Introduction
Antiretroviral therapy (ART) is increasingly used in resource-limited settings [1,2] [1,2] [1,3,4] [5–18] [19,20] [21–25] [13,26,27]
There has been concern that use of clinical rather than viral load monitoring will lead to a lower efficacy of the second-line regimen, due to increased levels of resistance to nucleoside analogue drugs used in the second-line regimen resulting from the delay in switching, and to increased rates of clinical progression and death. However, it has been demonstrated that delivery of ART and selection of patients for second-line therapy without viral load monitoring can still result in extremely low rates of clinical progression and mortality in parts of sub-Saharan Africa, such as was seen in the Development of Antiretroviral Therapy in Africa (DART) trial in Uganda and Zimbabwe over more than 5 years of follow-up (e.g. [7] [10] [18] [28–34] [32]
Methods
Scenario modelled
We used an individual-based stochastic computer simulation model with the scenario modelled being a setting with a high prevalence heterosexual epidemic which started in 1985. ART is assumed to have been introduced in 2003, with a policy of initiating therapy in those with WHO 4 disease or CD4 cell count below 200 cells/μl. The rate of diagnosis is assumed to be relatively low, such that only around 12% of all people with HIV are on ART by 2010 (44% of those in need, when ‘need’ is defined based on CD4 cell count 200), close to the overall estimate for sub-Saharan Africa [1] Fig. 1 ), and a switch in ART initiation strategy so that ART is initiated in people with CD4 cell count below 350 cells/μl, in line with new WHO advice [6]
Fig. 1: Trends from 2003 to 2020 in proportion of people with HIV who are diagnosed and proportion of people with HIV who are on ART. 2010 is the date from which diagnosis rates are increased. Based on one run pre-2010 and mean of 80 runs after 2010. Total number with HIV increases from 8920 in 2010 to mean over runs of 13158 in 2020 (range 11593 to 14548).
HIV synthesis transmission model
Sexual risk behaviour is modelled as the number of new and longer-term unprotected sex partners. As for all variables modelled, these are updated in 3-month periods. In any given period, the probability of an uninfected person having an unprotected sex partner who is infected with HIV depends on their number of partners and on the prevalence of HIV amongst partnerships formed by those of the opposite sex, accounting for patterns of age mixing. Given exposure to an infected partner, the probability of transmission depends on the viral load level of the partner (obtained by sampling from the distribution of viral load levels in partnerships formed by HIV-infected people, accounting for gender and age), on the estimated risk of transmission at that viral load, presence of a concurrent sexually transmitted infection and on gender. It should be noted that presence or not of resistance mutations does not influence the risk of transmission (i.e. virus with resistance mutations present is assumed equally transmissible as virus without such mutations, for a given viral load).
For people who have become infected with HIV the variables modelled include viral load, CD4 cell count, presence of resistance mutations, risk of AIDS and death. Resistance is modelled in terms of the presence or not of mutations specific to the drugs in use [e.g. number of thymidine analogue mutations (TAMS); presence of M184V (yes or no; y/n), K65R (y/n); L74V (y/n); Q151M (y/n), presence of a key non-nucleoside reverse transcriptase inhibitor (NNRTI) mutation (y/n); major protease inhibitor mutations (y/n)]. Distinction is made for each mutation as to whether it is only present in minority virus (if the patient has a mutation present but has stopped drugs that select for that mutation), so assumed not transmissible, or if it is present in majority virus, and hence assumed transmissible. The model of progression of HIV and the effect of ART has been shown to provide a generally close fit to observed data relating to natural progression of HIV infection and the effect of ART ([10,35,36] https://links.lww.com/QAD/A114
For a newly infected person, the probability of being infected by a person with resistant virus as their majority virus population is determined by the probability, for the given viral load level of the person from whom the virus has been acquired, that drug resistance mutations are present in the concurrent infected population, again taking into account gender, age and number of partnerships formed. It is not assumed that all resistance mutations present in majority virus of the source partner are established as a mutation in virus in the newly infected person. This is dependent on the specific mutation, in which M184V, for example, if present in the source partner is assumed to have only a 20% chance of being established as a mutation in virus in the new host (and hence compromising subsequent therapy), whereas for NNRTI mutations this is 80%. If a mutation is transmitted and established in the new host it is assumed to persist in majority virus indefinitely, with the exception of the M184V mutation which is assumed to revert to a minority variant immediately on infection. We also consider the possibility of a person who is already infected becoming super-infected, as this is potentially relevant for transmission of drug-resistant HIV. Full model details are in the Supplementary Methods (https://links.lww.com/QAD/A113
We performed uncertainty and sensitivity analysis in which we assessed the impact on our primary comparison (difference between scenarios in proportion of new infections with transmitted drug resistance in 2020) of changes in the assumptions in the model [37] https://links.lww.com/QAD/A115
Results
Figure 1 shows trends in the proportion of people with HIV who are diagnosed and on ART. These both show a modest rate of increase between 2003 and 2010, reflecting the kind of scale-up seen so far in many African countries. Table 1 shows the characteristics of the simulated HIV-infected population in 2010, which broadly reflects that of infected people in many countries (44% of those in need of ART being on ART corresponds to the UNAIDS estimate for end of 2008 for sub-Saharan Africa [1] Fig. 1 ), the proportion of people with HIV who are diagnosed is predicted to rise considerably, to 54% in 2015 and then more gradually to 63% in 2020, and likewise the proportion of all people with HIV who are on ART, predicted to rise to 37% in 2015 and 45% in 2020. This sharp rise is due to our optimistic assumption of an increase in diagnosis rate and availability of treatment after 2010.
Table 1: Breakdown of the simulated HIV infected population in 2010a .
Figure 2 shows the predicted outcomes between 2010 and 2020, broken down according to the timing of introduction of viral load monitoring. Figure 2 a shows that of those on ART, the proportion with viral load suppression is predicted to remain high, although this differs somewhat according to timing of introduction of viral load monitoring. Use of clinical monitoring to determine switch to second-line regimens often means that those people who have experienced virological failure of the first-line remain on that virologically failing first-line regimen for a sustained period of time, until such time as clinical failure occurs. For example, of those experiencing virologic failure within 5 years from start of ART, the median time from this failure to the time of fulfilling the switch criterion if clinical monitoring is used is 2.5 years. The predicted proportion of those on ART who are on second line is higher after viral load monitoring is introduced (Fig. 2 b), and is 4.0 times higher in 2020 if viral load monitoring was introduced in 2010, compared with if clinical monitoring is used throughout.
Fig. 2: Predicted outcomes from 2010–2020. Trends from 2010 to 2020 in (a) proportion of ART-treated patients with viral load suppression, (b) proportion of patients on ART who are on second-line, (c) proportion of those with viral load above 500 (including those undiagnosed and hence not on ART) with resistance in majority virus, and (d) proportion of new infections with resistance, according to timing of introduction of viral load monitoring. Based on mean of 80 runs. Standard deviations and standard errors for the estimates for 2020 are as follows: (a) VL from 2010: 0.2%, 0.04%, respectively, WHO 4 – VL from 2015: 0.2%, 0.05%, respectively, WHO 4: 0.3%, 0.05%, respectively; (b) VL from 2010: 0.2%, 0.05%, respectively, WHO 4 – VL from 2015: 0.3%, 0.06%, respectively, WHO 4: 0.1%, 0.03%, respectively; (c) VL from 2010: 0.4%, 0.1%, respectively, WHO 4 – VL from 2015: 0.5%, 0.1%, respectively, WHO 4: 0.5%, 0.1%, respectively; (d) VL from 2010: 1.2%, 0.3%, respectively, WHO 4 – VL from 2015: 1.2%, 0.3%, respectively, WHO 4: 1.7%, 0.3%, respectively. 95% confidence intervals are shown in graph (d) for 2020. For graphs (a–c) the confidence intervals are not shown as they are hardly discernable from the point estimates shown.
Since people with detectable viral load (defined here as >500 copies/ml) have a much greater infectivity than those with undetectable viral load [38] Fig. 2 c and indicates that this proportion is greater in the absence of viral load monitoring, with the difference reducing after introduction of viral load monitoring in 2015. Figure 2 d shows the proportion of newly infected people who carry drug resistance. Patterns tend to mirror those in Fig. 2 c, except percentages are lower due to that not 100% of people infected from a person with resistant virus will themselves carry resistant virus. Of new infections, the predicted percentage with resistance in 2020 is 5.4% if viral load monitoring was introduced in 2010, 6.1% if introduced in 2015, and 12.4% if clinical monitoring (WHO stage 4 condition) were used throughout.
Figure 3 shows the main results from multivariable uncertainty analysis, which indicates the overall level of uncertainty. The estimate and the spread around this estimate as depicted in the figure is derived from multivariable uncertainty analysis and indicates the overall level of uncertainty of the model taking various combinations of the assumptions into account. The analysis also indicates the effects of varying assumptions in the model on the predicted difference between strategies in the percentage of new infections with resistance in 2020 (see Supplementary Results 2 for details, https://links.lww.com/QAD/A115
Fig. 3: Uncertainty analysis on difference between viral load monitoring scenarios in proportion of new infections with resistance in 2020. Median (and range within which 95% of values lie, referred to as 95% plausibility limits), difference in percentage with resistance in 2020 between viral load monitoring scenarios over 10000 runs in which parameter estimates are chosen at random from two or three options, emphasizing the extremes of plausibility. This illustrates the overall level of uncertainty in our comparisons.
We also considered modifications to the viral load monitoring strategy in order to help understand what are the most essential elements of the viral load monitoring strategy. For these purposes, we assumed viral load monitoring was started in 2010. Use of a threshold of 5000 copies/ml, rather than 500 copies/ml, led to a predicted proportion of newly infected people who carry drug resistance in 2020 to 6.0% (compared with 5.4% in our main analysis). Reduction of the frequency of monitoring to once every 3 years, with the first measure at year 1, led to a value of 7.2%, whereas use of a single viral load measurement at 1 year (and no subsequent measures) led to a value of 8.5%.
Finally, we considered the death rate in the HIV-infected population between 2010 and 2020 (Table 2 ). This was 2.7 (2.4 when restricting to those on ART) per 100 person-years with use of viral load monitoring from 2010, 2.8 (2.7) with use of viral load monitoring from 2015, 3.1 (3.3) per 100 person-years with use of WHO 4 clinical monitoring throughout the period [2.9 (2.7) per 100 person-years with monitoring based on WHO clinical stage 3 or 4 conditions]. However, if the assumed marked increase in HIV diagnosis (and hence ART coverage) since 2010 was not to occur (and the diagnosis rate remains at the level in 2010), then the death rate in the HIV-infected population is predicted to be 4.7 per 100 person-years, even with use of viral load monitoring throughout the decade.
Table 2: Predicted death rates 2010–2020 for all those with HIV and for those on ART according to timing of introduction of viral load monitoring and diagnosis rate 2010–2020.
Discussion
Our results suggest that if we are to preserve current first-line drugs as widespread treatment options for future generations, there is a long-term need for introduction of some form of cheap, practical, and sustainable viral load monitoring in resource-limited settings with generalized epidemics which can be used in rural as well as urban settings. This represents a critical challenge for researchers over the next few years. These tests could be qualitative, that is do not need to be able to do more than distinguish those with viral load levels of above and below some low threshold such as 500 copies/ml. Even very infrequent (e.g. 3 yearly) monitoring is likely to provide significant benefit in reducing resistance transmission.
However, delay in introduction of viral load monitoring of around 5 years, whereas practical assays complete their development, is likely to result in only a small increase in transmission of resistant virus, compared with the theoretical, but not feasible, option of immediate use of viral load monitoring to determine regimen switching. We also found that the death rate in HIV-infected individuals can be lowered more substantially if the diagnosis rate can be increased to the level assumed in our main analysis (regardless of use of viral load monitoring), than by introduction of viral load monitoring that is not accompanied by an increase in diagnosis rate. These results indicate that increased and more geographically widespread testing and treatment access to ensure universal access to ART in those in need is the highest priority for resource-limited countries, and that lack of available viral load or other laboratory monitoring should not prevent this from occurring [39] [7]
The target of eventual introduction of viral load monitoring is also important for minimizing the mortality risk of people on ART. Our results (see also [10] [7] [10]
It is important to consider that the cost of viral load monitoring is not just in the cost of the tests and the infrastructure but also in the probable resulting increased number of people on second-line ART, which has a cost over six times higher (in drug costs alone) than first-line ART [40] [1] [41–43] [10]
Without eventual introduction of viral load monitoring and switching of drugs to second line based on virologic failure it is likely that the choice of standard first-line drugs in patients starting ART will at some point have to change, due to the high proportion of ART-naive patients who carry virus resistant to first-line drugs, although our results suggest this is unlikely to be necessary in the next 10 years. Loss of current drug options over the long term, due to substantial resistance transmission, might not have serious negative consequences, if there are sufficient newly developed and widely available drugs with nonoverlapping resistance profiles. Nevertheless, the prospect of extensive transmission of virus with NNRTI mutations associated with resistance to nevirapine and/or efavirenz would be likely to remain a concern for the foreseeable future. WHO has recently recommended that countries move towards replacement of d4T in first-line regimens with tenofovir, due to reduced toxicity [6] [44]
In conclusion, transmission of drug resistance represents a long-term threat to the continued optimal virologic efficacy of drugs currently used in resource-limited settings. Eventual introduction of viral load monitoring to help to determine the need for switching to second-line regimens will substantially reduce this threat. However, the number of people living with advanced HIV infection which is untreated should be the key priority for national ART programmes. Concern over lack of viral load monitoring must not be allowed to inhibit universal roll out of HIV diagnosis and ART availability over the coming few years.
Acknowledgements
The research leading to these results has received part funding from the European Community's Seventh Framework Programme (FP7/2007–2013) under the project ‘Collaborative HIV and Anti-HIV Drug Resistance Network (CHAIN)’ – grant agreement n° 223131. We would like to thank Azra Ghani and Rebecca Baggaley for valuable advice in developing this model. Support for development of an early version of this model was provided by Pfizer, UK. We also thank Research Computing, UCL for use of Legion distributed memory computing cluster, without which this work would not be possible.
Author contributions : All authors discussed the concept of the analysis and had substantive input into the decisions made about how to set up the scenario and the comparisons. In addition, D.P. and D.B. offered specific advice on modelling resistance emergence and transmission, J.L. provided advice from a clinical and epidemiologic perspective, M.V. and D.B. provided advice from a WHO standpoint, G.G. provided advice on aspects of the modelling, particularly as they related to UNAIDS work. A.P. and V.C. programmed the model and led on the work.
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