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Estimating recent HIV infections is crucial. This study evaluates two methods, finding the adjusted estimator more robust when assumptions of perfect test specificity and constant incidence are not met.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Infectious Disease Modeling

Background:

  • Longitudinal studies for HIV incidence are logistically difficult.
  • Recency testing offers an alternative by analyzing biomarker profiles in cross-sectional samples.
  • Current recency test estimators lack rigorous statistical evaluation.

Purpose of the Study:

  • To develop a theoretical framework for understanding HIV recency test estimators.
  • To assess the performance of two common estimators (snapshot and adjusted) under realistic conditions.
  • To provide recommendations for practical application and future methodological improvements.

Main Methods:

  • Theoretical framework development.
  • Simulation study using realistic HIV epidemiological dynamics.
  • Data analysis to evaluate estimator performance.

Main Results:

  • Both the snapshot and adjusted estimators perform well when their assumptions are met.
  • The adjusted estimator demonstrates greater robustness than the snapshot estimator when assumptions of constant incidence and perfect test characteristics are violated.
  • Performance is assessed under varying HIV epidemiological dynamics.

Conclusions:

  • The adjusted estimator is more reliable in scenarios with non-constant incidence or imperfect test specificity.
  • Recommendations are provided for the practical use of these HIV incidence estimation methods.
  • Future research should focus on enhancing methodological developments for improved HIV incidence estimation.