Modelling Health Impacts of Cycling Integrated Transport & Health Impact Modelling (ITHIM) tool approach 12/1/2016 Dr James Woodcock, University of Cambridge ITHIMs What Constitutes ITHIM? Physical Activity Modelling Physical Activity Characteristics of Physical Activity Model Variation in exposures Age & Gender Distributions or individual level data not means Combining different domains Non-linear relative risks Comparative risk assessment Baseline Vision 3Vision 2 Vision 1 Median: 9 min Median: 14 min Median: 19 min Median: 30 min Variation in Exposure Individual level exposures Synthetic individuals Individual level exposures National Travel SurveyHealth Survey for England Trips Non-travel physical activity Age, sex, socio-economic status, (geographic region, walking) Individual Data Representation of heterogeneous population Specification of scenarios based on characteristics of individuals and trips (e.g. age & trip distance) Investigation of how scenarios impact across multiple population subgroups Benefits depend on who is increasing their activity & what they would be doing otherwise Combining Different Activities Marginal Metabolically Equivalent Tasks (MMETs) Marginal METs (MMETs): METs above resting E.g. Ebikes 3.5, Walking 3.6, Cycling 5.4 ConditionStudies includedRelative Risk Exposure (Metabolic Equivalents)* Breast cancer19 cohort studies, 29 case control studies 0.94each additional h/wk Cardiovascular disease 18 cohort studies (459,833 people, 19,249 cases) hrs walking per week (7.5 METs/wk ) Colon cancer15 cohorts (7873 cases)Women: METs/wk Men: METs/wk DepressionCohort study (10,201 men, 387 first episodes physician-diagnosed depression) Kcal/wk 1< Diabetes10 cohort studies (301,211 people, 9367 cases) METs/wk Morbidity + Disease Specific Mortality Non-linear dose response curve (Type 2 Diabetes) GBD data allows estimation of mortality + morbidity World Health Organization data for every country Measure of health burden compared against age specific ideal life expectancy Deaths Disability Adjusted Life Year (DALY) Years of Life Lost (YLL) Years of Healthy Life Lost due to Disability (YLD) DALY = YLL + YLD Comparative Risk Assessment + Global Burden of Disease (GBD) Morbidity + Mortality Activity & Age Who: Older People are at Higher Risk Cycling: Benefits by Age Composition of Benefits Changes with Age Cycling minutes per day Air Pollution Modelling PM 2.5 Air pollution: two approaches Changes in background concentrations Changes in concentrations based on changes to emissions Mode specific exposure Different routes with different concentrations Different positions in the road Different ventilation rates Injury: Risk & Distance Model Modelling Changes in Injury Risk Bhalla et al 2007: Risk & distance model b p m c d h Bicyclebk bb k bp k bm k bc k bd k bb Pedestrianpk pb k pp.... Motorbikem k mb k mp k mm... Carck cb etc.... Busdk db..... HGV h k hb..... NOVnk b..... Striking Vehicle Injured party Road Traffic Injury Modelling Methods Additional Feature of the Model Non-linearity of risk / Safety in Numbers Age & gender variation in risk UK Risks by Age: Deaths per Billion Hours Mindell et al 2012: PloS One Exposure-Based, Like-for-Like Assessment of Road Safety by Travel Mode Using Routine Health Data Shift to Cycling: Central London Risks Shift to Cycling: Dutch Risks Uncertainty Key Differences to HEAT Physical Activity Model Morbidity + disease specific mortality Include all-age groups Include non-travel physical activity Non-linear dose response curve Distributions of physical activity or individual level data not averages Health not statistical value of a life Requires more data More skill required to run Conclusions Benefits depend on who is cycling Harms depend on who is cycling (& driving) Results are uncertain and (to an extent) this can be quantified ACKNOWLEDGEMENT Thanks for listening! This work was undertaken by the Centre for Diet and Activity Research (CEDAR), a UKCRC Public Health Research Centre of Excellence. Funding from Cancer Research UK, the British Heart Foundation, the Economic and Social Research Council, the Medical Research Council, the National Institute for Health Research, and the Wellcome Trust, under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged. Funding for this project for the NPCT from the Department for Transport is gratefully acknowledged. Changes in Background Concentrations Proportion of PM 2.5 due to local transport Apportioned to different motorised modes Change in distance by each mode Change in PM 2.5 emissions by mode Assume emissions equates to change in PM exposure Air pollution: route specific exposure Different routes/ different ventilation rates Ventilation factorConcentration Car/ Bus Bike Foot Publications Gotschi T, Tainio M, Maizlish N, Schwaneen T, Goodman A, Woodcock J. Contrasts in active transport behaviour across four countries: How do they translate into public health benefits? Preventive Medicine, 2015 Woodcock J, Goodman A. Transport and health in London: The main impacts of London road transport on health Greater London Authority February Woodcock J, Tainio M, et al. Health effects of the London bicycle sharing system: health impact modelling study. BMJ 2014 Maizlish N, Woodcock J, Co S, Ostro B, Fanai A, Fairley D. Health Cobenefits and Transportation-Related Reductions in Greenhouse Gas Emissions in the San Francisco Bay Area. AmJPH Woodcock J, Givoni M, Morgan A. Woodcock J, Givoni M, Morgan AS. Health Impact Modelling of Active Travel Visions for England and Wales Using ITHIM. PLoS One Jarrett J, Woodcock J, et al. Effect of increasing active travel in urban England and Wales on costs to the National Health Service The Lancet, 2012 Woodcock J, Edwards P, Tonne C, et al. Impact on Public Health of Strategies to Reduce Greenhouse Gas Emission: Urban Land Transport. Lancet, 2009