The number of people living with HIV has never been higher; UNAIDS estimates the global figure to be 34.2 million people . This reflects both the increase in the number of new infections and also the rise in access to antiretroviral therapy (ART) which has dramatically reduced mortality and morbidity [2–8]. Consequently, life expectancy has substantially improved to the extent that HIV is increasingly considered as a chronic illness, in which a near-normal lifespan is achievable with successful care [9,10].
In this review, we first define ‘life expectancy’ and discuss the main methods of calculation. A brief overview of how life expectancy has improved over time is then given, followed by the main focus of this study, which reviews the literature on the latest estimates of life expectancy. Finally, the literature is discussed in the context of what impact it will have in the future.
DEFINITION OF LIFE EXPECTANCY AND METHODS OF CALCULATION
Life expectancy is ‘the average number of years an individual of a given age is expected to live if current mortality rates continue to apply’ . It is commonly perceived as a useful indicator of population health and mortality and is easier to relate to than mortality rates.
Ideally, life expectancy would be calculated using data from a large cohort with very long follow-up such that 50% of deaths are observed. Although this approach would directly provide the median life expectancy, it is currently infeasible. Furthermore, the estimated life expectancy would be outdated and therefore not applicable to those infected or diagnosed today.
Instead, calculation of life expectancy is often done by constructing period life tables from the mortality experiences of a cohort over a short period of time, assuming the cohort is subject to age-specific death rates in any given year. Life tables can be complete or abridged, depending on whether death rates were discrete at each year of age, or grouped for similar ages (e.g. 5-year groups), respectively. The life expectancy of an individual is then calculated using projections of these age and sex-specific death rates, assuming they apply throughout an individual's lifespan . An alternative method to estimate life expectancy is to use mathematical models.
ESTIMATES OF LIFE EXPECTANCY (PUBLISHED 1980–2009)
Survival in people with HIV has progressively improved since AIDS was first seen in the United States . In those early days when treatment for HIV did not yet exist, the median time of survival following an AIDS diagnosis was no longer than about 20 months . Without treatment, it has been estimated that the median time from seroconversion to AIDS and death is approximately 9 and 10 years, respectively, but varies significantly by age at seroconversion .
Even after treatment was available, HIV was in the top 10 causes of deaths worldwide in 1997, partly because ART was not very effective at first and also because it was mainly limited to resource-rich countries . Numerous studies have since estimated the life expectancy of people infected with HIV and have demonstrated that survival has improved over time [17–22]. This has been a result of successful virologic suppression because of the availability of increasingly effective and tolerable treatment [23–25].
ESTIMATES OF LIFE EXPECTANCY (PUBLISHED 2010 ONWARDS)
Recently, there has been a surge in the number of research papers [9,10,26,27▪▪,28,29▪▪,30▪▪] which estimate life expectancy in people with HIV (Table 1). As these estimates apply to different circumstances (such as no hepatitis coinfections or injecting drug use) and to certain settings (most studies assume good access to ART and HIV care), it is difficult to directly compare the estimates against one another.
Life expectancy calculated using data from cohort studies
Life expectancy has been estimated by May et al.[30▪▪] using data from the UK Collaborative HIV Cohort study, which combines patient data from over 30 HIV clinics across the UK. Although life expectancy at age 20 increased from 30.0 years during 1996–1999 to 45.8 years during 2006–2008, the presence of HIV infection reduced average life expectancy by 13 years compared to someone without HIV. Men had lower life expectancy than women (39.5 years compared to 50.2 years) and people who started treatment later than guideline recommendations also had a worse prognosis.
Van Sighem et al. found that life expectancy of recently diagnosed asymptomatic individuals approached that of uninfected individuals using data from the AIDS Therapy and Evaluation Netherlands cohort. The median number of years lived from age 25 was 52.7 and 57.8 years for men and women, respectively, whereas it was 53.1 and 58.1 years for men and women, respectively, in the general Dutch population. The analysis was restricted to a selective group of healthy people who were still not eligible for treatment nor had experienced a HIV-related symptomatic event (except one associated with primary HIV infection) or AIDS as of 24 weeks from HIV diagnosis. However, these estimates may be biased upwards because of losses to follow-up with consequent under-ascertainment of deaths and to keeping age fixed over follow-up.
Mills and colleagues have investigated life expectancy and survival in people receiving ART in Uganda [27▪▪,32,33]. In one study, they found that life expectancy was 26.7 years at age 20, approaching the overall rate for all young adults in Uganda [27▪▪]. Similar to the observations seen in resource-rich settings, there was substantial variability in the life expectancy depending on the patient's age, sex, and CD4 count at baseline [27▪▪]. Most of these patients were treated with older regimens than those currently used in the USA and Western Europe.
Since the roll out of ART, adult life expectancy has increased in a South African population cohort, where 28% of adults are infected with HIV . In 2003, the expected age at death was 52 years, but by 2011, this figure was 61. In this study, Bor et al. also found that treatment costs were far outweighed by the economic value of gain in life expectancy.
Life expectancy calculated using other methods
Harrison et al. used U.S. surveillance data to find that the average life expectancy following diagnosis increased from 10.5 years in 1996 to 22.5 years in 2005. However, according to these estimates, HIV-positive people still have a lifespan 21 years shorter compared to HIV-negative people. Women had a better life expectancy compared to men, but this difference lessened by 2005. The researchers also found that life expectancy was shortest for black men, followed by Hispanic men and white men. This racial disparity was also found by Losina et al..
Although developed primarily for cost-effectiveness analyses, the CEPAC model (a state-transition Monte Carlo simulation of HIV-infected individuals) has been used extensively to answer many research questions [17,18,29▪▪,35,36]. In one study, Sloan et al.[29▪▪] estimated the mean life expectancy for a simulated cohort with mean age of 38 years to be 26.5 years, assuming ART was initiated when CD4 counts fell below a threshold of 350 cells/μl. If patients presented earlier and ART was assumed to be initiated when CD4 counts fell below 500 cells/μl or if the HIV viral load was over 100 000 copies/ml, life expectancy increased slightly to 27.4 years.
Another well validated simulation model of HIV, developed by Braithwaite et al., has been used to consider the impact of adherence interventions on cost-effectiveness by comparing the increase in life expectancy with increase in costs (mainly from the intervention itself). Although this study did not explicitly state the estimated life expectancy, results from previous studies have done [31,38,39]. For example, in one study they estimated that life expectancy for a 30 year old, assuming minimal toxicity from antiretrovirals, ranged from 19.3 to 33.5 years depending on the CD4 count at treatment initiation and adherence .
Nakagawa and colleagues used their previously developed stochastic simulation model of HIV progression to project the life expectancy of men who have sex with men (MSM) in the UK [10,40,41]. The median life expectancy for MSM infected in 2010 at age 30 was estimated to be 45 years . This assumed that no one had hepatitis coinfection, 40% were lifelong smokers and the median CD4 count at diagnosis was 432 cells/μl. If the median CD4 count at diagnosis declined to 140 cells/μl, the median life expectancy dropped to 41.5 years. The authors suggest that their estimates of life expectancy are much higher than in most other studies because the model takes into account the long-term durable effects of therapy using current estimates of rates of virologic failure and resistance emergence and, therefore, over time the CD4 count increases and mortality rates decrease.
INTERPRETATION OF LIFE EXPECTANCY ESTIMATES
The major assumption made in all life expectancy calculations is that age-specific mortality rates estimated at a given point in time will apply throughout the individual's entire lifespan. This does not allow for potential improvements in mortality rates over time, which has been observed in both the HIV-infected population and the general population [42,43], and any estimates may therefore be an underestimation. Some studies  have taken into account that relative to HIV-negative populations, there is a late fall in survival because of increased mortality associated with several non-AIDS-related causes.
Methods to estimate life expectancy can be broadly split into two approaches: life table approach and use of mathematical models. Both approaches rely on data from cohort studies to either construct life tables or to inform the models, but there are some limitations inherently associated with the use of cohort studies. For example, patients followed up in cohort studies may have better HIV care and therefore better overall prognosis because of their participation in various research studies, although this is not always the case as in some cohorts data is collected as part of routine care. Under-ascertainment of deaths can also occur, which would lead to an overestimation of life expectancy.
The main limitation when using the life table approach is the under-ascertaining of death, combined with the lack of mortality data in older age groups, where the majority of the deaths occur in the comparator background population. Also unlike regression analyses, life tables do not adjust for covariates and therefore results need to be interpreted carefully.
One advantage of mathematical models is the ability to account for the long-term durability of ART and the subsequent decrease in mortality rates, which should ensure that life expectancy is not underestimated in people with HIV. However, it is important to note that mathematical models make many assumptions about the natural course of HIV progression as well as the effect of ART. Additionally, in the case of estimating life expectancy, considerable uncertainty is introduced by simulating very long periods of time.
IMPLICATIONS OF INCREASED LIFE EXPECTANCY
A consequence of increased life expectancy is that the number of deaths from non-AIDS-related conditions now considerably exceeds the number of AIDS deaths in resource-rich settings . In particular, non-AIDS malignancies, cardiovascular disease, and hepatic disease (in those with hepatitis coinfection) are now amongst the leading causes of deaths [44,45]. HIV infection itself, as well as long-term use of ART, is also thought to increase the risk of non-AIDS-related diseases, probably, although to a lesser degree, even in people with viral suppression [45–51]. These data support the need for enhanced preventive measures more akin to those used in older people in the general population, such as screening programmes.
Another direct implication of increased life expectancy is that younger patients nowadays will expect to be on treatment for at least three or four decades. Results from the ongoing Strategic Timing of AntiRetroviral Treatment study, which is evaluating the risk/benefit of ART initiation in people with CD4 count greater than 500 cells/μl, compared with deferral to 350 cells/μl, could lead to patients being on treatment earlier . If ‘treatment as prevention’ strategies, such as ‘test-and-treat’ and preexposure prophylaxis (PrEP) regimens, are to be used widely, these could also add to the length of time people are on ART. A large proportion of the lifetime costs related to the care of HIV-infected patients is ascribed to antiretroviral drugs [29▪▪]. Therefore, further cost-effectiveness studies of different regimens need to be done  and it is vital for cheaper, generic drugs to be made available.
As a result of longer life expectancy, HIV will be more prevalent in older people. Ageing in HIV is not yet well studied as it is a relatively new occurrence ; however, the presence of HIV infection may accelerate ageing . The development of age-specific guidelines for HIV is now called for [56▪▪]. Other than guidance on HIV care and clinical management, the guidelines should provide recommendations on screening programmes and additional monitoring of biomarkers, especially for common comorbidities which occur more frequently with increasing age. Therefore, a more integrated approach to managing HIV-related and non-HIV-related diseases may be appropriate, such that people with HIV are seen in general practice and only referred to HIV specialist clinics when viral loads are unsuppressed. This could include lifestyle support, managing cardiovascular risk factors and other chronic diseases, as well as dealing with ART-associated complications such as drug–drug interactions.
As there is now sufficient evidence that the life expectancy of someone with HIV is nearly the same as that of an HIV-negative person, access to life insurance should be more widely available. The improved survival needs to be reflected in premium prices, particularly as survival often improves with age, which is unlike most other chronic diseases such as diabetes, because it takes several years of ART to restore CD4 counts to normal levels.
FURTHER IMPROVEMENTS IN LIFE EXPECTANCY?
Many of the studies considered above have shown that life expectancy is longer in people who have a higher CD4 count at HIV diagnosis [10,28,29▪▪]. In the UK, approximately 50% of people are diagnosed late (defined as a CD4 count <350 cells/μl within 3 months of diagnosis)  and there is strong evidence that late diagnosis is associated with poorer prognosis including worse response to treatment and an increased risk for HIV-related morbidity and mortality [58–61]. Therefore, one way to further increase life expectancy of people with HIV is to improve HIV testing rates, particularly in high prevalence areas and perhaps in nontraditional settings. Early diagnosis of HIV will result in individuals starting ART in accordance with guideline recommendations and people being be in care and routinely monitored, both of which will improve life expectancy.
In general, it is recognized that HIV-positive people have an inferior profile of lifestyle risk factors compared to the general population [62–67]. Many of these risk factors, whether HIV-related or not, and which include smoking, alcohol abuse, and recreational drug use, are associated with an increased risk of death [46,62,68▪,69▪▪], and hence investment into support programmes may help to reduce the mortality rates more. The effect of hepatitis C coinfection on HIV is also important to consider, because HIV immunosuppression is associated with faster progression of liver disease in people with hepatitis C .
The risk factors for acquiring HIV are also associated with lower life expectancy . Studies comparing mortality rates in people with HIV and on ART to people without HIV have reported conflicting results [9,72▪,73], although there is some evidence of an increased death rate in individuals who are not yet on treatment because of high CD4 counts compared with the general population (with the exception of MSM with CD4 count >500 cells/μl) . Even if the life expectancy of an HIV-infected person were found to be equal to that of an HIV-negative person, the quality-adjusted life years would probably be less, mostly because of the potential harm resulting from long-term ART usage , but also because of the psychological aspects for some of carrying a lifelong virus, despite it being controlled at very low levels. More research is required on ways in which quality of life could be improved.
The life expectancy of someone living with HIV, especially if they are diagnosed before ART is indicated, is approaching that of HIV-negative individuals; however, it is still not equal (Table 2). Even if life expectancy were to improve further such that it did not differ by HIV status, quality-adjusted life years would almost certainly be less.
The improvement in life expectancy will mean that the number of people with HIV increases and there will be a shift in the age distribution. Further research into the impact of ageing on HIV-positive people will become crucial. Therefore, age-specific HIV treatment guidelines may need to be developed and introduced. Screening programmes and more comprehensive patient management are also required to address comorbidities and risk factors associated with HIV that increasingly occur as people age.
Modelling studies suggest that life expectancy could potentially rise further. Ways in which this could be possible include increased uptake of HIV testing, better antiretroviral regimens and treatment strategies and the adoption of healthier lifestyles by those living with HIV. In particular, earlier diagnosis and enrolment into care is one of the most important factors associated with better life expectancy.
Margaret May is supported by the UK Medical Research Council grant MR/J002380/1.
Conflicts of interest
A.P. has received consultancy fees or funds for research from Johnson and Johnson, Gilead, Bristol-Myers Squibb and ViiV.
REFERENCES AND RECOMMENDED READING
Papers of particular interest, published within the annual period of review, have been highlighted as:
▪ of special interest
▪▪ of outstanding interest
Additional references related to this topic can also be found in the Current World Literature section in this issue (p. 100).
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