Impact of the Centers for Disease Control's HIV Preexposure Prophylaxis Guidelines for Men Who Have Sex With Men in the United States
Impact of the Centers for Disease Control's HIV Preexposure Prophylaxis Guidelines for Men Who Have Sex With Men in the United States
published online 1
. Correspondence: S. M. Jenness 1
Department of Epidemiology 1
Emory University 1
Clifton Rd 1
(). The Journal of Infectious Diseases® 1
0 Division of HIV/AIDS Prevention, Centers for Disease Control and Prevention , Atlanta , Georgia
1 The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions , e-mail
2 Global Health, Emory University
3 Center for Studies in Demography and Ecology, University of Washington , Seattle
4 Department of Anthropology
Background. Preexposure prophylaxis (PrEP) is effective for preventing human immunodeficiency virus (HIV) infection among men who have sex with men (MSM) within trial settings. Population impact will depend on clinical indications for PrEP initiation, coverage levels, and drug adherence. No modeling studies have estimated the impact of clinical practice guidelines for PrEP issued by the Centers for Disease Control and Prevention (CDC). Methods. Mathematical models of HIV transmission among MSM were used to estimate the percentage of infections averted (PIA) and the number needed to treat (NNT) under behavioral indications of the CDC's PrEP guidelines. We modeled the contribution of these indications while varying treatment coverage and adherence. Results. At 40% coverage of indicated MSM over the next decade, application of CDC guidelines would avert 1162 infections per 100 000 person-years, 33.0% of expected infections. The predicted NNT for the guidelines would be 25. Increasing coverage and adherence jointly raise the PIA, but reductions to the NNT were associated with better adherence only. Conclusions. Implementation of CDC PrEP guidelines would result in strong and sustained reductions in HIV incidence among MSM in the United States. The guidelines strike a good balance between epidemiological impact (PIA) and efficiency (NNT) at plausible scale-up levels. Adherence counseling could maximize public health investment in PrEP by decreasing the NNT.
The efficacy of daily oral antiretroviral preexposure prophylaxis
(PrEP) for the prevention of human immunodeficiency virus
(HIV) infection was established in several randomized
controlled trials (RCTs), including the iPrEx study that tested the
tenofovir disoproxil fumarate and emtricitabine formulation
among men who have sex with men (MSM) .
Intent-totreat analyses estimated a prevention benefit of 44%, with
efficacy at 73% among those with high self-reported adherence and
92% among those with serum-detectable drug levels . Poor
adherence had been a problem in establishing efficacy of
PrEP in some RCTs , but subsequent demonstration studies
have found stronger adherence in open-label settings [4, 5].
In response to these trial results, the US Food and Drug
Administration approved a label indication for the prescription of
Truvada for PrEP among uninfected persons at high risk of
infection , and the Centers for Disease Control and Prevention
(CDC) subsequently released guidelines for its use in clinical
practice . In these guidelines, PrEP is indicated for MSM
who are at “substantial risk” of infection, defined primarily by
3 behavioral criteria: unprotected (ie, condomless) anal
intercourse (UAI) in HIV status–unknown monogamous
partnerships, UAI outside a monogamous partnership, and anal
intercourse (AI) in a known-serodiscordant partnership.
Sexually transmitted infection diagnoses, another criterion, are
considered biological indications of risky sexual activity. For each
criterion, clinicians should query these indications over the
prior 6 months; any events during that “risk window” trigger
a possible indication for PrEP. The CDC supports PrEP use
as part of a comprehensive prevention plan that includes
other biomedical and behavioral prevention strategies.
The guidelines’ criteria were devised based on analyses of
RCT data . However, persons eligible for and willing to
participate in RCTs may not represent the broader target
population for interventions . Public PrEP programs also may not
replicate the extensive ancillary risk reduction and adherence
counseling components within research settings . It is
therefore critical to understand the impact of different schemes for
targeting PrEP on population-level HIV incidence.
Mathematical models provide one approach to estimating PrEP impact
[11, 12], but PrEP models of MSM to date have modeled uptake
schemes that differ from the CDC guidelines  or use static
modeling approaches that do not represent MSM sexual
partnerships relevant for the guidelines’ behavioral indications
. A model-based investigation of the CDC guidelines will
be helpful for state and local public health officials seeking to
estimate the impact of including PrEP within a comprehensive
HIV prevention plan.
In this study, we model HIV transmission dynamics among
MSM to estimate the proportion of infections averted, the
number needed to treat (NNT) with PrEP to prevent 1 new
infection, and related epidemiological outcomes after
implementing PrEP according to the CDC guidelines. The goal is to
quantify reductions in incidence associated with individual
guideline indications, separately and jointly, and to explore
the impact of varying conditions of coverage and adherence
patterns during the next 10 years.
This study uses a network-based mathematical model of HIV
transmission dynamics in an open population of MSM in the
United States. This work builds on an earlier modeling project
to investigate the causes and consequences of racial disparities
in HIV incidence among MSM in the United States .
Parameters for sexual behavior were drawn from 2 empirical studies of
MSM in Atlanta, Georgia . Our model was built and
simulated using the open-source EpiModel (version 1.2.5) software
package (http://epimodel.org) for the R statistical computing platform
. The full methodological framework for these mathematical
models, including the statistical estimation of dynamic network
models, model parameterization, simulation, and data analysis,
are provided in a Supplementary Appendix.
HIV Transmission and Progression
Similar to prior studies , this study modeled HIV
transmission over sexual contact networks using exponential random
graph models, a flexible statistical method for simulating
dynamic partnerships parameterized from behavioral data .
The modeled network had 3 components: main partners,
shorter-term casual partners with repeated contacts, and one-time
partners. The set of persons was the same in each component,
but the predictors of partnership formation varied by
partnership type, with different model terms for degree (number of
ongoing partners for each member of the pair), age homophily
(selecting partners of similar age), and sexual role segregation
(such that 2 exclusively receptive men cannot pair, nor can 2
exclusively insertive men). For main and casual partnerships,
there was a constant hazard of relationship dissolution,
reflecting the median duration of each type.
Clinical HIV progression followed the natural course of disease
and antiretroviral therapy (ART) treatment profiles . Persons
progressed through disease stages in the absence of ART with
evolving HIV viral loads that modified the rate of HIV
transmission in serodiscordant pairs. Per-act factors influencing the
transmission probability included viral load , condom use ,
receptive versus insertive sexual position , circumcision for
an insertive negative partner , and the presence of the
CCR5-Δ32 genetic allele [24, 25]. After infection, persons were
assigned into clinical care trajectories controlling rates of HIV
diagnosis, ART initiation, and HIV viral suppression, to match
empirical estimates of the prevalence of these states . ART was
associated with decreased viral load (and related transmission
risk)  and extended life span .
PrEP Indications and Uptake
The guidelines recommend assessing patients for PrEP at
diagnostic HIV testing, with a focus on the prior 6 months of
behavior (the risk window). Based on empirical data , a small
proportion of MSM (6.5%) never tested for HIV in our models,
and the remainder tested at regular intervals (approximately
yearly before and quarterly after PrEP initiation). We varied
the risk window (independent from the testing interval), from
6 months in the base scenario, to 3 and 12 months in sensitivity
analyses. Behavior was tracked over that window; any behavioral
events accumulated to establish an indication for PrEP at that
test visit. MSM were assessed for PrEP indications only at visits
in which their HIV test result was negative. At that time, MSM
were allowed to start PrEP only if the proportion of MSM on
this regimen had not surpassed a threshold coverage fraction,
which we varied from a default of 40% to between 10% and
90% in sensitivity analyses.
This model explicitly simulated PrEP eligibility based on the 3
behavioral conditions in the CDC guidelines: UAI in
monogamous partnerships with a partner not recently tested negative
for HIV, UAI outside a monogamous partnership, and AI in a
known-serodiscordant partnership . We modeled PrEP
indications for these 3 conditions separately and then jointly to
estimate their individual and combined prevention effects (Table 1).
Because of potential variations in clinical interpretation of the
guidelines, we explored 2 different functional definitions: a
“literal” version based on the specific guideline wording and a
“clinical” version that could be more realistically assessed in practice.
For condition 1, the literal version defined monogamy as both
partners in a long-term partnership having no outside
partnerships, whereas the clinical version required only the person
assessed for PrEP to exhibit monogamy . For condition 2, the
literal version considered any UAI outside a monogamous
partnership, where monogamy was defined by the number of
ongoing partnerships (sexual network degree, 1), an the clinical
version indicated PrEP if there was any UAI outside self-defined
“main” partnerships. For condition 3, the guideline definition
was AI in a known-serodiscordant partnership, but we also
modeled a high-risk variant requiring that condomless AI
Table 1. Scenario Definitions for Models of PrEP in MSM Based on CDC
Guidelines for Behavioral Indications
UAI in monogamous, HIV status–unknown partnership
2-sided monogamy assessment
1-sided monogamy assessment
UAI outside monogamous partnership
Nonmonogamy defined as degree >1 in interval
Nonmonogamy defined as nonmain partnership
AI in known-serodiscordant partnership
Any AI in partnership
Any UAI in partnership
Proportion of MSM with indications who start PrEP:
10%–90% (base model, 40%)
Proportion of MSM initiated on PrEP who are highly
adherent: 10%–90% (base model, 61.9%)
Length of historical time window to conduct risk
assessment: 3, 6 (base model), or 12 mo
Abbreviations: AI, anal intercourse; CDC, Centers for Disease Control and Prevention; HIV,
human immunodeficiency virus; MSM, men who have sex with men; PrEP, preexposure
prophylaxis; UAI, unprotected AI.
occurred in these partnerships. The CDC guidelines indicate
PrEP based on the union of conditions 1–3. We modeled 3
variants of this union, based on the different condition
combinations, comprising a plausible range of PrEP risk assessment
schemes in clinical practice.
Once men started PrEP, they returned for diagnostic
assessment at quarterly intervals. As stipulated by the guidelines, men
were assessed for ongoing risk indications yearly. MSM who
formerly had indications but then had none after a period of
no triggering behavioral conditions within the risk window
were removed from PrEP. Men starting PrEP were assigned a
fixed adherence profile reflecting an average weekly dosage.
Adherence profile parameters were drawn from an open-label
demonstration project reweighted by race to account for the
small proportion of nonwhite persons in that study . Our
base model assigned 21.1% of men as nonadherent (0 doses),
7.0% as taking <2 doses per week, 10.0% taking 2–3 doses per
week, and 61.9% taking ≥4 doses per week. In sensitivity
analyses for adherence, we varied the proportion in the highly
adherent group from 10% to 90% by proportionally reallocating
MSM into the lower 3 adherence groups. Use of PrEP resulted
in a reduction of the per-act probability of infection correlated
with adherence level: 0%, 31%, 81%, and 95% for the
nonadherent to high-adherence groups . To focus on coverage and
adherence, we did not model changes to risk behavior after PrEP
initiation in this study; to date, little evidence for risk
compensation among MSM has been found [2, 4, 29, 30].
Simulation and Analysis
Burn-in models establishing equilibrium in epidemiological
and demographic outcomes were simulated with a starting
population size of 10 000 MSM, with men entering the network at
sexual onset and exiting owing to death or reaching age 40 years.
This model was calibrated using approximate Bayesian
computation methods  fit to a stable HIV prevalence in the
population of 26%, consistent with the Atlanta studies supplying
many behavioral parameters . From there, each PrEP
scenario was simulated 250 times over 10 years.
HIV outcomes and time on PrEP were tracked under each
scenario. These were used to calculate the primary outcomes:
HIV incidence and prevalence; the number of infections averted
(NIA) and percentage of infections averted (PIA), relative to a
scenario in which there was no PrEP; and the number of
person-years on PrEP needed to prevent 1 infection (ie, the NNT).
The NIA was the difference in the cumulative incidence
between the active PrEP scenarios and a scenario in which no
PrEP was provided. The PIA was the NIA divided by the
cumulative incidence in that non-PrEP scenario. The NNT was the
person-time on PrEP divided by the reciprocal of the NIA for
each scenario. Given the stochastic framework of these models,
we presented the means and 95% credible intervals (middle
95% of simulated data) for each outcome. We developed an
online web application
(https://prism.shinyapps.io/cdc-prepguidelines) for further exploration of model results.
Table 2 provides the primary results for each behavioral
indication and the joint union of those indications, assuming 40%
coverage of indicated MSM and 62% high adherence among
those covered. Implementing PrEP consistent with CDC
guidelines under these assumptions resulted in a monotonic decline
in HIV prevalence and incidence. Under the best-performing
joint scenario for the guidelines’ indications (scenario J2),
PrEP would avert 33% of new infections among MSM over
the next 10 years. This would require treating 25 uninfected
MSM for 1 year per infection averted.
Figure 1 shows the cumulative PIA based on this J2 scenario
over 10 years. The PIA was lower at the introduction of PrEP,
with rapid, nonlinear growth as PrEP-naive MSM started
PrEP at their regular HIV testing visits. The PIA continued to
grow, more linearly, over the decade as infections were averted
through both the direct prevention benefit of PrEP among the
current users and the indirect benefit to the population by
lowering community HIV prevalence (the “downstream”
As shown in Figure 2 and Table 2, each behavioral indication
in the guidelines had a unique contribution to this overall
impact. Condition 1, targeting UAI in status-unknown
monogamous partnerships, yielded a lower PIA than the other 2
conditions. Condition 2, targeting UAI outside monogamous
25.9 (24.6–26.6) 3.55 (1.25–6.31)
Outcome (95% CrI)
12.7 (8.2–16.9) 18 (13–28)
23.2 (19.1–27.5) 23 (19–28)
partnerships, would prevent 13%–23% of infections, depending
on the definition of monogamy. PrEP indications for UAI in
non-main partnerships (condition 2b) yielded a higher PIA
but also a higher NNT than the concurrency-based definition
(condition 2a); the former was more prevalent but a
lowerrisk activity than the latter. Condition 3 had the best efficiency
(lowest NNT) of any indication but a relatively low PIA for
similar reasons. For the joint conditions reflecting the full scope of
the guidelines, the J2 scenario that combined individual
conditions 1b, 2b, and 3a averted the most infections. Given the
optimum performance of J2 in these simulations, we used it as the
indication variant for the sensitivity analyses on 2 key model
assumptions: coverage and adherence.
In Table 3, the sensitivity analyses for coverage assumed the
base adherence level (61.9% as highly adherent), whereas the
sensitivity analyses for adherence assumed the base coverage
level (40%). Both factors modified how PrEP decreased HIV
incidence among MSM. Coverage of 10% would prevent only 11%
of infections, whereas coverage of 90% would prevent 50% over
10 years. Similarly, increasing adherence among MSM with 40%
coverage lowered incidence and increased the PIA (from 33% to
40% if 90% of MSM were highly adherent). Under less
optimistic scenarios, poor adherence reduced the PIA by circulating
PrEP prescriptions among men who did not receive a
pharmacological benefit. The left panel of Figure 3 shows the
interaction between adherence and coverage for the PIA along a
continuous gradient. At low coverage levels (<30%), improving
adherence has only a marginal impact on the PIA, whereas at
higher coverage (>50%) the combined effects become linear
in their interaction.
Varying coverage and adherence also differentially modified
the efficiency of PrEP, measured by the NNT. Increasing or
decreasing coverage had minimal impact on the NNT, which
averaged 24–27 across all coverage levels; this was because the NNT
was a measure standardized to coverage as a function of
personyears on PrEP. This contrasts with adherence, because greater
adherence was associated with a lower NNT. The right panel of
Figure 3 depicts this finding; the contour bands are horizontally
oriented, indicating that increasing adherence, but not coverage,
will reduce the NNT. The efficiency of PrEP was lower with lower
Model Scenario Coverage, % 10 20
Outcome (95% CrI)
adherence, because more person-time on PrEP was diluted
among men who received no prevention benefit.
Table 3 also shows that increasing the length of the risk
assessment window during diagnostic HIV testing visits, from 6
to 12 months, had a minimal impact on the PIA (increase
from 33% to 36%). The PIA increased only marginally because
sexual behavior was temporally autocorrelated. Initiating PrEP
for MSM with less frequent risk indications would avert further
infections, yet assessment over this longer interval increased the
NNT from 25 to 27, because PrEP uptake would occur among
MSM who were at lower risk.
Implementation of CDC guidelines for PrEP would result in
significant and sustained declines in HIV prevalence and
incidence among MSM in the United States, according to our
modeling study. This assumes fixed sexual behaviors, clinical care
utilization patterns, and other factors potentially influencing
HIV transmission dynamics that could potentially reduce the
prevention benefits of PrEP. Under the 3 behavioral indications
for PrEP within the guidelines, 40% coverage of indicated MSM,
and 62% high adherence, 1162 new infections would be averted
per 100 000 person-years at risk, representing 33% of cases
expected over the next decade. This study therefore provides
strong support for the CDC HIV prevention guidelines from
a modeling framework.
The models in this study explicitly represented the CDC
guidelines’ behavioral indications for MSM based on the unique
aspects of their dynamic sexual partnership networks, using
robust statistical and mathematical modeling methods [17, 19].
The complex structure of main, casual, and one-time sexual
MSM partnership networks in which HIV infection risk occurs
contributes to the high prevalence of HIV among MSM in the
United States  and will be critical to target for any
intervention seeking to mitigate that epidemic .
Each of the behavioral indications for the guidelines resulted
in substantial averted infections in our model, but some more
than others. For conditions 1 (UAI in monogamous
status-unknown partnerships) and 2 (UAI outside monogamous
partnerships), we found important differences in prevention
benefits based on the indication definition, with the “clinical”
versions (conditions 1b and 2b) each averting nearly twice the
infections as their corresponding “literal” versions (conditions
1a and 2a). The clinical versions are therefore recommended
because of their optimal performance. Targeting MSM with any
AI in known-serodiscordant partnerships (condition 3a)
prevented more infections than limiting prescription to those
specifically with condomless AI in those partnerships (condition
3b), consistent with the guidelines’ indication of any AI .
The single greatest contributor to overall incidence reduction
in the joint scenario models was coverage level, the fraction of
the population with behavioral indications who started PrEP.
Achieving sustained high coverage will be challenging, owing
to losses at each step of the “PrEP continuum.”  Addressing
gaps in access to HIV testing and other clinical settings in which
PrEP assessment and prescription occur is critical in linking
PrEP availability to uptake. Adherence to PrEP after initiation is
the other component contributing to its success. Our base
scenarios used a heterogeneous adherence profile based on a recent
PrEP demonstration project of MSM . Open-label studies
such as ours and others  have shown greater adherence
than in blinded trials , yet both types of studies may not
reflect the long-term adherence patterns of MSM outside study
settings and throughout their sexual lifetimes. Our sensitivity
analyses included a broad range of adherence scenarios
reflecting possibilities over the next decade.
Intervention targeting generally requires a trade-off between
epidemiological impact (eg, PIA) and efficiency (eg, NNT) .
Analyses of iPrEx findings suggested that targeting PrEP more
broadly (to persons with any UAI) would prevent many more
infections than targeting a high-risk group, but at low efficiency
(NNT approaching 100) . Yet those analyses and static
transmission models were unable to account for the downstream
prevention effects of PrEP, wherein the benefits accrue from
both direct PrEP use and indirect community-level protection
Accounting for indirect effects can substantially improve the
efficiency of PrEP, lowering the NNT by 50%–100% compared
with estimates from models with direct effects only .
Although a formal economic analysis is outside the scope of this
study, the increased efficiency predicted by our findings will
translate into a higher cost-effectiveness for PrEP. Our
epidemiological model results may be incorporated into a cost-effectiveness
analysis, as others have done . Overall, taking into account
the direct and indirect effects, implementing PrEP based on the
indications in the CDC guidelines strikes a good balance
between impact and efficiency according to our study, with 33%
of infections averted and an NNT of 25.
Finally, our study highlights the critical role of adherence for
both effectiveness and efficiency. Across all levels of coverage,
increasing the proportion of MSM receiving PrEP who are
highly adherent will strongly effect the efficiency of PrEP: the
NNT could be reduced from approximately 50 with poor
adherence to 20 with optimal adherence. Increasing adherence to that
degree will be a challenge, requiring high-quality adherence
counseling , but will yield substantial public health cost
savings in scaling up PrEP for MSM in the United States .
Ongoing research is investigating mobile technologies and care
coordination models for improving PrEP adherence under
daily dosing schedules [37, 38], and event-based dosing and
long-acting PrEP formulations may provide alternative
approaches to the challenge of long-term adherence .
This study has 4 key limitations. First, we modeled the core
behavioral indications in CDC’s guidelines for PrEP use by
MSM, but not the sexually transmitted infection diagnosis
component. This would involve modeling the transmission of
multiple non-HIV infections, including their biological interactions
with HIV. This was outside the scope of the current models, but
it is planned for future research. Although diagnoses of sexually
transmitted infections may be important to consider as
independent risk factors for HIV infection , they are also typically
used in practice as indicators of behavioral risk more objective
than self-reports; these behaviors were already well captured in
our model. Second, our models assume unbiased recall of sexual
behaviors and reporting of those behaviors to clinicians
prescribing PrEP. To the extent that behaviors would be
underreported , this could overestimate PrEP performance. Third,
many of the underlying sexual behaviors, the basis of the
modeled PrEP indications in these models, have been parameterized
from 2 studies on MSM in Atlanta. Although risk behaviors of
MSM in these studies were similar to broader national data ,
the generalizability of our models to the larger population of
MSM in the United States is unknown.
In conclusion, PrEP provides a fundamental new opportunity
in HIV prevention among MSM in the United States, where
condoms and other behavioral methods of risk reduction
have been inconsistently or insufficiently used. The benefits of
PrEP in reducing HIV incidence in the next decade will require
sustained uptake and high adherence among MSM at risk of
infection through their networks of sexual partnerships. Because
the levels of PrEP use among MSM has been low to date, further
research and implementation efforts are needed to clarify the
long-term effectiveness of PrEP as part of a comprehensive
HIV prevention plan. That prevention plan should continue
to stress the importance of existing risk-reduction strategies,
such as condom use, along with PrEP for MSM as indicated.
Our study confirms that the indications for PrEP use in the
CDC guidelines strike a good balance with respect to
intervention impact and efficiency and should be used by clinicians in
determining their prescriptions. Under these conditions, PrEP
could reduce new infections by one-third over the next decade.
Acknowledgments. We thank members of the scientific and public
health advisory groups of the Coalition for Applied Modeling for Prevention
project for their input on this study, and specifically those members who
reviewed a previous version of this manuscript: James Curran, Carlos del
Rio, David Holtgrave, David Dowdy, Jane Kelley, Gregory Felzien, John
Douglas, and Nanette Benbow. We also thank Dr. Albert Liu and the research
team of the Preexposure prophylaxis (PrEP) Demonstration Project for
providing PrEP adherence data used in our model.
Disclaimer. The findings and conclusions in this article are those of the
authors and do not necessarily represent the views of the Centers for Disease
Control and Prevention (CDC).
Financial support. This work was supported by CDC (grant: U38
PS004646), the National Institutes of Health (grants: R01 HD068395, R21
HD075662, and R24 HD042828), and the Emory Center for AIDS Research
(grant: P30 AI050409).
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.
1. Grant RM , Lama JR , Anderson PL , et al. Preexposure chemoprophylaxis for HIV prevention in men who have sex with men . N Engl J Med 2010 ; 363 : 2587 - 99 .
2. Grant RM , Anderson PL , McMahan V , et al. Uptake of pre-exposure prophylaxis, sexual practices, and HIV incidence in men and transgender women who have sex with men: a cohort study . Lancet Infect Dis 2014 ; 14 : 820 - 9 .
3. Baeten JM , Grant R. Use of antiretrovirals for HIV prevention: what do we know and what don't we know ? Curr HIV/AIDS Rep 2013 ; 10 : 142 - 51 .
4. McCormack S , Dunn DT , Desai M , et al. Pre-exposure prophylaxis to prevent the acquisition of HIV-1 infection (PROUD): effectiveness results from the pilot phase of a pragmatic open-label randomised trial . Lancet 2016 ; 387 : 53 - 60 .
5. Volk JE , Marcus JL , Phengrasamy T , et al. No new HIV infections with increasing use of HIV preexposure prophylaxis in a clinical practice setting . Clin Infect Dis 2015 ; 61 : 1601 - 3 .
6. Food and Drug Administration . Background package for NDA 21-752/supplement 30 , 2012 . http://www.fda.gov/downloads/AdvisoryCommittees/CommitteesMeeting Materials/Drugs/AntiviralDrugsAdvisoryCommittee/UCM303213.pdf. Accessed 15 February 2016 .
7. Centers for Disease Control and Prevention. Preexposure prophylaxis for the prevention of HIV infection in the United States-2014: a clinical practice guideline , 2014 . http://www.cdc.gov/hiv/pdf/prepguidelines2014.pdf. Accessed 15 February 2016 .
8. Buchbinder SP , Glidden DV , Liu AY , et al. HIV pre-exposure prophylaxis in men who have sex with men and transgender women: a secondary analysis of a phase 3 randomised controlled efficacy trial . Lancet Infect Dis 2014 ; 14 : 468 - 75 .
9. Gandhi M , Ameli N , Bacchetti P , Sharp G . Eligibility criteria for HIV clinical trials and generalizability of results: the gap between published reports and study protocols . AIDS 2005 ; 19 : 1885 - 96 .
10. van der Straten A , Van Damme L , Haberer JE , Bangsberg DR . Unraveling the divergent results of pre-exposure prophylaxis trials for HIV prevention . AIDS 2012 ; 26 : F13 - 9 .
11. Cremin I , Alsallaq R , Dybul M , Piot P , Garnett G , Hallett TB . The new role of antiretrovirals in combination HIV prevention: a mathematical modelling analysis . AIDS 2013 ; 27 : 447 - 58 .
12. Desai K , Sansom SL , Ackers ML , et al. Modeling the impact of HIV chemoprophylaxis strategies among men who have sex with men in the United States: HIV infections prevented and cost-effectiveness . AIDS 2008 ; 22 : 1829 - 39 .
13. Kessler J , Myers JE , Nucifora KA , et al. Evaluating the impact of prioritization of antiretroviral pre-exposure prophylaxis in New York . AIDS 2014 ; 28 : 2683 - 91 .
14. Chen A , Dowdy DW . Clinical effectiveness and cost-effectiveness of HIV pre-exposure prophylaxis in men who have sex with men: risk calculators for real-world decision-making . PLoS One 2014 ; 9 : e108742 .
15. Sullivan PS , Rosenberg ES , Sanchez TH , et al. Explaining racial disparities in HIV incidence in black and white men who have sex with men in Atlanta, GA: a prospective observational cohort study . Ann Epidemiol 2015 ; 25 : 445 - 54 .
16. Hernández-Romieu AC , Sullivan PS , Rothenberg R , et al. Heterogeneity of HIV prevalence among the sexual networks of black and white men who have sex with men in Atlanta: illuminating a mechanism for increased HIV risk for young black men who have sex with men . Sex Transm Dis 2015 ; 42 : 505 - 12 .
17. Jenness S , Goodreau S , Morris M. EpiModel : mathematical modeling of infectious disease . 2015 ; doi:10.5281/zenodo.16767.
18. Goodreau SM , Carnegie NB , Vittinghoff E , et al. What drives the US and Peruvian HIV epidemics in men who have sex with men (MSM)? PLoS One 2012; 7:e50522 .
19. Krivitsky PN , Handcock MS. A separable model for dynamic networks . J R Stat Soc Ser B Stat Methodol 2014 ; 76 : 29 - 46 .
20. Carnegie NB , Goodreau SM , Liu A , et al. Targeting pre-exposure prophylaxis among men who have sex with men in the United States and Peru: partnership types, contact rates, and sexual role . J Acquir Immune Defic Syndr 2015 ; 69 : 119 - 25 .
21. Hughes JP , Baeten JM , Lingappa JR , et al. Determinants of per-coital-act HIV-1 infectivity among African HIV-1-serodiscordant couples . J Infect Dis 2012 ; 205 : 358 - 65 .
22. Weller S , Davis K. Condom effectiveness in reducing heterosexual HIV transmission . Cochrane Database Syst Rev 2002 ; 1 : CD003255 .
23. Wiysonge CS , Kongnyuy EJ , Shey M , et al. Male circumcision for prevention of homosexual acquisition of HIV in men . Cochrane Database Syst Rev 2011 ; 6 : CD007496 .
24. Zimmerman PA , Buckler-White A , Alkhatib G , et al. Inherited resistance to HIV-1 conferred by an inactivating mutation in CC chemokine receptor 5: studies in populations with contrasting clinical phenotypes, defined racial background, and quantified risk . Mol Med 1997 ; 3 : 23 - 36 .
25. Marmor M , Sheppard HW , Donnell D , et al. Homozygous and heterozygous CCR5-delta32 genotypes are associated with resistance to HIV infection . J Acquir Immune Defic Syndr 2001 ; 27 : 472 - 81 .
26. Cohen MS , Chen YQ , McCauley M , et al. Prevention of HIV-1 infection with early antiretroviral therapy . N Engl J Med 2011 ; 365 : 493 - 505 .
27. Goodreau SM , Carnegie NB , Vittinghoff E , et al. Can male circumcision have an impact on the HIV epidemic in men who have sex with men ? PLoS One 2014 ; 9 : e102960 .
28. Rosser B , Bockting W , Rugg D , et al. A randomized controlled intervention trial of a sexual health approach to long-term HIV risk reduction for men who have sex with men: effects of the intervention on unsafe sexual behavior . AIDS Educ Prev 2002 ; 14 (3 suppl A): 59 - 71 .
29. Liu AY , Cohen SE , Vittinghoff E , et al. Preexposure prophylaxis for HIV infection integrated with municipal- and community-based sexual health services . JAMA Intern Med 2015 ; 176 : 75 - 84 .
30. Marcus JL , Glidden DV , Mayer KH , et al. No evidence of sexual risk compensation in the iPrEx trial of daily oral HIV preexposure prophylaxis . PLoS One 2013 ; 8 : e81997 .
31. Toni T , Welch D , Strelkowa N , Ipsen A , Stumpf MPH . Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems . J R Soc Interface 2009 ; 6 : 187 - 202 .
32. Kelley CF , Kahle E , Siegler A , et al. Applying a PrEP continuum of care for men who have sex with men in Atlanta , GA. Clin Infect Dis 2015 ; 61 : 1590 - 7 .
33. Holtgrave D , Qualls N , Curran JW , Valdisseri R , Guinan M , Parra W. An overview of the effectiveness and efficiency of HIV prevention programs . Public Heal Rep 1995 ; 110 : 134 - 46 .
34. Ouellet E , Durand M , Guertin JR , LeLorier J , Tremblay CL . Cost effectiveness of “on demand” HIV pre-exposure prophylaxis for non-injection drug-using men who have sex with men in Canada . Can J Infect Dis Med Microbiol 2015 ; 26 : 23 - 9 .
35. Ware N , Wyatt M , Haberer J , et al. What's love got to do with it? explaining adherence to oral antiretroviral pre-exposure prophylaxis (PrEP) for HIV serodiscordant couples . J Acquir Immune Defic Syndr 2012 ; 59 : 463 - 8 .
36. Paltiel A , Freedberg K , Scott C , et al. HIV preexposure prophylaxis in the United States: impact on lifetime infection risk, clinical outcomes, and cost-effectiveness . Clin Infect Dis 2009 ; 48 : 806 - 15 .
37. Mayer KH , Hosek S , Cohen S , et al. Antiretroviral pre-exposure prophylaxis implementation in the United States: a work in progress . J Int AIDS Soc 2015 ; 18 ( 4 suppl 3 ): 19980 .
38. Lucas J , King G , Watkins P , et al. The utilization of good participatory practice (GPP) during the planning and implementation of a PrEP study among black MSM . AIDS Res Hum Retroviruses 2014 ; 30 :A52.
39. Molina J-M , Capitant C , Spire B , et al. On-demand preexposure prophylaxis in men at high risk for HIV-1 infection . N Engl J Med 2015 ; 373 : 2237 - 46 .
40. Crepaz N , Marks G , Liau A , et al. Prevalence of unprotected anal intercourse among HIV-diagnosed MSM in the United States: a meta-analysis . AIDS 2009 ; 23 : 1617 - 29 .
41. Prevalence and awareness of HIV infection among men who have sex with men21 cities , United States , 2008 . MMWR Morb Mortal Wkly Rep 2010 ; 59 : 1201 - 7 .