Using the capture-recapture method to estimate the human immunodeficiency virus-positive population

Area of Science:

• Epidemiology
• Biostatistics
• Public Health

Background:

• Accurate estimation of human immunodeficiency virus (HIV) prevalence is crucial for effective public health interventions.
• Existing data sources for HIV-positive individuals are often incomplete, necessitating advanced statistical methods for population estimation.

Purpose of the Study:

• To estimate the number of unregistered HIV-positive individuals in Lorestan Province, Iran, using the capture-recapture method.
• To assess the completeness of existing HIV surveillance data sources in the region.

Main Methods:

• A cross-sectional study utilizing a 3-source capture-recapture model with log-linear analysis was conducted in 2016.
• Data from a transfusion center, volunteer counseling and testing centers (VCTCs), and prisons were used to identify overlapping HIV-positive cases.
• Model selection was guided by the Akaike information criterion (AIC) and Bayesian information criterion (BIC).

Main Results:

• A total of 2,281 unique HIV-positive individuals were identified across the three sources.
• The capture-recapture method estimated 14,868 (95% CI: 9,923–23,427) unregistered HIV-positive individuals.
• The overall completeness of the three registries was estimated at approximately 13.3%, suggesting a substantial underestimation of the HIV burden.

Conclusions:

• A significant number of HIV-positive individuals remain unregistered in provincial data sources, indicating a critical gap in surveillance.
• These findings underscore the urgent need for policymakers to enhance HIV case reporting and healthcare planning.
• While the capture-recapture method is valuable, potential overestimation due to method limitations should be considered.