  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.

Methodology