Prevalence estimation when disease status is verified only among test positives: Applications in HIV screening...

Area of Science:

• Epidemiology
• Biostatistics
• Public Health

Background:

• The UN's 90-90-90 strategy aims for 90% of people living with HIV (PLWH) to know their status by 2020.
• Estimating HIV prevalence in target populations informs screening program effectiveness and resource allocation.

Purpose of the Study:

• To develop and evaluate statistical methods for estimating HIV prevalence using imperfect screening tests.
• To compare estimation methods under different data availability scenarios (known vs. unknown test negatives).

Main Methods:

• Development of maximum likelihood estimators and Bayesian approaches for prevalence estimation.
• Utilizing gold-standard verification for positive results from imperfect screening tests.
• Simulation studies to assess estimator performance under varying test accuracy and data conditions.

Main Results:

• The maximum likelihood estimator performed better when the total number of test negatives was known (Scenario 1).
• Estimator performance was comparable when test accuracy was below 90%, even with unknown test negatives.
• Bayesian methods are recommended for Scenario 2 (unknown negatives) due to sensitivity to test accuracy misspecification.

Conclusions:

• Accurate HIV prevalence estimation is feasible using imperfect screening tests.
• Recording the number of test negatives enhances estimation accuracy in public health screening.
• Bayesian approaches offer robust prevalence estimation, particularly when test characteristics have uncertainty.