Estimation of time since infection using longitudinal disease-marker data

Area of Science:

• Epidemiology
• Biostatistics
• Immunology

Background:

• Estimating time since human immunodeficiency virus type 1 (HIV-1) infection is crucial for disease management and research.
• Current methods for determining infection duration are often imprecise, especially for prevalent cases.

Purpose of the Study:

• To propose and validate a novel statistical method for estimating the time since HIV-1 infection.
• To apply this method to prevalent cohort data from intravenous drug users and homosexual/bisexual men.

Main Methods:

• The method assumes an exponential prior distribution for time since infection.
• It utilizes CD4 cell count at the first positive HIV-1 antibody test to derive a conditional truncated normal distribution for time since infection.
• The model was applied to prevalent cohort data and validated using seroconverter data.

Main Results:

• Estimated mean time since infection was 15.0 months for intravenous drug users and 16.7 months for homosexual/bisexual men.
• Validation in homosexual/bisexual men showed a median absolute difference of 4.6 months between estimated and known infection dates at 18 months post-infection.
• The model showed a tendency to overestimate short infection durations and underestimate long ones.

Conclusions:

• The proposed method offers a viable approach to estimate time since HIV-1 infection using CD4 counts.
• Further refinement of the mathematical model may be necessary to improve accuracy, particularly for shorter and longer infection times.