PRM161 A Paradigm Shift in Health Economic Evaluations for Developing Countries - Optimization Modelling for Assessing Which Mix of Malaria Prevention Strategies Achieves a Defined Public Health Target at the Lowest Budget

Abstract

PRM161 A PARADIGM SHIFT IN HEALTH ECONOMIC EVALUATIONS FOR DEVELOPING COUNTRIES OPTIMIZATION MODELLING FOR ASSESSING WHICH MIX OF MALARIA PREVENTION STRATEGIES ACHIEVES A DEFINED PUBLIC HEALTH TARGET AT THE LOWEST BUDGET Van Vlaenderen I1, Sauboin C2, Van Bellinghen LA1, Standaert B2 1CHESS, Ternat, Belgium, 2GlaxoSmithKline Vaccines, Wavre, Belgium Malaria remains one of the leading causes of ill health with the majority of cases and deaths occurring in young children in Sub-Saharan Africa. Key interventions currently recommended for preventing childhood malaria include Insecticide Treated Nets, Indoor Residual Spraying, Intermittent Preventive Treatment in infancy, and Seasonal Malaria Chemoprevention. One-to-one comparisons applied in cost-effectiveness and budget impact analyses fail to examine the health and economic impact of multiple interventions implemented simultaneously, and are therefore unable to evaluate the optimal integration of available malaria preventive interventions and partially efficacious vaccines currently in development. Moreover, an incremental cost-effectiveness ratio with recommended willingnessto-pay threshold provides limited guidance in developing countries. OBJECTIVE: Defining an approach to identify the optimal sequence of introducing different preventive interventions, achieving progressively increasing public health targets for malaria control in children 5years old at the lowest budget. METHODS: Our suggested optimization approach integrates two distinguished models to assess combinations of interventions. A vector model simulates the impact of varying coverages of vector control interventions on vector infectiousness capacity and associated reduction in Entomological Inoculation Rate (EIR) for children. A human host model applies this reduced EIR to simulate disease incidence at varying coverages of interventions directly acting within humans. These connected models provide all potential intervention combinations achieving a pre-defined public health target (e.g. reducing childhood mortality with 50%). Considering malaria policy evolutions at increasing public health targets, a lower bound is set on intervention coverages at each optimization step. The remaining options are ranked according to their budget impact considering cost of interventions and disease management in a health system perspective. CONCLUSIONS: Using the optimization process with progressively increasing public health targets provides an indication on the optimal sequence of introducing interventions at the lowest budget. Therefore, this approach can support decision-making in prioritization of malaria preventive interventions.