In this study we are developing the next generation of health impact models of transport and testing this in England. The study builds on the existing ITHIM tools.
This study represents a sister study to TIGTHAT. In TIGTHAT we are laying the foundation for a globally applicable model that can cover settings with diverse and typically limited data. In METAHIT we are investigating how far we can go with a setting with good data.
Key developments in METAHIT are:
Producing estimates at smaller spatial scales
Better representation of uncertainty, including using Value of Information analysis to prioritise future research
Inclusion of noise pollution
Improving and comparing health impact modelling methods (using a proportional multi-state life table model with Dr Belen Zapata-Diomedi)
City region scenarios in collaboration with the Department for Transport and city region authorities, and building on the Propensity to Cycle Tool
The model is in R and all code is available open source https://github.com/ITHIM/ITHIM-R/.
Investigators and researchers
University of Cambridge (Dr James Woodcock, Dr Soren Brage, Dr Ali Abbas, Dr Chris Jackson, Dr Rob Johnson)
Imperial College (Dr Audrey de Nazelle, Dr Tim Oxley)
University of Leicester (Prof John Gulliver)
London School of Hygiene and Tropical Medicine (Dr Anna Goodman)
Norwegian Centre for Transport Research (Dr Rune Elvik).
METAHIT is funded through the MRC Methodology Research Programme from 2017 to 2021
Outputs so far include:
METAHIT publications on the MRC Epidemiology Unit publications database.
Contextualising Safety in Numbers: a longitudinal investigation into change in cycling safety in Britain, 1991–2001 and 2001–2011 Injury Prevention 2019
Cycling injury risk in London: A case-control study exploring the impact of cycle volumes, motor vehicle volumes, and road characteristics including speed limits Accident Analysis and Prevention 2018
A guide to Value of Information methods for prioritising research in health impact modelling Preprint available
Safety-in-numbers: An updated meta-analysis of estimates. Accident Analysis and Prevention 2019
Data sharing
The MRC Epidemiology Unit is committed to sharing data to maximise the value of our work for the public good. Please see our Data Sharing pages for more information.
