PubMed · 16341316
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J P Van Niekerk. 2005. Disclosure please!. https://pubmed.ncbi.nlm.nih.gov/16341316/
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BACKGROUND: Estimating the time since HIV infection (TSI) at population level is essential for tracking changes in the global HIV epidemic. Most methods for determining TSI give a binary classification of infections as recent or non-recent within a window of several months, and cannot assess the cumulative impact of an intervention. RESULTS: We developed a Random Forest Regression model, HIV-phyloTSI, which combines measures of within-host diversity and divergence to generate continuous TSI estimates directly from viral deep-sequencing data, with no need for additional variables. HIV-phyloTSI provides a continuous measure of TSI up to 9 years, with a mean absolute error of less than 12 months overall and less than 5 months for infections with a TSI of up to a year. It performs equally well for all major HIV subtypes based on data from African and European cohorts. CONCLUSIONS: We demonstrate how HIV-phyloTSI can be used for incidence estimates on a population level.
Structural interventions addressing macro-social determinants of risk have been suggested as potentially important adjuncts to biomedical and behavioural interventions for the prevention of HIV and other diseases. A few interventions of this type have been evaluated using randomised controlled trials (RCTs), the most rigorous design to evaluate the effects of biomedical and behavioural interventions. The appropriateness of applying RCTs to structural interventions is however debated. This paper considers whether issues of ethics, feasibility and utility preclude the use of RCTs in evaluations of structural interventions for HIV prevention. We conclude there is nothing particular to this category of interventions prohibiting use of RCTs. However, we suggest that RCTs may prove unacceptable, unfeasible or not useful in certain circumstances, such as where an intervention brings important benefits other than HIV prevention (such as increased income); where leaders of clusters do not allow decisions about macro-social policies to be determined randomly; where the unit of social organization addressed by an intervention is so large that recruitment of adequate numbers of clusters is impossible; and where the period required to trial interventions extends beyond practical decision-making time-scales. In such cases, alternative evaluative designs must be assessed for their ability to provide evidence of intervention effectiveness.
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