A guide to value of information methods for prioritising research in health impact modelling

Health impact simulation models are used to predict how a proposed intervention or scenario will affect public health outcomes, based on available data and knowledge of the process. The outputs of these models are uncertain due to uncertainty in the structure and inputs to the model. In order to assess the extent of uncertainty in the outcome we must quantify all potentially relevant uncertainties. Then to reduce uncertainty we should obtain and analyse new data, but it may be unclear which parts of the model would benefit from such extra research. This paper presents methods for uncertainty quantification and research prioritisation in health impact models based on Value of Information (VoI) analysis. Specifically, we 1. discuss statistical methods for quantifying uncertainty in this type of model, given the typical kinds of data that are available, which are often weaker than the ideal data that are desired; 2. show how the expected value of partial perfect information (EVPPI) can be calculated to compare how uncertainty in each model parameter influences uncertainty in the output; 3. show how research time can be prioritised efficiently, in the light of which components contribute most to outcome uncertainty. The same methods can be used whether the purpose of the model is to estimate quantities of interest to a policy maker, or to explicitly decide between policies. We demonstrate how these methods might be used in a model of the impact of air pollution on health outcomes.