title: Integrated health impact assessment of travel behaviour: model exploration and application to a fuel price increase.
abstract: Transportation policy measures often aim to change travel behaviour towards more efficient transport. While these policy measures do not necessarily target health, these could have an indirect health effect. We evaluate the health impact of a policy resulting in an increase of car fuel prices by 20% on active travel, outdoor air pollution and risk of road traffic injury. An integrated modelling chain is proposed to evaluate the health impact of this policy measure. An activity-based transport model estimated movements of people, providing whereabouts and travelled kilometres. An emission- and dispersion model provided air quality levels (elemental carbon) and a road safety model provided the number of fatal and non-fatal traffic victims. We used kilometres travelled while walking or cycling to estimate the time in active travel. Differences in health effects between the current and fuel price scenario were expressed in Disability Adjusted Life Years (DALY). A 20% fuel price increase leads to an overall gain of 1650 (1010-2330) DALY. Prevented deaths lead to a total of 1450 (890-2040) Years Life Gained (YLG), with better air quality accounting for 530 (180-880) YLG, fewer road traffic injuries for 750 (590-910) YLG and active travel for 170 (120-250) YLG. Concerning morbidity, mostly road safety led to 200 (120-290) fewer Years Lived with Disability (YLD), while air quality improvement only had a minor effect on cardiovascular hospital admissions. Air quality improvement and increased active travel mainly had an impact at older age, while traffic safety mainly affected younger and middle-aged people. This modelling approach illustrates the feasibility of a comprehensive health impact assessment of changes in travel behaviour. Our results suggest that more is needed than a policy rising car fuel prices by 20% to achieve substantial health gains. While the activity-based model gives an answer on what the effect of a proposed policy is, the focus on health may make policy integration more tangible. The model can therefore add to identifying win-win situations for both transport and health.
authors.affiliation: Department of Medical Sociology and Health Sciences, Vrije Universiteit Brussel, Laarbeeklaan 103, B-1090 Brussels, Belgium. stijn.dhondt@vub.ac.be
authors.forename: Stijn
authors.initials: S
authors.lastname: Dhondt
authors.forename: Bruno
authors.initials: B
authors.lastname: Kochan
authors.forename: Carolien
authors.initials: C
authors.lastname: Beckx
authors.forename: Wouter
authors.initials: W
authors.lastname: Lefebvre
authors.forename: Ali
authors.initials: A
authors.lastname: Pirdavani
authors.forename: Bart
authors.initials: B
authors.lastname: Degraeuwe
authors.forename: Tom
authors.initials: T
authors.lastname: Bellemans
authors.forename: Luc
authors.initials: L
authors.lastname: Int Panis
authors.forename: Cathy
authors.initials: C
authors.lastname: Macharis
authors.forename: Koen
authors.initials: K
authors.lastname: Putman
mesh_terms: D000293:Adolescent; D000328:Adult; D000397:Air Pollution / Q000517:prevention & control / Q000706:statistics & numerical data*; D058735:Environmental Policy; D005260:Female; D005742:Gasoline / Q000191:economics* / Q000706:statistics & numerical data; D062489:Health Impact Assessment; D006291:Health Policy; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D009017:Morbidity; D018570:Risk Assessment; D013660:Taxes; D014186:Transportation / Q000191:economics* / Q000706:statistics & numerical data; D014195:Travel / Q000191:economics* / Q000523:psychology / Q000706:statistics & numerical data; D016138:Walking / Q000706:statistics & numerical data; D055815:Young Adult
chemical_list: D005742:Gasoline