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Kenya has the world’s 4 th  largest HIV burden. Various strategies to control the epidemic have been implemented, including the implementation of viral load (VL) testing to monitor HIV patients on ARVs. Like many resource limited settings, Kenya’s healthcare system faces serious challenges in effectively providing quality health services to its population. Increased investments to strengthen the country’s capacity to diagnose, monitor and treat diseases, particularly HIV and TB, continue to be made but are still inadequate in the face of global health goals like the UNAIDS 90:90:90 which require scaling up of VL tests amid existing constraints. In Kenya, there is an increase in the demand for VL tests amidst these existing constraints. The GeneXpert system is a diagnostic point-of-care technology that can quantify, amongst others, HIV VL. Currently, GeneXpert technology is widely distributed in Kenya for testing of tuberculosis. This study aimed to determine the economic and public health impact of incorporating VL test modules on the existing GeneXpert infrastructure. Markov models were constructed for different populations (non-pregnant adults, pregnant women and children). The scenarios analysed were 100% centralized VL testing compared to 50% GeneXpert plus 50% centralized VL testing, with time horizons of 5 years for the adult and child populations, and 31 months for the pregnant population. Incremental effectiveness was measured in terms of the number of HIV transmissions or opportunistic infections avoided when implementing the GeneXpert scenario compared to a 100% centralized scenario. The model indicated that, for all three populations combined, the GeneXpert scenario resulted in 117 less HIV transmissions and 393 less opportunistic infections. The cost decreased by $21,978,755 for the non-pregnant and pregnant adults and $22,808,533 for non-pregnant adults, pregnant adults and children. The model showed that GeneXpert would cost less and be more effective in terms of total cost per HIV transmission avoided and the total cost per opportunistic infection avoided, except for the pregnant population, when considered separately.

Economic and public health impact of decentralized HIV viral load testing: A modelling study in Kenya