The modellers do make one other crucial assumption:
they assumed that when gay men become aware of their HIV infection, they very
considerably reduce their sexual risk behaviour. In the model the researchers
reduced the likelihood of someone transmitting HIV by an average of two-thirds post-diagnosis, a figure based on US studies. These reductions have not necessarily been matched by figures from
other parts of the world,
and the researchers found that sexual risk behaviour was the assumption fed
into the model that had the biggest influence on cumulative new infections.
The researchers do point out that their model lacks
certain subtleties. Firstly, it doesn’t attempt to stratify HIV risk in
different age groups, ethnic groups, or by risk behaviour – it assumes all
sexually active gay men are approximately at the same risk of HIV. Secondly, it
does not add in any allowance for the possible future use of pre-exposure prophylaxis (PrEP). And thirdly,
they point out that "mathematical models are only as good as the available data
used for the parameters and calibration".
It is also interesting that they use estimates of the
average time between diagnosis and treatment as their parameter for the
influence of treatment on prevention, rather than using the more direct figure
of the estimated proportion of people with HIV with an undetectable viral load.
They explain that this is because we do not have high-level evidence for the
efficacy of viral load suppression as a prevention measure in anal sex. However,
they do feed in an assumption that anyone starting treatment at a CD4 count
over 350 cells/mm3 who mainains adherence becomes 96% less infectious.
The researchers suggest that, given that starting
people on treatment earlier leads to a prediction of higher rates of multidrug-resistant
HIV, and given that increased testing and more treatment do not seem to be
synergistic, it might be better to concentrate on getting people to test more
frequently rather than treating everyone diagnosed.
The finding that the number of patients with MDR-HIV will increase, however, is based on very old data. The figure of 3.1% the model uses for the proportion of people who start therapy with MDR-based HIV is derived by taking the median figure for primary MDR resistance from a single review (Van de Vijver) which was published in 2007, and includes no data collected after 2005. Even in this review it was noted that MDR resistance was lower in other parts of the world than the US. In addition, it was just after this point that studies started to find that drug resistance in people with HIV was starting to decline, and epidemiologists soon confirmed that it had in fact been doing so for several years (see Health Protection Agency). This trend has been sustained in more recent studies.
This brings the finding of the model that each 10% increase in average testing frequency, or each 10% decrease in average time between infection and starting ART, leads fairly consistently to a 0.45% absolute increase in the proportion of people starting therapy who have MDR-HIV, into some question.
In addition, however, they also use an outdated definition of multdrug resistance, namely resistance to two of thre three drug classes in use at the time of the 2007 review, nucleoside and non-nucleoside reverse transriptase inhibitors (NRTIs and NNRTIs) and protease inhibitors (PIs). Even this review noted that it was not taking into account resistance to the then-recently developed fusion inhibitor enfuvurtide (T-20, Fuzeon). Since then ARVs of two other classes (integrase inhibitors and CCR5 inhibitors) have been developed, as have a number of drugs of established classes that work against HIV with resistance mutations to those classes.
However, even with their own assumptions about resistance, the researchers found that the development of
multidrug resistance actually had relatively little
clinical effect and that even projecting the model into the far future, which
would lead to a 23% rate of multidrug-resistant HIV, would not lead to more
HIV cases or deaths than we have currently.
The model includes, buried within its parameters, a number of other interesting assumptions, which are not entirely explained. It assumes, for instance, that if people start therapy at a CD4 count over 350 cells/mm3 their adherence rate will be just under 90% but that if their CD4 count is under 350 cells/mm3 their adherence rate will be nearly 99%. It is not clear what data these inputs are based on.
However, even though some of its parameters are based on somewhat out of date findings, this model, by basing its assumptions
carefully on what has actually been observed to happen in gay men, may avoid
exaggerated predictions of the success of ‘test-and-treat’ for which some other models have been criticised.