Cost-effectiveness of the optimal mix of differentiated service delivery models for HIV care and treatment in Zambia: a mathematical modelling study

Abstract

Background: Zambia has scaled up differentiated service delivery (DSD) models for antiretroviral treatment (ART) to provide more client-centric care and increase service delivery efficiency. The current DSD landscape includes multiple models of care based on guidelines, resources, partner inputs, and other factors. We used local data to identify cost-effective combinations of DSD models that will maximize benefits and/or minimize costs to guide future DSD expansion. Methods: We developed a mathematical Excel-based model using retrospective retention and viral suppression data from a national cohort of ART clients (≥15 years) between January 2018-March 2022 stratified by age, sex, setting (urban/rural), and model of ART delivery. Outcomes (viral suppression and retention in care), provider costs, and costs to clients for each model were estimated from the cohort and previously-published data. For different combinations of the nine DSD models in use, we evaluated the incremental cost to the health system per additional ART client virally suppressed on treatment compared to the 2022 base case. To assess uncertainty in our model parameters, we conducted univariate and multivariate deterministic sensitivity analysis on key model input parameters. Results: Of the 125 scenarios evaluated, six were on the cost-effectiveness frontier: 1) six-month dispensing (6MMD)-only; 2) 6MMD and adherence groups (AGs); 3) AGs-only; 4) fast track refills (FTRs) and AGs; 5) FTRs-only; and 6) AGs and home ART delivery. 6MMD-only was cost-saving compared to the base case, increased the proportion of clients retained in care by 1.2% (95% CI, 0.7; 1.8), virally suppressed by 1.6% (95% CI, 1.0;2.6), and decreased costs to clients by 12.0% (95% CI, -10.8; -12.4). The next two scenarios on the cost-effectiveness frontier, 6MMD + AGs and AGs-only, each cost an additional $245 per person virally suppressed, increased the total number of individuals suppressed on treatment by 2.8% (95% CI, 2.2; 3.3) and 4.0% (95% CI, 3.5; 4.0) and increased costs to clients by 20.1% (95% CI, 9.5; 28.1) and 52.3% (95% CI, 29.8; 68.7), respectively. Under the base case, and scenarios on the cost-effectiveness frontier, cost of ART and laboratory tests were the most influential model parameters to the total health system costs and ICER, respectively. Conclusions: Mathematical modelling using existing data can identify cost-effective mixes of DSD models and allocations of clients to these models, while ensuring that all client sub-populations are explicitly considered. In Zambia, providing 6MMD to all eligible clients is likely to be cost-saving, while health outcomes can be improved by allocating clients to selected models based on sub-population.