Biomarker-based incidence estimation can be consistently adapted to a general context without the strong assumptions of previous work.
The ability to estimate reliable HIV incidence rate ratios (IRRs) using cross-sectional data has vast public health importance in HIV surveillance and in prevention studies; it would reduce the need to recruit and maintain large and costly longitudinal cohorts. In fact, the most common method to evaluate HIV IRR is through cohort studies which are designed to estimate HIV incidence and the effects of interventions. However, the development of biomarkers which identify recently HIV infected individuals has made it possible to estimate HIV incidence using a cross-sectional survey. Following that, one study used classical statistical methods to analyse risk factors of recent HIV infection identified with a biomarker. It is therefore important to determine how that technology can be used to estimate incidence rate ratios.
There are a number of approaches for estimating HIV incidence, with varying tractability, complexity and limitations. 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. 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. However, two key obstacles to cross-sectional biomarker-based incidence surveillance remain: the lack of standardisation of terminology and methodology, and poor characteristics, and characterisation, of currently available tests.