Upload
creal
View
0
Download
0
Embed Size (px)
Citation preview
1
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.
2
(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
Woodcock et al. 2013
Woodcock et al. 2014
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
3
A.2. Data extraction tool
Author HIA approach Policy focus Setting Mode shift Scenario Health endpoint
Woodcock et al. 2009
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
DALYs
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
Rojas-Rueda et al. 2011
… … … … …
(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
Rojas-Rueda et al. 2011
… … … ... ... ...
4
(Data extraction tool continued)
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
PM2.5
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
Rojas-Rueda et al. 2011
… ... … ... … ... ... ...
5
(Data extraction tool continued)
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 … … … … …
(Data extraction tool continued)
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)
(Data extraction tool continued)
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
6
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
7
Author (year) Dose-response function
Risk estimate Health outcome Data source Source type
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
All-cause mortality All-cause mortality
US DHHS, 2008 US DHHS, 2008
Report Report
Grabow et al. (2012) 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
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
All-cause mortality All-cause mortality
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
8
Author (year) Dose-response function
Risk estimate Health outcome Data source Source type
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)
RR 0.72/ 3 hours cycling/ week/ 36 weeks/ year/ at 14km/h All-cause mortality Andersen et al. 2000 Cohort study
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
9
Author (year) Dose-response function
Risk estimate Health outcome Data source Source type
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
Author (year) Dose-response function
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
Rojas-Rueda et al. (2012)
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
10
Author (year) Dose-response function
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)
Rojas-Rueda et al. (2013)
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.
11
(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
All-cause mortality All-cause mortality
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
Rojas-Rueda et al. (2011)
BADMg RR 1.04/ 10 µg/m3 PM2.5 RR 1.05/ 10 µg/m3 BS
All-cause mortality All-cause mortality
Krewski et al. 2009 Beleen et al. 2008
Cohort study Case-cohort study
Rabl & de Nazelle (2012)
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
12
Author (year) Tool/ Method Risk estimate Health outcome Data source Type of source
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
13
Author (year) Tool/ Method Risk estimate Health outcome Data source Type of source
(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)
Woodcock et al. (2013)
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)
Pope et al. 2002, in WHO, 2004 Pope et al. 2002, in WHO, 2004 WHO, 2004
Cohort study Cohort study Meta-analysis
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
Rojas-Rueda et al. (2013)
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
Woodcock et al. (2014)
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)
Pope et al. 2002, in WHO, 2004 Pope et al. 2002, in WHO, 2004 WHO, 2004
Cohort study Cohort study Meta-analysis
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
14
Author (year) Tool/ Method Risk estimate Health outcome Data source Type of source
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.
15
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
Rabl & de Nazelle (2012)
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)
INFRAS/ IWW 2003; INFRAS/ IWW 2004 (from CE Delft 2008)
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)
INFRAS/ IWW 2003; INFRAS/ IWW 2004 (from CE Delft 2008)
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
16
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.
-5
0
5
10
15
20
25
30
Roja
s-R
ueda e
t al. 2
013 (
S2
-S5)
Roja
s-R
ueda e
t al. 2
012 (
S2
-S6)
Roja
s-R
ueda e
t al. 2
011
Lin
dsay
et
al. 2
011 (
S1
-S4)
Macm
illan e
t al. 2
014
(S3-S
4)
Jarr
ett e
t al. 2
012
Rabl &
de N
azelle
et
al. 2
012
Woodcock e
t al. 2
009
(S3-S
4)
Saele
nsm
inde e
t al. 2
004
(Tro
ndheim
/ H
am
ar)
Deenih
an &
Caulfie
ld e
t al. 2
014
(S
1-S
3)
de H
art
og e
t al. 2
011
Maiz
lish e
t al. 2
013
(S
2-S
3)
Woodcock e
t al. 2
014
Edw
ard
s &
Mason 2
014
Gots
chi 2011
(World-c
lass-b
asic
pla
n)
Guo &
Guandavara
pu 2
010
Holm
et al. 2
012
* C
obia
c e
t al. 2
009
Maximal benefit scenario
Minimal benefit scenario
Maximal risk scenario
Minimal risk scenario
1:1
>30 >30 >30 >30
17
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.
18
(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
Woodcock et al. (2009)
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)
DALYs
76.1/(5.4+51.1) 1.345132743
Rojas-Rueda et al. (2012)
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
19
Woodcock et al. (2013) Benefits onlyb
Maizlish et al. (2013)
Scenario 2
4776 DALYs 101 DALYs -5907 DALYs (44776+101)/5907 7.597257491
Rojas-Rueda et al. (2013)
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.
20
(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.
21
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
0 0
459 25.67427386
1892 100
S=Scenario
22
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.
23
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.
24
(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)
Woodcock 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)
25
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)
0.95
0.85
0.77
0.72
26
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)
27
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)
28
(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.
29
(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)
30
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
31
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
Qualitative 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
Qualitative 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
Qualitative 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
32
Plan
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)
33
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