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BACKGROUND: Regimen simplification of ART, by administering them less frequently, has been suggested as a practical approach to improve adherence. The aim of this study was to assess the cost-utility of once-daily (QD) versus twice-daily (BID) antiretroviral (ART) regimens in the treatment of HIV.  METHODS: A Model-based Markov modelling of cost-effectiveness using secondary data sources was developed to determine the incremental cost per quality-adjusted life year (QALY) gained of QD versus BID ART regimen for a hypothetical cohort treatment-naive adults with HIV, from the Sub-Saharan African healthcare payer’s perspective.  RESULTS: At base-case values for all parameters, the total number of QALY gained by QD regimen was 0.27 and the incremental cost difference of $2147.04. The incremental cost-effectiveness ratio (ICER) of QD versus BID regimen was $8087/QALY gained. The ICER was most sensitive to the variations in the total medical cost of state A (asymptomatic, non-AIDS, CD4> 350 cells/μL), total medical Cost State D (symptomatic AIDS or severe symptoms), and utility of State A. In our bootstrap analysis, 60% of bootstrap replicates for the ICER shows that QD is more costly and more effective than BID regimen, while the remaining 40% replicates shows that QD is less costly and less effective than BID. If decision-makers were willing to pay $1000 per QALY gained, the probability of QD being cost-effective was 44%. The probability of QD regimen being cost-effective was 48% when the willing to pay was $5000.  CONCLUSIONS: From a sub-Saharan Africa societal perspective QD regimen cannot be regarded as cost-effective, although there is substantial decision uncertainty. Findings from the economic evaluation are important for low- and middle-income countries (LMIC) to consider as they decide whether to adopt the new branded single tablet regimen. Generic-based ART could yield substantial budgetary saving to HIV programmes in LMIC.

Cost-effectiveness of once-daily versus twice-daily regimens in the treatment of HIV infection in sub-Saharan Africa: a probabilistic decision model