Incidence estimation from cross-sectional surveys testing for biomarkers of 'recent infection'
Background: Estimating disease incidence from cross-sectional surveys, using biomarkers for “recent” infection, has attracted much interest. Despite widespread applications to HIV, there is currently no consensus on the correct handling of biomarker results classifying persons as “recently” infected long after the infections occurred.
Methods: We derive a general expression for a weighted average of recent incidence that—unlike previous estimators—requires no particular assumption about recent infection biomarker dynamics or about the demographic and epidemiologic context. This is possible through the introduction of an explicit timescale T that truncates the period of averaging implied by the estimator.
Results: The recent infection test dynamics can be summarized into 2 parameters, similar to those appearing in previous estimators: a mean duration of recent infection and a false-recent rate. We identify a number of dimensionless parameters that capture the bias that arises from working with tractable forms of the resulting estimator and elucidate the utility of the incidence estimator in terms of the performance of the recency test and the population state. Estimation of test characteristics and incidence is demonstrated using simulated data. The observed confidence interval coverage of the test characteristics and incidence is within 1% of intended coverage.
Conclusions: Biomarker-based incidence estimation can be consistently adapted to a general context without the strong assumptions of previous work about biomarker dynamics and epidemiologic and demographic history.