.06
Advanced Practice Registered Nurse 0.31 0.61 0.25 .618 1.36
Urban 2.16 0.54 16.02 < .001 8.65
Suburban 1.82 0.63 8.22 .004 6.14
Had PrEP training 2.91 0.41 50.19 < .001 18.41
TABLE 6: Ordinal logistic regression predicting the pre-exposure prophylaxis (PrEP) stage of
prescribing
Research Question 2: Do Primary Care Providers’ Personal and Practice Variables Predict Prescribing or Not
Prescribing PrEP to African Americans Residing in the Southern US?
To address the second research question, a binary logistic regression analysis was performed. This analysis
utilized the same personal and practice variables that were employed for the first research question. The
final independent variables included in the model were selected through the forward entry (conditional)
method. The dependent variable was whether the participant prescribed PrEP (categorized as prescribed or
not prescribed).
The overall model was statistically significant, χ2(8) = 136.34, p < .001, demonstrating that the predictors
collectively had a significant impact on PrEP prescription. Therefore, the null hypothesis was rejected. Table
7 presents the individual regression coefficients for the final model. Race emerged as a significant predictor,
with participants who did not identify as White being 5.86 times more likely to prescribe PrEP (p < .001).
Sexual orientation was also a significant predictor, with participants identifying as gay (OR = 359.51, p =
.002), lesbian (OR = 19.24, p = .034), and bisexual (OR = 62.75, p = .030) showing a higher likelihood of
prescribing PrEP. Additionally, the practice setting was significant, indicating that participants in urban
settings (OR = 11.36, p = .008) were more likely to prescribe PrEP. Furthermore, PrEP training was a
significant predictor, with those having received training being 40.26 times more likely to prescribe PrEP (p
< .001).
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Variable Estimate Standard error Wald's statistic Significance Odds ratio
Non-White 1.77 0.48 13.58 < .001 5.86
Heterosexual 2.11 1.17 3.22 .073 8.21
Gay 5.89 1.93 9.28 .002 359.51
Lesbian 2.96 1.40 4.47 .034 19.24
Bisexual 4.14 1.90 4.74 .030 62.75
Urban 2.43 0.92 6.98 .008 11.36
Suburban 1.81 1.04 3.01 .083 6.08
Had PrEP training 3.70 0.60 37.56 < .001 40.26
TABLE 7: Binary logistic regression predicting pre-exposure prophylaxis (PrEP) prescription
Research Question 3: What Is the Relationship Between TTM Decisional Balance and the Stage of Change of PrEP
Prescribing?
To address Research Question 3, an ordinal logistic regression analysis was conducted. In this analysis, the
independent variables consisted of individual survey items that assessed the importance of specific
facilitators and barriers to PrEP prescribing (based on the TTM decisional balance). The dependent variable
was the stage of change for PrEP prescribing. Multicollinearity was evaluated by calculating VIFs, and all
VIFs were below 10, indicating an absence of severe multicollinearity.
The overall model was statistically significant, χ2(20) = 135.78, p < .001, demonstrating that the predictors
collectively had a significant impact on the stage of change. Consequently, the null hypothesis was rejected.
Table 8 presents the individual regression coefficients for the model. Significant positive predictors included
access to resources (OR = 1.70, p < .001), streamlined insurance (OR = 1.52, p = .010), and lack of insurance
coverage (OR = 1.99, p = .004), indicating that participants who rated these factors as more important were
more likely to be in an advanced stage of change. Conversely, significant negative predictors were lack of
provider training (OR = 0.67, p = .020), likelihood of low adherence (OR = 0.66, p = .018), and likelihood of
HIV resistance developing (OR = 0.64, p = .012), indicating that participants who rated these factors as more
important were less likely to be in a higher stage of change.
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Variable Estimate Standard
error
Wald's
statistic Significance Odds
ratio
PrEP training during residency -0.04 0.11 0.14 .705 0.96
Staff or providers who are knowledgeable about PrEP provision 0.25 0.14 3.24 .072 1.28
Access to resources such as PrEP prescription guidelines and
protocols 0.53 0.14 14.02 < .001 1.70
On-site support -0.02 0.12 0.03 .853 0.98
Clinic in-service PrEP training -0.08 0.16 0.28 .597 0.92
Knowledge of PrEP's efficacy 0.07 0.15 0.21 .650 1.07
Patient motivation or "buy in" 0.22 0.16 1.85 .174 1.25
Peers who prescribe PrEP 0.00 0.12 0.00 .984 1.00
Patient access to financial incentives that would lower the cost of PrEP 0.14 0.14 1.02 .312 1.15
Streamlined insurance prior authorization procedures 0.42 0.16 6.72 .010 1.52
Lack of provider training/education regarding PrEP -0.40 0.17 5.40 .020 0.67
Lack of clinic leadership support regarding PrEP -0.26 0.14 3.50 .061 0.77
Lack of clinical guidelines/protocols for prescribing/monitoring PrEP -0.16 0.14 1.17 .279 0.86
Clinic and lab monitoring requirements 0.06 0.15 0.15 .697 1.06
Staffing time constraints related to risk reduction and PrEP adherence
counseling -0.03 0.19 0.03 .853 0.97
Lack of insurance coverage and out-of-pocket patient costs for PrEP 0.69 0.24 8.12 .004 1.99
Likelihood of low adherence to PrEP -0.41 0.17 5.63 .018 0.66
Likelihood of developing HIV resistance -0.45 0.18 6.34 .012 0.64
Patients may engage in riskier behavior while on PrEP -0.09 0.15 0.35 .554 0.91
Insufficient evidence of PrEP's effectiveness -0.07 0.18 0.13 .715 0.94
TABLE 8: Ordinal logistic regression predicting the pre-exposure prophylaxis (PrEP) stage of
prescribing
Research Question 4: Does a Primary Care Provider’s TTM Decisional Balance Predict Prescribing or Not
Prescribing PrEP to African Americans Residing in the Southern US?
To address Research Question 4, a binary logistic regression analysis was performed. The independent
variables in this analysis were the specific survey items concerning the perceived importance of facilitators
and barriers to PrEP prescribing, which were used in Research Question 3 (reflecting the TTM decisional
balance). The final set of independent variables included in the model was selected using the forward entry
(conditional) method. The dependent variable was whether the participant prescribed PrEP (yes or no).
The final model was statistically significant, χ2(9) = 131.51, p < .001, indicating that the predictors
collectively had a significant impact on the likelihood of prescribing PrEP, leading to the rejection of the null
hypothesis. Table 9 provides the individual regression coefficients for the final model. Significant positive
predictors included knowledgeable staff (OR = 1.51, p = .033), access to resources (OR = 1.85, p = .001),
patient motivation (OR = 2.19, p = .007), patient access to financial incentives (OR = 1.97, p < .001), and lack
of insurance coverage (OR = 2.74, p = .002), suggesting that participants who rated these factors as more
important were more likely to prescribe PrEP. Conversely, significant negative predictors were lack of
provider training (OR = 0.43, p = .003), lack of clinical leadership (OR = 0.65, p = .018), likelihood of low
adherence (OR = 0.53, p = .001), and likelihood of developing HIV resistance (OR = 0.43, p < .001), indicating
that participants who rated these factors as more important were less likely to prescribe PrEP.
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Variable Estimate Standard
error
Wald's
statistic Significance Odds
ratio
Staff or providers who are knowledgeable about PrEP provision 0.41 0.19 4.55 .033 1.51
Access to resources such as PrEP prescription guidelines and
protocols 0.61 0.18 11.93 .001 1.85
Patient motivation or "buy in" 0.78 0.29 7.20 .007 2.19
Patient access to financial incentives that would lower the cost of
PrEP 0.68 0.18 14.12 < .001 1.97
Lack of provider training/education regarding PrEP -0.86 0.29 8.92 .003 0.43
Lack of clinic leadership support regarding PrEP -0.43 0.18 5.57 .018 0.65
Lack of insurance coverage and out-of-pocket patient costs for
PrEP 1.01 0.33 9.29 .002 2.74
Likelihood of low adherence to PrEP -0.64 0.20 10.36 .001 0.53
Likelihood of developing HIV resistance -0.84 0.22 14.78 < .001 0.43
TABLE 9: Binary logistic regression predicting pre-exposure prophylaxis (PrEP) prescription
Discussion
The findings from this study provide significant insights into the factors influencing PrEP-prescribing
behaviors among PCPs in the Southern US. Here, the key results are interpreted, and their implications for
improving the PrEP uptake in this high-risk region are discussed.
The study revealed that race and practice setting are significant predictors of PrEP-prescribing behaviors.
Non-White PCPs and those practicing in urban and suburban settings were more likely to be in advanced
stages of change and to prescribe PrEP. This suggests that PCPs from diverse racial backgrounds and those in
urbanized areas may have better access to resources and training related to PrEP or may be more attuned to
the needs of high-risk populations. Tailored educational and support programs targeting PCPs in rural areas
and those from less diverse backgrounds are crucial. Such initiatives could help bridge the gap in PrEP-
prescribing practices and ensure more uniform access to PrEP across different regions.
Receiving specific training on PrEP significantly predicted higher stages of change and increased likelihood
of PrEP prescription. This underscores the importance of education in equipping PCPs with the knowledge
and confidence to prescribe PrEP. Integrating PrEP training into medical school curricula and continuing
medical education programs is essential. Training programs should focus on practical aspects of PrEP
prescription, including patient counseling, risk assessment, and adherence strategies.
The study found that perceived barriers such as lack of provider training, low adherence likelihood, and
potential HIV resistance negatively impacted PrEP prescribing. Conversely, facilitators such as access to
resources, streamlined insurance procedures, and patient motivation positively influenced prescribing
behaviors. Addressing provider concerns through evidence-based information and practical solutions is key.
For instance, providing data on PrEP adherence rates and addressing misconceptions about HIV resistance
can alleviate provider hesitations. Ensuring easy access to prescribing resources and simplifying insurance
processes can further support PCPs in prescribing PrEP.
PCPs who reported seeing a higher proportion of African American patients were more likely to be in
advanced stages of PrEP prescribing and to have prescribed PrEP. This aligns with the higher HIV burden
among African American communities in the Southern US and indicates a responsive adaptation by PCPs to
the needs of their patient populations. Public health initiatives should focus on areas with high African
American populations to enhance PrEP awareness and access. Collaborative efforts between community
organizations and healthcare providers can facilitate targeted outreach and support.
The study highlighted systemic barriers such as lack of insurance coverage and financial constraints as
significant impediments to PrEP uptake. These barriers were rated highly by PCPs, indicating their critical
role in limiting access to PrEP. Policy changes that expand insurance coverage for PrEP and reduce out-of-
pocket costs are vital. Advocacy efforts should focus on removing financial barriers and ensuring that PrEP
is accessible to all individuals at risk of HIV, regardless of their socioeconomic status.
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Understanding the landscape: barriers and facilitators to PrEP uptake
in the Southern United States
The Southern US presents a unique and challenging landscape for HIV prevention, particularly through the
utilization of PrEP. Despite the high efficacy of PrEP in reducing HIV transmission, its uptake remains
critically low in this region, especially among African American communities who are disproportionately
affected by HIV. This study sheds light on several factors that contribute to the low uptake of PrEP and offers
insights into potential interventions to address these barriers.
PCPs play a pivotal role in the dissemination and prescription of PrEP. However, many PCPs in the Southern
US are not adequately informed or trained about PrEP, its benefits, and its prescribing guidelines. This lack
of knowledge creates a significant barrier to the PrEP uptake. Studies have shown that providers who are
unaware of PrEP or its efficacy are less likely to discuss it with patients or prescribe it, thereby limiting
access to this vital preventive measure.
The stigma related to HIV and PrEP is a pervasive issue that affects both patients and healthcare providers.
PCPs may hold stigmatizing views towards individuals at high risk of HIV, such as African American
MSM and transgender individuals, which can hinder their willingness to prescribe PrEP. This stigma not only
impacts the provider's behavior but also discourages patients from seeking PrEP due to fear of judgment and
discrimination.
The demanding nature of primary care practice, characterized by high patient volumes and limited
consultation times, often results in preventive health discussions, including PrEP, being deprioritized.
Discussing PrEP requires a detailed conversation about HIV risk behaviors, medication adherence, and
potential side effects, and many PCPs feel that they do not have adequate time to cover it.
Systemic barriers such as lack of health insurance coverage, high out-of-pocket costs for PrEP, and
cumbersome insurance prior authorization procedures further impede the uptake of PrEP. Patients who
cannot afford PrEP or navigate the complexities of insurance may be unable to access this preventive
measure, despite its potential to significantly reduce HIV transmission.
Enhancing the knowledge and skills of PCPs through targeted training programs can significantly improve
PrEP-prescribing practices. Education that covers PrEP efficacy, prescribing guidelines, and patient
counseling can empower PCPs to incorporate PrEP discussions into their routine practice. Integrating PrEP
services into routine primary care can normalize its use and reduce stigma. This can be achieved by
incorporating PrEP discussions into standard HIV-screening protocols and utilizing electronic health record
prompts to remind providers to discuss PrEP with eligible patients.
Community-based interventions that involve partnerships between public health departments, federally
qualified health centers, and community organizations can enhance access to PrEP. These collaborations
can facilitate outreach and education efforts, particularly in underserved and high-risk communities.
Advocating for policy changes that reduce financial barriers to PrEP, such as expanded insurance coverage
and streamlined prior authorization processes, is crucial. Additionally, ensuring that PrEP is included in
medical school curricula and continuing medical education programs can address the knowledge gap among
healthcare providers.
Advancing health equity through targeted PrEP-prescribing strategies
The findings from this study provide valuable insights into how addressing the identified barriers and
facilitators of PrEP-prescribing behaviors among PCPs can promote health equity. By highlighting the role
of race and practice settings as significant predictors of PrEP prescribing, the study underscores the
necessity of tailoring educational and support programs to enhance PrEP access in rural areas and among
less diverse PCPs. These targeted interventions can help bridge the gap in PrEP-prescribing practices,
ensuring more uniform access across different regions and demographics. Furthermore, by focusing on
systemic barriers such as the lack of health insurance coverage and financial constraints, the study
emphasizes the need for policy changes to expand access to PrEP. These changes are crucial for ensuring
that all individuals at risk of HIV, regardless of socioeconomic status, have access to PrEP. Through these
efforts, public health initiatives can work toward reducing disparities in HIV prevention and enhancing
health equity in the Southern US, particularly among high-risk African American communities. Collaborative
efforts involving community organizations and PCPs can further facilitate targeted outreach and support,
ultimately contributing to a more equitable healthcare landscape.
Applying the TTM to PrEP prescribing
The TTM offers a robust framework for understanding and predicting PrEP-prescribing behaviors among
PCPs. By identifying where PCPs are in their readiness to prescribe PrEP, targeted interventions can be
designed to move them through the stages of change towards action and maintenance. For instance, PCPs in
the precontemplation stage may benefit from educational campaigns that raise awareness about the high
HIV burden in the Southern US and the effectiveness of PrEP. Those in the contemplation stage might
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require detailed information on how to discuss PrEP with patients and address common concerns. PCPs in
the preparation stage could benefit from practical training sessions that build their confidence and
competence in prescribing PrEP.
Limitations
While this study provides valuable insights into the factors influencing PrEP-prescribing behaviors among
primary care providers in the Southern United States, several limitations must be considered when
interpreting the findings. These limitations pertain to the sample size, recruitment methods, study design,
potential biases, and contextual factors. Understanding these limitations is crucial for accurately assessing
the study's contributions and for guiding future research in this area.
Sample Size
The sample size for this study was determined to be 219 participants based on a power analysis to achieve a
medium effect size with an alpha of .05 and a power of .80. However, the final dataset included 223
participants after removing incomplete responses. While this sample size was adequate for the statistical
analyses performed, it may not fully capture the diversity and variability of primary care providers'
experiences and perspectives across the entire Southern US region. The relatively small sample size limits
the generalizability of the findings and may overlook nuanced differences within subgroups of the
population.
Limitations of Internet Recruitment
Participants were recruited using social media and email due to COVID-19 lockdowns, which may have
introduced selection bias. Internet recruitment often leads to a sample that is more technologically savvy
and possibly more informed about current health trends and practices compared to the general population of
PCPs. This method might also exclude those without regular internet access or those who are less inclined to
engage with online platforms, potentially skewing the results towards more proactive and engaged
practitioners.
Cross-Sectional Study Design
The cross-sectional design of this study captures a snapshot of primary care providers' PrEP-prescribing
behaviors and attitudes at a single point in time. While this provides valuable insights, it does not allow for
the assessment of changes over time or the establishment of causal relationships. Longitudinal studies
would be more appropriate to observe trends, changes in behavior, and the impact of interventions over
time. Additionally, qualitative and mixed methods studies could be utilized to explore the underlying
reasons for providers' behaviors and attitudes, providing a deeper understanding of the contextual factors
influencing PrEP-prescribing practices.
Reporting Bias
Self-reported data are inherently susceptible to reporting bias. Participants might have provided socially
desirable responses, especially on sensitive topics like stigma and perceived barriers to PrEP prescribing. The
anonymity of the survey was intended to mitigate this, but it cannot completely eliminate the possibility of
biased reporting. Additionally, recall bias may affect the accuracy of responses related to past behaviors and
experiences.
Other limitations
Generalizability
The study focused on PCPs in 10 Southern states, which limits the generalizability of the findings to other
regions. The specific cultural, socioeconomic, and healthcare landscape of the Southern US may not be
applicable to other areas, and thus the results should be interpreted with caution when considering broader
applications.
Measurement Limitations
The survey instrument was adapted from the HIV PrEP survey used by Terndrup et al. (2019) and included
the transtheoretical model staging algorithm. Although permission was obtained to use the survey, the
validity and reliability of the instrument were not established for this specific study. Therefore, the
measurements may not accurately reflect the constructs they were intended to assess.
Impact of COVID-19
The timing of the study during the COVID-19 pandemic may have influenced the responses. The pandemic
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significantly altered healthcare practices, priorities, and resource allocation, which could have affected
PCPs' attitudes and behaviors towards PrEP prescribing. The long-term effects of these changes remain to
be seen and should be considered in future research.
These limitations highlight the need for further research with larger, more diverse samples, longitudinal
designs, and mixed methods approaches to fully understand and address the barriers and facilitators to PrEP
prescribing among primary care providers in the Southern United States.
Conclusions
In conclusion, addressing the barriers to PrEP uptake in the Southern US necessitates a comprehensive and
multifaceted strategy that encompasses enhancing PCPs' knowledge and skills, reducing stigma, improving
systemic support, and fostering community-based interventions. To achieve this, it is imperative to develop
comprehensive training programs that focus on the latest PrEP guidelines, risk assessment, and effective
communication strategies. These programs should be accessible through various platforms, including
workshops, online modules, and CME courses, facilitated by experts in HIV prevention and sexual health.
Ensuring these training opportunities are provided by reputable organizations in settings such as healthcare
conferences, clinics, and medical schools will ensure their accessibility for both current and future
healthcare professionals. Community-based interventions will play a crucial role in leveraging local
resources and cultural contexts to increase PrEP uptake. By partnering with local community centers, faith-
based organizations, and advocacy groups, educational outreach and stigma-reduction campaigns can be
effectively implemented. Initiatives such as peer-led support groups, PrEP navigation services, and mobile
health clinics are instrumental in increasing awareness about and access to PrEP. Tailoring these
interventions to the specific needs of the community and incorporating input from local stakeholders are
essential to ensure their relevance and effectiveness. Finally, employing frameworks like the TTM allows for
the development of targeted strategies that effectively increase the PrEP uptake and reduce HIV
transmission in this high-risk region. The findings from this study highlight the critical need for tailored
interventions that address the unique challenges faced by PCPs and patients in the Southern US. Ultimately,
these efforts will contribute to better health outcomes and greater health equity, underscoring the
importance of a coordinated approach in tackling the barriers to PrEP uptake in this region.
Appendices
Instrument
Informed Consent
University of Missouri Sinclair School of Nursing
Title of study: Primary Care Provider PrEP Prescribing Practices: Southern United States
Investigators(s): Daryl Traylor, PhD (c). For questions or concerns about the study, you may contact Daryl
Traylor at 480-482-0740 or via email dotcf2@mail.missouri.edu.
Purpose of the study
You are invited to participate in this research study. The purpose of this research study is to learn about
primary care provider (PCP) training experiences and knowledge, attitudes, and behaviors regarding
prescribing HIV pre-exposure prophylaxis (“PrEP”) for African Americans residing in the Southern United
States. This anonymous survey should take approximately 10 to 15 minutes to complete.
Participants
You are being asked to participate in this study because you fit these criteria: (1) You are a licensed and
practicing physician who has completed residency training, a nurse practitioner, or a physician assistant. (2)
You practice primary care medicine. (3) Your medical practice is located in one or more of the following
states: Alabama, Arkansas, Georgia, Florida, Louisiana, Mississippi, North Carolina, South Carolina,
Tennessee, or Texas.
Procedures
If you volunteer to participate in this study, you will be asked to do the following: Complete an online survey
using the Qualtrics platform.
Benefits of participation
There will be no direct benefits to you as a participant in this study. However, I hope to learn more about the
HIV PrEP prescribing practices of primary care as they pertain to African Americans in the Southern United
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States. I hope that the results will then be used to improve primary care provider HIV PrEP prescribing
practices for African Americans residing in Southern United States.
Risks of participation
There are minimal anticipated risks in this study. Personal questions are asked, which may make you feel
uncomfortable. However, the survey does not ask you to provide identifying information such as name,
address, email address, and phone number. Your answers will not be readily linked to you. In addition, all
results will be reported as a group.
Cost/compensation
There will be no financial cost to you to participate in this study. The study will take about 10-15 minutes of
your time.
Confidentiality
All information gathered in this study will be kept anonymous. As stated earlier, no reference will be made in
written or oral materials that could link you to this study. All records will be stored on a password protected,
encrypted hard drive for 3 years after completion of the study. After the storage time, the information
gathered will be destroyed as appropriate by me.
Voluntary participation
Your participation in this study is voluntary. You may refuse to participate in this study or in any part of this
study. You may withdraw at any time. You are encouraged to ask questions about this study at the beginning
or any time during the research study.
You may contact the University of Missouri Institutional Review Board (IRB) if you:
Have any questions about your rights as a study participant;
Want to report any problems or complaints; or
Feel under any pressure to take part or stay in this study.
The IRB is a group of people who review research studies to make sure the rights of participants are
protected. Their phone number is (573) 882-3181.
Participant Consent
I have read the above information and agree to participate in this study. I have been able to ask questions
about the research study. I am at least 18 years of age.
Do you consent to participate in this study?
Yes
No
The next set of questions will ask you about your eligibility for this study. Thank you for your responses.
Are you 18 years of age or older?
Yes
No
Are you a licensed physician, Advanced Practice Registered Nurse, or Physician Assistant?
Yes
No
Do you currently practice primary care medicine?
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Yes
No
In which of the following states do you practice primary care medicine in? Check all that apply:
Alabama
Arkansas
Georgia
Florida
Louisiana
Mississippi
North Carolina
South Carolina
Tennessee
Texas
None of the above
Block 1
PrEP stands for “Pre-Exposure Prophylaxis”, the use of any medicine to prevent a disease before exposure to
that disease. For the purposes of this survey, the term refers to the use of an oral antiretroviral medication
taken on a daily basis by people at high risk of exposure to HIV to prevent HIV infection. Truvada, a
combination of two antiretroviral medications tenofovir disoproxil fumarate and emtricitabine, was
approved for PrEP in 2012 by the FDA and recommended in 2014 by the CDC. Descovy, a combination of
emtricitabine and tenofovir alafenamide, was approved by the FDA in 2019. Any healthcare provider who is
authorized to write prescriptions can write a prescription for PrEP. You do not have to be an HIV specialist or
infectious disease specialist to write a prescription for PrEP.
1. Do you regularly write prescriptions for PrEP for your African American patients who have PrEP
indications?
Yes, I have been for less than 6 months.
No, but I intend to in the next 30 days.
No, but I intend to in the next 6 months.
No, and I do not intent to in the next 6 months.
Block 2
Listed below are several possible facilitators of prescribing PrEP. How important is each of these facilitators
to you in deciding whether or not to prescribe PrEP for your African American patients who have PrEP
indications?
1. How important are the following in your decision to prescribe PrEP?
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1. Not
important
2. Slightly
important
3. Moderately
important
4. Very
important
5. Extremely
important
PrEP training during residency ○ ○ ○ ○ ○
Staff or providers in your clinic who are knowledgeable
about PrEP provision ○ ○ ○ ○ ○
Access to resources such as PrEP prescription
guidelines and protocols ○ ○ ○ ○ ○
On-site support (i.e., risk reduction or adherence
counselors, social workers) ○ ○ ○ ○ ○
Clinic in-service PrEP training ○ ○ ○ ○ ○
Knowledge of PrEP's efficacy ○ ○ ○ ○ ○
Patient motivation or "buy in" to consistently and
properly use PrEP as prescribed ○ ○ ○ ○ ○
Peers who prescribe PrEP ○ ○ ○ ○ ○
Patient access to financial incentives that would lower
the cost of PrEP ○ ○ ○ ○ ○
Streamlined insurance prior authorization procedures ○ ○ ○ ○ ○
TABLE 10: Facilitators of PrEP prescribing
PrRP, pre-exposure prophylaxis
Listed below are several possible barriers of prescribing PrEP. How important is each of these barriers to you
in deciding whether or not to prescribe PrEP for your African American patients who have PrEP indications?
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1. Not
important
2.
Slightly
important
3.
Moderately
important
4. Very
important
5.
Extremely
important
Lack of provider training/education regarding PrEP ○ ○ ○ ○ ○
Lack of clinic leadership support regarding PrEP ○ ○ ○ ○ ○
Lack of clinical guidelines/protocols for prescribing/monitoring PrEP ○ ○ ○ ○ ○
Clinic and lab monitoring requirements (e.g., seeing patient and obtaining
HIV tests and STI screening every 3 months; checking renal function
every 6 months)
○○○ ○○
Staffing time constraints related to risk reduction and PrEP adherence
counseling ○○○ ○○
Lack of insurance coverage and out-of-pocket patient costs for PrEP and
related care (e.g., lab work) ○○○ ○○
Likelihood of low adherence to PrEP ○ ○ ○ ○ ○
Likelihood of HIV resistance developing ○ ○ ○ ○ ○
Patients may engage in riskier behavior while on PrEP ○ ○ ○ ○ ○
Insufficient evidence of PrEP's effectiveness ○ ○ ○ ○ ○
TABLE 11: Barriers to PrEP prescribing
PrRP, pre-exposure prophylaxis
Block 3
The following questions are about your current personal and practice demographics.
1. What is your age?
2. What is your gender?
Male
Female
Gender queer/Non-conforming
Transgender
Prefer not to answer
3. With respect to sexual orientation, how do you self-identify?
Heterosexual
Gay
Lesbian
Bisexual
Other
Prefer not to answer
4. With respect to race, how do you self-identify (select all that apply)?
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Asian or Asian American
Black or African American
Native Hawaiian and Pacific Islander
White
Multiracial
Other
Prefer not to answer
5. Do you identify as being Hispanic or Latino?
Yes
No
6. I am a...
Physician
Advanced Practice Registered Nurse
Physician Assistant
7. What is your highest degree completed?
Doctor of Osteopathic Medicine (D.O.)
Medical Doctor (M.D.)
Master of Science - Nursing (MSN) or equivalent
Doctor of Nursing Practice (DNP)
Master of Science - Physician Assistant (MSPA) or equivalent
Doctor of Medical Science - Physician Assistant (DMSPA) or equivalent
8. How would you classify the geographic area you practice in?
Urban - The U.S. Census Bureau defines an urban area as an area with 50,000 or more people.
Suburban - The U.S. Census Bureau defines suburban areas as those areas that lie on the fringes of urban
areas and are in easy commuting distance of urban areas.
Rural - The U.S. Census Bureau defines a rural area as any area outside of urban and suburban areas with a
population of 0 to 49,999 people.
9. What is the zip code of your primary care practice?
10. How many years have you practiced primary care healthcare?
11. For each racial/ethnic category, please mark the percentage of each group that makes up your clinic
patient population. Please give your best estimate.
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African American White American Indian/Alaska Native Latinx Asian Native Hawaiian or Pacific Islander
0% ○ ○ ○ ○ ○ ○
1%-25% ○ ○ ○ ○ ○ ○
26%-50% ○ ○ ○ ○ ○ ○
51%-75% ○ ○ ○ ○ ○ ○
76%-100% ○ ○ ○ ○ ○ ○
TABLE 12: Clinic patient population
12. Do you routinely screen your African American patients for HIV?
Yes
No
13. Have you ever written a prescription for PrEP for an African American patient?
Yes
No
14. Do you routinely write PrEP prescriptions for your African American patients?
Yes
No
15. Have you ever referred an African American patient for a PrEP prescription (e.g., to a PrEP provider or
Infectious Disease/HIV clinic)?
Yes
No
16. Do you routinely refer African American patients for PrEP prescriptions (e.g., to a PrEP provider or
Infectious Disease/HIV clinic)?
Yes
No
17. Before this survey, had you ever heard of PrEP?
Yes
No
18. Have you received specific training on PrEP?
Yes
No
Thank you for completing this survey!
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Additional Information
Author Contributions
All authors have reviewed the final version to be published and agreed to be accountable for all aspects of the
work.
Concept and design: Daryl O. Traylor, Melva Thompson-Robinson, Mansoo Yu, Tina Bloom, Linda Bullock,
Maithe Enriquez
Acquisition, analysis, or interpretation of data: Daryl O. Traylor
Drafting of the manuscript: Daryl O. Traylor, Maithe Enriquez
Critical review of the manuscript for important intellectual content: Daryl O. Traylor, Melva
Thompson-Robinson, Mansoo Yu, Tina Bloom, Linda Bullock, Maithe Enriquez
Supervision: Daryl O. Traylor
Disclosures
Human subjects: Consent was obtained or waived by all participants in this study. University of Missouri,
Columbia issued approval 2056192. Animal subjects: All authors have confirmed that this study did not
involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure
form, all authors declare the following: Payment/services info: All authors have declared that no financial
support was received from any organization for the submitted work. Financial relationships: All authors
have declared that they have no financial relationships at present or within the previous three years with
any organizations that might have an interest in the submitted work. Other relationships: All authors have
declared that there are no other relationships or activities that could appear to have influenced the
submitted work.
Acknowledgements
I extend my deepest gratitude to Dr. Eboni E. Anderson and Dr. Joy H. Lewis from the Department of Public
Health and Research at the A.T. Still University School of Osteopathic Medicine in Arizona. Their review and
invaluable feedback have greatly enhanced the quality and clarity of this manuscript. Their expertise and
guidance were instrumental in shaping the final version of this work. I also express my sincere appreciation
to Dr. Christopher Terndrup, Dr. Carl G. Streed Jr., Dr. Perry Tiberio, Dr. Marissa Black, Dr. John Davis, Dr.
Ariella Apfel, Dr. Oni J. Blackstock, Dr. E. Jennifer Edelman, and Dr. Gail Berkenblit for granting me
permission to adapt the survey instrument they developed for their study, "A Cross-Sectional Survey of
Internal Medicine Resident Knowledge, Attitudes, Behaviors, and Experiences Regarding Pre-exposure
Prophylaxis for HIV Infection." Their contributions have been invaluable to my research.
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