Cross-sectional HIV incidence estimation in an evolving epidemic

Area of Science:

• Epidemiology
• Biostatistics

Background:

• Cross-sectional studies are valuable for HIV incidence estimation but rely on initial training datasets.
• These datasets may become outdated due to epidemic evolution, such as increased antiretroviral therapy use.

Purpose of the Study:

• To develop and evaluate methods for adjusting initial training datasets to better reflect current target populations.
• To provide a cost-effective solution for applying cross-sectional incidence estimation methods to evolving epidemics.

Main Methods:

• Developed statistical methods to adjust existing training datasets.
• Evaluated the adjustment procedures through a simulation study.
• Applied the methods to a dataset of HIV subtype B infection.

Main Results:

• The developed adjustment procedures effectively updated initial datasets to better represent target populations.
• Simulations demonstrated the validity and utility of the proposed methods.
• The approach proved applicable beyond HIV incidence estimation.

Conclusions:

• The proposed adjustment methods offer a practical and cost-effective way to adapt cross-sectional incidence estimation to evolving epidemics.
• These techniques can be valuable in various statistical analyses requiring data transportability across populations.
• This approach enhances the utility of existing data for public health surveillance.