In the scenarios considered above, the risk of death for a person with a given AIDS condition was assumed to be the same, regardless of whether the AIDS event occurred at diagnosis or under follow-up. We performed a sensitivity analysis to estimate the life expectancy if in the low diagnosis rate scenario, we instead assumed a three-fold raised risk of AIDS-related deaths occurring at HIV diagnosis (on the basis that delays in diagnosis can lead to more serious disease). This resulted in a life expectancy of 68.0 years (Table 1). In other sensitivity analyses, we altered the rate of ART interruption under the assumption of a high diagnosis rate. Increasing the rate by 10-fold in those with low tendency to adhere (compared with 1.5-fold in the initial model) resulted in a life expectancy of 73.8 years, whereas reducing the rate of interruption to zero (regardless of adherence level) resulted in a life expectancy of 76.5 years. Assuming a high diagnosis rate, we also altered the probability of initiation of ART. We assessed the effect of both a higher rate and lower rate of uptake than what we speculate is being observed; however, both scenarios did not impact much on predicted life expectancy (Table 1).
In order to attempt to fully quantify the uncertainty associated with our life expectancy estimate, we investigated the effect of plausible variations in parameter values. A total of 10 000 runs of the model were made, each time sampling at random, values for a number of different key parameters in order to generate the distribution of life expectancy in the multivariable sensitivity analysis. The median life expectancy from this analysis was 73.8 years and the 95% uncertainty bounds were 68.0 and 77.3 years. Further details are given in the Supplementary Material (http://links.lww.com/QAD/A193).
It was projected that a relatively high proportion (41%) will at some point in their lifetime develop an AIDS condition. This arises largely due to the fact that in our model, there is a small risk of AIDS occurring even at higher CD4 cell counts and because of our assumption that a high proportion of people will interrupt ART at least once. There are also rapid consequences in terms of viral load increase and CD4 cell count depletion in our model. Estimated life expectancy increased by 1.5 years when it was assumed that no treatment interruptions occurred. Our rates of interruption were based on observed proportions of ART-experienced people on ART .
In the initial model, we assumed a high rate of diagnosis, consistent with that currently observed in data on MSM in the UK . When we instead assumed a much lower rate of diagnosis (such that the CD4 cell count at diagnosis was 140 cells/μl instead of 432 cells/μl), this increased the death rate within 10 years from diagnosis, but the effect on life expectancy after this period was more modest. This is due to the durable effects of ART, even in those who start ART when their CD4 cell count is low [32,33]. However, late diagnoses can and should be prevented by increased access and uptake of HIV testing. Figure 1 emphasizes the importance of early diagnosis and the substantial difference it makes to the death rates, especially in the first 20 years after infection. Furthermore, under the assumption of a low diagnosis rate, we estimated that 42% would present with AIDS at diagnosis (Table 2). Late diagnoses not only reduce the life expectancy of the HIV-positive person but also impact on the probability of onward transmission because treatment, which reduces infectivity, is also delayed.
We did not assume an increased risk of death associated with the use of any specific antiretrovirals. Despite evidence that some drugs lead to an increased risk of myocardial infarction and possible impairment of liver and kidney functions [21,34–38], we assumed that the direct effect of antiretrovirals on mortality would be minimal, as drugs with severe adverse effects would probably not be used from 2010 due to the considerable choice of drugs available. However, we did assume a lifelong 1.5-fold increased risk of non-AIDS deaths compared with the general population, due to presence of HIV infection per se. Bearing in mind that most people are predicted to have viral suppression and high CD4 cell counts for most of their lives, this may well be a pessimistic assumption, resulting in underestimation of life expectancy.
As our model takes into account the predicted durability of ART (using estimates of virologic failure and resistance emergence rates [32,39–41]), our approach to estimating life expectancy differs from that employed in analyses of cohort studies which have been based on the assumption that current death rates will remain unchanged [4,6,7]. The fact that risks appear to decrease as the CD4 cell count is increased with more durable viral load suppression means that such an assumption is also likely to be pessimistic . Another key difference with some previous assessments is that we started on the basis of a person being infected with HIV in 2010, rather than a person with HIV presenting for healthcare in 2010.
Our projected life expectancy after entry into healthcare (median 41.5 years) is much longer than the value of 24.2 years generated from the model used by Schackman et al. and values generated by many others [42–45]. It is also higher than those calculated from both cohort data and surveillance data in recent years; Lohse et al. calculated median survival to be 38.9 years among patients with HIV from age 25 years without hepatitis co-infection in the Danish HIV Cohort Study, whereas Harrison et al. estimated an average 22.5 years of survival after diagnosis in 2005 using surveillance data in the USA. It is likely that this difference in life expectancy is partially due to the fact that data emerging from treated patient cohorts in recent years have shown still increasing prevalence of viral load suppression in treated patients and continuing very low rates of virologic failure in patients with viral suppression and increased regimen stability [13,14,46,47]. Walensky et al. observed an increasing trend in life expectancy with growing numbers of antiretrovirals and increasing potency and tolerability. The higher life expectancy estimated by our model could also be explained by the fact that patients are assumed to start ART with combination therapy and because we also assumed that they are neither co-infected with hepatitis, nor do they inject drugs. In fact, it has been estimated recently that asymptomatic treatment-naive patients have near-normal life expectancy, based on their current death rates . Our life-expectancy estimate is also somewhat higher than that found in a study by the Antiretroviral Therapy Cohort Collaboration  which estimated that survival for a person aged 20 years initiating combination ART was 49.4 years, although the life expectancy of 69.4 years is within our uncertainty bounds of (68.0, 77.3).
There are several limitations that should be considered. The main limitations relate to those inherent in modelling a process and then using that model to predict many years into the future. Our model has been shown to encapsulate the main features of HIV progression and the effect of ART, as they are currently understood fairly closely (see reference [13,17] and Supplementary Material, http://links.lww.com/QAD/A193). However, any model is at best an approximation to the truth and the effects of any model misspecification could be amplified due to the extremely long simulation period. We also make several assumptions in the model in order to make these projections. We assume that testing and treatment guidelines and the current standard of care will remain as they are now and we do not incorporate changes which may happen in the future. We assume that all-cause death rates will remain fixed at 2009 levels, instead of assuming that the current downward trend will continue . We also assume that adherence to ART will remain stable over time and not decline. There is some recent evidence that this is the case for over 10 years , but only time will tell whether this is the case for the 40–50 years that will be required by many. In addition as discussed above, we take no account of any increased risk of death associated with some antiretroviral drugs. With currently available drugs, such risks are likely to be small, but in the future as more data on the long-term effects of drugs become available, it will be possible to refine and update our model, incorporating new drugs and resistance mutations. We did not estimate life expectancy for females which would require using death rates amongst females in the UK. However, as a large proportion of HIV-positive females living in the UK are not originally from the country (whereas a large proportion of MSM are), it is uncertain whether those rates apply. Finally, our estimates at this stage do not apply to people with hepatitis co-infection.
In summary, based on continuing low rates of virologic failure in treated patients, predicted life expectancy in people with HIV is high in settings with access to multiple antiretroviral drugs. Delays in diagnosis pose the greatest risk of excess mortality for people with HIV. Despite recent progress in the testing, treatment and care of HIV infected patients, there is still room for improvement such that life expectancy reaches the same as that of the non-infected population.
The authors acknowledge the use of the University College London Legion High Performance Computing facility, and associated services, in the completion of this work.
F.N. and A.N.P. were involved in model programming. A.N.P. and J.D.L. helped develop the original model. F.N. drafted the manuscript. All authors were involved in the conception of the article, interpretation of results, critical revisions of the paper and approved the final version.
There are no conflicts of interest.
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