Abstract
OBJECTIVES: To produce valid seroincidence estimates, the serological testing algorithm for recent HIV seroconversion (STARHS) assumes independence between infection and testing, which may be absent in clinical data. STARHS estimates are generally greater than cohort-based estimates of incidence from observable person-time and diagnosis dates. The authors constructed a series of partial stochastic models to examine whether testing motivated by suspicion of infection could bias STARHS. METHODS: One thousand Monte Carlo simulations of 10,000 men who have sex with men were generated using parameters for HIV incidence and testing frequency from data from a clinical testing population in Seattle. In one set of simulations, infection and testing dates were independent. In another set, some intertest intervals were abbreviated to reflect the distribution of intervals between suspected HIV exposure and testing in a group of Seattle men who have sex with men recently diagnosed as having HIV. Both estimation methods were applied to the simulated datasets. Both cohort-based and STARHS incidence estimates were calculated using the simulated data and compared with previously calculated, empirical cohort-based and STARHS seroincidence estimates from the clinical testing population. RESULTS: Under simulated independence between infection and testing, cohort-based and STARHS incidence estimates resembled cohort estimates from the clinical dataset. Under simulated motivated testing, cohort-based estimates remained unchanged, but STARHS estimates were inflated similar to empirical STARHS estimates. Varying motivation parameters appreciably affected STARHS incidence estimates, but not cohort-based estimates. CONCLUSIONS: Cohort-based incidence estimates are robust against dependence between testing and acquisition of infection, whereas STARHS incidence estimates are not.