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Interpopulation variation in HIV testing promptness may introduce bias in HIV incidence estimates using the serologic testing algorithm for recent HIV seroconversion


Objectives The serologic testing algorithm for recent HIV seroconversion (STARHS) calculates incidence using the proportion of testers who produce a level of HIV antibody high enough to be detected by ELISA but low enough to suggest recent infection. The validity of STARHS relies on independence between dates of HIV infection and dates of antibody testing. When subjects choose the time of their own test, testing may be motivated by risky behaviour or symptoms of infection and the criterion may not be met. This analysis was conducted to ascertain whether estimates of incidence derived using STARHS were consistent with estimates derived using a method more robust against motivated testing.

Methods A cohort-based incidence estimator and two STARHS methods were applied to identical populations (n=3821) tested for HIV antibody at publicly funded sites in Seattle. Overall seroincidence estimates, demographically stratified estimates and incidence rate ratios were compared across methods. The proportion of low-antibody testers among HIV-infected individuals was compared with the proportion expected given their testing histories.

Results STARHS estimates generally exceeded cohort-based estimates. Incidence ratios derived using STARHS between demographic strata were not consistent across methods. The proportion of HIV-infected individuals with lower antibody levels exceeded that which would be expected under independence between infection and testing.

Conclusions Incidence estimates and incidence rate ratios derived using methods that rely on the changing antibody level over the course of HIV infection may be vulnerable to bias when applied to populations who choose the time of their own testing.

  • bias
  • clinical populations
  • epidemiology
  • HIV
  • HIV incidence
  • laboratory diagnosis
  • recency of infection
  • surveillance
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