DATA-DRIVEN MODELING AND SIMULATION FOR THE DESIGN AND EVALUATION OF HIV VIRAL LOAD MONITORING POLICIES IN RESOURCE-LIMITED SETTINGS

Purpose: Early detection of virologic failure in HIV patients on antiretroviral therapy may improve the health outcomes of patients and reduce transmission. HIV RNA (viral load) monitoring is a costly yet common technology for detecting failure.  We assess the health benefits and cost-effectiveness of strategies for viral load monitoring in resource-limited contexts.

Methods: We created a microsimulation model parameterized using longitudinal cohort data.  We use the Swiss HIV Cohort Study (SHCS) data to estimate: 1) predictors of virologic failure; and 2) CD4 evolution during virologic failure and on antiretroviral therapy (ART).  We model the probability of virologic failure as a function of self-reported patient adherence, time on regimen, age and gender.  Adherence is also modeled as a time-varying process that depends on the patient's previous adherence status, age, gender and education level.  Individual CD4 counts are modeled using quantile regression models, where CD4 progression depends on failure status, previous CD4 count, time since ART initiation, CD4 nadir, age and gender.  We develop a microsimulation model informed by the data, and simulate 30,000 HIV patients in Uganda for 10 years following ART initiation. We then use our model to evaluate the total costs and QALYs achieved over a 10-year period by four viral load monitoring frequencies: every 3, 4, 6 or 12 months.

Results: The model was validated by matching the five-year survival rates and the opportunistic infection-free survival rates within the 95% confidence intervals of the DART randomized clinical trial.  The average total number of months spent in virologic failure per patient over the 10 years simulated ranged from 2.07 for the 3-month interval policy to 4.25 for the 12-month policy. The percentage of the patients who switched to second-line regimen by the end of the 10-year period ranged from 31.6% for the 12-month policy to 36.8% for the 3-month policy.  In comparison with monitoring viral load every 12 months, more frequent monitoring marginally increased QALYs.  Compared with 12-monthly monitoring, 3-monthly monitoring yields on average a gain of 0.0595 QALYs per patient, at an incremental cost of $821. 

Conclusions: In resource-limited settings, high-frequency viral load monitoring relative to yearly monitoring costs more per QALY gained than many HIV interventions.  Use of direct person-level data can inform model construction and improve parameter estimation for diverse populations.