UBDPolicy researchers joined 24 participants at the University of Cambridge for an intensive course on quantitative Health Impact Assessment (19–23 May 2025), combining lectures, group work and idea sharing to strengthen future cooperation in health research. The course on quantitative Health Impact Assessment took place from May 19th to May 23th 2025, organised and hosted by the Public Health Modelling Team at the MRC Epidemiology Unit, University of Cambridge (UK). A total of 24 participants, from eight different institutions, including 12 UBDpolicy researchers, convened in the West Hub campus for five intense days of learning new tools, discussing ideas, sharing insights and strengthening ties for present and future cooperation in health impact assessment research.
Overall, the training addressed a broad range of questions, including – but not limited to: why do we need mathematical models, and what are their limitations? How do our transportation systems affect our health? How can we measure the environmental factors that impact people’s health, and what are the strengths and weaknesses of each method? How can these environmental factors realistically be different from what they are now – also known as scenario development? How will people’s health change for different scenarios? How will a scenario unfold into the future, interacting with many other changes happening at the same time? What follows is a selection of two core themes of the course, distilled to their key messages.
AP and PA: very different, but both highly impactful
Transportation systems impact people’s health through many mechanisms, but two stand out as the most important: exposure to air pollution (AP) and the opportunity for transport-related Physical Activity (PA). We can draw an interesting contrast between these two factors:
- Air pollution increases the risk of many diseases and early death, while physical activity decreases these risks and increases the duration and quality of life.
- Air pollution affects many people at once (although not everyone equally), while physical activity only benefits the individual people who do it.
A change from doing no physical activity to doing a little will greatly improve a person’s health; further increases in the level of physical activity will bring further health improvements, but progressively smaller ones. Walking and cycling are considered active modes of transport because they provide the benefits of physical activity to the people who do them; these benefits outweigh the risks posed by air pollution under most circumstances. Public transport is not an active mode in itself, but most journeys by public transport start and finish with a walking stage, whose health benefits should not be dismissed.
Adding all up with ITHIM
The Public Health Modelling Team developed the Integrated Transport and Health Impact Model (ITHIM), a tool that applies the known links between transport systems and health to produce estimates of health impacts.
ITHIM takes in data describing the travel behaviour in a city of choice: how many trips the people are taking during an average week, how long are these trips, and which transport mode is used (among driving, walking, cycling, and riding public transport). Furthermore, it takes into account how polluted the air already is, how much physical activity the population is already doing, the distribution of the population among various age and sex categories (a.k.a. the demographic make-up), the mortality rate and the frequency of certain diseases of interest happening in the population (a.k.a. the local epidemiology). Finally, one or more transport scenarios are added to the mix. A transport scenario, in this case, is a hypothetical change in the travel behaviour: for example, what would happen if all driving trips under 5km were replaced by walking or cycling trips of the same distance?
ITHIM combines all the data and the scenarios provided to estimate the health impact that would result from each scenario becoming reality. The health impact is estimated in terms of premature deaths or other summary health metrics: for example, a result can look like 100 premature deaths or 40 cases of heart attack prevented every year.
