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MAJOR ARTICLE
Antiretroviral Therapy and Pre-exposure
Prophylaxis: Combined Impact on HIV
Transmission and Drug Resistance in South
Africa
Ume L. Abbas,1Robert Glaubius,1Anuj Mubayi,1,a Gregory Hood,2and John W. Mellors3
1
Departments of Infectious Diseases and Quantitative Health Sciences, Cleveland Clinic, Ohio;
2
Pittsburgh Supercomputing Center and
3
Division of
Infectious Diseases, School of Medicine, University of Pittsburgh, Pennsylvania
(See the editorial commentary by Celum et al on pages 189–91)
Background.The potential impact of antiretroviral therapy (ART) and pre-exposure prophylaxis (PrEP) with
overlapping and nonoverlapping antiretrovirals (ARVs) on human immunodeficiency virus (HIV) transmission
and drug resistance is unknown.
Methods.A detailed mathematical model was used to simulate the epidemiological impact of ART alone, PrEP
alone, and combined ART + PrEP in South Africa.
Results.ART alone initiated at a CD4 lymphocyte cell count <200 cells/µL (80% coverage and 96% effective-
ness) prevents 20% of HIV infections over 10 years but increases drug resistance prevalence to 6.6%. PrEP alone
(30% coverage and 75% effectiveness) also prevents 21% of infections but with lower resistance prevalence of 0.5%.
The ratio of cumulative infections prevented to prevalent drug-resistant cases after 10 years is 7-fold higher for
PrEP than for ART. Combined ART + PrEP with overlapping ARVs prevents 35% of infections but increases resis-
tance prevalence to 8.2%, whereas ART + PrEP with nonoverlapping ARVs prevents slightly more infections (37%)
and reduces resistance prevalence to 7.2%.
Conclusions.Combined ART + PrEP is likely to prevent more HIV infections than either strategy alone, but
with higher prevalence of drug resistance. ART is predicted to contribute more to resistance than is PrEP. Optimiz-
ing both ART and PrEP effectiveness and delivery are the keys to preventing HIV transmission and drug resistance.
Keywords.antiretroviral therapy; ART; chemoprophylaxis; HIV drug resistance; HIV epidemic; HIV transmis-
sion; mathematical model; pre-exposure prophylaxis; PrEP; South Africa.
Oral antiretroviral (ARV) pre-exposure prophylaxis
(PrEP) is a new biomedical intervention against human
immunodeficiency virus (HIV) transmission with
proven efficacy [1–3]. There is concern, however, about
the potential emergence and spread of HIV drug resis-
tance arising from the rollout of PrEP, particularly
in resource-constrained settings, where antiretroviral
therapy (ART) options are limited [4]. This concern is
amplified by the possibility that the same ARVs will
be used for both ART and PrEP. The combination of 2
nucleoside reverse-transcriptase inhibitors, tenofovir
(TDF) and lamivudine or emtricitabine (3TC or FTC,
respectively), with 1 nonnucleoside reverse-transcriptase
inhibitor (NNRTI), efavirenz or nevirapine, is the
World Health Organization–recommended first-line
ART regimen in several countries worldwide, including
South Africa [4], and TDF or TDF + FTC have shown
efficacy in HIV prevention trials [1–3]. Thus far, only 9
Received 22 October 2012; accepted 15 January 2013; electronically published 9
April 2013.
Presented in part: 18th Conference on Retroviruses and Opportunistic Infections,
Boston, Massachusetts, 27 February–2 March 2011. Abstract 98LB. 6th IAS Confer-
ence on HIV Pathogenesis, Treatment and Prevention, Rome, Italy, 17–20 July
2011. Abstract TUPE364.
a
Present affiliation/address: Department of Mathematics, NEIU, 5500 North St
Louis Ave, Chicago, Illinois.
Correspondence: Ume L. Abbas, MD, MS, Departments of Infectious Diseases
and Quantitative Health Sciences, Cleveland Clinic Foundation, 9500 Euclid Ave/
G21, Cleveland, OH 44195 (abbasu@ccf.org).
The Journal of Infectious Diseases 2013;208:224–34
© The Author 2013. Published by Oxford University Press on behalf of the Infectious
Diseases Society of America. All rights reserved. For Permissions, please e-mail:
journals.permissions@oup.com.
DOI: 10.1093/infdis/jit150
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drug-resistant cases have been observed among clinical trial
participants on PrEP, most of whom had unrecognized acute
infection at enrollment. However, clinical trials of PrEP are not
designed to address the population-level and/or long-term epi-
demiological impact of PrEP, including consequences of drug
resistance. We therefore used a mathematical model [5]to
examine the potential impact of orally administered overlap-
ping and nonoverlapping PrEP and ART on HIV transmission
and drug resistance in South Africa.
METHODS
Model Structure
We developed and analyzed a detailed mathematical model to
assess the impact of PrEP and ART implementation on the
adult population (aged 15–49 years) of South Africa, using de-
terministic and stochastic modeling techniques and the pro-
gramming language C/C++. The model describes population
and epidemiological stratifications based on gender (male;
female), sexual activity (high; medium; low; lowest), PrEP and
ART use status (on; not on), infection status (susceptible; in-
fected), stage of HIV infection (acute preseroconversion; acute
postseroconversion; early chronic; late chronic; AIDS), and
HIV drug susceptibility (drug-sensitive; drug-resistant). Model
parameter assignments are made using recent results from
PrEP trials [1–3,6,7] and data mainly from South/sub–
Saharan Africa on HIV disease progression [8], infectivity [9],
sexual behavior [10], ART rollout [4,11–18], and HIV drug re-
sistance [19–33]. The model is calibrated to simulate the HIV
epidemic in South Africa with adult HIV prevalence (Supple-
mentary Figure S1) reaching 17% at the end of 2003, having a
female-to-male prevalence ratio of 1.6 and HIV incidence near
2.4% [34]. A simplified model structure is shown in Supple-
mentary Figure S2 and model input parameters are shown in
Tables 1 and 2and Supplementary S1. Model equations and
details are provided in the Supplementary Text S1.
Table 1. Model ART-Related Input Parameters
Parameter
Uncertainty
Symbol Base Case LHS Range Unit Reference
ART coverage
Start of ART rollout 2004 [17]
% of eligible individuals enrolled in ART at 2010 55% [18]
% of eligible individuals enrolled in ART at 2012 Θ80% 65%–95% [11]
Coverage beyond 2012 80% 65%–95% Per year
ART dropout
During first year of ART 1/η
H
0.10 0.05–0.15 Per year [13,16]
During subsequent years of ART 1/η
T
0.05 0.025–0.075 Per year [13,16]
Infectivity relative to WT virus
On suppressive ART 4% 1%–27% [36]
With acquired ART resistance 75% 37.5%–100% [19,22,25,27]
With transmitted ART resistance 100% 50%–100% [24,27]
Disease progression
Mortality in first year of suppressive ART ϖ
H
0.1 0.05–0.15 Per year [13,14,16]
Mortality in subsequent years of suppressive ART ϖ
T
0.05 0.025–0.075 Per year [13,14,16]
Relative to WT disease progression with acquired
majority ART resistance, on or off ART
75% 37.5%–100% [19,22,25]
Relative to WT disease progression while
nonadherent to ART
100% 100%
Virologic failure
WT virus failure rate during first year of ART
^
LHW 20% 10%–30% Per year [19,30,32]
WT virus failure rate during subsequent years of ART
^
LTW 5% 2.5%–7.5% Per year [19,30]
DR virus failure rate during first year of ART
^
LHV 50% 25%–75% Per year [29]
DR virus failure rate during subsequent years of ART
^
LTV 15% 7.5%–22.5% Per year [29]
% failing first year ART due to NA (no acquired DR) ^
a
H4% Per year [32]
% failing in subsequent years of ART due to NA (no
acquired DR)
^
a
T2% Per year [32]
Persistence time of transmitted ART resistance ψ
R1
3 1.5–4.5 Years [23,26,33]
Persistence time of acquired ART resistance ψ
R2
0.25 0.125–0.375 Years [20,31]
Abbreviations: ART, antiretroviral therapy; DR, drug resistance; LHS, Latin hypercube sampling; NA, nonadherence; PrEP, pre-exposure prophylaxis; WT, wild-type.
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HIV Drug Resistance
We stratify HIV-infected individuals based on their ARV
status, HIV drug susceptibility, type of drug resistance, and
virus population dynamics of drug-resistant HIV, including
persistence and reversion of resistance [35]. The model tracks
individuals infected with different viral variants over time,
either untreated, on PrEP, or on ART. We do not explicitly rep-
resent different drug-resistant mutants but assume the emer-
gence and transmission of 184V with PrEP use [1,2,6]; and
although several different mutations may arise with ART use
(such as 103N, 106M, 181C, 184V, 65R), 184V is the most
common. “Transmitted resistance”may occur from a donor
either on PrEP, not on PrEP, on ART, or not on ART, having a
majority population of drug-resistant virus, to a recipient either
on or not on PrEP. “Acquired resistance”may occur due to de
novo selection on PrEP or ART in persons with wild-type in-
fection, reemerge from archived drug-resistant variants on
PrEP or ART, or persist/accumulate on ART. Upon removal of
drug pressure, either by discontinuation of ART or PrEP or
transmission to a recipient not on PrEP or ART, the drug-
Table 2. Model PrEP-Related Input Parameters
Parameter
Uncertainty
Symbol Base Case LHS Range Unit Reference
PrEP Program
% of individuals enrolled in PrEP (coverage)
w
30% 15%–45% . . .
% of inappropriate PrEP use among
individuals with established infection
~
w
2.5% . . . . . .
Initial year of PrEP deployment 2012 . . .
Time to reach target coverage 5 2.5–7.5 Years . . .
HIV testing frequency in PrEP program 1=
f
P63–9 Months . . .
HIV testing frequency in general population 1=
f
~
P1 . . . Years . . .
Average duration of PrEP use 1/σ5 2.5–7.5 Years . . .
Effects of PrEP
Efficacy of PrEP for WT or reverted virus ξ
W
90% 70%–99% [1,2]
Adherence when highly/poorly adherent θ95%/1% 80%–99%/1–79% [1]
Proportion highly/poorly adherent 88%/12% 10%–90%/90–10% [1]
Efficacy of PrEP against resistant virus ξ
R
,ξ
Q
0.25 ξ
W
0.125 ξ
W
–0.375ξ
W
Relative infectivity while on PrEP with WT or
reverted virus
100% 50%–100% [2]
Relative infectivity of acquired PrEP-resistant
virus, on or off PrEP
75% 50%–100% [2]
Relative infectivity of transmitted PrEP-
resistant virus, on or off PrEP
100% 50%–100% [2,21,27]
Time to acquisition of PrEP resistance with
WT virus in entire cohort
t
1
0.5 0.25–0.75 Years . . .
Time to acquisition of PrEP resistance with
reverted resistant virus in entire cohort
t
2
0.5t
1
0.25t
1
–0.75t
1
Years . . .
Rate of PrEP resistance acquisition with WT
virus
π
W
−ln (1−0.99θ)/t
1
−ln (1−0.99θ)/t
1
Per year . . .
Rate of PrEP resistance acquisition with
reverted resistant virus
π
r
1
,π
q
1
−ln (1−0.99θ)/t
2
−ln (1−0.99θ)/t
2
Per year . . .
Persistence time of transmitted PrEP
resistance
ψ
Q
1
21–3 Years [2,26,27,33]
Persistence time of acquired PrEP resistance ψ
Q
2
0.125 0.0625–0.1875 Years [2]
Relative disease progression rates
While on PrEP with WT infection 100% 50%–100% [2]
With acquired resistance to PrEP, on or off
PrEP
75% 50%–100% [2]
While on PrEP with transmitted or reverted
resistant infection
100% 50%–100% [2,21,27]
With transmitted resistant virus and no ARV
pressure
100% 50%–100% [2,21,24,27]
Abbreviations: ART, antiretroviral therapy; ARV, antiretroviral; HIV, human immunodeficiency virus; LHS, Latin hypercube sampling, PrEP, pre-exposure prophylaxis;
WT, wild-type.
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resistant virus may revert to drug-sensitive virus after a period
of persistence. Prior to reversion, drug-resistant variants com-
prise the majority population, whereas following reversion,
they become a minority population [35].
ARV Interventions, Base-Case Scenarios, and Model Analyses
We simulate 3 different rollout strategies for ARV-mediated HIV
prevention—ART alone, PrEP alone (a hypothetical illustration),
and ART + PrEP—and compare the epidemiological outcomes
with an ARV-naive epidemic. For each strategy, we first construct
and analyze a reference-case (base-case) scenario using a defined
set of input parameters, including estimates of the effectiveness
of ART and PrEP for prevention of HIV from the HPTN 052
clinical trial [36] and the Partners PrEP study [1], respectively;
followed by uncertainty and sensitivity analyses [37].
Base-Case Analyses
ART Rollout and Effectiveness. In our model, individuals
become treatment eligible at CD4 lymphocyte cell counts <200
cells/µL [11]. Treatment scale-up starts at the end of 2003 [17]
and the proportion of eligible persons on ART (ie, coverage)
reaches 55% by the end of 2009 [18] and 80% by the end of
2011 [11]. Coverage is then maintained at 80% throughout the
simulation [11]. To represent the current situation in South
Africa, we simulate 2 additional scenarios of expanded ART
rollout in which treatment eligibility threshold changes at the
end of 2009 to include individuals with CD4 counts between
200 and 350 cells/µL [4], reaching 66% coverage at CD4 count
threshold <350 cells/µL by the end of 2011 [15]. Coverage is
then: (1) maintained at the 66% level (termed status-quo cover-
age) or (2) increased to reach 80% at the end of 2016 [12] and
maintained thereafter (termed optimized coverage). We model
only first-line ART with conservative coverage to focus on the
interplay between first-line ART and PrEP, assuming that
access to second-line regimens [38] and drug-resistance testing
[39] is limited. In base-case analyses, we assume ART reduces
HIV transmission by 96% [36]. Our model represents virologic
suppression and failure (with/without drug resistance),
dropout, survival, and HIV transmission during the first and
subsequent years of ART.
PrEP Rollout and Effectiveness. The effectiveness of PrEP
against HIV acquisition is a composite of efficacy and adher-
ence [40]. The Partners PrEP study showed the effectiveness of
oral TDF + FTC PrEP to be 75% (95% confidence interval [CI],
55–87); with 90% efficacy of PrEP in those with near-perfect
adherence, and only 12% of subjects having less than 80% ad-
herence [1].
We therefore stratify individuals into 2 groups based on their
level of adherence to PrEP: high or low. For base-case analyses,
we assume that close to 90% of individuals have 95% adherence
and about 10% have low (near zero) adherence. However, given
the conflicting results from different PrEP trials (TDF + FTC
was ineffective in the Fem-PrEP trial [6], and oral TDF was in-
effective in the VOICE trial [7]), for uncertainty and sensitivity
analyses we use a wide range of input estimates for PrEP effica-
cy and adherence and the proportion of individuals in the 2
(high/low) adherence groups.
PrEP (TDF + FTC) scale-up starts in 2012 and achieves 30%
coverage over a 5-year period that is then maintained. We
assume that PrEP is about 90% efficacious against wild-type
virus [1,2] and that the average duration of PrEP use is 5 years
in susceptible individuals with HIV testing every 6 months
(and PrEP discontinuation if HIV infection occurs). For the
ART + PrEP strategy, in addition to our base-case scenario with
overlapping drugs (ie, cross-resistance) between PrEP (TDF +
FTC) and ART (TDF + FTC + NNRTI), we simulate an alter-
nate scenario with identical model input and structural as-
sumptions except for there being no overlap/cross-resistance
between ART and PrEP.
Uncertainty Analyses
We perform uncertainty analyses to estimate the extent of varia-
tion in our projections across a broad range of input parameter
estimates that include the following assumptions (Tables 1and
2): ART effectiveness is 73%–99%; PrEP efficacy against wild-
type virus is 70%–99%; PrEP adherence among individuals
highly adherent is 80%–99% and among poorly adherent is 1%–
79%; the proportion of individuals highly adherent is 10%–90%;
PrEP coverage is 15%–45%; average duration of PrEP use is 2.5–
7.5 years; the frequency of HIV testing under the PrEP program
is 3–9 months; and the time by which about 100% of wild-type
virus recipients acquire PrEP resistance from inappropriate PrEP
use with perfect adherence is 3–9 months with the median time
to acquired resistance of about 1 month [41]. We perform
50 000 simulations using Latin hypercube sampling (LHS) for
each ARV-based strategy, and compute the epidemiological out-
comes (median and interquartile range [IQR]) in comparison
with an ARV-naive baseline epidemic. We also calculate the out-
comes for the overlapping and nonoverlapping ART + PrEP
strategies in comparison with ART alone as baseline.
Sensitivity Analyses
We conduct sensitivity analyses to identify those parameters
that exert the greatest influence on the predicted model out-
comes for each strategy. For these time-dependent multivariate
analyses, we use the input and output data from our uncertain-
ty analyses to derive standardized regression coefficients. In ad-
dition, we examine the sensitivity of the model’s predictions to
the modeling technique by comparative analyses of our sto-
chastic and deterministic model simulations.
Inappropriate PrEP Use
We simulate 2 contexts of inappropriate PrEP initiation and
use by previously infected individuals by extending our
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PrEP-alone and ART + PrEP base-case scenarios. In the first,
individuals in the preseroconversion phase of acute HIV infec-
tion are started on PrEP (“window use”). In the second, indi-
viduals with undiagnosed established HIV infection start PrEP
inappropriately at a rate of 2.5% per year (“general use”). The
duration of inappropriate PrEP use following seroconversion is
determined by the HIV testing interval assumed for the PrEP
program (6 months for base-case; LHS range: 3–9 months). For
general use, the duration is determined by the frequency of
population surveillance (1 year for base-case).
RESULTS
Prevention of HIV Transmission
Base-Case Scenarios
Figure 1Ashows the impact of different ARV-based strategies
on HIV prevention after 10 years compared with an ARV-naive
epidemic. ART alone is projected to prevent 20% of HIV
infections (0.92 million). Similarly, PrEP alone prevents 21%
(0.96 million) of HIV infections. The combined strategy of
ART + PrEP is predicted to be most effective, reducing infec-
tions by 35% (>1.6 million) with overlapping regimens and
37% (>1.7 million) with nonoverlapping ARV regimens.
Expanded ART Rollout
The scenarios, which expand treatment rollout to include cov-
erage at a CD4 count <350 cells/µL, result in modest increase in
infections prevented when measured against the base-case sce-
narios of ART alone and overlapping ART + PrEP (Figure 1B).
Coverage at 66% (status-quo coverage) respectively prevents
23% and 38% of infections, while 80% coverage (optimized cov-
erage) prevents 28% and 41% of infections versus 20% and 35%
for the base-case ART-alone and ART + PrEP scenarios.
Prediction Uncertainty of HIV Prevention
Figure 2Ashows the results of uncertainty analyses for the 3
ARV-based strategies. The median decrease in HIV infections with
ART alone after 10 years is 15% (IQR, 12%–19%), PrEP alone is
14% (IQR, 10%–18%), overlapping ART + PrEP is 27% (IQR,
22%–31%) and non-overlapping PrEP is 28% (IQR: 23%–33%).
Overlapping ART + PrEP (Figure 2C) prevents a median of
12.7% (IQR, 9.1%–17.2%) more infections than ART alone.
Results are similar for nonoverlapping ART + PrEP (median,
14%; IQR, 10%–18.9%).
HIV Drug Resistance
Base-Case Scenarios
Figure 3Ashows the impact of different ARV-based strategies
on HIV drug resistance prevalence compared with an ARV-
naive epidemic. After 10 years of PrEP alone, the prevalence of
overall resistance is low at 0.5% (20 090 cases). Drug resistance
prevalence is higher from the ART-alone strategy at 6.6%
overall (307 254 cases) with 4.2% acquired (195 758 cases) and
2.4% transmitted resistance (111 497 cases). The prevalence of
resistance increases further from overlapping ART + PrEP to
8.2% (339 895 cases) with the prevalence of acquired and trans-
mitted ART resistance increasing to 4.6% and 3.3%, respective-
ly. With nonoverlapping ART + PrEP, drug resistance
prevalence falls modestly to 7.2% due to a lower prevalence of
transmitted ART resistance (2.2%). In terms of the number of
prevalent cases of drug resistance (data not shown), acquired
ART resistance falls modestly from both overlapping and non-
overlapping ART + PrEP, when measured against ART alone;
however, transmitted ART resistance rises with overlapping but
falls with nonoverlapping ART + PrEP. Both acquired and
transmitted cases of PrEP resistance fall from ART + PrEP
when measured against PrEP alone.
Expanded ART Rollout
The scenarios of expanded ART rollout result in a modest in-
crease in drug resistance prevalence when measured against the
Figure 1. A, Cumulative new HIV infections prevented after 10 years
(2012–2022) compared to a naive epidemic, assuming base-case scenari-
os. B, Cumulative new HIV infections prevented after 10 years (2012–
2022) compared to a naive epidemic, assuming scenarios with different
treatment eligibility thresholds and levels of coverage. Abbreviations: ART,
antiretroviral therapy; HIV, human immunodeficiency virus; PrEP, pre-expo-
sure prophylaxis.
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base-case scenarios of ART alone and overlapping ART + PrEP
strategies (Figure 3B). Drug resistance prevalence increases to
8.3% and 10.1% in status-quo coverage scenarios and 11.4%
and 13.4% in optimized coverage scenarios, versus 6.6% and
8.2% in the base-case scenarios of ART alone and ART + PrEP,
respectively.
Ratio of Cumulative Infections Prevented to Prevalent and
Incident Drug-Resistant Cases
To compare the resistance consequences of different ARV-based
strategies, we calculated ratios of cumulative infections prevented
to resistance over 10 years, either defined as prevalent cases (pre-
vailing cases with majority drug-resistant variants; Figure 4A)or
incident (new cases of transmitted or acquired drug resistance;
Figure 4B). PrEP alone prevents about 48 infections for each
prevalent drug-resistant case and more than 5 infections for each
incident drug-resistant case. Inappropriate window-use in the
PrEP-alone strategy decreases these ratios modestly to 46 and
4.8, respectively. By contrast, inappropriate general-use PrEP
markedly reduces the ratios to 10 and 1, respectively. ART alone
prevents about 7 infections for each prevalent drug-resistant case
and about 1 infection for each incident drug-resistant case,
which is 6- to 7-fold lower than for PrEP. The prevention-
resistance ratios for prevalent and incident cases are 9.8 and 1.4,
respectively, for overlapping ART + PrEP, and 14.7 and 1.7,
respectively, for nonoverlapping ART + PrEP.
Figure 3. A, Prevalence of drug resistance after 10 years (2012–2022),
assuming base-case scenarios. B, Prevalence of drug resistance after 10
years (2012–2022), assuming scenarios with different treatment eligibility
thresholds and levels of coverage. Columns of different colors represent
the prevalence of overall drug resistance and acquired and transmitted
drug resistance from ART and PrEP. Abbreviations: ART, antiretroviral
therapy; PrEP, pre-exposure prophylaxis.
Figure 2. Uncertainty analyses. Results of 50 000 simulations are
shown as columns representing the median values and bars representing
the interquartile range. A, Cumulative new HIV infections prevented after
10 years (2012–2022), compared to a naive epidemic. B, Prevalence of
drug resistance after 10 years (2012–2022). Panel C: Cumulative new HIV
infections prevented and prevalence of drug resistance from ART + PrEP
after 10 years (2012–2022), compared to an epidemic with ART alone. Ab-
breviations: ART, antiretroviral therapy; HIV, human immunodeficiency
virus; PrEP, pre-exposure prophylaxis.
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Prediction Uncertainty of HIV Drug Resistance
Figure 2Bshows the results of uncertainty analyses for HIV
drug resistance outcomes from different ARV-based strategies.
After 10 years, the median overall prevalence of drug resistance
from ART alone is 5.9% (IQR, 4.6%–7.4%), from PrEP alone is
0.5% (IQR, 0.3%–0.7%), from overlapping ART + PrEP is 7%
(IQR, 5.6%–8.8%), and from nonoverlapping ART + PrEP is
6.5% (IQR, 5.2%–8.1%). These finding are consistent with our
base-case scenarios.
Overlapping ART + PrEP compared to ART alone
(Figure 2C), increases the number of prevalent overall and
transmitted ART-resistant cases after 10 years by a median
8.8% (IQR, 5.8%–13.1%) and 15.9% (IQR, 11.4%–21.9%), re-
spectively, while modestly decreasing the number of acquired
ART-resistant cases (median, −0.9%; IQR, −1.8% to 0%). Non-
overlapping ART + PrEP decreases the overall drug resistance
prevalence at 20 years (median, −4%; IQR, −7.5% to −0.7%).
Inappropriate PrEP Use
Inappropriate PrEP use by persons infected at baseline increas-
es HIV drug resistance from PrEP. When measured against the
overlapping ART + PrEP base-case, an overlapping ART +
PrEP strategy that includes inappropriate window-use PrEP
prevents almost the same number of infections (1.63 million),
with a modest increase (8.3% vs 8.2%) in the prevalence of re-
sistance (data not shown). In contrast, overlapping ART + PrEP
with inappropriate general-use PrEP leads to an increase in the
overall resistance prevalence from 8.2% to over 10%, with ac-
quired PrEP resistance rising to 1.3% from 0.2% and transmit-
ted PrEP resistance to 0.4% from 0.1% (data not shown).
Nonoverlapping ART + PrEP with inappropriate general-use
PrEP raises the overall resistance prevalence to 8.5% (data not
shown).
Sensitivity Analyses
The results of the sensitivity analyses are described in detail in
Supplementary Text S1 and summarized in Table 3.
DISCUSSION
The important insights derived from our study are several.
First, an ART strategy of treatment initiation at a CD4 count
<200 cells/µL combined with PrEP prevents more infections
than either ART alone or PrEP alone; however, the incremental
benefit of PrEP critically depends on PrEP efficacy, adherence,
and coverage. Second, the prevalence of HIV drug resistance is
largely driven by ART in both ART alone and ART + PrEP
strategies. Third, PrEP alone results in low prevalence of drug
resistance; high PrEP adherence leads to fewer infections and
less opportunity for acquired resistance, while low adherence
leads to predominantly wild-type breakthrough infections
because of low drug pressure for emergence of acquired resis-
tance. Fourth, use of overlapping ARVs for both ART and
PrEP could increase drug-resistance prevalence compared to
ART alone due to more frequent transmitted resistance. By
contrast, resistance prevalence falls with non-overlapping
ART + PrEP; however, this decrease is modest because the prin-
cipal driver of resistance is ART, not PrEP. Fifth, inappropriate
PrEP initiation among individuals with undetectable HIV in-
fection produces only a minor increase in the overall resistance
prevalence; however, inappropriate PrEP use among persons
with established HIV infection could significantly increase
drug resistance from PrEP. Lastly, PrEP prevents many more
infections per case of resistance than ART does.
The extent of coverage and the degree of effectiveness against
HIV transmission are the principal determinants of the infec-
tions prevented with ART. Similarly, PrEP coverage and effec-
tiveness against HIV acquisition are the key determinants of
the additional preventive benefit of ART + PrEP. The paradigm
of test and treat [42] has gained considerable momentum, and
the HPTN 052 trial [36] has provided the needed proof of
concept for ART-based prevention, though its population-level
impact may be limited by potential reluctance of asymptomatic
Figure 4. A, Ratio of cumulative infections prevented to prevalent drug-
resistant cases (2012–2022). B, Ratio of cumulative infections prevented
to incident drug-resistant cases (2012–2022). Window use refers to inap-
propriate PrEP initiation by persons in the preseroconversion phase of
acute HIV. General use refers to inappropriate PrEP initiation by persons
with established HIV infection at a per capita rate of 2.5%/year. Abbrevia-
tions: ART, antiretroviral therapy; HIV, human immunodeficiency virus;
PrEP, pre-exposure prophylaxis.
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Table 3. Sensitivity Analysis of Outcomes With ARV Strategies Versus Naive Epidemic at 2022
a
Model Input
b
Standardized Regression Coefficients (% variance explained)
c
ART Alone PrEP Alone ART + PrEP
Infections prevented (%)
Reduction in WT infectivity on ART 0.70 (0.49) . . . 0.47 (0.22)
ART coverage 0.59 (0.35) . . . 0.37 (0.14)
Relative infectivity of virus with acquired ART resistance −0.24 (0.06) . . .
PrEP coverage . . . 0.67 (0.45) 0.50 (0.25)
PrEP proportion highly adherent . . . 0.45 (0.20) 0.33 (0.11)
PrEP adherence (low) . . . 0.40 (0.16) 0.30 (0.09)
PrEP efficacy against WT virus . . . 0.29 (0.08) . . .
Prevalence of overall drug resistance (%)
d
Survival time on ART with acquired resistance 0.57 (0.32) . . . 0.56 (0.31)
ART coverage 0.46 (0.22) . . . 0.44 (0.19)
WT virologic failure rate during first year on ART 0.36 (0.13) . . . 0.34 (0.11)
WT virologic failure rate during subsequent years on ART 0.31 (0.09) . . . 0.30 (0.09)
% failing subsequent years on ART due to nonadherence −0.25 (0.06) . . . −0.25 (0.06)
Persistence of transmitted ART resistance 0.23 (0.06) . . . 0.25 (0.06)
PrEP coverage . . . 0.55 (0.30) . . .
Frequency of HIV testing . . . −0.50 (0.25) . . .
PrEP adherence (low) . . . 0.30 (0.09) . . .
PrEP efficacy against WT virus . . . −0.26 (0.07) . . .
Development time for acquired PrEP resistance . . . −0.25 (0.06) . . .
Prevalence of transmitted ART resistance (%)
Persistence of transmitted ART resistance 0.57 (0.32) . . . 0.55 (0.30)
Relative infectivity of virus with acquired ART resistance 0.45 (0.20) . . . 0.46 (0.21)
Survival time on ART with acquired resistance 0.35 (0.12) . . . 0.35 (0.12)
ART coverage 0.30 (0.09) . . . 0.29 (0.08)
WT virologic failure rate during first yr on ART 0.25 (0.06) . . . 0.24 (0.06)
Prevalence of acquired ART resistance (%)
Survival time on ART with acquired resistance 0.61 (0.38) . . . 0.62 (0.39)
ART coverage 0.49 (0.24) . . . 0.49 (0.24)
WT virologic failure rate during first year on ART 0.37 (0.14) . . . 0.35 (0.12)
WT virologic failure rate during subsequent years on ART 0.32 (0.10) . . . 0.32 (0.10)
% failing subsequent years on ART due to nonadherence −0.26 (0.07) . . . −0.26 (0.07)
Prevalence of transmitted PrEP resistance (%)
PrEP coverage . . . 0.51 (0.26) 0.48 (0.23)
Persistence of transmitted PrEP resistance . . . 0.39 (0.15) 0.39 (0.15)
Frequency of HIV testing . . . −0.29 (0.09) −0.30 (0.09)
PrEP adherence (low) . . . 0.29 (0.09) 0.28 (0.08)
Development time for acquired PrEP resistance . . . −0.24 (0.06) −0.25 (0.06)
Prevalence of acquired PrEP resistance (%)
Frequency of HIV Testing . . . −0.56 (0.32) −0.56 (0.31)
PrEP coverage . . . 0.52 (0.27) 0.50 (0.25)
PrEP adherence (low) . . . 0.28 (0.08) 0.27 (0.07)
PrEP efficacy against WT virus . . . −0.28 (0.08) −0.29 (0.08)
Development time for acquired PrEP resistance . . . −0.24 (0.06) −0.24 (0.06)
Abbreviations: ART, antiretroviral therapy; HIV, human immunodeficiency virus; PrEP, pre-exposure prophylaxis; SRC, standardized regression coefficient; WT, wild-type.
a
The results of sensitivity analyses are described in the Supplementary Text S1. Briefly, the principal determinants of infections prevented by PrEP alone and/or
ART + PrEP include PrEP coverage, reduction in WT viral infectivity by ART, the proportion of persons highly adherent to PrEP, and the level of PrEP adherence.
Drug resistance prevalence from ART alone and ART + PrEP is most influenced by the duration of survival on ART with acquired ART resistance and the WT
virologic failure rate during the first year of ART. PrEP coverage and the frequency of HIV testing are the key determinants of drug resistance from PrEP.
b
Parameters that contribute 5% or more of the variance in the model outcome are shown (SRC
2
≥0.05). The reported coefficients were significant (P≤.05).
c
Percentage of the variance in the predicted outcome explained by the regression model. The respective R
2
values were 0.93 (cumulative infections prevented),
0.94 (overall prevalence of resistance), 0.90 (prevalence of transmitted ART resistance), 0.95 (prevalence of acquired ART resistance), 0.79 (prevalence of
transmitted PrEP resistance), and 0.81 (prevalence of acquired PrEP resistance) for ART + PrEP scenario.
d
Proportion of cases with drug-resistant infection in the infected population.
Overlapping and Non-Overlapping ARVs •JID 2013:208 (15 July) •231
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HIV-infected persons for ART initiation. Notwithstanding
scale-up efforts, there is considerable unmet need for ART in
resource-constrained settings; about 60% of those eligible did
not have access to ART at the end of 2010 [43]. Moreover, the
population-level effect of treatment as prevention could be
limited by the actual proportion of infected individuals opti-
mally and durably suppressed on ART. In 2010, of the 1.2
million infected persons in the United States, 80% were aware
of their status, but 41% were retained in care, and only 28% had
virologic suppression [44]. The situation is much worse in sub–
Saharan Africa, where about two-thirds of HIV-infected
persons are unaware of their seropositive status [45]. In a sys-
tematic review [46], fewer than one-third of HIV-positive
persons were retained in care between HIV testing and ART
initiation. Furthermore, studies show high rates of loss to
follow-up among patients starting ART [16]. Thus, PrEP could
play an important additional role in controlling the HIV pan-
demic. Prioritized coverage with effective PrEP of individuals at
highest risk of HIV acquisition and spread could potentially
yield the optimal public health and cost benefits [40]. ART
rollout is also limited by infrequent [18] access to second-line
regimens and CD4 cell count, rather than virological monitor-
ing [4]. As a result, there are high levels of drug resistance mu-
tations among individuals with prolonged virological failure
[22,32], which may compromise both first-line [29,47] and the
limited second-line [48] ART regimens available. Our model
shows that ART drives the prevalence of HIV drug resistance in
both ART alone and ART + PrEP strategies. The principal de-
terminants of the prevalence of acquired resistance include
ART coverage, survival on ART with acquired resistance, and
the rate of treatment failure. For the prevalence of transmitted
resistance, determinants include the infectiousness of persons
with acquired ART resistance and the persistence time of trans-
mitted resistance. We find that PrEP is about 6- to 7-fold more
efficient in HIV prevention than ART in terms of ratios of in-
fections prevented to incident/prevalent drug-resistant cases
generated. Thus, improving the effectiveness of first- and
second-line ART is critical for preventing HIV infection and
controlling drug resistance.
Our model projects a low prevalence of drug resistance from
PrEP. Highly effective PrEP results in few breakthrough infec-
tions and a chance for emergence of acquired resistance. By
contrast, poorly effective PrEP fails to protect from acquisition
of wild-type HIV but also fails to exert selective pressure for
emergence of acquired resistance. Both of these phenomena
have been observed in recent PrEP trials [1,2]. However, drug
resistance from PrEP at the population level could rise with
inappropriate PrEP use among those with undiagnosed HIV
infection. While this increase is modest from inappropriate
PrEP use during the preseroconversion phase of acute infec-
tion, it becomes more pronounced with inappropriate use
among persons with established HIV. The latter may be of
concern in potential situations of unsupervised PrEP use (eg,
black-market drugs and drug sharing [49]) or inaccurate HIV
testing [50].
There are some important limitations of our model. The accu-
racy of our predictions will be affected by variations in the model
structure and sexual activity details, for which data are very
limited. We therefore employed a well-established template of
sexual behavior [40] with robust epidemiological and demo-
graphic parameterization, broadly applicable to South Africa.
Nevertheless, the HIV epidemic in South Africa is heterogeneous
and incompletely understood, with significant differences
between the demographic and HIV/AIDS epidemiological esti-
mates predicted by different agencies. HIV incidence is also not
precisely known, even when measured directly at the population
level. Although there is uncertainty regarding ARV-related pa-
rameters, we employed ranges (within plausible bounds) and
performed extensive sensitivity and uncertainty analyses. We ex-
cluded population stratification by age and analysis of prioritized
ARV implementation, as this was addressed in previous work
[40]. Because of limited access to both second-line regimens [38]
and drug-resistance testing [39] in resource-limited settings, we
chose not to represent specific drug resistance mutations or
second- or third-line ART regimens, nor do we consider HIV
subtype polymorphism. We also did not explicitly include other
influences on transmission. These and other refinements will be
included in future work, although including such parameters
greatly increases model complexity.
A key conclusion of this study is that combined ART + PrEP
can have a greater public health impact than ART alone;
however, overlapping ARVs for both can increase drug resis-
tance in resource-limited settings. Drug resistance prevalence is
predominantly driven by ART and not PrEP; consequently,
nonoverlapping strategies will produce only modest declines in
resistance. Thus, it is critical to consider the impact of ARVs
not only on prevention but also drug resistance. Improved effi-
cacy of first-line therapy and timely switching of ART to effec-
tive second-line regimens are critical for controlling HIV drug
resistance. In addition, frequent and accurate HIV testing could
minimize resistance consequences of PrEP. Our study also
highlights that poor adherence to PrEP will undermine its po-
tential impact on HIV prevention. Thus, prioritization of PrEP
to groups at most risk of HIV acquisition and counseling about
PrEP adherence are likely to maximize efficiency of PrEP and
minimize drug resistance.
Supplementary Data
Supplementary materials are available at The Journal of Infectious Diseases
online (http://jid.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.
232 •JID 2013:208 (15 July) •Abbas et al
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Notes
Financial support. This work was supported by the Bill & Melinda
Gates Foundation (OPP1005974). The funders had no role in study design,
data collection and analysis, decision to publish, or preparation of the man-
uscript.
Potential conflicts of interest. U. L. A. acknowledges grant support
from the Bill and Melinda Gates Foundation (OPP1005974). J. W. M. is a
member of the scientific advisory board of Gilead Sciences, has share
options of RFS Pharmaceuticals, and acknowledges grant support from the
AIDS Clinical Trials Group (National Institute of Allergy and Infectious
Diseases [NIAID] U01AI38858), the Microbicide Trials Network (NIAID
U01AI068633), the National Cancer Institute (Science Applications Inter-
national Corporation [SAIC] contract 20XS190A), and the Bill and Melinda
Gates Foundation (OPP1005974). All other authors report no potential
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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