Background: There is no clear consensus on how best to use increasingly available data derived from large populationbased surveys featuring HIV infection status ascertainment. In particular, for the purpose of estimating HIV incidence, there is considerable scope for better elucidation of the benefit of adding ‘recent infection’ ascertainment, which adds considerable additional cost and complexity to surveys which are already costly and complex.
Methods: Using an epidemic/survey simulation tool developed for this and some closely related investigations, we explore the value added by ‘recent infection’ data from population surveys, to support HIV incidence estimation. This directly piggy-backs on to two companion pieces which have explored, independently, the use of the ‘synthetic cohort’ paradigm of Mahiane et al (analysing age/time structure of prevalence, in conjunction with estimates of mortality) and the paradigm of Kassanjee et al (focusing on ‘recent infection’ data).
Results: Our headline findings are that: 1) Recent infection data adds marginal benefit to surveillance focused on the early years after sexual debut, which can reasonably be taken to be a core sentinel group in which surveillance is significantly more efficient than attempts to cover all ages; and 2) by contrast, recent infection data is crucial for the reliable estimation of incidence trends when only two cross sectional surveys are available. We detail numerous components of a general and robust approach to analysing data when both the Mahiane and Kassanjee analyses are in play.
Conclusion: Our main results present non-trivial dilemmas for survey design, as recency data is crucial for stabilising the more timely estimates, but of marginal benefit for the most important sentinel group. We hope that adaptation of our analysis, to simulated scenarios closely aligned to specific contexts facing expensive choices, will support rational investments in, and use of, precious surveillance opportunities and data sets.