ITHIM-package: Integrated Transport and Health Impacts Model (ITHIM)
In syounkin/ITHIM: The Integrated Transport and Health Impacts Model

Description Details Author(s) References See Also Examples

ITHIM is a mathematical model that integrates data on travel patterns, physical activity, fine particulate matter, GHG emissions, and disease and injuries. Based on population and travel scenarios. The model has been used to calculate the health impacts of walking and bicycling short distances usually traveled by car or driving low-emission automobiles. Please Cite: Woodcock, J., Givoni, M., & Morgan, A. S. (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), e51462. and Maizlish, N., Woodcock, J., Co, S., Ostro, B., Fanai, A., & Fairley, D. (2013). Health cobenefits and transportation-related reductions in greenhouse gas emissions in the San Francisco Bay area. American Journal of Public Health, 103(4), 703-709.)

The model uses comparative risk assessment through which it formulates a change in the disease burden, resulting from the shift in the exposure distribution from a baseline scenario to an alternative scenario.

ITHIM characterizes exposure distributions in several ways:

– Physical Activity – Described as quintiles of a log-normal distribution on the basis of the mean weekly active transport time per person, its standard deviation and coefficient of variation (the standard deviation divided by the mean), mean weekly non-transport physical activity, and the ratio between bicycling and walking times. The activity times were multiplied by weights to give metabolic-equivalent task hours (METS), which reflect energy expenditures for walking and cycling at average speeds and for performing occupational tasks.

Descriptive statistics were obtained from published research on walking and bicycling speeds and analysis of travel and health surveys with large probability samples for the Bay Area.

– Air Pollution – To estimate exposure to air pollution, they used population-weighted means of airborne fine particulate matter (PM2.5), based on models calibrated for Bay Area automobile emissions and air shed. The RR-PM2.5 gradient in the comparative risk assessment analysis reflected the change in risk over an increment of 10 micrograms per cubic meter PM2.5.

– Traffic Injuries – Data on injuries was extracted from from a geocoded collision database of fatal and serious collisions reported to police.

Roadway type: determined roadway type associated with the collision by a spatial join in mapping software (ArcGIS 10, ESRI, Redlands, CA) to a street layer and categorized it as motorOnly, major, or minor on the basis of federal and state classifications of facility type.

Daily distances walked, bicycled, and driven by drivers and passengers of cars, buses, and rail from geocoded coordinates of trip origins and estimations recorded in diaries of participants of the 2000 Bay Area Travel Survey.

Samuel G. Younkin syounkin@wisc.edu

http://www.cedar.iph.cam.ac.uk/research/modelling/ithim/, https://ithim.ghi.wisc.edu/

createITHIM, getMeans, deltaBurden, getBurden, update

# Parametric
CRA(meanlog.baseline = log(10), meanlog.scenario = log(11), type = "parametric")
CRA(p0.baseline = 0.75, p0.scenario = 0.74, type = "parametric")

# Non-parametric
n <- 1e4
P <- qlnorm(p = 1/n*(1:(n-1)), meanlog = log(10))
Q <- qlnorm(p = 1/n*(1:(n-1)), meanlog = log(11))
CRA( P = P, Q = Q, type = "non-parametric")
CRA(meanlog.baseline = log(10), meanlog.scenario = log(11), p0.baseline = 0, p0.scenario = 0, meanlog.leisure = 1e-6, n = n)