Sensitivity and uncertainty analyses
Of the various sensitivity analyses (Table 3), the assumed level of the viral load value after failure (analysis 2) had the largest impact on the benefit of viral load monitoring relative to CD4 monitoring. Assuming 1000 copies/ml after failure resulted in a 5% reduction in CVL and a 16% reduction in number of new infections, relative to CD4 monitoring. If the viral load at failure was assumed to be similar to that at ART start, the corresponding reductions were 65 and 45%. Increasing the level of undetectable viral load to 100 copies/ml (analysis 1) led to an increase in CVL and the number of new infections. The benefit of routine viral load monitoring decreased; instead of 31 only 18% of infections were prevented, whereas the CVL decreased only slightly from 31 to 29%. The time that viral load started to increase before reaching the failure threshold value did not affect CVL or number of transmissions (analysis 3).
Lower mortality, which would be more realistic in a universal ‘test and treat’ strategy, did not affect CVL or number of transmissions (analysis 4). If failure rates were lower, both CVL and transmissions were reduced (analysis 5). If failure rates were assumed to be equally low in CD4 and viral load sites, 21% of transmissions were prevented and the reduction in CVL dropped to 22%. Assuming a lower failure rate in sites with routine viral load monitoring only, would prevent 37% of infections and lead to a 40% reduction in CVL.
The results of the two remaining sensitivity analyses (analyses S1 and S2) and the uncertainty analysis are shown in the web appendix (4.1–4.2, http://links.lww.com/QAD/A213).
There is an ongoing debate on the benefit of routine viral load monitoring. Regular viral load measurements help to detect treatment failure earlier and may, therefore, reduce mortality and HIV transmission. Our model shows that routine viral load monitoring reduces CVL substantially as compared to CD4 monitoring (currently the standard of care in many low-income countries [24,25]). Compared to CD4 monitoring, viral load measurements reduced the average CVL by more than 30% over 5 years, and the reduction in transmissions was similar.
At the end of 2009, ART coverage was below 40% in sub-Saharan Africa . The time from HIV infection to ART eligibility is typically several years ; most people are tested too late for HIV  and the majority of the HIV-infected population remains untreated. The proportion of new infections from treated individuals is, therefore, small, and even large reductions in transmission from treated individuals would hardly reduce the viral load at the population level. However, there is a trend toward earlier ART initiation; recently, WHO increased the CD4 threshold for ART eligibility from 200 to 350 cells/μl , and there is evidence of clinical benefits for starting ART with CD4 values above 350 [29–31]. An even more fundamental change to the treatment policy would be the implementation of the ‘test and treat’ strategy [6–8].
Our results should also be valid in a ‘test and treat’ situation. Although they are based on patients starting ART with low CD4 values, the sensitivity analyses showed that similar benefits can be achieved when patients start ART earlier. Lower mortality did not have an impact on the benefit of routine viral load monitoring. Decreasing the rate of virological failure reduced the benefit of viral load monitoring, but this was offset by better adherence with viral load monitoring. Due to the current WHO guidelines, to our knowledge no data on mortality and virological failure rates have been published for people starting ART shortly after diagnosis of HIV. The available data do, however, suggest that the reduction in mortality due to higher CD4 cell count becomes minimal above 350 cells/μl , and several studies have found no association between baseline CD4 cell counts and risk of virological failure [33,34].
We investigated two different ways of estimating HIV transmission and both have advantages and disadvantages. Correlation between community viral load and HIV incidence has been described by Das et al.  in San Francisco. However, it is not clear if community viral load is a good proxy for HIV incidence in low-income settings, especially when assuming that most patients would be treated. The main advantages of CVL are that it is easy to calculate and it is independent of risk behaviour. One of the main limitations is that CVL does not take into account the number of individuals; 100 000 patients with undetectable viral load of 10 copies/ml, 1000 patients with a detectable viral load of 1000 copies/ml and one patient with a very high viral load of 1 000 000 copies/ml will all contribute the same amount to CVL, but the transmission potential will probably differ. The actual numbers of transmissions may, therefore, vary substantially between cohorts with similar CVL but different viral load distributions.
The other method we used to calculate transmissions assumes a linear relationship between log10 viral load values and HIV transmission. The resulting number of new infections is more intuitive than CVL. It can, for example, be directly transformed into costs, or other measures including the number needed to treat or the cost of preventing one HIV infection. However, calculating the absolute number of prevented transmissions is challenging, as it is highly sensitive to behavioural factors, which often are difficult to estimate.
Furthermore, the approximate reduction of 30% in transmissions should be applicable for different risk behaviour scenarios. In our calculations we assumed relatively high-risk behaviour. Individuals with fewer sexual acts and higher rate of condom use would transmit less, but the relative reduction would remain the same. The rate of partner change is also not a key factor, as the per-act transmission probabilities are low (see web appendix, 2.3 for more details, http://links.lww.com/QAD/A213).
Our model focused on a group of treated patients without considering the entire population, and this approach limited us in several ways. Because the main focus was on comparing monitoring strategies on ART, we did not model the pre-ART period in detail. Therefore, our results remain dependent on local characteristics, including HIV prevalence, ART coverage and ART eligibility criteria.
Transmission during the acute stage of infection has recently been estimated to contribute to about 38% of new infections  but various other estimates exist  and the acute phase may remain an important source of transmission in a ‘test and treat’ strategy. We did not take into account an increase in risk behaviour after ART start  and we did not consider behavioural differences that could result from different monitoring strategies apart from adherence. For example, patients from CD4 monitoring sites with unobserved virological failure and high CD4 cell counts may be more likely to engage in unprotected sex because they are unaware of the risk of transmitting the virus. If this was taken into account, probably even more infections could be prevented by viral load monitoring.
Similarly, we did not investigate the effect of possible worse adherence in patients starting ART with higher CD4 cell counts. The higher virological failure rates, which remain partly undetected in CD4 sites, would increase the benefit of viral load monitoring further. We also did not consider (primary) drug resistance that could complicate the treatment of newly infected individuals . The assumption that the virologic failure rate, due to improved adherence counselling, would be 50% lower in viral load sites compared to sites without viral monitoring may appear high. It was an arbitrary choice that cannot be verified in our data. But a recent systematic review has shown that virologic failure rates vary substantially in sub-Saharan Africa  and they are also highly variable in viral load sites of the IeDEA-SA collaboration. Our results are only applicable for short term. To investigate the longer-term evolution of the epidemic, one would need a dynamic transmission model in which susceptible people get HIV infected and partnerships are modelled.
Our study was based on almost 10 000 adult patients from two public sector treatment programmes in South Africa. Results should, therefore, be applicable to many other patients in the region most heavily affected by HIV. We acknowledge that these treatment programmes will not be representative of all programmes in southern Africa; they are located in urban areas, are equipped with electronic medical record systems, and have access to regular CD4 cell determination, viral load monitoring and second-line therapy.
The availability of viral load monitoring may have led to an underestimation of immunologic failure rates, as patients should have switched after detection of virological failure. However, many patients never switched and the median time to switching from the estimated time of failure was 22 months. Moreover, limitations in the data required us to make assumptions about factors such as the effect of virological failure on mortality and the effect of the delay between failure and switching on second-line efficacy. However, in sensitivity analyses we found that these assumptions had little effect on the results (see web appendix, 4.1 for details, http://links.lww.com/QAD/A213).
The main barrier in providing routine viral load monitoring is its high cost. A recent randomized controlled trial estimated the difference in the unit cost between viral load and CD4 measurement to be approximately US$ 25. Therefore, the extra annual cost of treating 1000 patients with two viral loads instead of CD4 measurements per year would be about US$ 50 000. The net cost of preventing a new infection depends on the number of infections that can be prevented, as well as the total cost of treating and managing a new HIV-infected patient. In the United States, the discounted lifetime cost of a new HIV infection has been estimated to be over US$ 300 000 . In low-income countries, these costs are much lower, for example, in Uganda, the total cost of treating a patient with ART and CD4 monitoring for a year is US$ 467. Assuming that patients spend on average at least 20 years on ART , the lifetime treatment costs would be around US$ 10 000. A detailed cost-effectiveness analysis is needed to evaluate whether routine viral load monitoring would be cost-effective or even cost saving in the long term. Such an analysis would, however, require more detailed information on sexual behaviour.
After 15 years of ART and close to a decade of widespread ART use in low-income settings, it is still not clear if, when and how often viral load should be measured to optimize treatment outcomes. We found that viral load monitoring could be an important factor in reducing mortality , and could prevent HIV infections. Continuous evaluation of the role of routine viral load monitoring in terms of costs and effectiveness is necessary as new technologies are developed and new research findings become available. We emphasize that although the first priority should be providing ART, viral load monitoring could provide an additional benefit for ART as a preventive measure.
We thank all study participants, Franziska Schöni-Affolter for helpful comments and Kali Tal for commenting and editing the manuscript.
J.E., O.K. and M.E. designed the study. R.W. and D.G. were involved in data acquisition and data management. J.E., C.A. and O.K. performed the statistical analyses. J.E., C.A. and T.H. developed the mathematical model. J.E. and O.K wrote the first draft of the manuscript. All authors contributed to the interpretation of the results and to the final version of the manuscript.
This study was supported by the National Institute of Allergy and Infectious Diseases (NIAID), Grant 5U01-AI069924-05, a PROSPER fellowship to O.K. supported by the Swiss National Science Foundation (Grant 32333B_131629) and a PhD student fellowship to J.E. from the Swiss School of Public Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Conflicts of interest
There are no conflicts of interest.
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HIV transmission; mathematical model; second-line therapy; Southern Africa; test and treat; therapy failure; viral load monitoring
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