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AIDS:
doi: 10.1097/QAD.0b013e3283558526
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The impact of antiretroviral treatment on the age composition of the HIV epidemic in sub-Saharan Africa

Hontelez, Jan A.C.a,b,c; de Vlas, Sake J.a; Baltussen, Robb; Newell, Marie-Louisec; Bakker, Roela; Tanser, Frankc; Lurie, Markd; Bärnighausen, Tillc,e

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aDepartment of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands

bDepartment of Primary and Community Care, Nijmegen International Center for Health System Analysis and Education, Radboud University Nijmegen Medical Centre, The Netherlands

cAfrica Centre for Health and Population Studies, University of KwaZulu-Natal, Somkhele, KwaZulu-Natal, South Africa

dDepartment of Epidemiology and the International Health Institute, Warren Alpert Medical School, Brown University, Providence, Rhode Island

eDepartment of Global Health and Population, Harvard School of Public Health, Boston, Massachusetts, USA.

Correspondence to Jan A.C. Hontelez, Department of Public Health, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands. Tel: +31 30 704 46 32; e-mail: j.hontelez@erasmusmc.nl

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Website (http://www.AIDSonline.com).

Received 9 November, 2011

Accepted 1 May, 2012

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Abstract

Introduction: Antiretroviral treatment (ART) coverage is rapidly expanding in sub-Saharan Africa (SSA). Based on the effect of ART on survival of HIV-infected people and HIV transmission, the age composition of the HIV epidemic in the region is expected to change in the coming decades. We quantify the change in the age composition of HIV-infected people in all countries in SSA.

Methods: We used STDSIM, a stochastic microsimulation model, and developed an approach to represent HIV prevalence and treatment coverage in 43 countries in SSA, using publicly available data. We predict future trends in HIV prevalence and total number of HIV-infected people aged 15–49 years and 50 years or older for different ART coverage levels.

Results: We show that, if treatment coverage continues to increase at present rates, the total number of HIV-infected people aged 50 years or older will nearly triple over the coming years: from 3.1 million in 2011 to 9.1 million in 2040, dramatically changing the age composition of the HIV epidemic in SSA. In 2011, about one in seven HIV-infected people was aged 50 years or older; in 2040, this ratio will be larger than one in four.

Conclusion: The HIV epidemic in SSA is rapidly ageing, implying changing needs and demands in many social sectors, including health, social care, and old-age pension systems. Health policymakers need to anticipate the impact of the changing HIV age composition in their planning for future capacity in these systems.

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Introduction

The rapid and large scale-up of antiretroviral treatment (ART) for HIV in sub-Saharan Africa (SSA) constitutes an unprecedented global public health effort, resulting in great improvements in length and quality of life of those infected. The expansion of ART coverage since the early 2000s has led to a substantial increase in the number of HIV-infected people on ART, with nearly 4 million people initiated in SSA as of late 2009 [1]. In June 2011, the United Nations General Assembly High Level Meeting on AIDS renewed its commitment to achieving universal ART coverage, calling for a doubling in scale-up efforts to initiate another 10 million people, to achieve universal coverage of those in need by 2015 [2]. Yet, while ‘[t]he UN meeting was tasked with charting the future course of the global HIV response, … [it] failed to mention the ageing of the pandemic’ [3].

Effective ART increases survival [4–6] and can decrease HIV transmission probabilities [7–10]. Mills et al. [5] estimated that life expectancy of HIV-infected patients in SSA can approach the life expectancy of the uninfected population if treatment is initiated early (at CD4 cell counts >250 cells/μl). The results of the HIV Prevention Trials Network (HPTN) 052 trial show that HIV transmission rates can be reduced by as much as 96% in HIV-discordant stable partnerships [8], and results from observational studies show reductions of about 90% in transmission rates [7,9]. Thus, with expanding ART coverage HIV-infected people will live into older ages and HIV incidence in the young and middle-aged population is likely to decrease, resulting in a shift of the age composition of the HIV epidemic towards older ages. Such a shift has already occurred in developed countries. About 29% of HIV-infected people in the United States were aged over 50 years in 2008, whereas this proportion was only 17% in 2001 [11]. A previous study quantified the ageing of the HIV epidemic for the South African province of KwaZulu-Natal, estimating that the number of HIV-infected adults aged 50 years or older will double from 2004 to 2025 [12]. Similar projections for other parts of SSA are currently missing, and it is unlikely that the South African results can be generalized to countries with different demographic and behavioural characteristics as well as distinct HIV treatment and prevention efforts. Already, an estimated 3 million people aged 50 years or older live with HIV in SSA [13], and with a further 7 million HIV-infected people in SSA eligible for HIV treatment [14], there is a large pool of currently untreated HIV-infected adults who will be able to survive to older ages as treatment coverage expands.

Here, we predict age-specific HIV prevalence trends in 43 countries in SSA under different trajectories of ART coverage expansion. We used STDSIM, a stochastic microsimulation model that simulates individuals in a dynamic network of sexual contacts [15–17]. We developed an approach that can be applied to quantify all national HIV epidemics in 43 sub-Saharan African countries in the period 2000–2009 by using country-specific data on demographic composition [18–20], data on country-specific ART coverage [1] and country-specific circumcision prevalence rates [14,21], as well as epidemic-specific sexual behaviour profiles.

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Methods

Model

In the model, HIV is represented by four consecutive stages: early infection (0.25 years); asymptomatic infection (5.5 years); symptomatic infection (4 years); and AIDS (0.7 years). Median survival of an individual with untreated HIV infection is about 10 years (95% confidence interval 5–19 years) [22]. People on ART are assumed to have a 90% reduction in infectiousness [7,9] and their life expectancy at the time of treatment initiation is four times the remaining life expectancy without treatment (Fig. S1, http://links.lww.com/QAD/A226) [6]. More details about the model structure can be found in the supplementary material and in three previous publications [15,23,24].

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Model quantification
Demographics

Background mortality rates (mortality in the absence of HIV) were calculated using country-specific life tables [19] and burden of disease estimates published by the World Health Organization (WHO) [20]. For each country, we first calculated the proportion of deaths attributed to HIV through comparison of the age-specific and sex-specific burden of disease estimates [20], and the all-cause mortality rates in the WHO life tables [19]. We then used the ratio between these two mortality estimates (HIV-specific and all-cause) to compute background mortality rates for all causes except for HIV. Figure S2A and S2B, http://links.lww.com/QAD/A226, present the country-specific HIV-corrected background mortality rates for men and women, respectively. Age-specific and period-specific fertility rates for each country were obtained from the 2008 United Nations World Fertility Data [18]. We assumed that fertility rates remained constant after 2011.

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Antiretroviral treatment scale-up

We fitted ART coverage until 2009 to the coverage levels reported by WHO [1], using two submodels. The first submodel represents an individual's demand for ART as a function of HIV-disease stage; the second submodel describes the capacity of the health system to meet this demand. ART coverage in our model is the ART demand met by the capacity of the health system. To fit the modelled ART coverage to the annual coverage data reported by WHO (for the period 2004–2009) [1], we used a quadratic (αx2), linear (αx) or square-root (αx1/2) function of scale-up of ART capacity in the health system, while assuming the ART demand function to be the same as previously estimated for South Africa [15]. For each of the three scale-up functions, we calculated the annual ART coverage of those eligible (at CD4 cell counts ≤200 cells/μl) for all countries in SSA using the country-specific starting years of the ART scale-up (the scale-up started in all countries in the period 2001–2005). We chose the multiplication factors (α) in the different functions to maximize the model fit by minimizing the mean squared error (MSE) of the model predictions compared with the country-specific ART coverage estimates reported by WHO [1].

We assumed all countries to provide ART at CD4 cell counts ≤200 cells/μl up to 2009, with three exceptions: Botswana offered ART at CD4 cell counts ≤250 cells/μl for all HIV-infected individuals since the start of its ART scale-up in 2003 [25]; Rwanda switched to ART at CD4 cell counts ≤350 cells/μl for all HIV-infected individuals in 2007 [26]; and Namibia has offered lifelong ART at CD4 cell counts ≤350 cells/μl for all pregnant women since 2007 (about 20% of all HIV-infected women aged 15–49 years and with CD4 cell counts between 200 and 350 cells/μl who seek care) [27]. We assumed a baseline annual rate of stopping treatment of 5% [28], and that people who stopped will never re-initiate treatment. As retention in care varies with the capacity of the health system to deliver ART [29], we assumed that the annual rate of stopping treatment is reduced to 2.5% when the health system's capacity to provide ART reaches 80%, and is further reduced to 1% when the capacity is 100%.

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HIV epidemic and sexual behaviour profiles

To represent the different HIV epidemics in SSA, we defined five sexual behaviour profiles that differ in their age-specific and sex-specific rates of forming – and condom use during – three different types of sexual partnerships (Table S1, http://links.lww.com/QAD/A226): stable relationships (lasting on average 25 years); casual relationship (lasting on average 6 months); and commercial sex (a once-off contact) [23,24].

We named the sexual behaviour profiles according to the epidemics they have produced: concentrated risk profile (high risk of HIV among commercial sex workers (CSWs) and clients; low risk in the general population); mixed risk profile (high risk of HIV among CSWs and clients; medium risk in the general population); and generalized risk profile (high risk in the general population). Three of the four parameter settings of the ‘four cities study’ fitted these three profiles and were chosen accordingly: Cotonou, Benin (concentrated risk profile); Yaoundé, Cameroon (mixed risk profile); and Kisumu, Kenya (generalized risk profile) [24]. High levels of condom use among CSWs introduced in the early nineties in the concentrated risk profile and mixed risk profile resulted in declining HIV prevalence. To capture this distinction, we added two extra profiles: concentrated risk profile (low condom use) and mixed risk profile (low condom use), both with reduced condom use rates during commercial sex.

As in a previous study [24], sexual behaviour parameters for the population aged 15–49 years were stratified by 5-year age groups and fitted to represent the age-specific numbers of sex partners reported in the behavioural surveys of the ‘four cities study’ [30]. In order to derive parameter values for sexual behaviour for the people aged 50 years or older, for which measured data were not available in the study, we assumed that partner change rates and CSW visiting behaviour remained the same for all people aged 45 years or older. Within each partnership, we assumed a 25% reduction in the frequency of sexual contacts in people aged 50 years or older relative to those aged 45–49 years. This assumption fitted closely to the data from a HIV and sexual behaviour surveillance in the population aged 50 years or older in KwaZulu-Natal, South Africa [12,31].

For each country, we ran the model with all five sexual behaviour profiles and the country-specific circumcision prevalence [14,21] and ART scale-up function (see above). We then selected the profile that best described the HIV epidemic in a given county in the period 2000–2009. In order to do so, we constructed a ‘fit score’ that captures the development of HIV prevalence over time. The score is the sum of the MSE of HIV prevalence predictions over 2000–2009 (for fitting prevalence levels), and the squared error over the difference between prevalence in 2000–2004 and 2005–2009 (to fit the observed trend in HIV prevalence). We used UNAIDS estimates of the country-specific HIV prevalence in adults aged 15–49 years over the period 2000–2009 in order to assess fit [14].

Finally, we fine-tuned the model quantifications for each country by choosing the best-fitting combination of overall partner change rates (range ±25%; see Table S1, http://links.lww.com/QAD/A226) and year of HIV introduction that produced the lowest MSE on the HIV prevalence estimates in adults aged 15–49 years, as compared with UNAIDS estimates for the period 2000–2009. For the concentrated risk and mixed risk profiles, we allowed for a maximum of 25% reduction in CSW visit rates to further fine-tune predicted HIV epidemics, because the epidemics produced by these profiles are largely driven by commercial sex.

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Simulations

We predicted trends in HIV prevalence in the populations aged 15–49 years and 50 years or older over the period 2011–2040 in 43 countries in SSA. In our baseline estimate, we assumed ART to be scaled up continuously after 2009 according to the country-specific scale-up function of the health system's capacity (see above), until capacity reaches 100%. By October 2010, seven countries in SSA had adopted the 2010 WHO treatment guidelines that recommend ART initiation at CD4 cell counts ≤350 cells/μl into their national policy (Kenya, Lesotho, Malawi, Rwanda, Tanzania, Zambia and Zimbabwe) [1], whereas South Africa adopted the guidelines in August 2011 [32]. We assumed that all other countries will have adopted the new guidelines by January 2013.

We calculated country-specific trends in HIV prevalence and total number of HIV-infected people aged 15–49 years and 50 years or older. We assumed three alternative scenarios of scale-up of health system's capacity to provide ART: decline (reduction in capacity by 20% in 2012 and constant capacity levels thereafter); no further scale up (capacity remains constant at 2011 levels); rapid scale up (capacity increases to 100% for all countries by 2015).

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Results

Using the five predefined sexual behaviour profiles (Fig. 1), our model was able to accurately replicate the ART coverage scale-up (Fig. 2a) and HIV epidemics (Fig. 2b and c) of all 43 sub-Saharan African countries. For only nine countries, HIV prevalence projections differed more than 10% compared with UNAIDS estimates at some point during the period 2000–2009. The absolute number of HIV-infected people aged 50 years or older (2.6 million) and the number of HIV-infected people aged 15–49 years (17.8 million) in 2007 are very similar to the estimates that Negin and Cumming [13] derived using a different methodological approach (2.9 and 17.9 million, respectively). In addition, our model predictions regarding population growth over the period 2000–2040 are very similar to those provided by the United Nations Population Prospects (Fig. S3, http://links.lww.com/QAD/A226) [33]. A detailed description of the parameters for individual countries can be found in Table S2, http://links.lww.com/QAD/A226.

Fig. 1
Fig. 1
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Fig. 2
Fig. 2
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Figure 3 shows the HIV prevalence in the populations aged 15–49 years and 50 years or older for the years 2011, 2025 and 2040 under the baseline scenario of continued scale-up of ART. Overall, prevalence in the population aged 15–49 years will decline from 5% in 2011 to 3% in 2040, whereas prevalence in the population aged 50 years or older will increase from 3 to 4% over the same period. The number of countries with an HIV prevalence of less than 1% in the population aged 15–49 years will increase from six in 2011 to 17 in 2040, whereas the number of countries in this prevalence category for the population aged 50 years or older will halve in the same period, from 12 to six. HIV prevalence in older adults will be 2% or higher in 22 countries in SSA in 2040, whereas this is the case for only 11 countries regarding adult HIV prevalence. In countries with currently very high HIV prevalence rates in both younger and older adults, HIV prevalence in the population aged 50 years or older will increase dramatically (Table 1). For instance, in Botswana, HIV prevalence in the population aged 50 years or older was 15% in 2011, and will increase to 24% in 2040. Similar trends are predicted for South Africa (an increase in HIV prevalence in the population aged 50 years or older from 10 to 16%), Swaziland (15 to 27%) and Lesotho (13 to 25%) (Fig. 3).

Fig. 3
Fig. 3
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Table 1
Table 1
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The total number of HIV-infected people aged 50 years or older in SSA will increase rapidly over the coming decades, from 3.1 million in 2011 to 9.1 million in 2040, an increase of 190% (Fig. 4 and Table 2). At the same time, the number of HIV-infected young adults (aged 15–34 years) will rapidly decline: from 12.1 million in 2011 to 9.1 million in 2030 (a 25% reduction). While prevalence levels stabilize in 2030, the total number of HIV-infected people will increase again to 10.8 million in 2040. Overall, the total number of HIV-infected people aged 15 years or older will increase over the next three decades, from 22.4 million in 2011 to 32.4 million in 2040, an increase of 44%.

Fig. 4
Fig. 4
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Table 2
Table 2
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As a result of the disproportionate increase in the number of HIV-infected older adults (Fig. 4), the age composition of the HIV-infected population will change (Table 2). In 2011, about 13% of all HIV-infected people were aged 50 years or older; by 2040, this proportion will have more than doubled to 27% (Table 2). In contrast, young adults (aged 15–34 years) will contribute decreasing proportions of infections to the total number, from 52% in 2011 to 33% in 2040 (Fig. 5). Countries that have both a high ART coverage and declining HIV prevalence among the population aged 15–49 years will be faced with an especially dramatic shift in age composition of the HIV epidemic. The most extreme shift is observed in Zimbabwe, where the proportion of HIV-infected people aged 50 years or older will increase from 16% in 2011 to 62% in 2040. Countries like Kenya (13–51%), Tanzania (14–48%), Namibia (12–38%) and South Africa (14–36%) show similar trends. In contrast, countries with low and slowly expanding ART coverage show less rapid changes in age composition. In Sierra Leone, the proportion of HIV-infected people aged 50 years or older increases from 11% in 2011 to 18% in 2040, and similar trends are found in Democratic Republic of Congo (13–15%), The Gambia (9–15%), Somalia (14–21%) and Burundi (16–22%) (Table 2).

Fig. 5
Fig. 5
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In the decline scenario, with 20% decrease in ART capacity in 2012, we predict that the number of HIV-infected older adults will reach 6.9 million in 2040, or 22% of all HIV-infected people (Fig. 5). On the other hand, if treatment capacity remained at the level of 2011 (i.e. in the no further scale up scenario), the total number of HIV-infected older adults would be 7.4 million in 2040, which is 24% of all HIV-infected adults. Under the rapid scale up scenario, the number of HIV-infected older adults in 2040 would be 9.3 million in 2040, which is 28% of all HIV-infected people.

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Discussion

We estimate that the total number of HIV-infected older adults (aged 50 years or older) will nearly triple from about 3.1 million in 2011 to 9.1 million in 2040, assuming that ART scale-up continues at the current speed. In 2011, about one in seven HIV-infected people were aged 50 years or older in SSA, whereas in 2040, this ratio will be more than one in four. Due to an overall increase in the number of people aged 50 years or older in SSA, the increase in prevalence is relatively modest, from 3% in 2011 to 4% in 2040. In contrast, HIV prevalence among the population aged 15–49 years will decline over the coming decades, from 5% in 2011 to 3% in 2040.

This ageing of the HIV epidemic is likely to have broad and important consequences for the organization of healthcare services in SSA, as has been pointed out in a commentary on the results we present in this study [33]. Due to the increase in life expectancy due to the ART scale-up, populations will age, ‘unmasking’ the burden of noncommunicable diseases (NCDs) previously hidden due to high rates of HIV-related mortality [34]. Already, NCDs are becoming more important in low- and middle-income settings, where prevalence of risk factors is high and prevention efforts are limited [35–39]. In South Africa, 55% of all middle-aged women were found to be obese in a cross-sectional survey [40,41]. Smoking prevalence in SSA is high and increasing, and meals generally contain high levels of calories and salt [40,41]. Consequently, hypertension and diabetes are becoming more common in SSA [42,43]. As the contribution of these risk factors to the overall risk of NCDs accumulates over age, they become particularly important as the HIV epidemic ages. In addition, HIV infections in older adults is often complicated by preexisting or developing non-AIDS-related comorbidities such as cardiovascular diseases (CVDs) and metabolic diseases, which in turn might aggravate HIV disease progression [44]. Finally, HIV infection and ART are independent risk factors of many NCDs such as non-AIDS-related malignancies, CVDs, kidney and liver failure and osteoporosis [45–47]. Therefore, quantitative estimates on the impact of the ageing HIV epidemic on the overall disease burden in SSA are needed.

The predicted ageing of the HIV epidemic will also affect social sectors other than the health sector, particularly in countries where HIV prevalence in older adults will substantially increase over the coming decades. Currently, many countries in SSA have no, or very limited, pension programmes [48], and support for the elderly generally falls under the responsibility of the family [49]. As the numbers of HIV-infected adults who live into old ages increases due to ART, the need for financial and social support of older adults will increase as well. Policymakers need to consider how this need can be met in the specific contexts of their countries’ existing old-age pension and social care systems. At the same time, the increasing presence of older adults in the hyperendemic communities in SSA may bring important benefits to families and communities in the region, including improved childcare, social cohesion, and greater flexibility of middle-aged family members to temporarily migrate in search for work opportunities. Future empirical research needs to establish how the presence of older HIV-infected adults in sub-Saharan African households affects households’ social and economic well being, and which interventions can strengthen positive effects and mitigate negative ones.

Our results show that the total number of HIV-infected adults (aged 15 years or older) will increase by 44% over the next three decades, creating a continuously growing need for financial and human resources to provide ART. Already, financial and human resources to provide ART in SSA are stretched [50,51], emphasizing the need for continued scale-up of cost-effective prevention interventions alongside treatment in order to reduce incidence and thus future treatment needs [52–54]. In addition, it might be necessary to more closely integrate the delivery of treatment and care for different chronic diseases, in order to reduce the financial and time burdens that older ART patients bear in regularly utilizing healthcare for several conditions. Economies of scope might increase the efficiency of the healthcare delivery, and general health systems might be strengthened as vertical health system structures are integrated [55].

Our study has several limitations. We modelled countries as a homogenous mix of people, assuming country averages to apply to the entire population. However, in reality, there may be important differences in HIV epidemics within countries [56]. In addition, we assumed HIV survival and transmission probabilities to be universally applicable, whereas in reality, there may be differences in these parameters by strains of HIV virus in different parts of Africa [57]. The HIV-2 virus, which is only prevalent in some western African countries, is known to have a lower virulence and transmission potential compared with the more common HIV-1 strain [57]. Also, our model does not include mother-to-child transmission of HIV. As HIV-infected children can be treated effectively with ART [58], they may now live into young adulthood, increasing the number of HIV-infected people in this age category.

Both acquired resistance (development of resistance within an individual on treatment) and transmitted resistance (spread of drug-resistant strains) may reduce the effectiveness of treatment programs, and consequently result in a less profound effect of the ART scale-up on the population age composition. Patients who develop resistance might fail to suppress viral replication while on treatment, resulting in shorter survival times and higher infectiousness. While second-line or third-line therapies can be prescribed to treat those with resistance to first-line ART, many treatment programs in SSA are currently not well equipped to deal with drug resistance, as both monitoring for treatment resistance and providing second-line and third-line ART regimens is expensive and requires specialized expertise [59]. Therefore, if the prevalence of resistance increases, effective treatment coverage will decline. In our sensitivity analysis, we explore the effect of declining treatment coverage on the changes in age composition. We find that the changes in age composition are similar but somewhat reduced in magnitude if effective coverage is reduced substantially (e.g. by one fifth compared with the baseline case). It is currently unclear, however, in how far the fears of rapidly spreading drug resistance expressed at the start of the ART scale-up [60] are justified. The prevalence of drug resistance remains low in most countries in SSA after nearly 10 years of scaling up ART [61,62]. In addition, adherence to treatment in SSA is comparable to many high-income countries [63], and survival of patients on treatment in SSA approaches general life expectancy [5], suggesting that resistance may not become a major problem in the region in the near future.

In this study, we assumed that risk behaviour remained the same after age 45 years. Although detailed data on sexual risk taking in older age for SSA is lacking, it is plausible that the frequency of sexual activity declines to some extent in older adults [64]. On the contrary, there is evidence that older people are at increased risk for HIV through both biological mechanisms and increased riskiness in behaviour during sex. Postmenopausal women might be more susceptible to HIV because of the thinning of the vaginal wall [65], and data from the Demographic and Health Surveys show that condom use and knowledge about condoms is particularly low in older adults [13]. In the United States, condom use among older adults with known risk factors for HIV was about six times lower compared with that among adults aged 15–49 years [66]. Yet, despite the considerable and increasing burden of HIV in older adults in SSA, our understanding of sexual behaviour in this group remains limited. With increasing prevalence of HIV in older adults, HIV incidence in this age group is also likely to increase, warranting the need for age-appropriate prevention interventions.

It is important to note that our model accurately replicated the HIV epidemic in all the 43 SSA countries (Fig. 2), suggesting that the theoretical limitations we describe above do not substantially matter for our estimations. This claim is further supported by comparison of our estimates of a total of 2.6 million HIV-infected older adults in 2007 to the estimate published by Negin and Cumming (which is 2.9 million) [13].

In conclusion, we show that the HIV epidemic in SSA will rapidly age over the coming decades. This has important consequences for both the organization of healthcare services and the general organization of societies in the subcontinent, as older HIV-infected people require specialized treatment and care, as well as social and financial support. In addition, expanded treatment coverage is likely to increase the burdens of other diseases in SSA, in particular NCDs. Health policymakers need to anticipate the impact of the ageing HIV epidemic in their planning for the future capacity of health systems to prevent and treat diseases of old age in HIV-infected individuals.

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Acknowledgements

This work is supported by the National Institutes of Health (1R01MH083539-01 and R01 HD058482-01).

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Conflicts of interest

There are no conflicts of interest.

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Keywords:

ageing; antiretroviral therapy; epidemiological trends; HIV; mathematical model

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