Abstract
Malaria remains a major cause of morbidity in Uganda, despite the presence of effective and low-cost interventions such as artemisinin-based combination therapy (ACT) and sulfadoxine/pyrimethamine (SP). This is largely attributed to the prevalence of asymptomatic low-density infections that often go undetected and hence untreated. A new generation of highly-sensitive rapid diagnostic tests (HS-RDTs) has been shown to detect asymptomatic low-density infections, offering a potential tool to help move Uganda towards Malaria elimination. A pragmatic literature review informed the conceptualisation and structuring of a cost-effectiveness model to evaluate use of the HS-RDT compared with conventional diagnostic tests (c-RDT) in three patient cohorts – febrile patients presenting at community health facilities, pregnant women attending ante-natal clinics, and asymptomatic household members of febrile children aged <5. A decision tree model will be used for the analysis to compare clinical (DALY) and cost outcomes, with patients entering the model at diagnosis stage. Clinical inputs, direct and indirect costs (including testing, treatment, disease management and mortality) are sourced from local publications. Prevalence, rapid diagnostic test (RDT) sensitivity and false positive rates will be based on data from a community based observational study in Mpigi District, Uganda. Results from the observational study and model outputs, will be used to test the hypothesis that the higher detection rate using HS-RDT translates into a larger number of asymptomatic malaria cases detected in all three patient cohorts, leading to clinical and cost benefits. Targeting and treating asymptomatic infections is also anticipated to help reduce the asymptomatic Malaria reservoir, thereby contributing towards the goal of Malaria elimination. One way sensitivity analysis will be conducted to test model sensitivity to key inputs. A limitation of this approach is that the model is not a dynamic transmission model and does not therefore model long-term consequences and seasonal effects.
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