HIV

You are either affected or infected with HIV/AIDs

An estimated 34 million people are currently living with HIV worldwide, half of whom do not know their status.

Current challenges in HIV research

  • HIV remains a major global health problem, with an estimated 35.3 million people living with HIV in 2012. 25.0 million are in sub-Saharan Africa.
  • Effective treatment with antiretroviral therapy (ART) has enabled those living with HIV to have a near natural life expectancy. In particular, in low and middle-income countries, there has recently been a rapid increase in the provision of ART, with an estimated 9.7 million HIV-infected individuals receiving therapy in 2012.
  • At a population level, ART has potentially an additional benefit by reducing viral load to below detectable levels and hence reducing the onward transmission of the virus. Thus "test-and-treat" strategies are now being considered as a population-level intervention to substantially reduce the number of new HIV infections.
  • A major remaining challenge is to identify and diagnose persons early in their infection, ensure that all those who would benefit from ART access care in a timely manner, and retain them on effective treatment.

The HIV work of the Diagnostics Modelling Consortium focuses on 2 research areas: HIV incidence estimation and HIV testing strategies.


HIV Incidence Estimation

Project Methodology

Malaria Test

Countries need to measure HIV incidence nationally so that they can know the scale of their epidemic, plan interventions, anticipate future resource needs and track changes over time, as part of an evaluation of interventions. Incidence assays provide a way to estimate HIV incidence since they identify the presence of a marker that is associated with recent infection according to a known survival curve. A major application of this could be for countries to measure HIV incidence nationally, so that they can know the scale of their epidemic, plan interventions, anticipate future resource needs and track changes over time, as part of an evaluation of interventions. Information from incidence assays could be combined with substantial other sources of data (prevalence data in household surveys, antenatal care (ANC), in some cases Prevention of Mother-to-Child Transmission (PMTCT) and mortality data) which can indirectly be used in models to generate inferences on incidence.

These approaches are, however, incapable of the timely identification of changes in incidence and are increasingly challenged by the confounding effects of ART.  The question thus arises about the extent to which incidence assay – and how they can be applied – can add meaningfully to existing surveillance efforts.

The application of incidence assays is challenged by:

 

  • The survival curve of the biomarker being such that small numbers of persons in a survey have the biomarker; 
  • The shape of the survival curve having a ‘tail’, such that the proportion of people with the biomarker in a population is partly dependent on the historical course of the epidemic (which will vary across settings); 
  • Substantial uncertainties in knowledge about the values of these calibrating parameters.

As development of the tests continues and funders need to assess the likelihood of suitable products being developed it becomes crucial to know what properties of the test (mean window period, false recent rate and the precision with which these two properties are known) and what properties of their application (survey sample size, interval between surveys) would be sufficient for the incidence assay to:

  • Reduce uncertainty in estimates of current HIV incidence levels; and
  • Increase the probability of correctly detecting trends in HIV incidence due in a timely manner (definition also left open for now).

This can be referred to as Target Product Profile (TPP) of the incidence assay.

Key Research Questions

The primary research question aims to address "What Target Product Profile (TPP) is required for Incidence Assays and their applications in surveys for improving surveillance of national level HIV epidemics?"

Other secondary questions include:

  • Can incidence assays reduce the variance of current incidence estimates?
  • Can incidence assays help to detect changes in incidence in a timely manner?

The project is a joint venture between the Diagnostics Modelling Consortium and the HIV Modelling Consortium, also hosted from Imperial College London.


HIV Testing Strategies

Why are testing strategies important in HIV control strategies?

Knowing one’s HIV serostatus is key to accessing services, especially to antiretroviral therapy (ART), which significantly improves survival as well as reducing the potential for onward transmission. There are multiple ways in which people learn their HIV status and each of these identifies a different sub-set of the population. E.g. household testing favours identification of women whilst work-place testing favours identification of men. HIV testing strategies should aim to identify all of those at risk in the most cost-effective manner. Furthermore each is associated with a different cost per finding undiagnosed individuals and the cost function for scale-up will likely take different forms for the different modalities. The modelling project aims to review the current range of HIV testing modalities.

Project Methodology

Know your HIV Status

The exercise involves a systematic review of the current literature including collating and summarising the data available so that it can be used as a next step with transmission models to estimate impact.

Key issues being addressed include: 

  • What is the impact of broad versus targeted coverage and what levels of coverage are needed in different strata?
  • How many will self-test and link to care?
  • What are the operational challenges with self-testing?
  • What further data are needed ?

Key Research Questions

The primary research question aims to address "What the current range of HIV testing modalities are, their cost and their likely impact on the HIV epidemic in different settings?"

Other secondary questions include:

  • What is the value of broad versus targeted testing coverage?
  • For self-testing, who will seek care and how many will test and link to care?

The project is also a joint venture between the Diagnostics Modelling Consortium and the HIV Modelling Consortium.

Key people: 
Prof Tim Hallett
Prof Ruanne Barnabas
Dr Le Bao
Dr Christine Rousseau
Prof Geoff Garnett
Dr Jennifer Osborn
Dr Melissa Mugambi