Continued correspondence ‘Will ART rollout in Africa drive an epidemic of drug-resistant HIV?’

Field: Medicine (Health Sciences)

We were interested to read the comments by Blower et al. [1] on our recent correspondence [2]. We agree that combination antiretroviral therapy (ART) has been a huge success in developed countries, and the spread of resistance has been slowed compared with monotherapy. Treatment is guided by close clinical monitoring of patients, and switches in drug regimen(s) are driven by virological rebound, adverse drug reactions or when resistance mutations are detected. Blower et al. [1] acknowledged that their ‘central conclusion is based upon an extensive review of the literature of the experiences with ART in Europe and North America’, and we are less confident that these observations can be extended to African settings. Here, most deployment will occur without close clinical monitoring, and changes in treatment regimens will be based on clinical indicators of failure (such as AIDS-defining secondary infections) rather than viral load or the detection of resistance mutations. Consequently, a large number of patients with highly viraemic resistant infections may be sexually active. Problems with infrastructure may interrupt drug supplies, and we can easily envisage a significant black market of antiretroviral drugs, both factors contributing to potential widespread non-adherence to ART regimens and the use of monotherapies on a casual ad hoc basis. Furthermore, the vast majority of data on HIV-1 drug resistance derives from studies of subtype B viruses, representing the major male homosexual epidemics in north America and Europe. This subtype represents less than 5% of global HIV-1, with subtype C representing over 50%, and it remains unclear whether the genetic barriers to resistance are identical in different subtypes [3]. We therefore urge caution against over-complacency about the transmission of resistance during the roll-out phase. If treatment ‘failure’ pushes resistance to even a ‘low’ level of 1–2% it may create the conditions ideal for the rapid expansion of resistance once ART scale-up occurs. Initial results suggest resistance will arise relatively rapidly: a recent trial in Uganda and Zimbabwe using World Health Organization standard therapies and no viral load or resistance mutation monitoring [4] found that at least 18 out of 300 recruits (i.e. > 6%) had ‘key resistance mutations in reverse transcriptase’ 24 weeks after starting therapy. The strength of mathematical modelling is that different beliefs about clinical settings and underlying biology can be investigated by different calibrations of models to assess their impact on disease epidemiology. This is what we argued should happen, and is what we set out to achieve. We are disappointed that Blower et al. [1] did not address our chief point, which was that the underlying assumptions of their previous model may not be appropriate for the African setting, instead concentrating on four technical aspects of our approach in their penultimate paragraph. This calls into question the accuracy of our approach, and thus our central conclusion, so we feel it incumbent to respond as follows. First, they say that our modifications to their equations are ‘incorrect’ and that we have ‘misunderstood’ their approach. We remain deeply sceptical, and a detailed explanation of our concerns is available on request or from http://pcwww.liv.ac.uk/hastings/HIV_models/HIVcalculations.doc. Specialists can decide which approach is more appropriate, but we have been explicit to allow non-specialists also to reach a decision. Second, they state that our use of difference equations would result in ‘numerical errors’ in our calculations. This is clearly not the case or else this approach would not be so widely used. For the non-specialist the distinction is as follows: differential equations with integration occur in infinitesimally small time periods, whereas difference equations ‘update’ the number of individuals in each group after daily time periods. The small daily probabilities of events means that essentially the same results will be obtained from both approaches. We did also use the differential approach with integration, and the numerical differences (or ‘errors’) between the two methods were imperceptible. The reason we chose to show difference equations is because the models can be easily distributed on an Excel spreadsheet, and most people are comfortable working with this program. Third, they point out that we did not perform an uncertainty analysis. Editorial space constrained us, but as our main point is that changes in underlying (and we think inappropriate) assumptions lead to qualitatively different predictions for the spread of ART resistance, there is little point in undertaking a sensitivity analysis until the most appropriate basic model has been identified. Fourth, they assert that we assumed that 40% of infected individuals would receive treatment. In fact, we state quite clearly in the figure caption that it is 20% per year, a figure we chose because it appeared that this was used in their original calculations [5]. We now learn that it was 5–10%. We found it impossible to repeat their calculation, and thus gauge the validity of their argument, because such key details about the underlying parameters used to calibrate their model were not provided. Mathematical modelling may vary in accuracy and usefulness, and it is important first to be explicit about the inherent assumptions made in the calculations, and to provide details about exactly what parameter values were selected and why. This is why our calculations are freely available as an Excel spreadsheet (http://pcwww.liv.ac.uk/hastings/HIV_models/2006_HIVdynamics.xls). Researchers can then select parameter values reflecting their own beliefs about the underlying epidemiology, play around with the inputs values, and gain an understanding of how the various clinical and epidemiological factors contribute to driving ART-resistant HIV through a population. This type of engagement between theoreticians and empiricists is mutually beneficial and contributes to a better understanding of how to slow the spread of resistance. Lack of transparency all too easily invites the ethos of ‘trust me I'm a mathematician’, which is almost as alarming as ‘trust me I'm a doctor’, both being fairly robust indicators of impending doom.