Mueller HIAreview PM 2015 supp

Appendices Health impact assessment of active transportation: a systematic review

Natalie Mueller MSc, David Rojas-Rueda PhD, Tom Cole-Hunter PhD, Audrey de Nazelle PhD, Evi Dons PhD, Regine Gerike Prof,

Thomas Götschi PhD, Luc Int Panis PhD, Sonja Kahlmeier PhD, Mark J Nieuwenhuijsen Prof

Appendix A - METHODS

A.1. Systematic search strategy and study selection

(a) MEDLINE search strategy

(((((((((((((((((((((((((Active mobility OR active commute OR commut* OR active transport* OR active travel* OR non motor* OR travel OR transportation OR bik* OR bicycl* OR cycling OR cyclist OR walk* OR pedestrian OR car OR automobile OR motor vehicles OR "Bicycling"[Mesh] OR "Walking"[Mesh] OR "Walkers"[Mesh] OR "Automobiles"[Mesh] OR "Automobile Driving"[Mesh] OR "Motor Vehicles"[Mesh] OR "Transportation"[Mesh] OR "Travel"[Mesh] OR "Motorcycles"[Mesh])) AND (Risk Assessment[Mesh] OR Health Impact Assessment[Mesh] OR "Costs and Cost Analysis"[Mesh] OR "Cost-Benefit Analysis"[Mesh] OR "Urban Health"[Mesh] OR "Environmental Health"[Mesh] OR "Gases/analysis"[Mesh] OR "Vehicle Emissions/analysis"[Mesh] OR risk assessment OR health impact assessment OR comparative risk assessment OR quantitative risk assessment OR health risk assessment OR benefit-risk assessment OR risk-benefit assessment))) AND ("Motor Activity"[Mesh] OR "Exercise"[Majr] OR physical activity OR "Transportation/injuries"[Mesh] OR traffic injuries OR road traffic accidents OR traffic accidents OR "Transportation/adverse effects"[Mesh] OR "Accidents, Traffic"[Majr] OR traffic crashes OR "Wounds and Injuries/prevention and control"[Mesh] OR air pollution OR "Air Pollution"[Mesh] OR "Particulate Matter/adverse effects"[Mesh] OR "Air Pollutants"[Majr] OR pollution OR noise OR "Noise, Transportation"[Mesh] OR social network OR "Social Environment"[Majr] OR "Community Networks"[Mesh] OR "Social Support"[Mesh] OR crime OR "Crime/ prevention and control"[Mesh] OR "Diet"[Majr] OR diet OR heat OR "Extreme Heat"[Mesh] OR "Ultraviolet Rays"[Mesh] OR ultraviolet rays OR "Socioeconomic Factors"[Mesh] OR health inequalities))) NOT (randomized OR trial OR "Clinical Trial"[Publication Type] OR "Randomized Controlled Trial"[Publication Type] OR "Case-Control Studies"[Majr] OR case-control OR cohort OR "Cohort Studies"[Majr] OR cluster OR "Cluster Analysis"[Mesh] OR case-crossover OR "Cross-Over Studies"[Majr] OR nested OR cells OR "Cells"[Mesh] OR plants OR "Plants"[Mesh] OR bacteria OR "Bacteria"[Mesh] OR animals OR aircrafts OR airplanes OR "Aircraft"[Mesh] OR airports OR water OR "Water"[Mesh] OR deserts OR violence OR "Violence"[Mesh] OR "Tropical Medicine"[Mesh] OR "Genetics"[Mesh] OR genetics OR biotechnology OR biology OR "Biology"[Mesh] OR microorganisms OR "Microbiology"[Mesh] OR biotechnology OR "Biotechnology"[Majr] OR DNA OR "DNA"[Mesh] OR enzymes OR "Enzymes"[Mesh] OR surgical OR "Surgical Procedures, Operative"[Mesh] OR clinical OR "Pathology, Clinical"[Mesh] OR diagnosis OR "Diagnosis"[Mesh] OR "Vaccination"[Mesh] OR vaccination))))))))))))))))))))

(b) Other data bases

Search strategies for Web of Science and Transportation Research International Documentation (TRID) and internet searches were simplified.

(c) Study selection by source

MEDLINE Web of Science Transport Research International Documentation (TRID)

Experts Internet Reference

Cobiac et al. 2008 Creutzig et al. 2012 Mulley et al. 2013 Macmillan et al. 2014 Guo & Guandavarpu 2012 Boarnet et al. 2008

Mooy & Gunning-Schepers 2001 Rabl & de Nazelle 2012 Stipdong & Reurings 2012 Deenihan & Caulfield 2014 Schepers & Heinen 2012

Rojas- Rueda et al. 2011 Saelensminde et al. 2008 James et al. 2014

Rojas- Rueda et al. 2012 Edwards & Mason 2014 Xia et al. 2015

Rojas-Rueda et al. 2013

De Hartog et al. 2010

Dhondt et al. 2013

Gotschi et al. 2012

Grabow et al. 2012

Holm et al. 2012

Jarrett et al. 2012

Lindsay et al. 2011

Maizlish et al. 2013

Olabarria et al. 2012

Woodcock et al. 2009

17 4 2 4 2 1

(d) Study exclusion with exclusion criteria (Figure 1, manuscript)

Source Included Review Methodological study Epidemiological study No HIA Qualitative HIA No peer review Not accessible Total

Experts 4 5 3

Internet 2 4 1 2 30 38

References 1 2 1 4

Web of Science 4 4 1 1 7 16

MEDLINE 17 11 5 2 10 5 51

TRID 2 2 1 2 1 3 11

30 21 10 5 20 5 36 3 129

TRID=Transport Research International Documentation

A.2. Data extraction tool

Author HIA approach Policy focus Setting Mode shift Scenario Health endpoint

Comparative risk assessment

Transportation CO2

reduction London (UK)/ Delhi (India) Age 15 years

Motorized transportation low emission vehicles, walking, bike, walking, public transportation years

S1: BAU 2013 S2: Low carbon driving S3: Active transportation S4: Short distance active transportation

de Hartog et al. 2010

Comparative risk assessment

Cycling stimulation Netherlands 500,000 people Age 18-64 years

Car bike S1: Bike 7.5km daily S2: Bike 15km daily

Life expectancy

Lindsay et al. 2011

Cost-benefit analysis Cycling stimulation New Zealand Age 18-64 years Stratified by ethnicity (New Zealanders, Maoris, Pacifics)

Car bike Shift 5%(1%, 10%, 30%) of car trip distances ≤7km to bike

Health costs ($NZ) (mortality; morbidity); mortality; adipose tissue

… … … … …

(Data extraction tool continued)

Physical activity

Author Method/ tool

Parameter Dose-response function

Relative Risk Health outcome Risk estimate source

Woodcock et al. 2009 Literature review for Relative Risk (RR)

MET; time Linear with threshold

RR 0.84/ 7.5 METs/ week RR 0.83/10 METs/ week RR 0.94/ additional hour/ week RR 0.80/ 30.1 METs/ week; RR 0.86/ 30.9 MET/ week RR 0.83/ 24.2 METs/ week RR 0.72/ 33 METs/ week

CVD Diabetes type 2 Breast cancer (female) Colon cancer (male); (female) Depression (male) Dementia

Hamer & Chida, 2008 Jeon et al. 2007 Monninkhof et al. 2007 Harriss et al. 2009 Pfaffenbarger et al. 1994 Hamer & Chida, 2009

de Hartog et al. 2010 Literature review for RR

Kcal; PA recommendations; PA categories; kJ; time; MET

Linear RR 0.70-0.80/ 1,000 kcal/week RR 0.70/ 1000 kcal/week RR 0.70/ meeting PA recommendations RR 0.70-0.87/ moderate PA; RR 0.46-0.92/ vigorous PA RR 0.65-0.80/ meeting PA recommendations RR 0.50-0.77/ 4,100-7,908 kJ/ week RR 0.55-0.73/ 3 hours cycling/ 36 week/ year RR 0.71-0.79/ walking and cycling to work RR 0.66-0.79/ cycling to work (MET-hours/day)

All-cause mortality All-cause mortality All-cause mortality All-cause mortality All-cause mortality All-cause mortality All-cause mortality All-cause mortality All-cause mortality

Lee and Skerrett, 2001 Kesaniemi et al. 2001 Bauman 2004 Bucksch & Schlicht, 2006 Warburton et al. 2006 Vogel et al. 2009 Andersen et al. 2000 Hu et al. 2004 Matthews et al. 2007

Lindsay et al. 2011 HEAT tool Time Linear RR 0.72/ 3h cycling/ week/ 36 weeks/ year/ at 14km/h All-cause mortality Andersen et al. 2000

… … … ... ... ...

Air pollution

Author Population Method/ tool

Parameter Unit Dose-response function

Relative Risk Health outcome Risk estimate source

Woodcock et al. 2009 General population

Literature review for risk estimate London: Emission dispersion model Delhi: Sim-air

µg/m3 London: Linear

Delhi: Log-linear 0.00893 coefficient London: RR=exp(b(x1-x2)) Delhi: RR=[(x1+1)/(x2+1)]b linear

Cardio-respiratory disease ….

WHO, 2004

de Hartog et al. 2010 Active traveler

Literature review for RR CAR dispersion model

PM2.5; Black smoke

µg/m3 Linear RR 1.06/ 10µg/m3 PM2.5 (average) RR 1.05/ 10 µg/m3 BS

All-cause mortality All-cause mortality

Pope et al. 2002 Beelen et al. 2008

Lindsay et al. 2011 General population

Literature review for RR VEPM version 2.3

PM2.5; PM10; CO; NO2; benzene

µg/m3 Linear RR 1.043/ 10 µg/m3 PM10 RR 1.0000058/ 1 µg/m3 CO1hr average max RR 1.00013/ 1 µg/m3 NO2 annual average RR 1.00018/ 1 µg/m3 NO2 annual average RR 1.079 *10-8/ 1 µg/m3 CO annual average RR 1.00001/ 1 µg/m3 CO 1-hr average max RR 1.01/ 10 µg/m3 increase annual PM10

RR 1.0015/ 1 µg/m3 24-hrs average NO2 RR 1.013/ 10 µg/m3 increase annual PM10

RR 1.003/ 1 µg/m3 24-hrs average NO2 RR 1.214/ 10 µg/m3 increase annual PM10 RR 6*10-6/ 1 µg/m3 Benzene average lifetime RR 9.1 cases/ 100 persons/ 1 µg/m3 annual PM2.5

(All-cause) mortality (PM10) Non external-cause mortality (CO) Non external-cause mortality (NO2) Circulatory + respiratory mortality (NO2) Congestive heart failure mortality (CO) CVD admissions <64 years (CO) CVD admissions (PM10) CVD admission (NO2) Respiratory admissions (PM10) Respiratory admissions >65 years (NO2) COPD admission (PM10) Leukemia (cancer) (Benzene) Restricted-activities days (PM2.5)

Kunzli et al. 2000 Denison et al. 2000 Scoggins et al. 2004 Scoggins et al. 2004 DEFRA, 2005 Denison et al. 2001 Dockery& Pope, 1994 Codde et al. 2003 Dockery & Pope, 1994 Codde et al. 2003 Dockery & Pope, 1994 HAPiNZ Study, 2007 Fisher et al. 2004, based on Wilton, 2001

… ... … ... … ... ... ...

Traffic incidents

Author Method/ tool Parameter Dose-response function Risk estimate Risk estimate source

Woodcock et al. 2009 Based on local or national traffic incident statistics

Fatalities; Serious injuries; Slight injuries

Linear Number of fatalities or injuries by mode/ distance travelled by victim and striking mode in London or Delhi (specific estimates not available)

STATS19 (traffic crash database for the UK); police records for Delhi

de Hartog et al. 2010 Based on local or national traffic incident statistics

Fatalities Linear 8.2 bicycle deaths/billion bicycle km 1.9 car deaths/ billion car km

Statistics Netherlands (Central Bureau voor de Statistiek); data for 2008

Lindsay et al. 2011 Based on local or national traffic incident statistics

Fatalities; Hospitalized injuries

Non-linear; Safety-in-numbers effect of 34% reduction in fatal injuries for doubling cycling usage

Fatalities or hospitalized injuries/ km cycled (specific estimates not available)

National Injury Query System (2008)

Rojas-Rueda et al. 2011 … … … … …

Noise Author Method/ tool Parameter Dose-response function Risk estimate Risk estimate source

Woodcock et al. 2009 / / / / /

de Hartog et al. 2010 / / / / /

Lindsay et al. 2011 / / / / /

Rabl & de Nazelle (2012) Cost- function Noise costs (including health costs) Linear Day time: Urban 0.76 €ct/ car km; Suburban 0.12 €ct/ car km; Rural 0.01 €ct/ car km

INFRAS/ IWW 2003; INFRAS/ IWW 2004 (from CE Delft 2008)

Environmental pathways Author Parameter Unit

Woodcock et al. 2009 CO2 emission Tones

de Hartog et al. 2010 / /

Lindsay et al. 2011 Fuel costs CO2eq emission Vehicle emissions (CO; NOx, PM10, VOC, CH4, N2O)

$NZ Tones Tones

Appendix B - RESULTS

B.1. Health pathways

(a) Table 1. Physical activity risk estimates and associated health outcomes

Author (year) Dose-response function

Risk estimate Health outcome Data source Source type

Mooy & Gunning-Schepers (2001)

Categorical RR 1.9–1.6 ‘not active’; RR 1.6-1.3 ‘moderately active’ CHD CHD

Berlin et al. 1990 Powell et al. 1989

Meta-analysis Review

Sælensminde (2004) Categorical NA

2 NOK/ km 7300 NOK/ inactive person/ year (specific savings NA)

Feeling of insecurity Feeling of insecurity Medical, treatment and productivity loss costs of cancer (5 types), high blood pressure, diabetes type 2, musculoskeletal ailments

Stangeby, 1997 Elvik, 1998 Norwegian Council on Nutrition and Physical Activity, 2000

Cohort study Report (available in Norwegian) Report (available in Norwegian)

Boarnet et al. (2008) Categorical RR 0.63/ 48 vs. 16 min of walking

RR 0.63/ 48 vs. 16 min of walking RR 0.71/ 48 vs. 16 min of walking

CHD mortality Sudden death All-cause mortality

Leon et al. 1987 Leon et al. 1987 Leon et al. 1987

Intervention trial study Intervention trial study Intervention trial study

Cobiac et al. (2009) Categorical RR 1.31-1.71 inactive, RR 1.20-1.44 insufficient active

RR 1.24-1.53 inactive, RR 1.05-1.10 insufficient active RR 1.20-1.45 inactive, RR 1.11-1.24 insufficient active RR 1.16-1.34 inactive, RR 1.06-1.13 insufficient active RR 1.30 -1.68 inactive, RR 1.09-1.18 insufficient active

IHD Ischemic stroke Type-2 diabetes Breast cancer Colon cancer

WHO, 2004 WHO, 2004 WHO, 2004 WHO, 2004 WHO, 2004

Meta-analysis Meta-analysis Meta-analysis Meta-analysis Meta-analysis

Woodcock et al. (2009) Linear with

maximum threshold RR 0.84/ 7.5 METs/ week RR 0.83/10 METs/ week RR 0.94/ additional hour/ week RR 0.80/ 30.1 METs/ week; RR 0.86/ 30.9 MET/ week RR 0.83/ 24.2 METs/ week RR 0.72/ 33 METs/ week

CVD Diabetes type 2 Breast cancer (female) Colon cancer (male); (female) Depression (male) Dementia

Hamer & Chida, 2008 Jeon et al. 2007 Monninkhof et al. 2007 Harriss et al. 2009 Paffenbarger et al. 1994 Hamer & Chida, 2009

Meta-analysis Systematic review Systematic review Meta-analysis Cohort study Systematic review

Guo & Gandavarapu (2010) Linear 150 lb/ 1.7 min Weight gain Calculated / de Hartog et al. (2010) Linear RR 0.70-0.80/ 1,000 kcal/week

RR 0.70/ 1000 kcal/week RR 0.70/ meeting PA recommendations RR 0.70-0.87/ moderate PA; RR 0.46-0.92/ vigorous PA RR 0.65-0.80/ meeting PA recommendations RR 0.50-0.77/ 4,100-7,908 kJ/ week RR 0.55-0.73/ 3 hours cycling/ 36 week/ year RR 0.71-0.79/ walking and cycling to work RR 0.66-0.79/ cycling to work (MET-hours/day)

All-cause mortality All-cause mortality All-cause mortality All-cause mortality All-cause mortality All-cause mortality All-cause mortality All-cause mortality All-cause mortality

Lee and Skerrett, 2001 Kesaniemi et al. 2001 Bauman 2004 Bucksch & Schlicht, 2006 Warburton et al. 2006 Vogel et al. 2009 Andersen et al. 2000 Hu et al. 2004 Matthews et al. 2007

Review Expert review (qualitative) Review (qualitative) Review (qualitative) Review Review Cohort study Cohort study Cohort study

Gotschi (2011) Log-linear (HEAT

cycling with RR 0.72/ 3 hours cycling/ week/ 36 weeks/ year/ at 14km/h All-cause mortality Andersen et al. 2000 Cohort study

maximum threshold RR 0.5)

Lindsay et al. (2011) Log-linear (HEAT

cycling with maximum-threshold RR 0.5)

RR 0.72/ 3 hours cycling/ week/ 36 weeks/ year/ at 14km/h All-cause mortality Andersen et al. 2000 Cohort study

Rojas-Rueda et al. (2011) Linear RR 0.72/ 3 hours cycling/ week/ 36 weeks/ year/ at 14km/h All-cause mortality Andersen et al. 2000 Cohort study Rabl & de Nazelle (2012) Curvilinear;

non-transportation PA

RR 0.709/ 3.3 hours cycling/ week RR 0.73/ 3.38 hours walking/ week

US DHHS, 2008 US DHHS, 2008

Report Report

Grabow et al. (2012) Log-linear (HEAT

cycling with maximum threshold RR 0.5)

Olabarria et al. (2012) Log-linear (HEAT

walking with maximum threshold RR 0.5)

RR 0.78/ 29 min walking/ day/ at 4.8km/h All-cause mortality WHO, 2011 Meta-analysis

Jarrett et al. (2012) Linear with

maximum threshold RR 0.84/ 7.5 METs/ week RR 0.84/ 7.5 METs/ week RR 0.83/ 10 METs/ week RR 0.94/ additional hour/ week RR 0.80/ 30.1 METs/ week RR 0.83/ 24.2 METs/ week RR 0.72/ 33 METs/ week

CBVD IHD Diabetes type 2 Breast cancer (female) Colon cancer (male) Depression (male) Dementia

Hamer & Chida, 2008 Hamer & Chida, 2008 Jeon et al. 2007 Monninkhof et al. 2007 Harriss et al. 2009 Paffenbarger et al. 1994 Hamer & Chida, 2009

Meta-analysis Meta-analysis Systematic review Systematic review Meta-analysis Cohort study Systematic review

Holm et al. (2012) Categorical

RR 1.71 inactive, RR 1.44 moderately active RR 1.53 inactive, RR 1.10 moderately active RR 1.45 inactive, RR 1.24 moderately active RR 1.25 inactive, RR 1.13 moderately active RR1.68 inactive, RR 1.18 moderately active

IHD Ischemic stroke Diabetes type 2 Breast cancer Colon cancer

WHO, 2004 WHO, 2004 WHO, 2004 WHO, 2004 WHO, 2004

Meta-analysis Meta-analysis Meta-analysis Meta-analysis Meta-analysis

Rojas-Rueda et al. (2012) Linear RR 0.78/ 29 min walking/ day/ at 4.8km/h

RR 0.72/ 3 hours cycling/ week/ 36 weeks/ year/ at 14km/h All-cause mortality All-cause mortality

WHO, 2011 Andersen et al. 2000

Meta-analysis Cohort study

Creutzig et al. (2012) Log-linear (HEAT

walking/ cycling with maximum threshold RR 0.5)

RR 0.78/ 29 min walking/ day/ at 4.8km/h RR 0.72/ 3 hours cycling/ week/ 36 weeks/ year/ at 14km/h

WHO 2011 Andersen et al. 2000

Meta-analysis Cohort study

Dhondt et al. (2013) Curvilinear; non-

transportation PA RR 0.81/ 11 MET hours All-cause mortality Woodcock et al. 2011 Meta-analysis

Woodcock et al. (2013) Curvilinear; non-transportation PA

RR 0.81/ 11 MET hours / week RR 0.84/ 8 MET hours/ week RR 0.89/ 11 MET hours/ week RR 0.94/ 5 MET hours/ week RR 0.80/ 31 MET hours/week; RR 0.86/ 30 MET hours / week RR 0.83/ ? (Age >29), RR 0.93/ ? (15-29) RR 0.72/ 32 MET hours/ week

All-cause mortality CVD Diabetes type 2 Breast cancer (female) Colon cancer (male); (female) Depression (male) Dementia

Woodcock et al. 2011 Hamer & Chida, 2008 Jeon et al. 2007 Monninkhof et al. 2007 Wolin et al. 2009 Paffenbarger et al. 1994 Hamer & Chida, 2009

Meta-analysis Meta-analysis Systematic review Systematic review Systematic review Cohort study Systematic review

Maizlish et al. (2013) Log-linear with

square-root function for higher PA levels; non-transportation PA

RR 0.84/ 7.5 METs/ week RR 0.83/ 10 METs/ week RR 0.80/ 30.1 METs/ week; RR 0.86/ 30.9 MET/ week RR 0.94/ 5 MET hours/ week RR 0.72/ 33 METs/ week

CVD Diabetes type 2 Colon cancer (male); (female) Breast cancer (female) Dementia

Hamer & Chida, 2008 Jeon et al. 2007 Harriss et al. 2009 Monninkhof et al. 2007 Hamer &Chida, 2009

Meta-analysis Systematic review Meta-analysis Systematic review Systematic review

Rojas-Rueda (2013) Linear RR 0.84/ 7.5 METs/ week

RR 0.83/10 METs/ week RR 0.94/ 5 MET hours RR 0.80/ 30.1 METs/ week; RR 0.86/ 30.9 MET/ week RR 0.72/ 33 METs/ week

CVD Diabetes type 2 Breast cancer (female) Colon cancer (male); (female) Dementia

Hamer & Chida, 2008 Jeon et al. 2007 Monninkhof et al. 2007 Harriss et al. 2009 Hamer & Chida, 2009

Meta-analysis Systematic review Systematic review Meta-analysis Systematic review

Mulley et al. (2013) Linear $AUS 1.68/ km walked

$AUS 1.12/ km cycled Health benefit including mortality and morbidity changes

Genter et al. 2008 Report

Woodcock et al. (2014) Non-linear; non-

transportation PA RR 0.81/ 8.6 METs/ week RR 0.84/ 5.4 METs/ week RR 0.83/ 5.6 METs/ week RR 0.94/ 3.5 METs/ week RR 0.80/ 31.0 METs/ week; RR 0.86/ 30.0 METs/ week RR 0.96/ 0.8 METs/ week RR 0.72/ 24.5 METs/ week

All-cause mortality IHD & CBVD Diabetes type 2 Breast cancer (female) Colon cancer (male); (female) Depression (male) Dementia

recalculated from Woodcock et al. 2009

Deenihan & Caulfield (2014) Log-linear; (HEAT

cycling with maximum threshold RR 0.5)

Edwards & Mason (2014) Linear RR 0.72/ 3 hours cycling/ week/ 36 weeks/ year/ at 14km/h All-cause mortality Andersen et al. 2000 Cohort study Macmillan et al. (2014) Linear RR 0.72/ 3 hours cycling/ week/ 36 weeks/ year/ at 14km/h All-cause mortality Andersen et al. 2000 Cohort study James et al. (2014) Log-linear; (HEAT

walking with maximum threshold RR 0.5)

RR 0.78/ 29 min walking/ day/ at 4.8km/h All-cause mortality WHO, 2011 Meta-analysis

Xia et al. (2015) Categorical RR 1.68; sedentary, RR 1.18 insufficient

RR 1.29 sedentary; RR 1.19 insufficient RR 1,71 sedentary, RR 1.44 insufficient RR 1.53 sedentary, RR 1.10 insufficient

Colon cancer Breast cancer (females) IHD Stroke

Ezzati et al. 2004 Ezzati et al. 2004 Ezzati et al. 2004 Ezzati et al. 2004

Meta-analysis Meta-analysis Meta-analysis Meta-analysis

RR 1.45 sedentary, RR 1.24 insufficient RR 2.50 sedentary, RR 2.50 insufficient RR 1.30 sedentary, RR 1.30 insufficient

Type 2 diabetes Falls Depression

Ezzati et al. 2004 Begg et al. 2007 Begg et al. 2007

Meta-analysis Report Report

CBVD = cerebrovascular disease; CHD = coronary heart disease; CVD = cardiovascular disease; IHD = ischemic heart disease; HEAT = Health Economic Assessment Tool; kcal = kilocalories;

kJ = kilojoules; MET = Metabolic equivalents of task, express energy expenditure of physical activity with the reference value of 1 MET set as 1 kcal·kg−1

·h−1

expressing the energy expenditure while sitting at rest; NA = not available; NOK = Norwegian Kroner; PA = physical activity. Linear dose-response function assumes an equal increment in risk by increase in exposure. Curvilinear/ square-root dose-response function assumes non-linear increments in risk over the exposure range.

(b) Table 2. Traffic incident risk estimates and associated health outcomes

Risk estimate Health outcome Data source

Woodcock et al. (2009) Linear Number of victim mode fatalities or injuries per victim mode distance traveled x striking mode distance traveled in London or Delhi (specific estimates not available)

Fatalities and injuries; Serious injuries = hospital admission Slight injuries = minor injuries

STATS19 (traffic crash database for the UK); police records for Delhi

de Hartog et al. (2010) Linear 8.2 bicycle fatalities/billion bicycle km

1.9 car fatalities/ billion car km Fatalities Statistics Netherlands (Central Bureau voor de Statistiek); data for

2008 Gotschi (2011) Non-linear Absolute number of fatalities/ injuries Fatalities and injuries Local Portland statistics (2006) Lindsay et al. (2011) Non-linear

Number of fatalities or hospitalized injuries per km cycled accounting safety-in-numbers (specific estimates not available)

Fatalities and hospitalized injuries National Injury Query System (2008)

Rojas-Rueda et al. (2011)

Linear 4.54 bicycle deaths/ billion bicycle km 3.72 car deaths/ billion car km

Fatalities

Barcelona Public Health Agency ( Agencia de Salut Publica de Barcelona) (2008)

Rabl & de Nazelle (2012)

Linear 6.6E-05/ fatalities/year/ cyclist in Paris 2.5E-05/ fatalities/ year/ cyclist in Amsterdam

Fatalities

Paris (Préfecture de Paris, personal communication) Amsterdam (Kentucky Institute for the Environment and Sustainable Development)

Jarrett et al. (2012) Linear Number of victim mode fatalities or injuries per victim mode distance

traveled x striking mode distance traveled to estimate percentage change of absolute fatality/ injury numbers (specific estimates not available)

Injuries (head and spinal) STATS19 (traffic crash database for the UK) (2010)

Stipdonk & Reurings (2012)

Linear Fatalities or injuries by mode per mode mobility accounting for striking mode, age, and gender (specific estimates not available)

Fatalities and inpatient injuries with injury severity of MAIS≥2

National Travel Survey (2011)

Holm et al. (2012) Linear 0.18 fatal or serious injuries/ million bicycle km

0.01 fatal or serious injuries/ million car km Fatalities and injuries

Danish National Travel Survey (2002-2007); police-reported accident data from Statistics Denmark

Linear 4.54 bicycle deaths/ billion bicycle km 3.72 car deaths/ billion car km

Fatalities

Barcelona Public Health Agency (Agencia de Salut Publica de Barcelona) (2002–2010)

Creutzig et al. (2012) Linear Number of fatalities or injuries by mode per distance traveled (specific Fatalities and injuries Municipal transportation reports

Risk estimate Health outcome Data source

estimates not available) Dhondt et al. (2013) Linear Number of fatalities or hospitalized injuries per distance travelled by traffic

mode, conflict type, age and gender (specific estimates not available) Fatalities and hospitalized injuries Police registries (2007)

Woodcock et al. (2013) Non-linear

Victim mode fatalities or injuries per victim mode distance traveled x striking mode distance traveled accounting for speed, road type and safety-in-numbers (specific estimates not available)

Fatalities and serious injuries STATS19 (traffic crash database for the UK) (2002–2008)

Maizlish et al. (2013) Non-linear Number of victim mode fatalities or injuries per victim mode distance

traveled x striking mode distance traveled accounting for road type and safety- in-numbers (specific estimates not available)

Fatalities and serious injuries California Highway Patrol (2008) Safety Transport Research and Education Center (2011)

Linear 1469 minor injuries/ billion bike km/ year 339 minor injuries/ billion bus km/ year 783 minor injuries/ billion walking km/ year 2489 minor injuries/ billion car km/ year 51 major injuries/ billion bike km/ year 4 major injuries/ billion bus km/ year 69 major injuries/ billion walking km/ year 26 major injuries/ billion car km/ year

Minor and major injuries Barcelona Public Health Agency (Agencia de Salut Publica de Barcelona) (2002-2009) F

Schepers & Heinen (2013)

Non-linear Number of fatalities and serious injuries per volume of vehicles and bicycles accounting for age, population density, single-bicycle crashes and safety-in-numbers (specific estimates not available)

Fatalities and serious injuries injury (severity of MAIS≥2 c)

Police registries (2004-2009)

Woodcock et al. (2014) Linear 4.35 serious injuries/ million hours cycle hire cycling (m)

6.40 serious injuries/ million hours cycle hire cycling (f) 21.26 slight injuries/ million hours cycle hire cycling(m) 11.52 slight injuries/ million hours cycle hire cycling f)

Serious and slight injuries (observed injury rate), comparison to modeled background injury rates

Transportation for London (STATS19) (2010-2012)

Edwards & Mason (2014)

Linear 41.5 bicycle fatalities/ billion bicycle km (age 20-64 years) 4.2 car fatalities/ billion car km (age 20-64 years)

Fatalities National Highway Traffic Safety Administration (2009) for entire USA; National Household Travel Survey (NHTS)

Macmillan et al. (2014) Non-linear

Fatalities and serious injuries per 1,000 cyclists hit by vehicle accounting for road type, injury type (adjusted for underreporting), car speed and safety-in-numbers with a threshold at a mode-share of 0.025 (specific estimates not available)

Fatalities and serious injuries NZ Transportation Agency (2005)

James et al. (2014) Linear 0.61 fatalities/ 100 million vehicle-miles-travelled Fatalities National Highway Traffic Safety Administration (2009) for

Massachusetts Xia et al. (2015) Non-linear Number of victim mode fatalities or injuries per victim mode distance

traveled x striking mode distance traveled accounting for safety-in-numbers (specific estimates not available)

Fatalities Road Crashes in South Australia reported by the Department for Transportation, Energy and Infrastructure (2010) Transportation Use Survey (ABS, 2012)

MAIS = Maximal Abbreviated Injury Scale (trauma severity measurement). Linear dose-response function assumes a proportional increase in traffic incidents by increase in exposure. Non-linear dose-response function assumes non-linear increments in traffic incident risk over the exposure range.

(c) Table 3. Air pollution risk estimates and associated health outcomes

Author (year) Tool/ Method Risk estimate Health outcome Data source Type of source

Woodcock et al. (2009)

London: ERG Air Pollution Toolkita; ADMS4b; OSPMv5.0.64c Delhi: SIM-AIR version 1.3d

RR= exp ( (x1-x2)) (linear exposure); =0.00893 <40 µg/m3 PM2.5 (London) (

RR= ((x1+1)/(x2+1) (log-linear exposure); =0.15515 ≥40 µg/m3 PM2.5 (Delhi) RR= exp ( (x1-x2)) (linear exposure); =0.01267 <40 µg/m3 PM2.5 (London)

RR= ((x1+1)/(x2+1) (log-linear exposure); =0.23218 ≥40 µg/m3 PM2.5 (Delhi) RR= exp ( (x1-x2)) (linear exposure); =0.00332 ( = 0.00332 obtained by doubling = 0.0016 to apply to PM2.5) X1= current PM2.5 concentration (µg/m3) X2= 7.5 µg/m3 PM2.5 (theoretical minimum concentration set by Burnett in Cohen et al. in Ezzati et al. 2004)(WHO, 2004)

Cardiopulmonary mortality. (≥30 years) Lung cancer mortality (≥30 years) Acute respiratory infection mortality (children <5years) (London or Delhi)

Pope et al. 2002, in WHO, 2004 Pope et al. 2002, in WHO, 2004 WHO, 2004

Cohort study Cohort study Meta-analysis

de Hartog et al. (2010)

CAR dispersion modele RR 1.06/ 10µg/m3 PM2.5 (average) RR 1.05/ 10 µg/m3 BS

Pope et al. 2002 Beelen et al. 2008

Cohort study Case-cohort study

Lindsay et al. (2011) (HAPiNZ Study, 2007)

VEPMf version 2.3 RR 1.043/ 10 µg/m3 PM10 RR 1.0000058/ 1 µg/m3 CO1-hr average maximum RR 1.00013/ 1 µg/m3 NO2 annual average RR 1.00018/ 1 µg/m3 NO2 annual average RR 1.079 *10-8/ 1 µg/m3 CO annual average RR 1.00001/ 1 µg/m3 CO 1-hr average maximum RR 1.01/ 10 µg/m3 increase annual PM10

RR 1.0015/ 1 µg/m3 24-hrs average NO2 RR 1.013/ 10 µg/m3 increase annual PM10

RR 1.003/ 1 µg/m3 24-hrs average NO2 RR 1.214/ 10 µg/m3 increase annual PM10 RR 6*10-6/ 1 µg/m3 Benzene average lifetime RR 9.1 cases/ 100 persons/ 1 µg/m3 annual PM2.5

(All-cause) mortality (PM10) Non external-cause mortality (CO) Non external-cause mortality (NO2) Circulatory and respiratory mortality (NO2) Congestive heart failure mortality (CO) CVD admissions <64 years (CO) CVD admissions (PM10) CVD admission (NO2) Respiratory admissions (PM10) Respiratory admissions >65 years (NO2) COPD admission (PM10) Leukemia (cancer) (benzene) Restricted-activities days (PM2.5)

Kunzli et al. 2000 Denison et al. 2000 Scoggins et al. 2004 Scoggins et al. 2004 DEFRA, 2005 Denison et al. 2001 Dockery& Pope, 1994 Codde et al. 2003 Dockery & Pope, 1994 Codde et al. 2003 Dockery & Pope, 1994 HAPiNZ Study, 2007 Fisher et al. 2004, based on Wilton, 2001

Review NA Time-series analysis Time-series analysis UK Expert group report Technical report NA NA NA NA NA Report Technical report

BADMg RR 1.04/ 10 µg/m3 PM2.5 RR 1.05/ 10 µg/m3 BS

Krewski et al. 2009 Beleen et al. 2008

Cohort study Case-cohort study

Copert4h version 8.0 ExternEi (2005)

RR 1.05/ 10 µg/m3 PM2.5 All-cause mortality Pope et al. 2002 Cohort study

Grabow et al. (2012)

CMAQj version 4.6; BenMAPk version 4.0.35

RR 1.06/ 10 µg/m3 PM2.5 (average) RR 1.16/ 10 µg/m3 annual mean PM2.5 RR 1.04/ 11.8 µg/m3 PM2.5 in 1-day lag GAM stringent model

All-cause mortality All-cause mortality Asthma hospitalization

Pope et al. 2002 Laden et al. 2006 Sheppard 2003

Cohort study Cohort study Revised time-series analysis

RR 1.17/ 9.5 µg/m3 PM2.5 OR 1.03-1.08/ 30 µg/m3 in 12-h average PM2.5 OR 1.08/ 10 µg/m3 increase above mean daily PM10 of 27.3 µg/m3 RR 1.81/ 45 µg/m3 PM2.5 OR 1.5/ 20.7 versus 5.8 µg/m3 PM2.5 RR 1.043/ 36 µg/m3 PM2.5 RR 1.02/ 10 µg/m3 PM2.5 in 2-day lag model RR 1.0185/ 10 µg/m3 PM2.5 OR 1.33/ 15 µg/m3 PM2.5 RR 1.154/ 36 µg/m3 PM2.5 RR 1.046/ 36 µg/m3 PM2.5 RR 1.11/ 36 µg/m3 PM2.5 RR 1.053/ 36 µg/m3 PM2.5 RR 1.62/ 20 µg/m3 increase in 24-h average PM2.5 RR 1.014/ 10 µg/m3 PM2.5 in zero-lag model RR 1.058/ 10 µg/m3 PM2.5 RR 1.0158/ 1 µg/m3 PM2.5 RR 1.04/ 10 µg/m3 PM2.5 NA NA RR 1.003908/ 10 ppb increase in daily average O3

RR 1,998738/ 10 ppb increase in daily average O3 RR 1.0125/ 10 ppb O3 RR 1.043/ 10 µg/m3 O3 RR 1.035/ 20 ppb change in daily 24-h average O3 RR 1.0037/ 10 ppb in daily 1-h maximum O3 RR 1.33/ 45.2 ppb increase in 5-day moving average of 1-h max O3 RR 1.20/ 50 µg/m3 increase in average daily RR 1.07/ 50 µg/m3 increase in average daily O3 RR 1.042/15 ppb change in daily average O3 RR 1.057/ 15 ppb change in daily average O3 RR 1.22/ 50 ppb increase in daily average O3 RR 1.028/ 5 ppb increase in 24-h O3 RR 1.022/ 25 ppb increase of maximum daily 8-h average O3 RR 1.02/ 10 µg/m3 O3 RR 1.05/ 0.01 ppb increase in 8-h mean daily O3 RR 1.1301/ 50 ppb O3 RR 1.629/ 20 ppb short-term change O3 NA RR 1.00185/ 1 µg/m3 O3

Asthma ER visit Asthma (symptoms) Cough among asthma diagnosed in children Chronic Bronchitis Acute bronchitis Chronic lung disease hospitalization COPD hospitalization COPD hospitalization Lower respiratory symptoms Pneumonia hospitalization Dysrythmia hospitalization Congestive heart failure hospitalization IHD hospitalization Acute MI CVD hospitalization CVD hospitalization Respiratory restricted activity days Work-loss days Respiratory disease Work-loss day Non-accidental mortality All-cause mortality Cardiopulmonary mortality All-cause mortality Non-accidental mortality Non-accidental mortality All respiratory hospitalizations All respiratory hospitalizations (Tacoma) All respiratory hospitalizations (New Haven) Chronic lung disease hospitalizations Pneumonia hospitalizations Pneumonia hospitalization COPD other than asthma Asthma ER visits Asthma ER visits Asthma ER visits School absenteeism Illness-related absenteeism Worker productivity Minor-restricted activity days

Norris 1999 Ostro 2001 Vedal et al. 1998 Abbey et al. 1995 Dockery et al. 1996 Ito 2003 Moolgavkar 2000a Moolgavkar 2003 Schwartz & Neas 2000 Ito 2003 Ito 2003 Ito 2003 Ito 2003 Peters et al. 2001 Moolgavkar 2000b Moolgavkar 2003 Ostro & Rothschild, 1989 Ostro, 1987 Pope et al. 1991 Adams et al. 1999 Bell, 2004 Bell, 2005 Huang et al. 2005 Levy 2005 Ito 2005 Schwartz, 2005 Burnett et al. 2001 Schwartz 1995 Schwartz 1995 Moolgavkar et al.1997 Moolgavkar et al. 1997 Schwartz 1994a Schwartz 1994b Peel et al. 2005 Wilson, Wake & Kelly 2005 Jaffe et al. 2003 Chen et al. 2000 Gilliland et al. 2001 Crocker et al. 1981 Ostro & Rothschild 1989

Time-series analysis Cohort study Cohort study Cohort study Cohort study Revised time series analysis Time-series analysis Revised time-series analysis Comparison of cohort studies Revised time series analysis Revised time series analysis Revised time series analysis Revised time series analysis Case-crossover study Time-series analysis Reanalysis of time-series study Cross-sectional study Cross-sectional study NA NA Time-series analysis Meta-analysis Time-series study Meta-analysis Meta-analysis Case-crossover study Time-series analysis Time-series analysis Time-series analysis Time-series analysis Time-series analysis Time-series analysis Time-series analysis Time-series analysis Time-series analysis Time-series analysis Cohort study Cohort study Case-study Time-series analysis

Holm et al. (2012) Danish Air Quality

Monitoring Program RR 1.059/ 10 µg/m3 PM2.5 RR 1.082/ 10 µg/m3 PM2.5

Cardiopulmonary disease mortality Lung cancer mortality

Pope et al. 2002, in WHO, 2004 Pope et al. 2002,in WHO, 2004

Cohort study Cohort study

Rojas-Rueda et al. BADMg RR 1.04/ 10µg/m3 PM2.5 All-cause mortality Krewski et al. 2009 Cohort study

(2012) RR 1.05/ 10 µg/m3 BS All-cause mortality Beleen et al. 2008 Case-cohort study Creutzig et al. (2012)

Municipal transportation reports

2.0 €ct/vehicle km/ petrol car >1.4L (EURO-classl0) 1.1 €ct/ vehicle km/ diesel car <1.4L (EURO-class 2) 9.1 €ct/ vehicle km/ truck <7.5t ( EURO-class 0)

Air pollution costs/ petrol car km Air pollution costs/ diesel car km Air pollution costs/ truck km

Maibach et al. 2008 Maibach et al. 2008 Maibach et al. 2008

Report Report Report

Dhondt et al. (2013) MIMOSA4m; IFDMn;

AURORAo; APHEIS methodp

= 0.0582/ 1 µg/m3 EC = 0.0129/ 1 µg/m3 EC = 0.179/ 1 µg/m3 EC

All-cause mortality CVD hospital admission CVD hospital admission (>64 years)

Janssen et al. 2011 Janssen et al. 2011, based on Tolbert et al. 2011; Peng et al. 2009 (>64 years)

Meta-analysis Time-series studies (Tolbert et al. 2011 and Peng et al. 2009)

UK National Air pollution Emissions Inventory

RR= exp ( (x1-x2)) (linear exposure); =0.00893 <40 µg/m3 PM2.5 RR= exp ( (x1-x2)) (linear exposure); =0.01267 <40 µg/m3 PM2.5 RR= exp ( (x1-x2)) (linear exposure); =0.00332 ( = 0.00332 obtained by doubling = 0.0016 to apply to PM2.5) X1= current PM2.5 concentration (µg/m3) X2= 7.5 µg/m3 PM2.5 (theoretical min. concentration set by Burnett in Cohen et al. in Ezzati et al. 2004)

Cardio-respiratory mortality (≥30 years) Lung cancer mortality (≥30 years) Acute respiratory infection mortality (children <5 years)

Maizlish et al. (2013)

EMFAC2007q; BAAQMD air shed modelr

RR 1.14/ 10 µg/m3 PM2.5 (average) RR 1.07-1.12/ 10 µg/m3 PM10 (4-12 months of age); RR 1.12-1.16/ 10 µg/m3 PM10 (7-12 months of age) RR 1.089/ 10µg/m3 PM2.5

Lung cancer mortality Respiratory infant mortality Cardio-pulmonary mortality (1979-1983)

Pope et al. 2002 Ritz et al. 2006 Krewski et al. 2009

Cohort study Case-control study Cohort study

BADMg RR 1.0081/ 10 µg/m3 PM2.5

RR 1.0092/ 10 µg/m3 PM2.5

RR 1.15/ 10 µg/m3 PM2.5

RR 1.1/ 10 µg/m3 PM2.5

RR 1.025/ 10 µg/m3 PM2.5

Cerebrovascular disease Lower respiratory tract infection Preterm birth Low birth weight CVD

Dominici et al. 2006 Dominici et al. 2006 Sapkota et al. 2010 Dadvand et al.2013 Mustafic 2012

Time-series analysis Time-series analysis Meta-analysis Meta-analysis Meta-analysis

Routinos; London Atmospheric Emissions Inventory 2008 Concentration Maps

RR= exp ( (x1-x2)) (linear exposure); =0.00893 <40 µg/m3 PM2.5

RR= exp ( (x1-x2)) (linear exposure); =0.01267 <40 µg/m3 PM2.5 RR= exp ( (x1-x2)) (linear exposure); =0.00332 ( = 0.00332 obtained by doubling = 0.0016 to apply to PM2.5) X1= current PM2.5 concentration (µg/m3) X2= 14.91 µg/m3 PM2.5 (min. PM2.5 concentration inner London 2008 average)

Cardiopulmonary mortality (>30 years) Lung cancer mortality (>30 years) Respiratory mortality, children (<5 years)

Macmillan et al. (2014)

VEPMf version 5.0; Longitudinal emission measurements

RR 1.043/ 10 µg/m3 PM10 RR 1.0000058/ 1 µg/m3 CO 1-hr average maximum RR 1.079 *10-8/ 1 µg/m3 CO annual average

(All-cause) mortality (PM10) Non external-cause mortality (CO) Congestive heart failure mortality (CO)

Kunzli et al. 2000 Denison et al. 2003 DEFRA, 2005

Review NA UK Expert group report

RR 1.00001/ 1 µg/m3 CO 1-hr average maximum RR 1.01/ 10 µg/m3 increase annual PM10

RR 1.013/ 10 µg/m3 increase annual PM10

RR 1.214/ 10 µg/m3 increase annual PM10 RR 6*10-6/ 1 µg/m3 Benzene average lifetime RR 9.1 cases/ 100 persons/ 1 µg/m3 annual PM2.5

CVD admissions <64 years (CO) CVD admissions (PM10) Respiratory admissions (PM10) COPD admission (PM10) Leukemia (cancer) (Benzene) Restricted-activities days (PM2.5)

Denison et al. 2001 Dockery& Pope, 1994 Dockery & Pope, 1994 Dockery & Pope, 1994 HAPiNZ Study, 2007 Fisher et al. 2004, based on Wilton, 2001

Technical report NA NA NA Report Technical report

James et al. (2014) MOBILE 6.2t

BenMapk RR 1.10/ 10 µg/m3 PM2.5 NA RR 1.04 11.8 μg/m3 PM2.5 (including SO2) GAM-default model RR 1.62/ 20 µg/m3 increase in 24-h average PM2.5 RR 1.058/ 10 µg/m3 PM2.5 RR 1.62/ 20 µg/m3 increase in 24-h average PM2.5 RR 1.02/ 10 µg/m3 PM2.5 in 2-day lag model

Mortality Mortality Asthma hospitalization CVD hospitalization (acute MI) CVD hospitalization Acute MI hospitalization COPD hospitalization

Schwartz et al. 2008 Roman et al. 2008 Health Effect Institute, 2003 Peters et al. 2001 Moolgavkar et al. 2000b Peters et al. 2001 Moolgavkar 2000a

Time-series analysis Expert opinion Reanalysis of time-series study Case-crossover study Time-series analysis Case-crossover study Time-series analysis

Xia et al. (2015) TAPMu RR 1.004; 1.005/ 1 µg/m3 PM2.5

RR 1.006/ 1 µg/m3 PM2.5

RR 1.0013/ 1 µg/m3 PM2.5

RR 1.003/ 1 µg/m3 PM2.5

RR 1.003-1.004/1 µg/m3 PM2.5

RR 1.0013/ 1 µg/m3 PM2.5

CVD mortality >75 years; <75 years Respiratory disease mortality; (65 years) Lung cancer mortality CVD morbidity Respiratory disease morbidity Lung cancer morbidity

EPHC, 2010 EPHC, 2010 Pope et al. 2002 EPHC, 2010 EPHC, 2010 Pope et al. 2002

Report Report Cohort study Report Report Cohort study

BS =black smoke; CO = carbon monoxide; CVD = cardiovascular disease; COPD = chronic obstructive pulmonary disease; EC =elemental carbon; ER = emergency room visit; IHD: ischemic heart disease; MI: myocardial infarction; NA =not available; NO2 = nitrogen dioxide;

O3 = ozone; PM2.5 = particulate matter less than 2.5 μm; PM10 = particulate matter less than 10 μm;

ppb = parts per billion; SO2 = sulfur dioxide a

ERG Air Pollution Toolkit (dispersion model). b

ADMS: Atmospheric Dispersion Modeling System. c

OSPM: Parameterized Street Pollution Model. d

SIM-AIR: Simple interactive models for better air quality. e

CAR: Calculation of Air pollution from Road traffic (dispersion model). f

VEPM: Auckland’s Vehicle Emission Prediction Model. g BADM: Barcelona Air Dispersion Model.

h Copert4: Computer Program to calculate Emissions from Road Transport.

i ExternE: External Costs of Energy.

j CMAQ: Community Multiscale Air Quality Model.

k BenMAP: Environmental Benefits Mapping and Analysis Program.

l Euro-Class = European Union exhausts emission standards.

m MIMOSA4: Emission model.

n IFDM: Imission Frequency Distribution Model (dispersion model).

o AURORA: Air quality modeling in Urban Regions using an Optimal Resolution Approach (Eulerian dispersion model).

p APHEIS method: Air Pollution and Health: A European Information System.

q EMFAC: Emission Factor model.

r BAAQMD: Bay Area Air Quality Management District shed model.

s Routino: Router for OpenStreetMap Data.

t MOBILE: Vehicle emission factor model.

u TAMP: The Air Pollution Model (dispersion model).

(d) Table 4. Noise exposure risk estimates and associated health outcomes

Author (year) Risk function shape Risk estimate

Health outcome Data source Source type

Linear Day time: Urban 0.76 €ct/ car km; Suburban 0.12 €ct/ car km; Rural 0.01 €ct/ car km Costs of annoyance and health costs (medical costs, costs of productivity loss and costs of increased mortality)

NA (Report) NA (Report)

Creutzig et al. (2012)

Linear Day: Urban 0.76 €ct/ car km; Suburban 0.12 €ct/ car km; Rural 0.01 €ct/ car km Night: Urban 1.39 €ct/ car km; Suburban 0.22 €ct/ car km; Rural 0.03 €ct/ car km Day: Urban 1.53 €ct/ motorcycle km; Suburban 0.23 €ct/ motorcycle km; Rural 0.03 €ct/ motorcycle km Night: Urban 2.78 €ct/motorcycle km; Suburban 0.44 €ct/motorcycle km; Rural 0.05 €ct/ motorcycle km Day: Urban 3.81 €ct/ bus km; Suburban 0.59 €ct/ bus km; Rural 0.07 €ct/ bus km Night: Urban 6.95 €ct/ bus km; Suburban 1.10 €ct/ bus km; Rural 0.12 €ct/ bus km Day: Urban 3.81 €ct/ LGV km; Suburban 0.59 €ct / LGV km; Rural: 0.07 €ct/ LGV km Night: Urban 6.95 €ct/ LGV km; Suburban 1.10 €ct/ LGV km; Rural 0.13 €ct/ LGV km Day: Urban: 7.01 €ct/ HGV km; Suburban 1.10 €ct/ LGV km; Rural 0.13 €ct/ LGV km Night: Urban 12.78 €ct/ HGV km; Suburban 2.00 €ct/ LGV km; Rural 0.23€ct/ HGV km Day: Urban 23.65€ct/ train km; Suburban 20.61€ct/ train km; Rural 2.58€ct/ train km Night: Urban 77.99€ct/ train km; Suburban 34.40 €ct/ train km; Rural 4.29 €ct/ train km Day: Urban 41.93 €ct/ freight train km; Suburban 40.06 €ct/ freight train km; Rural 5.00 €ct/ freight train km; Night: Urban 171 .06 €ct/ freight train km; Suburban 67.71 €ct/ freight train km; 8.46€ct/ freight train km

Costs of annoyance and health costs (medical costs, costs of productivity loss and costs of increased mortality)

NA (Report) NA (Report)

James et al. (2014)

Linear Vehicle volume (based on vehicle-miles-travelled in traffic analysis zone) and speed were related to noise levels

People exposed to more than 60 dB of noise on average per day (associated with hypertension)

Federal Highway Administration Traffic Noise Model, 1998

NA (Technical Manual)

HGV = heavy goods vehicle; LGV = light goods vehicle

B.2. Benefit-risk/ benefit-cost ratios

(a) Range of estimated health benefit-risk or benefit-cost ratios of active transportation mode shift scenarios by study

* Cobiac et al. 2009(Cobiac et al., 2009) are comparing TravelSmart (information and merchandise campaign) investment costs with TravelSmart health cost offset. The minimal benefit and maximal benefit scenarios describe the range of estimated health benefits across the different scenarios. The benefit-cost ratio or benefit-risk ratio was taken directly from the study if provided. Reported cost-benefit ratio may include environmental and economic impacts and may compare to policy investment costs. If the study did not report a benefit-risk or benefit-cost ratio, a benefit-risk or benefit-cost ratio was calculated.

ueda e

t al. 2

ueda e

t al. 2

ueda e

t al. 2

illan e

t al. 2

Rabl &

azelle

Woodcock e

t al. 2

inde e

t al. 2

ndheim

Deenih

Caulfie

t al. 2

lish e

t al. 2

Woodcock e

t al. 2

Mason 2

chi 2011

(World-c

lass-b

Guandavara

et al. 2

t al. 2

Maximal benefit scenario

Minimal benefit scenario

Maximal risk scenario

Minimal risk scenario

>30 >30 >30 >30

The ratio could not be calculated if only health benefits, or only health risks were found, or health impacts were expressed in different units. Therefore excluded: Mooy and Gunning-Schepers 2001; Boarnet et al. 2008; Creutzig et al. 2012; Olabarria et al. 2012; Grabow et al. 2012; Stipdonk and Reurings 2012; Mulley et al. 2013; Schepers and Heinen 2013; Dhondt et al. 2013, Woodcock et al. 2013; James et al. 2014, Xia et al. 2015.

(b) Calculation of benefit-risk or benefit-cost ratio for most conservative active transportation mode shift scenario (minimum benefit scenario) by study (manuscript, Table 2)

Author (year) Physical activity Air pollution general

population

Air pollution active

traveler

Traffic incidents Calculation Benefit-risk/

benefit-cost ratio

Mooy & Gunning-Schepers (2001) Benefits onlyb

Sælensminde (2004) Reporteda

Boarnet et al. (2008) Benefits onlyb

Cobiac et al.( 2009)

TravelSmart intervention

Intervention costs / cost offset

410 NZ$/ 220 NZ$

-1.863636364

Scenario 3

7742 DALYs 220 DALYs -519 DALYs (7742+220)/519 15.34104046

Guo & Gandavarapu (2010) Reporteda

de Hartog et al. (2010) Reporteda

Gotschi (2011) Reporteda

Lindsay et al. (2011)

Scenario 1

212150000 NZ$

5582250 NZ$ -16589000 NZ$ (212150000+5582250)/16589000 13.12509796

Rojas-Rueda et al. (2011) Reporteda

Rabl & de Nazelle (2012) 1310 € 33 € -19 € -53 € (1310+33)/(19+53) 18.91549296

Grabow et al. (2012) Benefits onlyb

Olabarria et al. (2012) Benefits onlyb

Jarrett et al. (2012) 17000000000 £ - 722000000 £ 17000000000/ 722000000 23.54570637

Stipdonk & Reurings (2012) Risks onlyb

Holm et al. (2012) 5634.8-5558.7 DALYs 9784.3-9789.7 DALYs (-303.2)-(-354.3)

76.1/(5.4+51.1) 1.345132743

Scenario 2

67.46 deaths 10.03 deaths -1.15deaths -0.17 deaths (67.46+10.03)/(1.15+0.17) 58.70454545

Creutzig et al. (2012) Benefits onlyb

Dhondt et al. (2013) Benefits onlyb

Woodcock et al. (2013) Benefits onlyb

Maizlish et al. (2013)

Scenario 2

4776 DALYs 101 DALYs -5907 DALYs (44776+101)/5907 7.597257491

Scenario 2

283.74 DALYs 0.38 DALYs -3.17 DALYs -5.37 DALYS ((283.74+0.38)/(3.17+5.39) 33.26932084

Mulley et al. (2013) Benefits onlyb

Schepers& Heinen 2013 Risks onlyb

Woodcock et al. (2014) 105 DALYs -17 DALYs 105/17) 6.176470588

Deenihan & Caulfield (2014) Reporteda

Edwards & Mason (2014) 0.847 life years -0.317 life years (0.847/0.137) 6.182481752

Macmillan et al. (2014) Reporteda

James et al. (2014) Risks onlyb

Xia et al. (2015) Benefits onlyb

a Reported benefit-cost ratio or benefit-risk ratio was taken directly from the study. Reported cost-benefit ratio may include environmental and economic

impacts and may compare to investment costs. If the study did not report a ratio and if possible, a benefit-risk or benefit-cost ratio was calculated based on change in health pathway exposure distribution, except for Cobiac et al. 2009 were only a comparison between investment costs and cost offset was possible. b The ratio could not be calculated if only health benefits, or only health risks were found, or health impacts were expressed in different units. Therefore

excluded: Mooy and Gunning-Schepers 2001; Boarnet et al. 2008; Creutzig et al. 2012; Olabarria et al. 2012; Grabow et al. 2012; Stipdonk and Reurings 2012; Mulley et al. 2013; Schepers and Heinen 2013; Dhondt et al. 2013, Woodcock et al. 2013; James et al. 2014, Xia et al. 2015.

(c) Calculation of median benefit-risk or benefit-cost ratio across the most conservative and most ambitious benefit scenarios (minimum and maximum benefit scenarios) of a mode shift to active transportation by study (reported in manuscript) Author (year) Calculation of benefit-risk or

benefit-cost ratio Minimum benefit scenario Benefit-risk or benefit-cost ratio

Maximum benefit scenario Benefit-risk or benefit-cost ratio

Median Benefit-risk or benefit cost ratio (in-between the scenarios)

Order Number of studies considered

Saelensminde 2004 Reporteda 2.9 (S3) 14.3 (S2) 8.6 9 1

Cobiac 2009 Intervention costs/ cost offset -1.86 - -1.86 1 2

Woodcock et al. 2009 DALYs(PA, air pollution, traffic incidents)

15.3 (S3) 15.5 (S4) 15.4 12 3

de Hartog et al. 2009 Reporteda 9 - 9 10 4

Guo & Guandavarapu 2010 Reporteda 1.87 - 1.87 3 5

Lindsay et al. 2012 NZ$ (PA, air pollution, traffic incidents)

13.13 (S1) 43.8 (S4) 28.5 15 6

Rojas-Rueda et al. 2011 Reporteda 77 - 77 16 7

Gotschi 2011 Reporteda 1.3 (S1) 3.8 (S3) 2.55 4 8

Holm et al. 2012 DALYs pre/post intervention (PA, air pollution, traffic incidents)

1.37 - 1.37 2 9

Jarrett et al. 2012 £ (PA, traffic incidents) 23.54 - 23.54 14 10

Rabl & de Nazelle 2012 € (PA, air pollution, traffic incidents)

18.65 - 18.65 13 11

Rojas-Rueda et al. 2012 Mortality (PA, air pollution, traffic incidents)

58.7 (S2) 195.33 (S6) 127.02 17 12

Maizlish et al. 2013 DALYs (PA, air pollution, traffic incidents)

7.59 (S2) 7.63 (S3) 7.61 8 13

Rojas-Rueda et al. 2013 DALYs (PA, air pollution, traffic incidents)

33.27 (S2) 362.25 (S5) 197.76 18 14

Woodcock et al. 2014 DALYs (PA, traffic incidents) 6.18 - 6.18 6 15

Deenihan & Caulfield Reporteda 2.22 (S1) 11.77 (S3) 6.995 7 16

Edwards and Mason 2014 Life years gained/ lost (PA, traffic incidents)

6.18 - 6.18 5 17

Macmillan et al. 2014 Reporteda 6 (S3) 24 (S4) 15 11 18

Overall MEDIAN 8.8

S=Scenario a

Reported benefit-cost ratio or benefit-risk ratio was taken directly from the study. Reported cost-benefit ratio may include environmental and economic impacts and may compare to investment costs.

B.3. Health pathway contribution

(a) Calculation of health pathway contribution to estimated health impact for the most conservative scenario (minimum benefit scenario) of a mode shift to active transportation (manuscript, Figure 2)

Author Unit Physical activity Air pollution general population Air pollution active traveler Traffic incidents Sum

Woodcock et al. 2009 (S3) DALYs 7742 200 0 -519 8461 % 91.28640491 2.363786786 0 -6.134026711 100

de Hartog et al. 2010 Life expectancy (days) 240 0 -21 -7 268 % 89.55223881 0 -7.835820896 -2.611940299 100

Linsay et al. 2011 (S1) NZ $ 212150000 5582250 0 -16589000 234321250 % 90.53809674 2.382306342 0 -7.079596921 100

Rojas-Rueda et al. 2011 Mortality (cases) 12.46 0 -0.13 -0.03 12.62 % 98.73217116 0 -1.030110935 -0.237717908 100

Grabow et al. 2012 Mortality (cases) 687 608 0 0 1295 % 53.05019305 46.94980695 0 0 100

Jarrett et al. 2012 £ (British pounds) 17000000000 0 -722000000 17722000000 % 95.92596772 0 0 -4.074032276 100

Holm et al. 2012 DALYs 76.1 0 -5.4 -51.1 132.6 % 57.39064857 0 -4.07239819 -38.46153846 100

Rabl & de Nazelle 2012 € 1310 33 -19 -53 1415 % 92.5795053 2.332155477 -1.342756184 -3.745583039 100

Rojas-Rueda et al. 2012 (S2) Mortality (cases) 67.47 10.03 -1.15 -0.7 79.35 % 85.02835539 12.64020164 -1.449275362 -0.882167612 100

Woodcock et al. 2013 (S1) DALYs 3503 47 0 228 3778 % 92.72101641 1.244044468 0 6.034939121 100

Dhondt et al. 2013 Years life gained 171 529 747 1447 % 11.81755356 36.55839668 0 51.62404976 100

Maizlish et al. 2013 (S2) DALYs 44776 101 -5907 50784 % 88.16950221 0.198881537 0 -11.63161626 100

Rojas-Rueda et al. 2013 (S2) DALYs 283.74 0.38 -3.17 -5.37 292.66 % 96.95209458 0.129843504 -1.083168182 -1.834893733 100

Macmillan et al. 2014 (S3) NZ $ 2000 40 -750 2790 % 71.68458781 1.754385965 0 -26.88172043 100.3206942

Woodcock et al. 2014 DALYs 105 0 0 -17 122 % 86.06557377 0 0 -13.93442623 100

Edwards & Mason 2014 Life years lived 0.847 0 0 -0.137 0.984 % 86.07723577 0 0 -13.92276423 100

Xia et al. 2015 (S3) DALYs %

1381 71.62863071

52 2.697095436

459 25.67427386

1892 100

S=Scenario

The health pathway contribution could not be calculated for studies that assessed only one health pathway; for studies where the health impact could not be untangled from environmental and economic impacts; for studies where the health pathway contributions were expressed in different units. Therefore excluded: Mooy and Gunning-Schepers 2001; Sælensminde 2004; Boarnet et al. 2008; Cobiac et al. 2009; Guo and Gandavarapu 2010; Gotschi 2011; Olabarria et al. 2012; Stipdonk and Reurings 2012; Creutzig et al. 2012; Mulley et al. 2013; Schepers and Heinen 2013; Deenihan and Caulfield 2014; James et al. 2014.

B.4. Health impact assessment modeling

(a) Conceptual framework of health impact assessment modeling assumptions of a mode shift to active transportation

DALYs = disability-adjusted-life-years; DRF = dose-response function; HEAT = Health Economic Assessment Tool (WHO). The framework presents the different health impact assessment model components considered by the individual studies.

Stratifications were age, sex, ethnicity, or population density. HEAT applies threshold for PA at RR 0.5 after which no additional health benefit can be obtained. Woodcock et al. 2009, Jarrett et al. 2012, Maizlish et al. 2013 applied a threshold or a square-root function for higher PA exposure levels. Safety-in-numbers describes a disproportional increase in traffic incidents with increasing modal share. Time-lags describes potential delays until health benefit or risk occurs in the lifespan.

(b) Health impact assessment modeling assumptions of a mode shift to active transportation, applied by the individual studies

Author (Year) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Mooy & Gunning-Scheppers (2001)

Sælensminde (2004)

Boarnet et al. (2008)

Cobiac et al.( 2009)

Guo & Gandavarapu (2010)

de Hartog et al. (2010)

Gotschi (2011) Lindsay et al. (2011)

Rojas-Rueda et al. (2011) Rabl & de Nazelle (2012)

Grabow et al. (2012)

Olabarria et al. (2012)

Jarrett et al. (2012) Stipdonk & Reurings (2012)

Holm et al. (2012) Rojas-Rueda et al. (2012)

Creutzig et al. (2012) Dhondt et al. (2013)

Woodcock et al. (2013) Maizlish et al. (2013) Rojas-Rueda et al. (2013)

Mulley et al. (2013) Schepers & Heinen (2013)

Woodcock et al. (2014) Deenihan & Caulfield (2014)

Edwards & Mason (2014) Macmillan et al. (2014)

James et al. (2014)

Appendix C - DISCUSSION

C.1. Physical activity

(a) Risk differences for physical activity and all-cause mortality using a linear/ curvilinear DRF adjusted from Rojas-Rueda et al. 2012(Rojas-

Rueda et al., 2012)

METs/hours/week = Metabolic equivalent of task per hour per week; DRF = Dose-response function; DRF Linear Walk from a meta-analysis reported in HEAT for walking (WHO, 2010); DRF Curvilinear from a meta-analysis for physical activity and all-cause mortality;(Woodcock et al., 2011a) DRF Linear Bike from HEAT for cycling.(Andersen et al., 2000)

Modeling the association between PA and all-cause mortality with a linear versus a curvilinear DRF, yield distinctive RRs for the same PA exposure. In the example by Rojas-Rueda et al. 2012, PA exposures less than approximately 14 MET hours/ week a linear DRF generally yields a smaller risk reduction than a curvilinear DRF. For PA exposures of approximately 19 MET-hours/ week we obtain a greater risk reduction for all-cause mortality with the linear DRF yielding an approximate risk reduction of 28% (RR = 0.72) compared to a curvilinear DRF, which yields an approximate risk reduction of 22% (RR = 0.78). Illustrating this risk difference emphasizes the need for consensus on the shape of the PA DRF. In this context, a curvilinear risk function has been claimed to be more biologically plausible(Physical Activity Guidelines Advisory Committee, 2008; Woodcock et al., 2011b) as a change in PA exposure does not result in equal health benefits for all exposure levels. Especially people with low levels of PA changing to moderate levels of PA were shown to benefit the greatest.(Woodcock et al., 2011b)

C.2. Susceptible populations

(a) Trade-off of benefits to harms for cycling in central London: effects by age and sex from Woodcock et al. 2014(Woodcock et al., 2014)

“Benefits come through impacts on diseases related to physical activity; harms come from exposure

to road traffic injuries. Results use background injury rates and so should be interpreted as the

trade-off for cycling in general in the cycle hire zone and not for specifically using cycle hire bicycles

(which may carry lower risks of injury)”.(Woodcock et al., 2014)

(b) Years Lived with Disability (YLD) change of the fuel scenario for non-fatal road traffic injuries from Dhondt et al. 2013(Dhondt et al., 2013)

Dhondt et al. 2013 report younger age groups to benefit the greatest from reduced motorized traffic collision risk resulting of a mode shift to (more) public transport and (more) car-sharing (as passenger) (low risk modes) and (less) AT (high risk modes), as injuries at younger age translate into more Years Lived with Disability (YLD).(Dhondt et al., 2013) Woodcock et al. 2014, Dhondt et al. 2013, Edwards and Mason 2014 and Xia et al. 2015 note that despite increased traffic incident risk for older ages, rising disease incidence increases the magnitude of benefits resulting of PA and air pollution.(Dhondt et al., 2013; Edwards and Mason, 2014; Woodcock et al., 2014; Xia et al., 2015) At older ages, benefits and harms increase greater compared to younger ages, but the benefits increase faster. At younger ages, both the harms and the benefits are much smaller in absolute terms than at older age. Therefore, Woodcock et al. 2014 and Edwards and Mason 2014 suggest that at younger ages the harms can outweigh the benefits, as the absolute increase in mortality associated with cycling is high relative to the low baseline mortality younger ages experience.

(c) Effects of mode shift to active transportation on the U.S. traffic fatality rates and death rates from Edwards and Mason 2014(Edwards and Mason, 2014)

Appendix D

D.1. Gray literature

Author (year) Title Health and environmental exposures

Source: Experts

1) James 2000 Determinants in health outcomes in switching to electric bikes

Physical activity, traffic incidents, air pollution

2) Metropolitan Area Planning Council (MAPC), 2011

Healthy T for a Healthy Region: Health impact assessment of proposed MBTA service cuts and fare increases (cost-benefit analysis)

Time costs, fuel costs, traffic accident costs, health costs due to air pollution exposure, health costs due to physical inactivity, costs of carbon emissions

3) Thomas Götschi, Sonja Kahlmeier, 2012 Economic assessment of macro-economic benefits of active transport in Switzerland (Ökonomische Abschätzung der volkswirtschaftlichen Gesundheitsnutzen des Langsamverkehrs in der Schweiz)

Physical activity (walking and cycling)

4) Rails to Trails, 2008 Active Transportation for America - The Case for Increased Federal Investment in Bicycling and Walking

Fuel savings, CO2 savings, physical activity

5) Bundesamt für Raumentwicklung (ARE) Eidgenössisches Departement für Umwelt, Verkehr, Energie und Kommunikation (UVEK), 2010

External costs of transport in Switzerland (Bundesamt für Raumentwicklung ARE (2014), Externe Kosten und Nutzen des Verkehrs in der Schweiz. Strassen-, Schienen-, Luft- und Schiffsverkehr 2010 und Entwickungen seit 2005)

Traffic incidents (walking and cycling)

Source: Bibliographic review

1) Foltynova H & Braun Kohlova M, n.d.

Cost-benefit analysis of cycling infrastructure: a case study of Pilsen (cost-benefit analysis)

Physical activity, traffic accidents, air pollution , insecurity

Source: Internet searches

1) Swedish Road Administration, 2009

Health impact assessment and public health costs of the road transportation sector

Traffic injuries; air pollution; noise pollution; physical inactivity, global climate change

2) Mineta Transportation Institute, 2007 Bicycling Access and Egress to Transit: Informing the Possibilities

Qualitative assessment

3) North East Public Health Observatory, 2005

A Screening Health Impact Assessment for the provisional second County Durham Local Transport Plan

Security and Safety; physical activity; access to the NHS.

4) National Collaboration Center for Environmental Health, 2010

Active Transportation in Urban Areas: Exploring Health Benefits and Risks

Injury risk; air pollution; greenhouse gas emissions; health care costs; noise pollution and congestion; urban design for connectivity and accessibility;

5) Metrolinx, n.d. Active and Sustainable School Transportation in Ontario: Qualitative assessment

Barriers and Enablers

6) Eastern Regional Health Authority, 2004 A Health Impact Assessment of Traffic and Transport in Ballyfermot

Physical activity; safety; public transport, mental health and well-being, traffic lights and parking, air quality and noise, access, journey time

7) County of San Mateo, 2010 Building health into San Mateo County Cities: Resources and Case Studies

Access to food; public safety; housing density; housing placement; transportation/ transit; environment

8) North Coast Area Health Service; Population Health, Planning & Performance Directorate, 2007

Coffs Harbor Our Living City Settlement Strategy: Health Impact Assessment

9) Christchurch City Council, 2012 Christchurch Transport Strategic Plan 2012-2042 Traffic injuries; pollution; physical inactivity; mobility access and independence

10) Clark County Public Health, 2010 Rapid Health Impact Assessment: Clark County Bicycle and Pedestrian Master Plan

Income; race/ethnicity; housing affordability; access; walkability; bike ability

11) Federal Highway Administration 2012 Report to the U.S. Congress on the Outcomes of the Non-motorized Transportation Pilot Program, SAFETEA‐LU Section 1807

12) Healthy Canada; Erna van Balen & Meghan Winters, n.d.

Health and active transportation: an inventory of municipal data collection and needs in the Lower Mainland of B.C.

Physical activity (HEAT); air quality; injuries and safety

13) Waikato District Health Board Population Health, 2012

Hamilton Central City Residential Intensification Health Impact Assessment

14) Upstream Public Health Portland, 2009 Health Impact Assessment on policies reducing vehicle miles traveled in Oregon Metropolitan area

Physical activity; air pollution; traffic incidents

15) Massachusetts Department of Public; Health Bureau of Environmental Health, 2013

Health Impact Assessment of the Massachusetts Department of Transportation (MassDOT) Grounding McGrath Study

Air quality; noise; mobility and connectivity; public safety;

16) Humboldt County Public Health Branch; Humboldt Partnership for Active Living Human Impact Partners, 2008

Humboldt County General Plan Update: Health Impact Assessment

17) Health Impact Assessment Project ; UCLA School of Public Health; Cole B, Agyekum G, Hoffman S, Shimkhada R, 2008

Mass Transit Health Impact Assessment: Potential health impacts of the Governor’s Proposed Redirection of California State Transportation Spillover Funds

Air pollution; water pollution; noise pollution; physical activity; discretionary time; social capital; traffic incidents; personal/community economics; land use

18) Plan ET – A regional partnership of East Tennesee Communities, 2013

Health Impact Assessment Access to care; healthy foods; physical activity; walk ability; bike ability; air quality; water quality;

19) City of Decatur; Community Transportation Plan, n.d.

Pathways to a Healthy Decatur: A Rapid Health Impact Assessment of the City of Decatur Community Transportation

Qualitative HIA

20) Oregon Health Authority, n.d. Climate Smart Communities Scenarios (comparative risk assessment DALYs)

Physical activity; air pollution; roar traffic crashes

21) Ribeiro P, Arsenio E, Mendes J, 2012 Assessing the potential health benefits of cycling at the city of Viana do Castelo

Physical activity (HEAT)

22) San Francisco Department of Public Health; Program on Health, Equity and sustainability; Technical Report, 2011

Health Effects of Road Pricing In San Francisco, California (cost-benefit analysis)

Air pollution; noise pollution; physical activity (HEAT); vehicle-pedestrian injury traffic incidents

23) Cavill N, Cope A, Kennedy A, Sustrans & Cavill Associates, 2009

Valuing increased cycling in the Cycling Demonstration Towns Physical activity (HEAT); air pollution

24) New Zealand Transport Agency, 2008 Valuing the health benefits of active transport modes (cost-benefit analysis)

Physical activity; injuries; air pollution exposure

25) Campbell R, Wittgens M; Better Environmentally Sound; Transportation, 2004

The Business Case for Active Transportation, The Economic Benefits of Walking and Cycling (cost-benefit analysis)

Air quality; physical activity

26) SQW, 2007 Valuing the benefits of cycling: A report to Cycling England (cost-benefit analysis)

Physical activity; NHS savings; productivity costs; obesity, air pollution; greenhouse gas emissions; congestion; accidents

27) Meggs, J., Schweizer, n.d. Effects of bicycle facility provision on mortality prevention and GHG Reduction: Cost-benefit Analysis within the BiCY project

Physical activity (HEAT); greenhouse gas emissions

28) Roads and Traffic Authority of NSW and the Department of Environment and Climate Change, 2009

Evaluation of the costs and benefits to the community of financial investment in cycling programs and projects in New South Wales: Final Report

Physical activity; nutrition; traffic safety

29) Tri-County Health Department (TCHD), 2007

Derby redevelopment - Health Impact Assessment- Historic commerce city, Colorado

Physical activity; nutrition; traffic safety; personal safety

30) Sieg 2014 Costs and benefits of a bicycle helmet law for Germany Physical activity, traffic incidents, environmental costs (including air pollution)

REFERENCES

Andersen, L.B., Schnohr, P., Schroll, M., Hein, H.O., 2000. All-cause mortality associated with physical activity during leisure time, work, sports, and cycling to work. Arch Intern Med 160, 1621–8.

Cobiac, L.J., Vos, T., Barendregt, J.J., 2009. Cost-effectiveness of interventions to promote physical activity: a modelling study. PLoS Med 6, e1000110. doi:10.1371/journal.pmed.1000110

Dhondt, S., Kochan, B., Beckx, C., Lefebvre, W., Pirdavani, A., Degraeuwe, B., Bellemans, T., Int Panis, L., Macharis, C., Putman, K., 2013. Integrated health impact assessment of travel behaviour: model exploration and application to a fuel price increase. Environ Int 51, 45–58. doi:10.1016/j.envint.2012.10.005

Edwards, R.D., Mason, C.N., 2014. Spinning the wheels and rolling the dice: life-cycle risks and benefits of bicycle commuting in the U.S. Prev Med (Baltim) 64, 8–13. doi:10.1016/j.ypmed.2014.03.015

Physical Activity Guidelines Advisory Committee, 2008. Physical Activity Guidelines Advisory Committee Report [WWW Document]. URL http://www.health.gov/paguidelines/report/pdf/committeereport.pdf (accessed 12.13.13).

Rojas-Rueda, D., de Nazelle, A., Teixidó, O., Nieuwenhuijsen, M.J., 2012. Replacing car trips by increasing bike and public transport in the greater Barcelona metropolitan area: a health impact assessment study. Environ Int 49, 100–9. doi:10.1016/j.envint.2012.08.009

Woodcock, J., Franco, O.H., Orsini, N., Roberts, I., 2011a. Non-vigorous physical activity and all-cause mortality: systematic review and meta-analysis of cohort studies. Int J Epidemiol 40, 121–38. doi:10.1093/ije/dyq104

Woodcock, J., Franco, O.H., Orsini, N., Roberts, I., 2011b. Non-vigorous physical activity and all-cause mortality: systematic review and meta-analysis of cohort studies. Int J Epidemiol 40, 121–38. doi:10.1093/ije/dyq104

Woodcock, J., Tainio, M., Cheshire, J., O’Brien, O., Goodman, A., 2014. Health effects of the London bicycle sharing system: health impact modelling study. BMJ 348, g425. doi:10.1136/bmj.g425

Xia, T., Nitschke, M., Zhang, Y., Shah, P., Crabb, S., Hansen, A., 2015. Traffic-related air pollution and health co-benefits of alternative transport in Adelaide, South Australia. Environ Int 74, 281–290. doi:10.1016/j.envint.2014.10.004

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