Future Work

Given the importance of side effects of HIV-treatment as reasons for switching regimens and for reduced adherence, it is disappointing that they are studied so poorly [148]. For regulatory approval, only side effects affecting major organ systems, such as the central nervous, cardiovascular, and respiratory systems have to be reported [148]. In terms of post-marketing data collection, the reporting of side effects occurs passively, with little consensus on the definition of side effects or method of collection [148]. There is an unmet need for more detailed data about treatment tolerability, drug interaction between ARVs and co-medication, and the effect of short- and long-term toxicity of cART, especially in ageing HIV-patients [15]. Long-term post marketing surveillance of cART was one of the primary reasons that the ATHENA cohort was set up [176], and in the immediate future it will be important to indicate to such long-term cohorts on the additional data that they should be collecting going forward. Many studies into toxicity have until recently excluded patients of advanced age or with co-morbid disease [15]. International consensus on definitions of toxicity and collaborative collection of side effects will be important steps in the continued research on toxicity and HIV.

Chapter 6 – Discussion

In addition, studies and controlled trials on therapeutic and clinical outcomes of older HIV- infected patients are needed. Until recently, this population has been relatively ignored, with most randomized trials of cART excluding older patients or those with co-morbid disease [15]. Consequently, many questions remain about the clinical care of ageing HIV-patients, the tolerability of cART in patients with multiple morbidities, the impact of large pill burdens on treatment outcomes, pharmacodynamics and pharmacokinetics in older patients, and the drug interactions between cART and co-administered medication. It remains unclear whether guidelines for the treatment of older HIV-patients should be modified. For example, should the CD4-threshold for cART initiation be changed in older patients to counteract the role of inflammation on the development of co-morbidities [227–229]? How do the risks and benefits of early cART initiation differ in older patients; what are the benefits of early initiation versus the long-term toxicity of cART on the development of co-morbid disease, and does this differ with a patient’s age? How should screening and treatment protocols be changed in light of the changing demography? What interventions are the most effective for the primary prevention of co-morbid disease in HIV-patients and how can they best implemented to respond to the unique needs of these complex patients as they age?

Despite the importance of these questions they have so far received little attention. Clinical research will have to evaluate the efficacy, safety and tolerability of these programs in HIV-cohorts, as well as potential interactions with ARVs and effects on adherence. Behavioural research will have to be carried out to evaluate the feasibility and acceptability of these interventions and how best to tailor them to this sub-population. Systems-based research will have to be carried out to identify the barriers to implementation and how best to address them.

New cohorts will bring a valuable tool to answering some of the many questions that remain. Cohorts such as the AGEhIV Cohort Study, a prospective cohort study in the Netherlands established

in 2010, are increasingly investigating the therapeutic and clinical outcomes of older HIV-infected patients [326]. These studies will provide vital information on some of the key questions, including the role of inflammation in the early onset of non-communicable diseases amongst HIV-patients. They will provide the means to expand the model presented in this thesis to provide a more rounded picture of the future of the clinical care of an ageing HIV-population. In the first analysis of their cohort data, they found that after controlling for a number of key factors, HIV-infected individuals were at an increased risk of having one or more co-morbidities compared to non-infected individuals. Their study showed that HIV-patients had a significantly higher prevalence of hypertension, MIs,

Chapter 6 – Discussion

My future aims are to continue researching this topic. Through continued collaboration with the ATHENA cohort and thanks to a joint interest with the AGEhIV Cohort Study, the model will be

further developed in the future. While there are a number of improvements that can be made to the model, the main points that will become the focus of continued collaboration with ATHENA are outlined in Figure 6.1, Part 1, Section A.

First, the goal will be to include key lifestyle factors such as smoking status, alcohol consumption and diet in the model and estimate their impact on the development of co-morbidities (Figure 6.1, Part 1, Section A, Point 1). In order to simulate the impact of lifestyle factors on the development of malignancies, the model should simulate malignancies in more detail, for example by including key cancer types (Figure 6.1, Part 1, Section A, Point 5). The model should also simulate recurrent MIs and strokes and incorporate a CVD framework, similar to the Framingham or SCORE. In the future the aim is to incorporate CVD risk, such as the D:A:D and Framingham framework to better evaluate a number of different CVD interventions independently and in combination.

The AGEhIV Cohort Study will form a valuable source of data for model expansion. For

example, the cohort collects information on additional co-morbidities which should be incorporated into the model (Figure 6.1, Part 1, Section A, Point 2). In particular, the focus should be on incorporating neurodegenerative disorders such as Alzheimer’s disease into the model. Neurocognitive conditions are expected to become a major problem amongst older HIV-patients, with conditions related to memory loss resulting in adherence issues, compromising the efficacy of HIV- treatment [15,309–312]. Any additional co-medication for those conditions should be included in the model (Figure 6.1, Part 1, Section A, Point 6) and information on drug interactions with additional co- medications updated (Figure 6.1, Part 1, Section A, Point 7).

In addition, incidence data on the co-morbidities already incorporated in the model will be checked against D:A:D estimates and estimates of the AGEhIV Cohort Study, to determine whether

and how the current model is underestimating the true burden of co-morbidity (Figure 6.1, Part 1, Section A, Point 4), and to update estimates where necessary. As mentioned previously, the ATHENA cohort does not systematically collect data on co-morbidity resulting in a potential underestimation of the true burden of co-morbidities in the cohort.

The AGEhIV Cohort Study will also provide additional information on the independent effect

of HIV-infection, including inflammation and HIV-treatment on the development of co-morbidities to include in the model (Figure 6.1, Part 1, Section A, Point 3). As the first analyses of the cohort are only just being undertaken, it was not possible to utilize the AGEhIV cohort data in the work

Chapter 6 – Discussion

Figure 6.1. Details of future work on Part 1: Model expansion and Part 2: Adaptation of the model to different geographical and demographic settings. Part 1 Sections B to D show the basic model structure. Current model follows HIV-patients from treatment initiation until death or closing year of model (2030) (Section C). It simulates how HIV-patients ageing over time (Section D), develop co-morbidities, start co- medication for these conditions and how these co-medications affect HIV-treatment (Section B). Future plans for model expansion are listed in Section A. Part 2 shows plans to adapt the model from a European

1. African setting 2. General population 3. Economic analysis

1. African setting 2. General population 3. Economic analysis

Chapter 6 – Discussion

Finally, in addition to the above model extensions, the next important step will be to expand the model, for example to explore these issues in an African setting (Figure 6.1, Part 2, Section 2, Point 1), where health care systems currently largely provide episodic care for acute symptomatic conditions [350,453,454] or services for maternal and infant health [350,455]. Recent studies have

found similar trends of shifting demography of HIV-patients in sub-Saharan Africa, where life expectancy of HIV-infected individuals has increased considerably over the past decade [12]. In South Africa life-expectancy of people living with HIV has been reported to have increased from 56.5 years in 2009 to 60 years in 2011 [12], probably due to the increasing delivery of HIV-treatment and prevention of mother-to-child-transmission [456]. An ageing population, both in HIV-infected and non-infected, will put new demands on health systems, and require continuity of care with specialists in chronic disease management. The model will provide a valuable tool in evaluating this need.

As mentioned previously, HIV-infection is likely to serve as a model for accelerated ageing, with the lessons learned regarding the complex management of patients with multiple morbidities likely to be applicable to geriatric services for the general population (Figure 6.1, Part 2, Point 2). Adaptation of the model to an African setting or for the general population and integration of relevant health economic data (Figure 6.1, Part 2, Point 3) providing valuable insight and inform Health Ministries on investing in future care of ageing populations in settings with high HIV prevalence. These expansions will require extensive data for parameterization. Adapting the model to the general population in particular is going to have data or make assumptions about the causes of ageing and reasons why patients with the same age suffer disproportionately differently from co-morbid disease.