Table 1.

Summary of methods to measure HIV incidence

Research Method		Principle	Data Source	Advantages	Disadvantages	Going Forward
Prospective Cohort Studies		Directly observed measure of incidence - Follow HIV-uninfected people over time and test for HIV at fixed intervals	Observational studies HIV prevention preparedness studies. Clinical trials	Considered “gold standard” Direct measure of HIV seroconversion	Large sample size required Costly Logistically challenging Loss to follow-up, particularly those at high-risk Intrinsic bias (participant behaviour change) Infrequently conducted	Improve retention of study participants
Biological Assays Testing for acute/new and recent/early infections	Antigen based tests for acute/new HIV infection	Uses ‘biomarkers’ such as HIV-1 RNA or P24 antigen that develop during the recent/acute stage of infection prior to seroconversion	Large scale cross-sectional surveys	Single sample required Follow-up not required Can distinguish between acute HIV infection in absence of HIV antibodies and post seroconversion. Acute HIV infection stage used for incidence estimation	Inadequate sensitivity and specificity Short duration of detection period (~28 days) Misclassification of acute with post seroconversion infections Requires confirmatory testing No ‘gold standard’ test	Development of new fourth generation HIV antibody assays decreases the “window- period” of acute HIV infection in absence of HIV antibodies
	Antibody avidity maturation based tests for recent/early HIV infection	Several tests available which use antibody avidity maturation following seroconversion to determine recent/early HIV infections	Large scale cross-sectional surveys	Follow-up not required Detection period (~130 days) Can distinguish between early and established HIV infection Early HIV infection used for incidence estimation	Requires confirmatory testing Requires additional information on ART exposure and HIV-1 RNA viral load No ‘gold standard’ test	Requires monitoring and estimation of incidence as HIV treatment is moving towards test and treat strategy – more recently infected individuals may be on ART, and this could affect accuracy of incidence measurement
Combination of HIV testing algorithms		Combination and sequence of assay tests for detecting ‘recency’ of HIV infection with clinical data	Cross-sectional surveys	Multiple tests selected to streamline for improving accuracy Addition of clinical data to reduce correct for misclassifications for interpreting results	Expensive Still requires correction for misclassification despite addition of clinical data	Data obtained with this approach is crucial to compare with other incidence estimates obtained by other methods such as cohorts and mathematical modelling) Field validation using population-based surveys is required With increase in ART roll-out, surveys that include individuals on ART are required
Mathematical modelling		Several models based on parameters of HIV prevalence data, modes of transmission, assumptions about survival after infection and mortality, risk behaviour, population size, STI prevalence, incubation information and ART coverage	Cross sectional surveys providing HIV prevalence trend data Sentinel HIV prevalence data routinely collected	Cost-effective Moderate-high levels of confidence Relatively simple	Depends on accuracy of prevalence and measurements of population size, ART coverage, mortality Can have high uncertainty range Time between studies may affect precision of results Key populations may not be representatively sampled	Cross sectional surveys need to have greater inclusion of adolescents and key populations Less reliance on young people as proxy for sexual debut and recent infection