SACEMA had the privilege of hosting an intense (Gates Foundation Funded) workshop of the international ‘HIV modelling consortium’, focusing on models of early treatment and their potential impact. Model scenarios were intriguing, debate was intense and constructive, and the prognosis was sobering. Treatment alone, at conceivable levels, given the current financial and infrastructural outlook, can curtail HIV incidence, but does not appear to open a road to HIV eradication. So we must accept that we must look to the ‘multiple prevention methods’ paradigm, new technologies, and cleverer healthcare systems if there is to be a future where HIV is a tolerable burden on those societies currently bearing the greatest impact.

An interesting theme worth watching, in the context of debate on interventions in the face of what will inevitably be a multi-generational epidemic, is the limits of orthodox discourse offered by various professional fraternities like ‘modellers’, ‘biostatisticians’, and ‘health economists’. The lengthy time scales, over which interventions and their consequences must play out, stretch all known, and perhaps all conceivable, formal methods to their limits, and demand that interpretation be a nuanced and careful technical execution. Should the model outputs be treated as crystal ball predictions, warnings, structural insights, or mere mental gymnastics. Perhaps it depends on the actual model in question, but formal predictions, in anything resembling the predictions which are the bread and butter of natural science, are hardly likely to be what epidemiological modelling is about.

On a narrower scope than steering us towards an HIV free generation, this edition of the SACEMA Quarterly epidemiological update offers, as usual, three perspectives on current challenges and recent progress. Carel Pretorius and Samuel Manda explore superficially very different, but, at a deeper level, analogous analytical problems relating to making sense of epidemiological ‘clusters’. It is tempting to attach interpretations to limited data which often does not support our pat conclusions, and we hope these pieces offer a useful view of on-going technical developments to support sound joining of the dots. A popular theme here at SACEMA, HIV incidence estimation, returns once again with a piece by Reshma Kassanjee, demonstrating how existing, previously neglected, specimens from the blood donation industry can be used to perform low cost, low risk, preliminary investigations into surveillance-application performance of new laboratory assays.

The first SACEMA Quarterly update of 2010 was devoted to the topic of HIV incidence (the rate of occurrence of new infections in a population). Incidence will always remain a crucial measure in epidemiology, providing a direct and current indication of the spread of disease. Prevalence (the fraction of the population with a condition at a point in time) is a metric more commonly measured, but is less informative as it emerges from historic incidence, demography and survival dynamics. South Africa has the largest HIV-positive population, exceeding 5 million individuals in 2011 (1). This year the government will launch its second 5-year National Strategic Plan (NSP) against HIV for 2012-2016. The headline goal of the first 5-year NSP (2007 – 2011) was to halve incidence, but it will be difficult to assess whether this goal has been achieved.

There are a number of approaches for estimating HIV incidence, with varying tractability, complexity and limitations. (i) In a prospective longitudinal study of a cohort of initially uninfected subjects, infection events are directly counted. However, such studies can be costly and prone to unrepresentative sampling. (ii) HIV prevalence, measured in sentinel or general populations, is often modelled to estimate incidence. For example, in age groups with low HIV mortality, differences in prevalence by age may be attributed to new infections. Alternatively, prevalence, measured at multiple time points in the past, could be used to estimate historic incidence. However, prevalence data has become increasingly complex to interpret as epidemics mature, and knowledge of post-infection mortality is often limited. (iii) Alternatively, back-calculation from AIDS cases involves estimating the historic HIV incidence that produced the observed AIDS incidence. However, this method provides little indication of recent incidence. An extension of this method utilises reported HIV diagnoses too. (iv) There are also a number of detailed ‘microscopic’ models, such as the UNAIDS Modes of Transmission model (see SACEMA Quarterly, March 2010 ), and dynamical models. These typically explicitly model the mechanisms of transmission of the virus through the population, requiring a number of quantitative assumptions.

Additionally, in recent years, there has been considerable interest in estimating HIV incidence from single cross-sectional surveys testing for ‘recent infection’ through laboratory-measured host or viral biomarkers (2). In a survey, the sizes of the HIV-negative, ‘recently infected’ and ‘non-recently infected’ populations can be measured, and incidence estimated using knowledge of the dynamics of the ‘recent infection’ biomarker (3,4,5).

Given the potential benefits arising from using single cross-sectional surveys for incidence estimation, this approach has been applied in numerous studies, and has caught the attention of prominent organisations worldwide. The World Health Organisation (WHO) Technical HIV Incidence Assay Working Group (HIVIWG) produced an extensive guide on the use of biomarkers for ‘recent infection’ for incidence estimation. The Centers for Disease Control and Prevention (CDC) continues to actively improve laboratory tests used to measure biomarkers that identify ‘recent infection’, and, earlier this year, supported the WHO’s efforts by hosting the latest HIVIWG meeting (August 2011). Notably, the Bill and Melinda Gates Foundation (BMGF) awarded the Health Protection Agency (HPA) a grant to assess, compare and optimise recent infection tests. The group working on this three-year project (2011-2013) is called CEPHIA, the Consortium for the Evaluation and Performance of HIV Incidence Assays, and comprises HPA, Blood Systems Research Institute (BSRI); University of California, San Francisco (UCSF); and SACEMA.

Two key obstacles to cross-sectional biomarker-based incidence surveillance remain: the (i) lack of standardisation of terminology and methodology, and (ii) poor characteristics, and characterisation, of currently available tests.

Characterising a Test for Recent Infection

Testing for recent infection for the purposes of incidence estimation differs, in key respects, from estimating how long each subject in a study has been infected. It is generally accepted that, in the context of incidence surveillance, there are two crucial characteristics of a test for recent infection (5,6).

Firstly, a mean duration of recent infection captures the average time spent ‘recently infected’. To produce reliable incidence estimates, the times that seroconverters spend ‘recently infected’ should be sufficiently large so that an adequate number of subjects are observed in this state in a survey of feasible sample size. For example, testing for the absence of HIV-antibodies amongst those with detectable HIV viral loads in principle identifies recent infection. However, the transiency of this state implies that very few ‘recently infected’ subjects would be found in a cross-sectional survey. Conversely, the ‘recent infection’ classification should not endure for extended periods of time post infection, as subjects who were infected long into the past will appear ‘recently infected’ in the survey, making incidence estimates less informative about current incidence.

Due to the substantial variability of the virus progression and immunological response, individual seroconverters can spend vastly different times classified as ‘recently infected’. To capture that some seroconverters are classified as ‘recently infected’ long after infection, a second characteristic, termed the false-recent rate, has been introduced. This measures the proportion of individuals that appear ‘recently infected’ a long time post infection. This proportion needs to be small to limit uncertainty in incidence estimates.

For a test for recent infection to be potentially useful for incidence surveillance, a mean duration of recent infection of 4 to 12 months and a small false-recent rate, less than 2%, are considered acceptable (6). Crucially, to produce robust incidence estimates, the characteristics of the recent infection test must be well-known (7).

Seroconverting Blood Donors as a Resource for Characterising

Recent Infection Tests In the past, estimation of recent infection test characteristics has relied on detailed longitudinal data. Specifically, data obtained from the regular follow-up of HIV-negative subjects, and then repeated testing for ‘recent infection’ amongst seroconverters, with small inter-test intervals, has been used. Such data is typically used to model the time each seroconverting subject spends ‘recently infected’, and this information is in turn used to estimate the average dynamic that is captured by the mean duration of recent infection. However, such data is expensive and logistically difficult to collect, and this has been an obstacle to the development of recent infection tests. Methods for using less well-characterised, but more easily captured, data could therefore greatly advance developments in this field. One such innovation is explored in the article ‘Seroconverting Blood Donors as a Resource for Characterising and Optimising Recent Infection Testing Algorithms’ (8), as briefly summarised below.

In the work, a readily-available source of specimens was identified, namely that of serocoverting blood donors. Utilising specimens from blood donors provides unique efficiencies as blood for transfusions is routinely collected and tested for HIV in most countries. In the study, repeat donors (in South Africa and the USA in the period 2001-2009) who were observed to seroconvert were tested for ‘recent infection’, using the specimens collected at the times of the first seropositive donations.

Data captured from such study designs has been overlooked in the past, because there is no follow-up of seroconverters and typically large intervals between HIV-tests (or donations). Such data provides little detail at an individual subject level. A method of estimation that draws meaningful information from this data, about average biomarker dynamics, without focusing on individuals, was employed. The approach is based on maximising the likelihood of the overall set of observed ‘recent’ and ‘non-recent’ classifications. The technical details of the method are provided in the abovementioned article (8). For example, if all inter-donation intervals were fixed at one year, then (for each donor equally likely to have been infected at any time in the year between his/her HIV-negative and HIV-positive donation) the overall collection of classifications provides information about the average dynamics of the biomarker for one year post infection. In the work, this principle is extended to allow for varying inter-donation intervals.

While the estimates of the test characteristics obtained using this method are likely not sufficiently robust for the purposes of incidence estimation, the work demonstrates an approach to perform preliminary characterisations of tests for recent infection, using a readily available source of specimens. More precise data could subsequently be used to better characterise only the most promising tests.

In conclusion, the cost and difficulty in collecting very detailed data describing the dynamics of a biomarker testing for ‘recent infection’ has been an obstacle to the characterisation of these tests, and hence incidence estimation. The innovative preliminary characterisation of recent infection tests, utilising more easily-sourced data, is therefore a promising development in the field. Using tests for recent infection to estimate times since infection for individual subjects is potentially of great interest in public health – this is a fundamentally different approach and would require an appropriately modified way to characterise tests for recent infection. Of interest in this discussion is the application of biomarkers testing for ‘recent infection’ for incidence surveillance. The ability to estimate incidence from a study performed at a single point in time offers great benefits, and therefore cross-sectional biomarker-based incidence estimation has drawn much interest in recent years. With the collective efforts of leading organisations and experts to address the remaining limitations in the field, there seems to lay a promise for many exciting developments in this area. Cross-sectional incidence estimation is certainly a topic that should be closely followed by epidemiologists, and could greatly advance population-level HIV incidence surveillance in years to come.  

The dataset consists of registered TB events, corresponding to the approximate time when a TB transmission event occurred (1). Clusters formed by TB strains isolates W451 and CC100 among HIV+ cases were particularly striking. These clusters may point to ongoing transmission, which could exasperate the already desperate situation in the township. A number of questions arise. The data show apparent clusters, but what are the properties of a typical cluster? Are the clusters we see the result of correlated infection events, e.g. an infection chain between HIV+ TB cases, or do they simply appear to be clustered when in reality there is no connection (correlation in statistical parlance) between them?

Mathematical theory of point processes

The best guide for this journey is the statistical theory of point processes. It helps us frame the questions we need to ask in order to interpret TB event data correctly. We developed a point process theory for TB events starting from the simple differential equation dynamical model we previously developed to understand the population level (macroscopic) aspects of TB in this community (2). To start building a point process theory for the TB model, we developed a dynamical description of all the random events that occur in the model, comprising birth, death, primary infection, re-infection and endogenous-activation events among susceptible, latently and actively infected sub-populations. To make this step analytically tractable we used only one HIV state in the model.

We then used van Kampen’s ‘population size’ expansion to derive a differential equation for the variances and co-variances of these random and fluctuating events (3,4). This is a so-called Fokker–Planck equation (FPE) and it describes the fluctuations as Gaussian noise around the equilibrium population level model. We solved the differential FPE with a standard ordinary differential equations (ODE) solver in Matlab, and checked the result against Gillespie’s stochastic simulation technique (5). Finally, we used the FPE to study the temporal aspects of TB clusters, and obtained an understanding of the timescale between active TB events.

Insights from point process theory

The first insight from the model applies to many deterministic models (see (2) for a summary)) used nowadays to model epidemics at the community level. We showed that fluctuations in the population variables (i.e. the variables that keep track of the number of susceptible, latently and actively infected individuals) become small relative to the size of the sub-populations when the population size is closer to 40,000. Given that many TB interventions and trials are often run and evaluated at a community level, we should expect a significant level of uncertainty in population-level estimates derived from macroscopic models. This uncertainty is seldom explicitly handled in the growing field of epidemic modeling, even for models applied to small communities.

The two-time correlation function

The next important insight from the model derives from the so-called two-time correlation function for events g2(t1,t2) (3, p. 41). It measures increased probability of observing an event of a particular type at time t2, given an event of a certain type at time t1. This is equivalent to measuring the degree to which the joint density of events at t1 and t2 is greater than the density at t1 and t2. This can be thought of as reflecting the causal influence of the first event on the second via both direct (e.g. infection, reactivation) and indirect routes (chains of such events): what is the increase in probability for an event to occur at t2 knowing that another event occurred at t1? Note that the events do not have to be of the same type.

Implications for TB control

Thus, even though chains of causation are not explicitly tracked in this type of framework, influence can be assessed at the statistical level by examining the correlations. This gives us a fairly detailed statistical description of clusters of events. For example, events that are separated by a timescale longer than the correlation timescale are unlikely to be part of the same transmission cluster. The method can therefore be used as the basis for understanding the temporal component of TB strain clusters, which are currently defined mostly in terms of DNA type, with geographical linkage, social interaction and other processes also playing a role.

Our analysis shows that endogenous activation events are correlated over long time scales, and are statistically likely to be part of short timescale clusters. This casts reasonable doubt around the presence of apparent clusters of isolates W451 and CC100 among HIV+ TB cases, and whether the data support the assumption of ongoing transmission of these strains. If the modeled intensities of active TB events are validated, then the model can be used to warn against spurious conclusions from measured clustered data. For example, a correlation effect may be incorrectly attributed to an infection trend, or even to a particular type of infection chain, while in reality it could be purely due to chance stemming from fluctuation. These observations have direct implication for TB control measures in the community: ongoing infection chains require more forceful intervention than reactivation events, which can be handled through standard TB control strategies.

Improvement and further work

Previous modeling work (6,7) has highlighted the potential for study time-windows and case-detection rates to bias interpretations of clustering statistics. Far less is known about how these may differ between data derived from HIV+ and HIV- TB cases. To do a complete analysis of a model with both HIV- and HIV+ TB cases is possible but cumbersome. We simply relabeled the HIV state in our model to HIV+, and changed all the dynamical parameters to ones corresponding to HIV+ individuals, who experience higher rates of primary infection, re-infection and endogenous reactivation. Higher rates of reactivation among HIV+ individuals may mean that their strains are a priori more likely to be drawn from the latent pool. Indeed, the analysis shows shorter correlation timescales among HIV+ individuals which suggests that a more stringent criterion of temporal linkage may be needed for cluster determination among HIV+ TB cases, compared with HIV- TB cases.

As more detailed data spanning a longer time interval become available it may be possible to evaluate if clustered active TB episodes are consistent with the dynamics of a closed community. If they are not, and therefore require exposure to external sources of infection to explain the observed clustering of TB events, it raises concerns for TB treatment programs. Treating TB cases only in a particular community will not reduce its TB burden: TB treatment programs would have to reach the wider community in order to be effective.

This analysis could find broad validation in epidemiological models where the transmission term is an assumed non-linear term, with few possibilities of validating the assumption against real data. The model is usually checked against aggregated population count data, which can be fit by many functional forms for the transmission term. Our approach of studying the underlying point process may shed light on whether a mass action model can produce the observed clustering of TB events. A model accounting for local contacts (household, schools) as well as global contacts (e.g. in the wider community) may be essential for modeling temporally clustered active TB events.

Note that the above is an abbreviated version of the following article: Pretorius C, Dodd P, Wood R. An investigation into the statistical properties of TB episodes in a South African community with high HIV prevalence. J Theor Biol. 2011;270(1):154-63. Link to article  

Disease mapping models are used in spatial epidemiological studies to investigate the causes and distributions of diseases. An ecologic investigation is usually adopted to assess disease risk in relation to risk exposure factors measured in a small area such as an electoral ward. When the spatial data have been collected over many time periods, temporal trends are included. The models often incorporate expected disease incidence based on age and gender stratification and account for dependence of disease incidence on contextual area factors such as deprivation and population density. For small areas or rare diseases such as cancer, the usual estimates of disease risk may be unreliable and unstable. This has necessitated identification of models that stabilise risk estimates in small geographic areas by using hierarchical random effects models (1).

In a standard univariate spatial model, the underlying area-specific disease risk is split into two parts; one that has a global mean relating to all the areas (heterogeneity or unstructured area effect) and the other that has a mean depending on neighbouring areas’ risks and has a variance which is inversely proportional to the number of neighbours (local or structured area effect) (2). Recently, there has been considerable methodological and application research on modelling and analysing multiple disease outcomes using joint mapping models. These extensions enable analysts to make an assessment on similarities as well as differences between risk factors among diseases purportedly sharing common risk profiles (3-4). These models, by combining data from different diseases, improve precision and efficiency of estimates, especially for rare diseases.

Most of the studies looking at mapping of health problems in the Sub-Saharan African region have concentrated on using univariate spatial models (5-7). Thus, there is a need to use and apply joint mapping models to measure co-morbidities of common illnesses in the region. A few studies measuring co-morbidity of diseases by using multivariate spatial modelling within the region have recently appeared (8-9). This article aims to show the utility of joint mapping models in estimating co-morbidities in two important health problems in South Africa.

The Shared-Spatial Component Model: Theory

An analyst can use a multivariate normal model to assess covariances and correlations within and between diseases underlying spatial risks (10). However, we use a shared component model which fits common and disease-specific unobserved and unmeasured spatial risks (3). The relative risk of each disease depends on a latent spatial component shared by all the diseases under study and the respective disease-specific latent component. For instance, if we have prevalence data on two diseases, then the risk for each disease is modelled as:

where P_ij is the prevalence rate of disease j in area i; θ_i is the shared component, common to both diseases; ω_i1 and ω_i2are the disease-specific spatial risk components, respectively. The unknown parameter k>0 is included to allow for a differential gradient for the shared component for the two diseases. The ratio of the scaling parameters k to 1/k compares the weight of disease one relative to disease two associated with the shared component. Usually the spatial components are modelled using both local dependence structures as well as unstructured heterogeneous effects to capture possible extra-variation in the data not captured by all the terms in the model affecting the risks.

Examples of Multiple Health Outcomes in South Africa

We illustrate the application of a shared-component model to two multiple health outcomes in South Africa. The first health problem concerns HIV and Syphilis prevalence data among pregnant women attending public antenatal clinics (ANC) in South Africa between 2007 and 2009 (11). The second concerns four vascular diseases: high blood pressure; heart attack or angina; stroke and high blood cholesterol available from the 2003 South African Demographic and Health Survey (12). The four chronic diseases are becoming a growing public health problem associated with lifestyle (smoking, alcohol, lack of physical activity) and dietary patterns. We measure distribution of disease at the health-district level. A district is the basis unit through which the delivery of Primary Health Care is undertaken in South Africa.

The number of ANC attendees that were sampled per district ranged from 51 to 2627. The prevalence of HIV and Syphilis ranged from 0 to 46.4% and from 0 to 12.6%, respectively. This shows great variation in both HIV and Syphilis prevalence between the districts. However, some of the estimates, especially for Syphilis, were based on small sample sizes. Observed geographical trends indicated that HIV prevalence was highest in the North-Eastern parts of the country and lowest in the South-Western parts. Lower HIV prevalence rates were found in the least populated and most rural areas compared to the metropolitan areas. Syphilis prevalence shows contrasting patterns to those observed for HIV prevalence, with high rates in the South-Western parts of the country.

For the four vascular diseases, the number of sampled adults in a health district ranged from 10 to 900. High blood pressure and heart rate were more prevalent than stroke and high blood cholesterol. There were 15 districts out of the 52 that had a prevalence of 0% for stroke, and 13 out of 52 had a prevalence of 0% for high blood cholesterol. High prevalence of high blood pressure and heart attacks are in the districts in the South-Western parts of the country and the lowest in the North-Eastern parts. For stroke and high blood cholesterol, prevalence rates appear to be relatively evenly distributed across the country.

The shared-spatial component model: practice

The observed prevalence maps (not shown) showed a large amount of variation especially for the rare diseases, which makes it difficult to discern any geographical trends in prevalence rates. Thus, it was necessary to use spatial models to smooth out the instability in the risk estimates. For both sets of multiple health outcomes, a shared component model (1) was fitted. In the case of HIV and Syphilis three spatial risk effects were used: a shared component that can be interpreted as representing risky sexual behaviours (e.g. multiple and concurrent sexual partners and unprotected sex) and two disease-specific components representing unmeasured risk factors associated with the individual diseases. In addition the effects of two contextual district level factors, social and material deprivation and population density, were measured. For the four vascular diseases, a shared component that can be interpreted as representing nutritional and lifestyle factors and four disease-specific risk components were used. In addition, individual subject risk factors were controlled for in the vascular diseases models. The district-level effects that remained after controlling for the effects of the included observed and measured risk factors were assessed on the disease-odds scale where values above 1 indicate increased prevalence in the corresponding district.

Table 1 shows the effects of deprivation and population density on the prevalence of HIV and Syphilis. For each disease, the effects shown are the odds of disease prevalence for different levels of the factor compared to the odds of the prevalence in the reference category (lowest), whereby a ratio value higher or lower than 1 indicates that the odds of disease prevalence increases or decreases compared to the reference category. The values in brackets are ranges of the ratio of the odds, indicating a significant difference between the odds if the range does not include 1. Thus, high values of deprivation and population density are associated with increasing HIV prevalence. On the other hand, Syphilis prevalence is inversely associated with both deprivation and population density. This shows that HIV and Syphilis have contrasting dependence on the two contextual factors.

Table 1: Estimates from a joint model for antenatal HIV and Syphilis Prevalence, South Africa, 2007-2009 

HIV and Syphilis specific-excess risk maps (after measuring the effects of important contextual predictor variables) showed high HIV rates in North-Eastern parts of the country, and high Syphilis in the districts around the South-Western corridor (maps not shown). The shared component (representing risky sexual behaviour) distribution shown in Figure 1 has a larger effect on HIV and Syphilis in the North-Western to South-Eastern corridor of the country.

Figure 1: Shared risk component for HIV and Syphlis Prevalence

The results from estimating co-morbidly of the four vascular diseases are presented in Table 2. Following the same interpretation of significance as before, results show that increasing age is positively associated with increased risks of all of the four vascular diseases. Being obese significantly increases the risk for all of the four vascular diseases. Lifestyle factors such as smoking and drinking alcohol appear not to have any adverse effects on the vascular diseases. Other factors (gender, education, population group, urban setting) affected the risks in non-systematic ways (data not shown).

Table 2: Median (95% CI) Odds Ratios estimates from a joint model for four high blood pressure, heart attack, stroke and high blood cholesterol, South Africa, 1998.

Vascular disease-specific maps (not shown) of excess risks (after measuring the effects of important subject-level predictor variables) showed that high blood pressure and stroke rates were concentrated highly in the South-Western parts of the country. Heart attack was highly concentrated in the central North-Eastern corridor; and high blood cholesterol had high rates in the top North-Eastern corridor. The distribution of the shared component (representing nutrition and lifestyle) shown in Figure 2 has a larger effect on vascular disease prevalence in the South-Western areas of the country.

Figure 2: Shared risk component for high blood pressure, heart attack, stroke and high blood cholesterol prevalence

Better understanding of co-morbidity

This article has demonstrated the application of recently developed methodology and estimation techniques in spatial epidemiology to model multiple health outcomes. In particular, we used the shared component model to assess common and divergent putative risk factors in the context of the burden of multiple sexually transmitted and multiple vascular diseases in South Africa. The results have shown that HIV and Syphilis have largely divergent risk and spatial factors. The common risk factors are mainly concentrated in the North-Western to South-Eastern corridor. This may suggest that interventions aimed at modifying risky sexual behaviours in this corridor will have greater impact on reducing the burden of HIV and Syphilis. Increasing age and obesity were significantly positively associated with all of the four vascular diseases. The common risk factors were more concentrated in the South-Western parts, implying that lifestyle modifications will make greater impact in reducing the burden of diseases in these areas.

In conclusion, multivariate mapping models provide a better understanding of co-morbidity between health outcomes than using separate univariate models. In particular, the modeller and the analyst of multiple disease outcomes can assess the underlying common and divergent spatial distributions of the diseases to optimally integrate disease management required to address the multiple burden of diseases in South Africa and the Sub-Saharan African region.

To assess the public health and policy implications of this new method of control, a dynamical model of HIV transmission was used to determine the population level impact of the microbicide on the HIV epidemic in South Africa as well as the cost effectiveness of the gel, under a range of scenarios.2 A compartmental model previously developed to explore the impact of medical male circumcision3 was adapted for this study and fitted to trends in adult HIV prevalence. If the relative risk of HIV infection for a woman using the gel is RR, then the risk, averaged over male-to-female and female-to-male transmission, is

and the average risk reduction at a coverage, c, is

Using this expression to determine the reduction in transmission the dynamical model shows that the use of tenofovir gel could have a significant impact on the future course of the epidemic in South Africa. Compared to the baseline scenario in which tenofovir gel is not used, the model shows that consistent use of the gel in 80% or more of sexual encounters (high coverage) could prevent 2.33 (0.12 to 4.63) million or 21% of new HIV infections and avert 1.30 (0.07 to 2.42) million or 12% of AIDS related deaths over the next 20 years. Even at low coverage (gel use in 25% of sexual encounters) it could prevent 0.5 (0.04 to 0.77) million or 4% of new HIV infections and avert 0.29 (0.02 to 0.44) million or 3% of AIDS related deaths over 20 years.

In the intervention trial the cost of the gel was US$ 0.50 per application but most of the cost was for the applicator, not for the tenofovir. The estimated cost per infection averted at low gel coverage is then US$2,392 (US$562 to US$4,222), or about 28% of the estimated life-time cost of providing ART to 1 person; whereas the cost per disability-adjusted life year (DALY) saved is US$104 (US$27 to US$181) or about 2% of the estimated per capita gross national income per year. At high coverage, use of the gel is even more cost effective at US$1,701 per infection averted and US$74 per DALY saved. Furthermore, the cost-effectiveness of using tenofovir gel compares favorably with other prevention interventions. In countries where HIV incidence is lower than in southern Africa, the cost-effectiveness of tenofovir gel will be less favorable because the cost per infection averted rises as the incidence falls. However, in such settings, the impact of the gel on HIV incidence would be increased if targeted at women at high risk of infection such as sex workers.

The CAPRISA study provides the first evidence that the use of an antiretroviral drug in the form of a microbicide gel can significantly reduce the risk of HIV infection among women. The mathematical modelling shows that even at low coverage it could have a substantial population level impact and in southern Africa, where HIV incidence is high, it will be highly cost effective. While the results of the CAPRISA trial need to be confirmed by further research to meet the requirements for licensure by FDA and other regulatory bodies, the findings are an important and exciting step forward for HIV prevention. Used in combination with other prevention methods, such as male circumcision, condom promotion and HIV testing and treatment, we may finally begin to bring the epidemic under control.

To accomplish this, we are conducting a cross-sectional survey that uses a questionnaire exploring one-year sexual histories, with a focus on timing and age differences in relationships, as well as other risk factors such as unprotected intercourse and the use of alcohol and recreational drugs. We administer it in a safe and confidential mobile interview space, using audio computer-assisted self-interview (ACASI) technology on touch screen computers. The ACASI features a choice of languages—English, Afrikaans and Xhosa— and visual feedback of temporal information. The survey is taking place in three urban disadvantaged communities in the greater Cape Town area that have a high burden of HIV. The study communities participated in a previous TB/HIV study, from which HIV test results will be anonymously linked to the survey dataset. Statistical analyses of the data will include descriptive statistics, linear mixed-effects models for the inter- and intra-subject variability in the age difference between sexual partners, survival analysis for correlated event times to model concurrency patterns, and logistic regression for association of HIV status with age disparity and sexual connectedness.

This study was designed in such a way that it facilitates more accurate recall of sensitive sexual history data and provides substantial insights into the relationship between key sexual network attributes and additional risk factors for HIV infection. Ultimately, we hope it will inform the design of context-specific HIV prevention programmes. For more information about the background and context of the study as well as about the study design, please see the reference below.

A cohort of 336 ARV naive HIV positive individuals enrolled into the Sinikithemba study conducted in McCords hospital, Durban, South Africa was studied. Patients were followed for a median of 3.54 years (IQR 1.91 – 4.52 years) and had a median of 12 visits (IQR 4 – 17 visits). The median time between visits was 0.26 years.

HIV progression was investigated through the application of a five state Markov model with reversible transitions. The four transient states are based on CD4 count intervals with ARV initiation as an absorbing fifth state. The rate of CD4 count decline was examined and predictions of the HIV state trajectory are made through the use of transition probability matrices. The effect of age, gender and baseline CD4 count on individual state transition rates was examined using the Cox proportional hazards model.

A key finding, consistent with previous research, was that the rate of decline in CD4 count tends to decrease at lower levels. It was also noted that patients enrolling with a CD4 count less than 350 have a far lower chance of immune recovery, and a substantially higher chance of immune deterioration compared to patients with a higher CD4 count. This study found multistate models to be a powerful tool in HIV/AIDS research which can offer a deeper understanding of the natural progression of the disease.

The focus of this study was immune deterioration in ARV naive patients. It is of further interest to explore the rates of immune recovery in patients on antiretroviral therapy. The heterogeneity between individuals cannot always be completely explained by observed covariates. In such cases this residual heterogeneity should be modeled as a random effect. Furthermore, the CD4 count is a marker which is subject to great variability and measurement error. An area of further work would be to formulate a Bayesian model which models two processes: the disease process (as a Markov process) and the measurement process.

The work presented here is based on: Reddy T. The Application of Multistate Markov Models to HIV Disease Progression (Master's thesis). Durban: University of KwaZulu Natal; 2010.

In light of expanding access to antiretroviral therapy (ART) and increasing calls for utilizing treatment as prevention in resource-limited settings, understanding sexual risk behaviours among HIV-infected individuals and their sex partners is necessary to inform secondary prevention interventions and to better understand the potential HIV transmission implications of greater access to ART and care. Reframing HIV prevention as a couples-centred approach could enhance prevention efforts currently underway in sub-Saharan Africa. This study answered the important question of whether differential behavioural patterns by partner HIV status occurs in the primarily heterosexual HIV epidemic of southern Africa.

We found that 40% of our sample of HIV-infected South African men and women in an urban primary care program reported having sex with an HIV-positive partner, 40% with a partner of unknown HIV status, and 20% with a HIV-negative partner. Those who reported having a HIV-negative or status unknown partner were more likely to be women and ART-naïve. Individuals with an HIV-infected partner were only slightly more likely to engage in sexual risk behaviours, though this was not statistically significant. Notably, sexual risk behaviours persisted among those participants who reported having a HIV-negative of status unknown partner.

These data suggest that selectively engaging in sexual risk behaviours by partner HIV status does not appear to be extensively practiced in this setting where most HIV transmission is heterosexual, which is different than data from the developed world among some high-risk groups. Given the large proportion of participants who remained unaware of their partners HIV status, further interventions like partner voluntary counselling and testing (VCT) and effective counselling messages for serodiscordant couples are needed to support testing the partners of HIV-infected individuals in care. HIV care and treatment programs need to expand couples- and family-based VCT services so that more HIV-infected Africans know their partners’ HIV status, which could have an impact on HIV transmission risk behaviours.