Prioritizing the scale-up of interventions for malaria control and elimination

Abstract

Background: A core set of intervention and treatment options are recommended by the World Health Organization for use against falciparum malaria. These are treatment, long-lasting insecticide-treated bed nets, indoor residual spraying, and chemoprevention options. Both domestic and foreign aid funding for these tools is limited. When faced with budget restrictions, the introduction and scale-up of intervention and treatment options must be prioritized.

Methods: Estimates of the cost and impact of different interventions were combined with a mathematical model of malaria transmission to estimate the most cost-effective prioritization of interventions. The incremental cost effectiveness ratio was used to select between scaling coverage of current interventions or the introduction of an additional intervention tool.

Results: Prevention, in the form of vector control, is highly cost effective and scale-up is prioritized in all scenarios. Prevention reduces malaria burden and therefore allows treatment to be implemented in a more cost-effective manner by reducing the strain on the health system. The chemoprevention measures (seasonal malaria chemoprevention and intermittent preventive treatment in infants) are additional tools that, provided sufficient funding, are implemented alongside treatment scale-up. Future tools, such as RTS,S vaccine, have impact in areas of higher transmission but were introduced later than core interventions.

Conclusions: In a programme that is budget restricted, it is essential that investment in available tools be effectively prioritized to maximize impact for a given investment. The cornerstones of malaria control: vector control and treatment, remain vital, but questions of when to scale and when to introduce other interventions must be rigorously assessed. This quantitative analysis considers the scale-up or core interventions to inform decision making in this area.

Keywords: Cost-effective; Interventions; Malaria; Plasmodium falciparum; Prioritization; Scale-up.

Fig. 1

Cost-effective prioritization of LLINs and treatment. The average cost-effective scale-up of access to LLINs (blue bars) and coverage of treatment (red bars) for a low (baseline PfPr_2-10: 10%), b medium (baseline PfPr_2-10: 30%) and c high (baseline PfPr_2-10: 60%) non-seasonal transmission settings. Outcomes are similar for the seasonal setting (Additional file 2: Figure S1). Coverage does not reach 100% in the low-transmission scenario as elimination is achieved

Fig. 2

The standardized marginal cost of increasing treatment coverage. Treatment coverage is increased from 0 to 50% (blue boxes) or 75% (orange boxes) with respect to LLIN access. Increasing LLIN coverage prevents cases and therefore reduces the cost of increasing treatment coverage. In a low (baseline PfPr_2-10: 10%) and b medium (baseline PfPr_2-10: 30%) transmission settings the marginal impact of scaling LLINs decreases and treatment becomes a relatively cost-effective choice before coverage of LLINs is maximized. In the c high (baseline PfPr_2-10: 60%) transmission settings transmission continues to decrease even as LLINs reach very high levels of coverage

Fig. 3

Cost-effective prioritization of LLINs, treatment and IPTi or SMC. The average cost-effective scale-up of access to LLINs (blue bars) and coverage of treatment (red bars) with IPTi (light green bars) in perennial transmission settings or SMC (purple bars) in seasonal transmission settings. Scale-up is shown for a, c medium (baseline PfPr_2-10: 30%) and b, d high (baseline PfPr_2-10: 60%) transmission settings

Fig. 4

Cost-effective prioritization of LLINs, treatment and the RTS,S vaccine. The average cost-effective scale-up of access to LLINs (blue bars), coverage of treatment (red bars) and the RTS,S vaccine (orange bars) for a medium (baseline PfPr_2-10: 30%) and b high (baseline PfPr_2-10: 60%) perennial transmission settings

Fig. 5

The relative impact of treatment coverage on case incidence and mortality rates. In the a low (baseline PfPr_2-10: 10%) transmission setting increasing treatment coverage leads to reductions in the mortality rate and incidence. In the b medium (baseline PfPr_2-10: 30%) and c high (baseline PfPr_2-10: 60%) transmission settings increasing treatment coverage is still associated with declines in the mortality rate. However, as transmission increases the impact of increasing treatment coverage on incidence becomes less. For all examples LLIN access is fixed at 25%