Economic evaluation in schistosomiasis: using the Delphi technique to assess effectiveness

Abstract

The limited resources allocated to Schistosomiasis Control Programme in Kenya are not adequate to implement and sustain all technically efficacious interventions simultaneously. This fact has dawned upon both policy-makers and medical personnel who hitherto advocated a multi-faceted approach to 'eradicate' schistosomiasis. Given that Kenya cannot afford to implement all interventions that might conceivably be of some benefit, it is necessary to establish priorities based on considerations of both costs and benefits. An important part of the benefit of any health intervention is its effect on health (quality and quantity of life) of potential beneficiaries. The objectives of this study were to: identify relevant types of data needed in estimating impacts of schistosomiasis interventions on health status; develop an instrument which can be used to obtain subjective probabilistic impacts of interventions; test its operational feasibility; develop a conceptual framework within which impacts of interventions can be estimated; illustrate how the probabilities could be used in calculating the 'expected quality adjusted life years' (EQALY); and identify obstacles that need to be overcome within a developing country context. In this study, a 'modified Delphi' approach was used to elicit expert subjective judgements on the effectiveness of ten schistosomiasis interventions. The paper compares and contrasts the expected quality adjusted life years estimated using values obtained from two local schistosomiasis experts with those obtained from a foreign expert. The panelists appeared to be consistent in their ranking of the effectiveness of treatment vs. non-treatment options. However, there were large differences in the magnitudes of estimates obtained from the local and the foreign experts. Given that the Delphi technique has been widely and fruitfully used in industry and commerce (among other areas) in developed countries, African countries health policy-makers cannot possibly do worse (than the current practice where decisions to commit resources are based on 'what we did last time' and 'gut feelings') by using systematic decision analyses based on data from such a technique.