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Related Experiment Video

Updated: Jun 25, 2026

Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3

Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3

Published on: December 27, 2010

Testing bias in calculating HIV incidence from the Serologic Testing Algorithm for Recent HIV Seroconversion.

Robert S Remis¹, Robert W H Palmer

¹Ontario HIV Epidemiologic Monitoring Unit, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada. rs.remis@utoronto.ca

AIDS (London, England)

|February 26, 2009

Summary

Estimating recent HIV infections using the Serologic Testing Algorithm for Recent HIV Seroconversion (STARHS) can be biased. Early testing by recently infected individuals may lead to overestimation, especially in men who have sex with men.

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Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3

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Published on: December 27, 2010

The α-test: Rapid Cell-free CD4 Enumeration Using Whole Saliva

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Published on: May 16, 2012

Area of Science:

Epidemiology
Public Health
Biostatistics

Background:

Monitoring HIV incidence is crucial for public health interventions.
The Serologic Testing Algorithm for Recent HIV Seroconversion (STARHS) offers a cost-effective method for estimating HIV incidence from single specimens.
HIV testing patterns can introduce bias into STARHS estimates, complicating interpretation.

Purpose of the Study:

To investigate potential biases in HIV incidence estimates derived from the STARHS assay.
To evaluate the impact of testing patterns, specifically the 'seroconversion effect' (SCE), on STARHS accuracy.
To develop methods for adjusting STARHS estimates to correct for identified biases.

Main Methods:

A hypothetical cohort of homosexual men was modeled to calculate HIV incidence using STARHS parameters.
The 'seroconversion effect' (SCE), representing increased testing likelihood around seroconversion, was incorporated.
Empirical STARHS data were fitted to an algebraic formula to adjust incidence estimates for bias.

Main Results:

STARHS estimates were unbiased in the absence of SCE or incidence density-interval interactions.
The presence of SCE led to overestimation of HIV incidence density, in some cases up to seven-fold.
Goodness-of-fit analyses indicated that the adjusted estimates were plausible and well-supported by the data.

Conclusions:

HIV incidence estimates from STARHS can be significantly overestimated due to early testing behavior in recently infected individuals, particularly among men who have sex with men.
STARHS-derived incidence estimates require cautious interpretation due to potential overestimation.
Adjusted STARHS estimates can provide more accurate and unbiased measures of HIV incidence.