To the Editor—We read with interest the article by Karris et al [1], which describes that although a majority of North American infectious disease physicians (74%) support the provision of human immunodeficiency virus (HIV) preexposure prophylaxis (PrEP) to at-risk individuals, strikingly few (9%) had prescribed PrEP. However, there remains a paucity of data regarding the views of primary care providers, who are best poised to prescribe PrEP to at-risk, HIV-negative patients. We conducted an online survey in December 2012 to understand factors associated with intentions to prescribe PrEP among physicians in the United States. Using a model based on the theory of planned behavior, we hypothesized that physicians’ intentions to prescribe PrEP are driven by attitudes, self-efficacy, and normative beliefs [2–4].
Attitudinal questions assessed beliefs regarding PrEP-related safety, efficacy, adherence, antiretroviral resistance, risk compensation, and malpractice and insurance coverage. Self-efficacy questions evaluated physicians' confidence in identifying at-risk patients, prescribing PrEP, and monitoring patients based on the current guidance from the Centers for Disease Control and Prevention (CDC) [5, 6]. Normative beliefs were assessed by perceived peer approval of PrEP prescribing. There was good internal consistency in each domain (Cronbach α > .70), and composite domain scores were divided into quartiles for analysis.
Intentions to prescribe PrEP were assessed with the question, “In the next year, how likely are you to prescribe PrEP to the following patients?” asked for 3 populations: men who have sex with men (MSM), at-risk women, and HIV-uninfected patients in serodiscordant relationships. We compared those who responded “very likely” with those who responded “somewhat likely” or “not likely at all” to best characterize early adopters of PrEP prescribing [4].
Email messages with a link to the survey (Supplementary Figure 1) were sent to 5672 physicians in 13 metropolitan areas with the highest HIV incidence selected from the American Medical Association Physician Masterfile, with up to 3 reminder requests over 4 weeks. Of 1545 physicians who opened at least 1 invitation email, 212 clicked on the survey link, 37 were ineligible (not involved in direct patient care or a related specialty), and 146 completed the survey (cooperation rate of 9.7%) [7]. Those who clicked on the survey link were more likely to be <50 years old (62% vs 48%; P < .001) and infectious disease physicians (14% vs 3%; P < .001).
The majority of respondents were primary care providers (84%) and in private practice (59%). The sample included physicians who practiced family medicine (34%), internal medicine (38%), obstetrics/gynecology (14%), and infectious disease (14%). The average patient population was 57% female, 12% MSM, 2% HIV-infected, and 1% in an HIV-serodiscordant relationship. Most providers had heard of PrEP (86%), nearly half (47%) were aware of CDC guidance on PrEP, and 21% had cared for a patient prescribed PrEP.
Overall, 28% of physicians reported that they would be willing to prescribe PrEP to MSM, 30% to at-risk women, and 45% to HIV-negative patients in serodiscordant relationships in the next year. We used multiple logistic regression to estimate intentions to prescribe PrEP based on domain quartile score, adjusting for significant covariates (P < .05) from univariate analyses (having cared for a patient on PrEP or postexposure prophylaxis). Self-efficacy and normative belief scores in the highest quartile were independently associated with intentions to prescribe PrEP, whereas attitudes and other covariates were not (Table 1).
Table 1.
Adjusteda Odds Ratios and 95% Confidence Intervals Estimating Intentions to Prescribe Preexposure Prophylaxis to At-Risk Patients Based on Self-efficacy, Attitudes, and Normative Beliefs Quartile Scoreb
| Variable | At-Risk Female Patients |
Intentions to Prescribe PrEPc |
|||||||
|---|---|---|---|---|---|---|---|---|---|
| P Value | MSM Patients |
P Value | HIV-Uninfected Patient in Serodiscordant Relationship |
||||||
| OR | 95% CI | OR | 95% CI | OR | 95% CI | P Value | |||
| Self-efficacy score | |||||||||
| Q4 | 3.39 | (1.21, 9.52) | .02 | 4.08 | (1.42, 11.70) | .009 | 3.25 | (1.26, 8.39) | .015 |
| Q3 | 1.74 | (.62, 4.85) | .294 | 2.62 | (.94, 7.34) | .066 | 1.9 | (.78, 4.64) | .156 |
| Q2 | 1.66 | (.51, 5.34) | .40 | 0.95 | (.35, 3.63) | .937 | 1.45 | (.52, 4.04) | .476 |
| Q1 | Ref | … | Ref | … | Ref | … | |||
| Attitudes score | |||||||||
| Q4 | 0.98 | (.37, 2.60) | .971 | 0.81 | (.30, 2.17) | .67 | 0.85 | (.36, 2.01) | .719 |
| Q3 | 1.79 | (.48, 6.77) | .389 | 1.12 | (.29, 4.40) | .869 | 0.98 | (.27, 2.51) | .976 |
| Q2 | 0.9 | (.35, 2.32) | .826 | 0.87 | (.34, 2.21) | .766 | 0.55 | (.23, 1.31) | .177 |
| Q1 | Ref | … | Ref | … | Ref | … | |||
| Normative beliefs score | |||||||||
| Q4 | 6.58 | (2.09, 20.75) | .001 | 4.83 | (1.60, 14.58) | .051 | 5.51 | (1.80, 16.82) | .003 |
| Q3 | 4.33 | (1.56, 12.04) | .005 | 2.12 | (.88, 6.33) | .088 | 4.19 | (1.65, 10.63) | .003 |
| Q2 | 1.89 | (.64, 5.64) | .252 | 0.75 | (.22, 2.29) | .568 | 1.45 | (.58, 3.65) | .426 |
| Q1 | Ref | … | Ref | … | Ref | … | |||
Abbreviations: CI, confidence interval; HIV, human immunodeficiency virus; MSM, men who have sex with men; OR, odds ratio; PrEP, preexposure prophylaxis.
a Adjusted model includes having cared for a patient on PrEP or postexposure prophylaxis (these variables were not significant in the final model).
b Higher quartile indicates more favorable views toward PrEP prescribing.
c Dichotomized as those reporting that they are “very likely” to prescribe vs “somewhat likely” or “not likely” to prescribe in the next year.
Given the multiple barriers identified by Karris et al [1], our study provides insights that could guide the development of evidence-based interventions to increase PrEP prescribing. Our data support interventions that increase clinician self-efficacy (ie, online risk calculators or adherence monitoring tools) and influence normative beliefs (ie, comprehensive guidelines or endorsement from medical societies). We concur with the conclusion by Karris et al that the success of real-world PrEP will likely require a multifaceted approach [1].
Supplementary Data
Supplementary materials are available at Clinical Infectious Diseases online (http://cid.oxfordjournals.org). Supplementary materials consist of data provided by the author that are published to benefit the reader. The posted materials are not copyedited. The contents of all supplementary data are the sole responsibility of the authors. Questions or messages regarding errors should be addressed to the author.
Notes
Acknowledgements. We thank Litjen Tan, PhD, Magdalena Sobieszczyk, MD, and Jennifer Briscoe for assisting in the survey design; Eric Vittinghoff, PhD, for assisting in the data analysis, and Nancy Hessol, MSPH, for assisting in the manuscript preparation.
Financial support. This study was funded by the AMA Foundation Seed Grant Research Program and Ruth Kirschstein National Research Service Award (5T32 MH019105–25) and by the National Institutes of Mental Health of the US Public Health Service (T32 MH19105).
Potential conflicts of interest. All authors: No reported conflicts.
All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.
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