Integrated Transport and Health Impact Modelling Tool (ITHIM) ) was originally developed out of work for the Lancet series on climate change mitigation and health 2009. Initially created as a spreadsheet model, a more recent Analytica model has been used in studies including São Paulo and the London Cycle Hire scheme. An international team of researchers is moving the model to R with a Shiny interface. This is a work in progress involving partners from UK, Switzerland, Brazil, USA and Canada. The work also involves collaboration with the World Health Organization, under the Urban Health Initiative, to adapt the tool for settings with limited evidence and data availability.

  • Please select User Case Study where you need to provide data for both baseline and scenarios

  • Please select Predefined Case Studies to see the ITHIM results for predefined locations.

Data Localization:

Using GBD to derive local disease and mortality rates.

Data Harmonization:

The process of conversion or matching of various data formats and variable definitions of external data to the generic data formats used in ITHIM. Including

  • Transport mode categories
  • Age bands
Synthetic population:

The process of creating a Synthetic population (sample of 10k individuals), through probabilistic matching of separate input data from population, travel, and health surveys, for baseline.

Scenario Definitions:

The process of creating scenarios based on user-inputs. Users are given options to describe scenarios, by modifdyign baseline's data.

Physical activity:

ITHIM uses non-linear dose response relationships based on total physical activity and applies these individual diseases. This approach allows that baseline travel and non-travel physical activity vary between populations and that the relative burden of diseases varies between countries.

Air Pollution:

Just like Physical Activity, ITHIM uses a non-linear dose relationship for calculating Air Pollution impacts.

Road traffic injuries:

Unlike most other models of walking and cycling ITHIM estimates injuries taking into account all the parties involved in collision. ITHIM uses a non-linear distance based method. That is the number of injuries is dependent on the distance travelled by all modes, but it is non-linear because it includes ‘safety-in-numbers’.

Health Impacts:

Calculates health gains measured as Years of Life Lost (YLL) and Premature Deaths Averted. The baseline for health comes from Global Burden of Disease Study

Help?
Displays plots for mode share of trips based on main mode only. A scenario is selected by a combination of three inputs: % of Population who are Regular Cyclists, Equity and Ebike. Users can choose to compare mode share between selected sub-populations and the total population, and/or between selected scenarios and baseline.
Help?

Displays histogram of total physical activity and also the percentage of the population meeting the physical activity guidelines of the World Health Organization (WHO).

The WHO guidelines are for 150 minutes of moderate intensity or 75 minutes of vigorous intensity activity, with additional benefits by achieving 300 minutes of moderate intensity or 150 minutes of vigorous intensity activity. We have translated these guidelines into Marginal Metabolic Equivalent Task (MMET) hours per week. MMETs represent the body mass adjusted energy expenditure above resting. To do this we have assumed that moderate intensity activity is 3.5 MMETs, meaning that the lower target is 8.75 MMET hours per week, and the higher target is 17.5 MMET hours per week. We have assumed the MMET rates are 3.6 for walking, 5.4 for cycling, and 3.5 for ebikes (Costa et al., 2015 and Sperlich et al., 2012). Thus the lower target could be achieved by 145 minutes per week of walking, 97 minutes of cycling, or 150 minutes of ebiking.

Non-travel activity is estimated using self-reported data from probabilistically matched individuals of a similar age, gender, and ethnicity from the Health Survey for England 2012.

Users can choose to compare physical activity between selected sub-populations and the total population, and/or between selected scenarios and baseline.

Help?
Population distributions of CO2 from car travel - for CO2 reduced see Summary tab. Displays two plots for CO2 produced during car travel, defined as travel as a car/van driver or car/van passenger. Users can choose to compare CO2 emissions from car travel between selected sub-populations and the total population, and/or between selected scenarios and baseline.
Air Pollution:

Just like Physical Activity, ITHIM uses a non-linear dose relationship for calculating Air Pollution impacts.

Road traffic injuries:

Unlike most other models of walking and cycling ITHIM estimates injuries taking into account all the parties involved in collision. ITHIM uses a non-linear distance based method. That is the number of injuries is dependent on the distance travelled by all modes, but it is non-linear because it includes ‘safety-in-numbers’.

Help?
Displays two plots for health gains measured as Years of Life Lost (YLL) and Premature Deaths Averted. YLLs are taken from the Global Burden of Disease Study for the UK 2013. YLL is an estimate of the age specific life expectancy against an 'ideal' reference population. A scenario is selected by a combination of three inputs: % of Population who are Regular Cyclists, Equity and Ebike - this scenario can then be compared against baseline or against an alternative scenario. Results are presented by age and gender, or the display can be restricted to particular age and gender groups using the subpopulation option.

Welcome to the Integrated Transport and Health Impact Modelling Tool (ITHIM)

ITHIM is released under an Affero GPL and we accept no liability, included but not limited to loss or damages. The code of which is hosted on GitHub.

The ITHIM has been created by CEDAR. Those currently working on ITHIM R include:

Publications

Brazil

  • de Sá TH, Tainio M, Goodman A, Edwards P, Haines A, Gouveia N, Monteiro CA, Woodcock J. (2017). The São Paulo we want? Health impact modelling of different travel patterns for São Paulo, Brazil. Environment International 108: 22–31. http://dx.doi.org/10.1016/j.envint.2017.07.009

  • Sá TH, Duran AC, Tainio M, Monteiro CA Woodcock J. (2016). Cycling in São Paulo, Brazil (1997–2012): Correlates, time trends and health consequences. Preventive Medicine Reports 4: 540–545.

England

  • Goodman, A., Green, J., & Woodcock, J. (2014). The role of bicycle sharing systems in normalising the image of cycling: An observational study of London cyclists. Journal of Transport & Health, 1(1), 5–8. doi: 10.1016/j.jth.2013.07.001

  • Woodcock J., Tainio M., Cheshire J., O’Brien O., Goodman A. (2014). Health effects of the London bicycle sharing system: health impact modelling study. British Medical Journal (BMJ) 348: g425. http://www.bmj.com/content/348/bmj.g425.long

  • Götschi, T., Tainio, M., Maizlish, N., Schwanen, T., Goodman, A., & Woodcock, J. (2015). Contrasts in active transport behaviour across four countries: How do they translate into public health benefits? Preventive Medicine. doi:10.1016/j.ypmed.2015.02.009

Malaysia

  • Kwan SC, Tainio M, Woodcock J, Sutan R, Hashim JH. (2017). The carbon savings and health co-benefits from the introduction of mass rapid transit system in Greater Kuala Lumpur, Malaysia. Journal of Transport & Health 6: 187-200. doi.org/10.1016/j.jth.2017.06.006

  • Kwan SC, Tainio M, Woodcock J, Hashim JH. (2016). Health co-benefits in mortality avoidance from implementation of the mass rapid transit (MRT) system in Kuala Lumpur, Malaysia. Reviews on Environmental Health 31(1):179-83. doi: 10.1515/reveh-2015-0038.

USA

  • Maizlish N,Woodcock J, Co S, Ostro B, Fanai A, Fairley D. (2013). Health cobenefits and transportationrelated reductions in greenhouse gas emissions in the San Francisco Bay area. Am J Public Health. 103(4):703-709.

  • N. Maizlish, N.J. Linesch, J. Woodcock. (2017). Health and greenhouse gas mitigation benefits of ambitious expansion of cycling, walking, and transit in California. J. Transp. Health., 6, pp. 490-500

Multiple locations

  • Woodcock J, Edwards P, Tonne C, Armstrong BG, Ashiru O, Banister D, et al (2009). Public health benefits of strategies to reduce greenhouse-gas emissions: urban land transport. Lancet 374:1930-43.

  • Woodcock J, Givoni M, Morgan AS. (2013). Health Impact Modelling of Active Travel Visions for England and Wales Using an Integrated Transport and Health Impact Modelling Tool (ITHIM). PLoS ONE. 8(1).

For more information or questions, please contact us at: jw745@cam.ac.uk