Uncertainty and Value of Information analysis in the Integrated Transport and Health Impact Modelling Tool for Global Cities (ITHIM-Global)

Abstract Health impact assessment models are a key tool for stakeholders and policymakers to understand the effect of transport on public health. The Integrated Transport and Health Impact Model for Global Cities (ITHIM-Global) is an open-source model developed specifically for low- and middle-income countries to assess the impact of changes in transport on population health at a city level. It models impacts through three different pathways: air pollution, physical activity and road traffic fatalities. As there is uncertainty in input parameters, ITHIM-Global can be set up to use a Monte Carlo simulation, sampling from pre-defined probability distributions describing inputs to create credible intervals for the various model outputs. A Value of Information analysis is used to determine the effect of these various input parameters on the uncertainty of the outputs. Using Bogotá as an example, this article explains how this uncertainty analysis works in ITHIM-Global. We demonstrate the effect of uncertainty on estimates of years of life lost at a city level using three hypothetical scenarios resulting in 5% increases in cycling, public transport and car journeys, respectively. The use of a Value of Information analysis and the impact of the various input parameters on the uncertainty in the results is also demonstrated. We show that an increase in cycling or public transport always has a positive effect on population health, independently of the uncertainty in our input parameters, whereas an increase in car journeys always has a negative impact. We also show that having perfect knowledge of some of the input parameters individually could reduce the standard deviation of the credible intervals of our results by up to 7%.