Addressing Population Heterogeneity for HIV Incidence Estimation Based on Recency Test.

Area of Science:

• Epidemiology
• Biostatistics
• Public Health

Background:

• Cross-sectional HIV incidence estimation uses recency tests to identify recent infections, avoiding longitudinal follow-up.
• Conventional methods require repeated testing and long-term monitoring, which can be resource-intensive.
• Estimating incidence in specific subpopulations or with external data presents challenges due to heterogeneity.

Purpose of the Study:

• To extend cross-sectional HIV incidence estimation to new target populations, addressing heterogeneity.
• To develop a general framework applicable to subset and external target populations.
• To incorporate HIV subtypes as covariates influencing recency test properties.

Main Methods:

• Proposed a general statistical framework for cross-sectional HIV incidence estimation in extended populations.
• Developed estimators to integrate HIV subtype information into the incidence estimation framework.
• Validated the proposed methods using simulation studies and a real-world data application.

Main Results:

• The proposed framework effectively estimates HIV incidence in target populations with heterogeneity.
• Incorporating HIV subtypes improved the accuracy of incidence estimations.
• The methods demonstrated robust performance in both simulated and applied scenarios.

Conclusions:

• The developed framework provides a flexible and accurate approach for cross-sectional HIV incidence estimation.
• The methods are valuable for public health surveillance, particularly when dealing with diverse populations and subtypes.
• Further research should focus on sensitivity analyses and framework enhancements.