Sustainable HIV treatment in Africa through viral-load-informed differentiated care

Abstract

There are inefficiencies in current approaches to monitoring patients on antiretroviral therapy in sub-Saharan Africa. Patients typically attend clinics every 1 to 3 months for clinical assessment. The clinic costs are comparable with the costs of the drugs themselves and CD4 counts are measured every 6 months, but patients are rarely switched to second-line therapies. To ensure sustainability of treatment programmes, a transition to more cost-effective delivery of antiretroviral therapy is needed. In contrast to the CD4 count, measurement of the level of HIV RNA in plasma (the viral load) provides a direct measure of the current treatment effect. Viral-load-informed differentiated care is a means of tailoring care so that those with suppressed viral load visit the clinic less frequently and attention is focussed on those with unsuppressed viral load to promote adherence and timely switching to a second-line regimen. The most feasible approach to measuring viral load in many countries is to collect dried blood spot samples for testing in regional laboratories; however, there have been concerns over the sensitivity and specificity of this approach to define treatment failure and the delay in returning results to the clinic. We use modelling to synthesize evidence and evaluate the cost-effectiveness of viral-load-informed differentiated care, accounting for limitations of dried blood sample testing. We find that viral-load-informed differentiated care using dried blood sample testing is cost-effective and is a recommended strategy for patient monitoring, although further empirical evidence as the approach is rolled out would be of value. We also explore the potential benefits of point-of-care viral load tests that may become available in the future.

Figure 1

Overall programme costs in ($m per 3 months) according to monitoring strategy (mean over 2015–2034, discounted at 3% per annum from 2015)

Figure 2

Cost effectiveness plane showing clinical- and CD4-based monitoring strategies along with viral load-informed differentiated care using DBS. ICER - incremental cost-effectiveness ratio

Figure 3

Indication of whether viral load-informed differentiated care is the most cost effective monitoring strategy according to (i) cost of viral load tests and (ii) reduction in non-ART programme costs in people with viral suppression. In context of cost-effectiveness threshold $500. Colours indicate which monitoring strategy is economically preferred.

Figure 4

Incremental cost-effectiveness ratio (ICER) for viral-load-informed differentiated care using dried blood spot (DBS) (compared with next less effective strategy on the efficiency frontier) according to changes in assumptions.

Figure 5

Cost effectiveness plane showing the current sitiation - CD4 count (WHO) monitoring with a low rate of switching in those meeting the failure criteria (0.05 per 3 months) - and viral load informed differentiated care with switch rate as in our base case (0.5 per 3 months)

Figure 6

Cost-effectiveness planes showing the effect of viral load measurement frequency, format and threshold, all in the context of viral-load-informed differentiated care. a, Viral load monitoring every 12-months is compared with every 6 months (every 2-year monitoring is excluded from the cost-effectiveness frontier due to unproven ability to base differentiated care on a 2-yearly value; however, if less frequent monitoring could be implemented without adverse health outcomes this would be cost-effective). b, Laboratory whole blood corresponds to dried blood spot (DBS). c, Alternative thresholds to define failure (viral load >200, >1,000 and >5,000 cps ml1) are compared in the context of laboratory monitoring every 12 months using plasma.

Figure 6

Cost-effectiveness planes showing the effect of viral load measurement frequency, format and threshold, all in the context of viral-load-informed differentiated care. a, Viral load monitoring every 12-months is compared with every 6 months (every 2-year monitoring is excluded from the cost-effectiveness frontier due to unproven ability to base differentiated care on a 2-yearly value; however, if less frequent monitoring could be implemented without adverse health outcomes this would be cost-effective). b, Laboratory whole blood corresponds to dried blood spot (DBS). c, Alternative thresholds to define failure (viral load >200, >1,000 and >5,000 cps ml1) are compared in the context of laboratory monitoring every 12 months using plasma.

Figure 6

Cost-effectiveness planes showing the effect of viral load measurement frequency, format and threshold, all in the context of viral-load-informed differentiated care. a, Viral load monitoring every 12-months is compared with every 6 months (every 2-year monitoring is excluded from the cost-effectiveness frontier due to unproven ability to base differentiated care on a 2-yearly value; however, if less frequent monitoring could be implemented without adverse health outcomes this would be cost-effective). b, Laboratory whole blood corresponds to dried blood spot (DBS). c, Alternative thresholds to define failure (viral load >200, >1,000 and >5,000 cps ml1) are compared in the context of laboratory monitoring every 12 months using plasma.