supplementary material contents details of studies
TRANSCRIPT
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Supplementary Material
Contents
Details of studies excluded from meta-analysis for other causes or mortality
Tables
eTable 1 Coding of categorise of cause of death
eTable 2 Study and cohort characteristics
Figures
eFigure 1 All-cause mortality
eFigure 2a All-cause mortality - fixed effects model
eFigure 2b All-cause mortality - random effects model
eFigure 3 All-cause mortality stratification by pre-existing disease on recruitment
eFigure 4 All-cause mortality stratification by age range at cohort recruitment
eFigure 5 Cardiovascular mortality
eFigure 6a Cardiovascular mortality - fixed effects model
eFigure 6b Cardiovascular mortality - random effects model
eFigure 7 Cardiovascular mortality - stratification pre-existing disease on recruitment
eFigure 8 Cardiovascular mortality - stratification by age range at cohort recruitment
eFigure 9 Cardiovascular mortality - stratification by level of confounder control
eFigure 10 Cardiovascular mortality - stratification by spatial resolution of NO2 concentration estimates
eFigure 11 CHD mortality
eFigure 12a CHD mortality - fixed effects model
eFigure 12b CHD mortality - random effects model
eFigure 13 CHD mortality - stratification by age range at cohort recruitment
eFigure 14 CHD mortality - stratification by level of confounder control
eFigure 15 CHD mortality - stratification by spatial resolution of NO2 concentration estimates
eFigure 16 Cerebrovascular mortality
eFigure 17a Cerebrovascular mortality - fixed effects model
eFigure 17b Cerebrovascular mortality - random effects model
eFigure 18 Cerebrovascular mortality - stratification by age range at cohort recruitment
eFigure 19 Cerebrovascular mortality - stratification by level of confounder control
eFigure 20 Cerebrovascular mortality - stratification by spatial resolution of NO2 concentration
eFigure 21 Respiratory mortality
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eFigure 22a Respiratory mortality - fixed effects model
eFigure 22b Respiratory mortality - random effects model
eFigure 23 Respiratory mortality - stratification by pre-existing disease at cohort recruitment
eFigure 24 Respiratory mortality - stratification by age range at cohort recruitment
eFigure 25 Respiratory mortality - stratification by level of confounder control
eFigure 26 Respiratory mortality - stratification by spatial resolution of NO2 concentration
eFigure 27 COPD mortality
eFigure 28a COPD mortality - fixed effects model
eFigure 28b COPD mortality - random effects model
eFigure 29 COPD mortality - stratification by age range at cohort recruitment
eFigure 30 COPD mortality - stratification by level of confounder control
eFigure 31 COPD mortality - stratification by spatial resolution of NO2 concentration
eFigure 32 Pneumonia mortality
eFigure 33 Lung cancer mortality
eFigure 34a Lung cancer mortality - fixed effects model
eFigure 34b Lung cancer mortality - random effects model
eFigure 35 Lung cancer mortality - stratification by age range at cohort recruitment
eFigure 36 Lung cancer mortality - stratification by level of confounder control
eFigure 37 Lung cancer mortality - stratification by spatial resolution of NO2 concentration
eFigure 38 Diabetes mortality
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Details of studies excluded from meta-analysis for other causes or mortality (references refer to
main manuscript)
CHD: Twelve studies were meta-analysed after exclusions of one study [53] included in ESCAPE [21]
and 3 analysed in other publications [31, 43, 62].
Cerebrovascular: Seven studies were meta-analysed after exclusions of one study [53] included in
ESCAPE [21]; one study reporting an abnormally high HR (>2.4) from the Shenyang Cohort [63] and 2
studies where the same cohorts were analysed in other publications [31, 62].
COPD: Only a single study [62] analysed in a subsequent publication was excluded.
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eTable 1 Coding of categorise of cause of death
Mortality Coded
All-causes "All Causes" , "All Cause (after ACS)" , "All Causes (after stroke)" , "All causes" , "Natural Causes" , "Non Accidental"
Cardiovascular "Cardiovascular" , "Circulatory"
Cerebrovascular "Cerebrovascular"
CHD "CHD" , "IHD"
Respiratory "Respiratory" , "Pulmonary" , "Non-malignant Respiratory"
COPD "COPD" , "COPD & allied conditions"
Pneumonia "Pneumonia" , "Pneumonia & Influenza"
Lung cancer "Lung Cancer" , "Trachea, bronchus and lung cancers"
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eTable 2: Cohort and study characteristics (ordered by cohort/date of publication); citation numbers correspond with main paper
Cohort Name and brief description
Country Enrolment (baseline)
Publication and Date
Number of Subjects
Follow-up Dates / Length
Exposure Period
Exposure assessment
Covariate adjustment includes#:
Mortality Cause
Date Age
European Cohorts
ESCAPE 22 population based cohorts from 13 European Countries
Europe mainly 1990s
all ages [21] Beelen R et al 2014b
367,251
Average 13.9 years
1 year Oct 2008-May 2011 with back-extrapolation to baseline
LUR + back-extrapolation: Address-level
Age, sex, smoking, BMI, occupational and educational factors
All-cause
[22] Beelen R et al 2014a
367,383 Average 13.9 years
1 year Oct 2008-May 2011 with back-extrapolation to baseline
LUR + back-extrapolation: Address-level
Age, sex, smoking, BMI, occupational and educational factors
CV, CHD, MI, Cerebrovascular
[33] Dimakopoulou K et al 2014
307,553 (16 cohorts)
16.3 to 18.6 years
Annual average (baseline)
LUR: Address-level
Age, sex, smoking, BMI, occupational and educational factors
Respiratory
Diet Cancer and Health cohort (DCH) Population based sample with no cancer history living in areas of Copenhagen and Aarhus.
Denmark 1993-1997
50-64 [54] Raaschou-Nielsen O et al 2012
52,061 to end 2009 1971 onwards: Annual mean exposure, time varying
DM: Address-level allowing for change of residence
Age, sex, smoking, BMI, employment status, length of school attendance
All-cause CV, CHD, Cerebrovascular
[55] Raaschou-Nielsen O et al 2013
52,061 to end 2009 1971 onwards: Annual mean exposure, time varying
DM: Address-level allowing for change of residence
Age, sex, smoking, BMI, length of school attendance
Diabetes
[19] Andersen ZI et al 2012
52,215 to June 27th 2006
1971 onwards: Annual mean exposure, time varying
DM: Address-level allowing for change of residence
Age, sex, smoking, BMI, educational level.
Fatal stroke (and sub-types)
6
[58] Sorensen M et al 2014
51,569 1st July 1997 to 30th Nov 2009
1987-2009: 10-years exposure, time varying
DM: Address-level allowing for change of residence
Age, sex, smoking, BMI, length of school attendance
Fatal stroke
Clinical Practice Research Datalink (CPRD) Patients in GP practices participating in CPRD
England 2003 40-89 [25] Carey IM et al 2013
830,429 2003-2007 2002 DM: 1 km grid square-level
Age, sex, smoking, BMI, area-level income
All-cause, CV, CHD, MI, HF, Cerebrovascular, Stroke, Respiratory, COPD, Pneumonia, Lung Cancer
South London Stroke Register (SLSR) Patients in the South London Stroke Register who experienced their first ever stroke between 1995 and 2005.
England 1995-2005
Mean (SD) 70.4 (14.6)
[51] Maheswaren R et al 2010
3,320 to mid-2006 2002 Modelled: 20x20m grid level
Age, sex, smoking, social class but not BMI
All-cause
MINAP (Acute Coronary Syndrome survivors) Patients admitted to hospital in England and Wales with acute coronary syndrome and recorded in MINAP (Myocardial Ischaemia National Audit Project) who are still alive 28 days post admission.
England & Wales
2004-2007
Mean (SD) 68 (13) >25
[59] Tonne C et al 2013
154,204 3.7 years 2004-2010: Annual average, time varying
DM: 1km x 1km level
Age, sex, smoking, area-level income but not BMI
All-cause
Greater London
2003-2007
>25 [65] Tonne C et al 2016
18,138 4.0 years 2003-2010 DM: 20m x 20m level
Age, area-level income deprivation, but not BMI. Smoking, and ethnicity in a sensitivity analysis after imputation of large number of missing values
All-cause
7
Pollution Atmosphérique et Affections Respiratoires Chroniques (PAARC) Adults from French family households resident in 24/18 areas of 7 Cities.
France 1974-1976
25-59 [35] Filleul L et al 2005
14,284 1974-2000 1974-1976 (August excluded)
Monitoring data: Area-level (areas 0.5 to 2.3 km in diameter)
Age, sex, smoking, BMI, educational level
All-cause, Cardio-pulmonary, Lung Cancer
GAZEL Cohort EDF-GDF workers
France 1989-2013
Mean (SD) 43.7 (3.5) 35-50
[23] Bentayeb et al, 2015
20,327 1989-2013 1989-2008 Chemistry Transport Model (resolution 2km) linked to ZIP code, allowing for change in residence
Age, sex, smoking, BMI, highest level of education, occupational level
All-cause, CV, Respiratory, Lung Cancer
German cohort Women sampled at random from cross-sectional studies conducted in North Rhine-Westphalia in the 1980s and 1990s.
Germany 1985-1994
50-59 (92% aged 53-55)
[39] Gehring U et al 2006
4,752 to May 2003 5-year average prior to baseline
Monitoring data: Area-level
Smoking, educational level but not BMI (all female cohort of similar age)
All-cause, Cardio-pulmonary
[57] Schikowski T et al 2007
4,750 to May 2003 5-year average prior to baseline
Monitoring data: Area-level
Smoking, educational level but not BMI (all female cohort of similar age)
CV
[41] Heinrich J et al 2013
4,752 to Oct 2008 1-year average (baseline)
Monitoring data: Nearest monitor to residence
Smoking, educational level but not BMI (all female cohort of similar age)
All-cause, Cardio-pulmonary Respiratory, Lung Cancer
CHD survivors cohort Population based cohort of CHD survivors (Rome)
Italy 1998-2000
35-84 [56] Rosenlund M et al 2008
6,513 29th day after event to end June 2005
1995/1996: Annual mean
LUR: Census block-level
Age, sex, area-based socioeconomic status but not smoking or BMI
All-cause
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Rome longitudinal study Population based cohort of long-term (5+ years) residents of Rome.
Italy 2001 >=30 [26] Cesaroni G et al 2013
1,265,058 Oct 2001-Dec 2010
Oct 1996-Dec 2010: Cumulative mean exposure, time varying
LUR: Address-level allowing for change of residence
Age, sex, education, occupation but not smoking or BMI
All-cause, CV, CHD, Cerebrovascular, Respiratory, Lung Cancer
[27] Cesaroni G et al 2012
684,204 (subset age 45-80 in 2001)
2001-2006 1995/1996: Annual mean
LUR: Address-level
Age, sex, education, occupation but not smoking or BMI
All-cause
Dutch Environmental Longitudinal Study DUELS Dutch inhabitants who had lived at the same address between 1/1/1999 and 1/1/2004
Nether-lands
2004 >=30 [36] Fisher PH et al 2015
7,218,363 2004-2010 2001 LUR: 100 x 100m level
Age, sex, standardised disposable household income but not smoking or BMI
All-cause, CV, Respiratory, Lung Cancer
Netherlands Cohort Study (NLCS-AIR) Subjects selected from 323 of the 714 municipalities of the Netherlands
Nether-lands
1986 55-69 [20] Beelen et al, 2009
117,528 1987-1996 1987-1996 LUR, Monitoring, GIS: Address-level (baseline address)
Age, sex, smoking, neighbourhood indicators of socioeconomic status but not BMI
CV, CHD, HF, Cerebrovascular, Cardiac Dysrhythmia
[24] Brunekreef B et al 2009
117,528 1987-1996 1987-1996 LUR, Monitoring, GIS: Address-level (baseline address)
Age, sex, smoking, neighbourhood level and COROP area-level percentage of persons with high & low income but not BMI
All-cause, CV, Respiratory, Lung Cancer
[42] Hoek et al 2002
Random sample: 4,492
Sept 1986-Oct 1994
1987-1990 LUR, Monitoring, GIS: Address-level (baseline address)
Age, sex, smoking, BMI, education, occupation
All-cause, Cardio-pulmonary
Oslo cohort Inhabitants of Oslo
Norway 1992 51-90 [53] Naess O et al 2007
143,842 1992-1998 1992-1995 DM: Neighbourhood level
Age, education, occupational class but not smoking or BMI
CV, COPD, Lung Cancer
9
(sex-specific analyses)
North American Cohorts
Adventist Health Study on the Health Effects of Smog (AHSMOG) Sub-sample of the Adventist Health Survey. All subjects, Seventh Day Adventists, white, non-Hispanic and resident in California.
USA 1977 27-95
[29] Chen LH et al 2005
3,239 [1,149 M; 2090 F]
1977-1998 1973-1998: Annual mean, time varying (4-year window)
Monitor data: Interpolation to ZIP code centroid (<50km from monitor), allowing for change of residence
Age, sex, past smoking, BMI, years of education.
CHD
[18] Abbey D et al 1999
5,652 [~3621 F; ~2031 M]
1977-1992 1973-1992: Cumulative monthly mean exposure, time varying
Monitor data: Interpolation to ZIP code centroid (<50km from monitor), allowing for change of residence
Age, sex, past smoking, BMI, years of education (sex-specific analyses)
All-cause, Cardio-pulmonary, Respiratory, Lung Cancer
American Cancer Society Prevention II Study (ACS CSP-II) Friends, neighbours, acquaintances of American Cancer Study (ACS) volunteers
USA 1982 >=30 [61] Turner et al, 2016
669,046 1982-2004 Annual average for 2006
LUR (~30 m): Address-level
Age, sex, smoking, BMI, occupational and educational factors
All-cause, CV, CHD, Cerebrovascular,Respiratory, COPD, Pneumonia, Diabetes, Lung Cancer
[44] Jerrett M et al 2013
73,711 California
1982-2000 1988-2002 LUR: Address-level
Age, sex, smoking, BMI, occupational and educational factors.
All-cause, CV, CHD, Stroke, Respiratory, Lung Cancer
[47] Krewski et al 2009
406,917 1982-2000 1980 Monitoring Data: MSA level
Age, sex, smoking, BMI, occupational and educational factors.
All-cause, Cardio-pulmonary, CHD, Lung Cancer
10
[52] McKean-Cowen et al, 2009
527,123 1982-2000 1982-1998 Monitoring Data: MSA level
Age, sex, smoking, educational level but not BMI
Brain cancer
[43] Krewski et al, 2000
552,138 (295,223 with PM2.5)
1982-1989 1980 Monitoring data: City level
Age, sex, smoking, BMI, educational level
All-cause, Cardio-pulmonary, Lung Cancer
California Teachers Study (CTS) Prospective cohort of female public school professionals living in California.
USA 1995 >=30 [50] Lipset MJ et al 2011
12,336 June 1997-Dec 2005
June 1996-Dec 2005: Cumulative mean exposure, time varying
Monitor data & IDW: 250x250m grid level , allowing for change of residence
Age, smoking, BMI, ecological variables for income, education, unemployment
All-cause, CV, CHD, Cerebrovascular, Respiratory, Lung Cancer
Six Cities Random sample from white subjects in six communities
USA 1974-1977
25-74 [43] Krewski et al, 2000
8,111 1974-1989 1977-1985 Monitor data: City-level
Age, sex, smoking, BMI, education level
All-cause, Cardio-pulmonary, Lung Cancer
US trucking industry cohort Men employed in four trucking companies
USA 1985 15.3-84.9 [40] Hart JE et al 2011
53,814 1985-2000 1985-2000 LUR + spatial smoothing: Address-level
Age, years of work in each of 8 job groups but not smoking or BMI (all male cohort)
All-cause, CV, CHD, Respiratory, COPD, Lung Cancer
Washington University-EPRI Veterans cohort US male veterans with a diagnosis of hypertension
USA 1975-1976
Mean (SD) 51 (12)
[48] Lipfert et al 2006a
~15,200 survivors (1997)
1997-2001 1997-2001 Monitor data: County-level
Age, smoking, BMI, zip code and /or country-level education and income (male only cohort)
All-cause
[49] Lipfert et al 2006b
28,635 survivors (1997)
1997-2001 1999-2001 Monitor data: County-level
Age, smoking, BMI, zip-code level: income, education and poverty status (male only cohort)
All-cause
Canadian Census Health and Environment Cohort (Can CHEC)
Canada 1991 25-89 [32] Crouse DL et al 2015a
735,590 [10 cities]
Jun 1991-Dec 2006
1984-2006: Annual mean exposure, time varying (7-year moving window)
LUR: Post-code level allowing for change in residence
Age, sex, education, employment status, occupational classification,
All-cause, CV, CHD, Cerebrovascular, Respiratory
11
Population based cohort of residents
household income but no direct adjustment for smoking or BMI
[31] Crouse DL et al 2015b
2,521,525 Jun 1991-Dec 2006
1984-2006: Annual mean exposure, time varying (7-year moving window)
LUR: Post-code level allowing for change in residence
Age, sex, education, employment status, occupational classification, household income but no direct adjustment for smoking or BMI
All-cause, CV, CHD, Cerebrovascular, Respiratory, COPD, Diabetes, Lung Cancer
Ontario tax cohort Retrospective cohort study. Random sample from federal family income tax database of subjects living in 3 cities in Ontario.
Canada 1982-1986
35-85 [28] Chen H et al 2013
205,440 1982-2004 1982-2004: 3-year moving average, time varying
LUR: Post-code level allowing for change of residence
Age, sex, annual household income, but not smoking or BMI
CV, CHD, Cerebrovascular
Toronto respiratory cohort Subjects treated at a respiratory disease clinic in Toronto
Canada 1992-1999
Median (IQR) 60 (49-69)
[45] Jerrett M et al 2009
2,360 1992-2002 Average (Autumn 2002 and Spring 2004)
LUR: Address-level
Age, sex, smoking, BMI, EA-level deprivation index
All-cause, CV, Respiratory
Vacouver cohort Long-term (5+ years) residents of Metropolitan
Canada 1999 45-85 [38] Gan WQ et al 2011
452,735 1999-2002 1994-1998 LUR (10m): Address-level allowing for change of residence
Age, sex, neighbourhood socioeconomic status but not smoking or BMI
CHD
12
Vancouver with no CHD history.
[37] Gan WQ et al 2013
467,994 1999-2002 1994-1998 LUR: Address-level allowing for change of residence
Age, sex, neighbourhood socioeconomic status but not smoking or BMI
COPD
Chinese Cohorts
Shenyang cohort Population based retrospective cohort of family members living in 5 urban districts of Shenyang city.
China 2009 35-103 [64] Zhang P et al 2011
9,941 1998-2009 1998-2009: Annual mean exposure, time varying
Monitoring data: District-level
Age, sex, smoking, BMI, educational level, personal income
CV, Cerebrovascular
[34] Dong G-H et al 2012
9,941 1998-2009 1998-2009: Annual mean exposure, time varying
Monitoring data: District-level
Age, sex, smoking, BMI, educational level, household income
Respiratory
Four northern Chinese cities Random sample of neighbourhoods within 1km of a monitor.
China 1998 23-89 [30] Chen et al, 2016
39,054 1998-2009 1998-2009 Annual average, time varying OR 1998 annual average
Monitor data: nearest monitor <1km.
Age, sex, BMI, household income, occupation
All-cause, Lung Cancer
Japanese Cohorts
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Shizuoka elderly cohort Age-sex - stratification random sample of residents from 74 municipalities of Shizuoka
Japan Dec 1999
65-84 [63] Yorifuji T et al 2010
13,444 Dec 1999-Mar 2006
Apr 2000-Mar 2006
LUR: Address-level
Age, sex, smoking, BMI, financial capability
All-cause, CV, CHD, Cerebrovascular, Cardio-pulmonary, Respiratory, COPD, Pneumonia, Lung Cancer
[62] Yorifuji T et al 2013
13,412 Dec 1999-Jan 2009
Apr 1996-March 2009: Annual mean exposure, time varying
LUR: Address-level
Age, sex, smoking, BMI, financial capability
All-cause, CV, CHD, Cerebrovascular (and sub-types), Cardio-pulmonary, Respiratory, COPD, Pneumonia, Lung Cancer
3 Japanese Prefectures Subjects living in 6 areas in 3 prefectures
Japan 1983-1985
>=40 [46] Katanoda K et al 2011
63,520 10 years (max to Oct 1995)
1974-1983 Monitor data: area-level
Age, sex, smoking, health insurance type / occupational exposure, but not BMI
Respiratory, COPD, Pneumonia, Lung Cancer
Taiwanese Cohort
TCS Civil servants in Greater Taipei area
Taiwan 1989-1992
Employed Mean (SD) 41.3 (10.5)
[60] Tseng et al, 2015
43,227 1992-2008 2000-2008 Monitor data: District-level
Age, sex, smoking, BMI, income, educational level
CV
¶ lung cancer and for female all natural causes not adjusted for BMI; # This is not meant to be a complete list of covariates but focusses only on a few “key” variables.
14
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36. Fischer PH, Marra M, Ameling CB, Hoek G, Beelen R, de Hoogh K, Breugelmans O, Kruize H, Janssen NA, Houthuijs D. Air Pollution and Mortality in Seven Million Adults: The Dutch Environmental Longitudinal Study (DUELS). Environ Health Perspect 2015;123(7):697-704.
37. Gan WQ, FitzGerald JM, Carlsten C, Sadatsafavi M, Brauer M. Associations of ambient air pollution with chronic obstructive pulmonary disease hospitalization and mortality. American Journal of Respiratory and Critical Care Medicine 2013;187(7):721-727.
38. Gan WQ, Koehoorn M, Davies HW, Demers PA, Tamburic L, Brauer M. Long-term exposure to traffic-related air pollution and the risk of coronary heart disease hospitalization and mortality. Environmental Health Perspectives 2011;119(4):501-507.
16
39. Gehring U, Heinrich J, Kramer U, Grote V, Hochadel M, Sugiri D, Kraft M, Rauchfuss K, Eberwein HG, Wichmann HE. Long-term exposure to ambient air pollution and cardiopulmonary mortality in women. Epidemiology 2006;17(5):545-551.
40. Hart JE, Garshick E, Dockery DW, Smith TJ, Ryan L, Laden F. Long-Term Ambient Multipollutant Exposures and Mortality. American Journal of Respiratory and Critical Care Medicine 2011;183(1):73-78.
41. Heinrich J, Thiering E, Rzehak P, Kramer U, Hochadel M, Rauchfuss KM, Gehring U, Wichmann HE. Long-term exposure to NO2 and PM10 and all-cause and cause-specific mortality in a prospective cohort of women. Occupational & Environmental Medicine 2013;70(3):179-86.
42. Hoek G, Brunekreef B, Goldbohm S, Fischer P, Van Den Brandt PA. Association between mortality and indicators of traffic-related air pollution in the Netherlands: A cohort study. Lancet 2002;360(9341):1203-1209.
43. Institute HE. Reanalysis of the Harvard Six Cities Study and the American Cancer Society Study of Particulate Air Pollution and Mortality: A Special Report of the Institute’s Particle Epidemiology Reanalysis Projec. 2000.
44. Jerrett M, Burnett RT, Beckerman BS, Turner MC, Krewski D, Thurston G, Martin RV, van Donkelaar A, Hughes E, Shi Y, Gapstur SM, Thun MJ, Pope CA, 3rd. Spatial analysis of air pollution and mortality in California. Am J Respir Crit Care Med 2013;188(5):593-9.
45. Jerrett M, Finkelstein MM, Brook JR, Arain MA, Kanaroglou P, Stieb DM, Gilbert NL, Verma D, Finkelstein N, Chapman KR, Sears MR. A cohort study of traffic-related air pollution and mortality in Toronto, Ontario, Canada. Environmental Health Perspectives 2009;117(5):772-777.
46. Katanoda K, Sobue T, Satoh H, Tajima K, Suzuki T, Nakatsuka H, Takezaki T, Nakayama T, Nitta H, Tanabe K, Tominaga S. An association between long-term exposure to ambient air pollution and mortality from lung cancer and respiratory diseases in Japan. Journal of Epidemiology 2011;21(2):132-143.
47. Krewski D, Jerrett M, Burnett RT, Ma R, Hughes E, Shi Y, Turner MC, Pope 3rd CA, Thurston G, Calle EE, Thun MJ, Beckerman B, DeLuca P, Finkelstein N, Ito K, Moore DK, Newbold KB, Ramsay T, Ross Z, Shin H, Tempalski B. Extended follow-up and spatial analysis of the American Cancer Society study linking particulate air pollution and mortality. Research report (Health Effects Institute) 2009(140):5-114; discussion 115-136.
48. Lipfert FW, Baty JD, Miller JP, Wyzga RE. PM2.5 constituents and related air quality variables as predictors of survival in a cohort of U.S. military veterans. Inhalation Toxicology 2006;18(9):645-57.
49. Lipfert FW, Wyzga RE, Baty JD, Miller JP. Traffic density as a surrogate measure of environmental exposures in studies of air pollution health effects: Long-term mortality in a cohort of US veterans. Atmospheric Environment 2006;40(1):154-169.
50. Lipsett MJ, Ostro BD, Reynolds P, Goldberg D, Hertz A, Jerrett M, Smith DF, Garcia C, Chang ET, Bernstein L. Long-term exposure to air pollution and cardiorespiratory disease in the California teachers study cohort. American Journal of Respiratory and Critical Care Medicine 2011;184(7):828-835.
51. Maheswaran R, Pearson T, Smeeton NC, Beevers SD, Campbell MJ, Wolfe CD. Impact of outdoor air pollution on survival after stroke: Population-based cohort study. Stroke 2010;41(5):869-877.
52. McKean-Cowdin R, Calle EE, Peters JM, Henley J, Hannan L, Thurston GD, Thun MJ, Preston-Martin S. Ambient air pollution and brain cancer mortality. Cancer Causes and Control 2009;20(9):1645-1651.
53. Naess O, Nafstad P, Aamodt G, Claussen B, Rosland P. Relation between concentration of air pollution and cause-specific mortality: Four-year exposures to nitrogen dioxide and
17
particulate matter pollutants in 470 neighborhoods in Oslo, Norway. American Journal of Epidemiology 2007;165(4):435-443.
54. Raaschou-Nielsen O, Andersen ZJ, Jensen SS, Ketzel M, Sorensen M, Hansen J, Loft S, Tjonneland A, Overvad K. Traffic air pollution and mortality from cardiovascular disease and all causes: a Danish cohort study. Environmental Health: A Global Access Science Source 2012;11:60.
55. Raaschou-Nielsen O, Sorensen M, Ketzel M, Hertel O, Loft S, Tjonneland A, Overvad K, Andersen ZJ. Long-term exposure to traffic-related air pollution and diabetes-associated mortality: a cohort study. Diabetologia 2013;56(1):36-46.
56. Rosenlund M, Picciotto S, Forastiere F, Stafoggia M, Perucci CA. Traffic-related air pollution in relation to incidence and prognosis of coronary heart disease. Epidemiology 2008;19(1):121-128.
57. Schikowski T, Sugiri D, Ranft U, Gehring U, Heinrich J, Wichmann HE, Kramer U. Does respiratory health contribute to the effects of long-term air pollution exposure on cardiovascular mortality? Respiratory research 2007;8:20.
58. Sorensen M, Luhdorf P, Ketzel M, Andersen ZJ, Tjonneland A, Overvad K, Raaschou-Nielsen O. Combined effects of road traffic noise and ambient air pollution in relation to risk for stroke? Environmental Research 2014;133:49-55.
59. Tonne C, Wilkinson P. Long-term exposure to air pollution is associated with survival following acute coronary syndrome. European Heart Journal 2013;34(17):1306-1311.
60. Tseng E, Ho WC, Lin MH, Cheng TJ, Chen PC, Lin HH. Chronic exposure to particulate matter and risk of cardiovascular mortality: cohort study from Taiwan. BMC Public Health 2015;15:936.
61. Turner MC, Jerrett M, Pope CA, Krewski D, Gapstur SM, Diver WR, Beckerman BS, Marshall JD, Su J, Crouse DL, Burnett RT. Long-Term Ozone Exposure and Mortality in a Large Prospective Study. American Journal of Respiratory and Critical Care Medicine 2016;193(10):1134-1142.
62. Yorifuji T, Kashima S, Tsuda T, Ishikawa-Takata K, Ohta T, Tsuruta K, Doi H. Long-term exposure to traffic-related air pollution and the risk of death from hemorrhagic stroke and lung cancer in Shizuoka, Japan. Science of the Total Environment 2013;443:397-402.
63. Yorifuji T, Kashima S, Tsuda T, Takao S, Suzuki E, Doi H, Sugiyama M, Ishikawa-Takata K, Ohta T. Long-term exposure to traffic-related air pollution and mortality in Shizuoka, Japan. Occupational and Environmental Medicine 2010;67(2):111-117.
64. Zhang PF, Dong GH, Sun BJ, Zhang LW, Chen X, Ma NN, Yu F, Guo HM, Huang H, Lee YL, Tang NJ, Chen J. Long-Term Exposure to Ambient Air Pollution and Mortality Due to Cardiovascular Disease and Cerebrovascular Disease in Shenyang, China. Plos One 2011;6(6).
65. Tonne C, Halonen JI, Beevers SD, Dajnak D, Gulliver J, Kelly FJ, Wilkinson P, Anderson HR. Long-term traffic air and noise pollution in relation to mortality and hospital readmission among myocardial infarction survivors. International Journal of Hygiene & Environmental Health 2016;219(1):72-8.
18
eFigure 1 All-cause mortality
* indicates exclusion from meta-analysis. Refer to methods/results sections of manuscript.
Crouse et al *
Crouse et al
Jerrett et al
HEI *
Krewski et al *
Jerrett et al *
Turner et al
Abbey et al
Lipsett et al
HEI
Hart et al
Lipfert et al *
Lipfert et al
Raaschou-Nielsen et al *
Carey et al
Maheswaren et al
Tonne et al
Beelen et al
Bentayeb et al
Filleul et al
Gehring et al *
Heinrich et al *
Rosenlund et al
Cesaroni et al *
Cesaroni et al
Tonne et al *
Fischer et al
Hoek et al *
Brunekreef et al
Chen et al
Yorifuji et al *
Yorifuji et al
Study
2015a
2015b
2009
2000
2009
2013
2016
1999
2011
2000
2011
2006a
2006b
2012
2013
2010
2013
2014b
2015
2005
2006
2013
2008
2012
2013
2016
2015
2002
2009
2016
2010
2013
Year
CanCHEC
CanCHEC
Toronto respiratory cohort
ACS CPS-II
ACS CPS-II
ACS CPS-II
ACS CPS-II
AHSMOG
CTS
Six Cities
US trucking industry cohort
Washington University-EPRI Veterans cohort
Washington University-EPRI Veterans cohort
DCH
CPRD
SLSR
MINAP (ACS survivors)
ESCAPE
Gazel
PAARC
German cohort
German cohort
CHD survivors cohort
Rome longitudinal study
Rome longitudinal study
MINAP (ACS survivors)
DUELS
NLCS-AIR
NLCS-AIR
Four northern Chinese cities
Shizuoka elderly cohort
Shizuoka elderly cohort
Cohort
Canada
Canada
Canada
USA
USA
USA
USA
USA
USA
USA
USA
USA
USA
Denmark
England
England
England & Wales
Europe
France
France
Germany
Germany
Italy
Italy
Italy
London
Netherlands
Netherlands
Netherlands
China
Japan
Japan
Setting
735,590
2,521,525
2,360
552,138
406,917
73,711
669,046
5,652
12,336
8,111
53,814
~15,200
28,635
52,061
830,429
3,320
154,204
367,251
20,327
14,284
4,752
4,752
6,513
684,204
1,265,058
18,138
7,218,363
2,788
117,528
39,054
13,444
13,412
N
FM
FM
FM
FM
FM
FM
FM
FM
F
FM
M
M
M
FM
FM
FM
FM
FM
FM
FM
F
F
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
Sex
25-89
25-89
60 (49 69)
>=30
>=30
>=30
>=30
27-95
>=30
25-74
15.3-84.9
51 (12)
51 (12)
50-64
40-89
70.4 (14.6)
>25
All
35-50
25-59
50-59
50-59
35-84
45-80
>=30
>25
>=30
55-69
55-69
23-89
65-84
65-84
Age
Indirect smoking and BMI
Indirect smoking and BMI
No BMI, Smoking
No BMI
No BMI
No BMI, Age
No BMI, Age(?)
No BMI, Smoking
No BMI, Smoking
No BMI, Smoking
No BMI
No BMI, Smoking
No BMI
Adjustment
Confounder
1.05 (1.03, 1.07)
1.03 (1.03, 1.04)
1.23 (1.00, 1.51)
0.99 (0.99, 1.00)
1.00 (0.99, 1.00)
1.04 (1.01, 1.07)
1.02 (1.01, 1.03)
1.00 (0.98, 1.02)
0.98 (0.95, 1.02)
1.08 (1.02, 1.14)
1.05 (1.03, 1.08)
1.02 (0.98, 1.07)
1.03 (0.99, 1.07)
1.08 (1.02, 1.15)
1.02 (1.00, 1.05)
1.41 (1.14, 1.75)
1.01 (0.98, 1.04)
1.01 (0.99, 1.03)
1.07 (1.00, 1.15)
1.14 (1.03, 1.26)
1.12 (1.01, 1.23)
1.11 (1.04, 1.18)
0.95 (0.89, 1.02)
1.06 (1.04, 1.08)
1.03 (1.02, 1.03)
1.05 (0.98, 1.12)
1.03 (1.02, 1.03)
1.08 (0.94, 1.23)
1.03 (1.00, 1.05)
0.93 (0.90, 0.95)
1.02 (0.96, 1.08)
1.12 (1.07, 1.18)
ES (95% CI)
1.05 (1.03, 1.07)
1.03 (1.03, 1.04)
1.23 (1.00, 1.51)
0.99 (0.99, 1.00)
1.00 (0.99, 1.00)
1.04 (1.01, 1.07)
1.02 (1.01, 1.03)
1.00 (0.98, 1.02)
0.98 (0.95, 1.02)
1.08 (1.02, 1.14)
1.05 (1.03, 1.08)
1.02 (0.98, 1.07)
1.03 (0.99, 1.07)
1.08 (1.02, 1.15)
1.02 (1.00, 1.05)
1.41 (1.14, 1.75)
1.01 (0.98, 1.04)
1.01 (0.99, 1.03)
1.07 (1.00, 1.15)
1.14 (1.03, 1.26)
1.12 (1.01, 1.23)
1.11 (1.04, 1.18)
0.95 (0.89, 1.02)
1.06 (1.04, 1.08)
1.03 (1.02, 1.03)
1.05 (0.98, 1.12)
1.03 (1.02, 1.03)
1.08 (0.94, 1.23)
1.03 (1.00, 1.05)
0.93 (0.90, 0.95)
1.02 (0.96, 1.08)
1.12 (1.07, 1.18)
ES (95% CI)
1.9 1 1.1 1.2
19
eFigure 2a All-cause mortality – fixed effects model
eFigure 2b All-cause mortality - random effects model
Overall (I-squared = 83.9%, p = 0.000)
Crouse et al
Jerrett et al
Carey et al
HEI
Fischer et al
Lipsett et al
Lipfert et al
Yorifuji et al
Abbey et al
Study
Filleul et al
Maheswaren et al
Bentayeb et al
Cesaroni et al
Chen et al
Tonne et al
Beelen et al
Hart et al
Turner et al
Brunekreef et al
Rosenlund et al
2015b
2009
2013
2000
2015
2011
2006b
2013
1999
Year
2005
2010
2015
2013
2016
2013
2014b
2011
2016
2009
2008
CanCHEC
Toronto respiratory cohort
CPRD
Six Cities
DUELS
CTS
Washington University-EPRI Veterans cohort
Shizuoka elderly cohort
AHSMOG
Cohort
PAARC
SLSR
Gazel
Rome longitudinal study
Four northern Chinese cities
MINAP (ACS survivors)
ESCAPE
US trucking industry cohort
ACS CPS-II
NLCS-AIR
CHD survivors cohort
Canada
Canada
England
USA
Netherlands
USA
USA
Japan
USA
Setting
France
England
France
Italy
China
England & Wales
Europe
USA
USA
Netherlands
Italy
2,521,525
2,360
830,429
8,111
7,218,363
12,336
28,635
13,412
5,652
N
14,284
3,320
20,327
1,265,058
39,054
154,204
367,251
53,814
669,046
117,528
6,513
1.03 (1.02, 1.03)
1.03 (1.03, 1.04)
1.23 (1.00, 1.51)
1.02 (1.00, 1.05)
1.08 (1.02, 1.14)
1.03 (1.02, 1.03)
0.98 (0.95, 1.02)
1.03 (0.99, 1.07)
1.12 (1.07, 1.18)
1.00 (0.98, 1.02)
ES (95% CI)
1.14 (1.03, 1.26)
1.41 (1.14, 1.75)
1.07 (1.00, 1.15)
1.03 (1.02, 1.03)
0.93 (0.90, 0.95)
1.01 (0.98, 1.04)
1.01 (0.99, 1.03)
1.05 (1.03, 1.08)
1.02 (1.01, 1.03)
1.03 (1.00, 1.05)
0.95 (0.89, 1.02)
100.00
15.10
0.01
1.00
0.22
51.16
0.50
0.47
0.27
1.23
Weight
0.06
0.01
0.11
13.49
0.73
0.67
1.69
1.10
11.08
%
0.96
0.13
1.03 (1.02, 1.03)
1.03 (1.03, 1.04)
1.23 (1.00, 1.51)
1.02 (1.00, 1.05)
1.08 (1.02, 1.14)
1.03 (1.02, 1.03)
0.98 (0.95, 1.02)
1.03 (0.99, 1.07)
1.12 (1.07, 1.18)
1.00 (0.98, 1.02)
ES (95% CI)
1.14 (1.03, 1.26)
1.41 (1.14, 1.75)
1.07 (1.00, 1.15)
1.03 (1.02, 1.03)
0.93 (0.90, 0.95)
1.01 (0.98, 1.04)
1.01 (0.99, 1.03)
1.05 (1.03, 1.08)
1.02 (1.01, 1.03)
1.03 (1.00, 1.05)
0.95 (0.89, 1.02)
100.00
15.10
0.01
1.00
0.22
51.16
0.50
0.47
0.27
1.23
Weight
0.06
0.01
0.11
13.49
0.73
0.67
1.69
1.10
11.08
%
0.96
0.13
1.9 1 1.1 1.2
NOTE: Weights are from random effects analysis
. (0.99, 1.06)with estimated predictive interval
Overall (I-squared = 83.9%, p = 0.000)
Lipsett et al
Rosenlund et al
Maheswaren et al
Chen et al
Beelen et al
Carey et al
Abbey et al
Yorifuji et al
Filleul et al
HEI
Hart et al
Lipfert et al
Brunekreef et al
Turner et al
Study
Bentayeb et al
Tonne et al
Cesaroni et al
Jerrett et al
Crouse et al
Fischer et al
2011
2008
2010
2016
2014b
2013
1999
2013
2005
2000
2011
2006b
2009
2016
Year
2015
2013
2013
2009
2015b
2015
CTS
CHD survivors cohort
SLSR
Four northern Chinese cities
ESCAPE
CPRD
AHSMOG
Shizuoka elderly cohort
PAARC
Six Cities
US trucking industry cohort
Washington University-EPRI Veterans cohort
NLCS-AIR
ACS CPS-II
Cohort
Gazel
MINAP (ACS survivors)
Rome longitudinal study
Toronto respiratory cohort
CanCHEC
DUELS
USA
Italy
England
China
Europe
England
USA
Japan
France
USA
USA
USA
Netherlands
USA
Setting
France
England & Wales
Italy
Canada
Canada
Netherlands
12,336
6,513
3,320
39,054
367,251
830,429
5,652
13,412
14,284
8,111
53,814
28,635
117,528
669,046
N
20,327
154,204
1,265,058
2,360
2,521,525
7,218,363
1.02 (1.01, 1.03)
0.98 (0.95, 1.02)
0.95 (0.89, 1.02)
1.41 (1.14, 1.75)
0.93 (0.90, 0.95)
1.01 (0.99, 1.03)
1.02 (1.00, 1.05)
1.00 (0.98, 1.02)
1.12 (1.07, 1.18)
1.14 (1.03, 1.26)
1.08 (1.02, 1.14)
1.05 (1.03, 1.08)
1.03 (0.99, 1.07)
1.03 (1.00, 1.05)
1.02 (1.01, 1.03)
ES (95% CI)
1.07 (1.00, 1.15)
1.01 (0.98, 1.04)
1.03 (1.02, 1.03)
1.23 (1.00, 1.51)
1.03 (1.03, 1.04)
1.03 (1.02, 1.03)
100.00
4.31
1.60
0.19
5.26
7.28
6.06
6.58
2.90
%
0.86
2.45
6.31
4.17
5.97
9.65
Weight
1.45
5.07
9.76
0.21
9.81
10.12
1.02 (1.01, 1.03)
0.98 (0.95, 1.02)
0.95 (0.89, 1.02)
1.41 (1.14, 1.75)
0.93 (0.90, 0.95)
1.01 (0.99, 1.03)
1.02 (1.00, 1.05)
1.00 (0.98, 1.02)
1.12 (1.07, 1.18)
1.14 (1.03, 1.26)
1.08 (1.02, 1.14)
1.05 (1.03, 1.08)
1.03 (0.99, 1.07)
1.03 (1.00, 1.05)
1.02 (1.01, 1.03)
ES (95% CI)
1.07 (1.00, 1.15)
1.01 (0.98, 1.04)
1.03 (1.02, 1.03)
1.23 (1.00, 1.51)
1.03 (1.03, 1.04)
1.03 (1.02, 1.03)
100.00
4.31
1.60
0.19
5.26
7.28
6.06
6.58
2.90
%
0.86
2.45
6.31
4.17
5.97
9.65
Weight
1.45
5.07
9.76
0.21
9.81
10.12
1.9 1 1.1 1.2
20
eFigure 3 All-cause mortality - stratification by pre-existing disease on recruitment
NOTE: Weights are from random effects analysis
.
.
General
Crouse et al
Hart et al
Lipsett et al
Abbey et al
HEI
Turner et al
Carey et al
Beelen et al
Filleul et al
Bentayeb et al
Cesaroni et al
Fischer et al
Brunekreef et al
Chen et al
Yorifuji et al
Subtotal (I-squared = 86.1%, p = 0.000)
Preexisting
Jerrett et al
Lipfert et al
Maheswaren et al
Tonne et al
Rosenlund et al
Subtotal (I-squared = 76.3%, p = 0.002)
Study
2015b
2011
2011
1999
2000
2016
2013
2014b
2005
2015
2013
2015
2009
2016
2013
2009
2006b
2010
2013
2008
Year
CanCHEC
US trucking industry cohort
CTS
AHSMOG
Six Cities
ACS CPS-II
CPRD
ESCAPE
PAARC
Gazel
Rome longitudinal study
DUELS
NLCS-AIR
Four northern Chinese cities
Shizuoka elderly cohort
Toronto respiratory cohort
Washington University-EPRI Veterans cohort
SLSR
MINAP (ACS survivors)
CHD survivors cohort
Cohort
Canada
USA
USA
USA
USA
USA
England
Europe
France
France
Italy
Netherlands
Netherlands
China
Japan
Canada
USA
England
England & Wales
Italy
Setting
2,521,525
53,814
12,336
5,652
8,111
669,046
830,429
367,251
14,284
20,327
1,265,058
7,218,363
117,528
39,054
13,412
2,360
28,635
3,320
154,204
6,513
N
FM
M
F
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
M
FM
FM
FM
Sex
25-89
15.3-84.9
>=30
27-95
25-74
>=30
40-89
All
25-59
35-50
>=30
>=30
55-69
23-89
65-84
60 (49 69)
51 (12)
70.4 (14.6)
>25
35-84
Age
1.03 (1.03, 1.04)
1.05 (1.03, 1.08)
0.98 (0.95, 1.02)
1.00 (0.98, 1.02)
1.08 (1.02, 1.14)
1.02 (1.01, 1.03)
1.02 (1.00, 1.05)
1.01 (0.99, 1.03)
1.14 (1.03, 1.26)
1.07 (1.00, 1.15)
1.03 (1.02, 1.03)
1.03 (1.02, 1.03)
1.03 (1.00, 1.05)
0.93 (0.90, 0.95)
1.12 (1.07, 1.18)
1.02 (1.01, 1.03)
1.23 (1.00, 1.51)
1.03 (0.99, 1.07)
1.41 (1.14, 1.75)
1.01 (0.98, 1.04)
0.95 (0.89, 1.02)
1.04 (0.98, 1.10)
ES (95% CI)
11.34
7.01
4.69
7.34
2.62
11.14
6.72
8.18
0.91
1.53
11.28
11.74
6.62
5.78
3.11
100.00
6.73
31.07
6.23
32.14
23.84
100.00
Weight
%
1.03 (1.03, 1.04)
1.05 (1.03, 1.08)
0.98 (0.95, 1.02)
1.00 (0.98, 1.02)
1.08 (1.02, 1.14)
1.02 (1.01, 1.03)
1.02 (1.00, 1.05)
1.01 (0.99, 1.03)
1.14 (1.03, 1.26)
1.07 (1.00, 1.15)
1.03 (1.02, 1.03)
1.03 (1.02, 1.03)
1.03 (1.00, 1.05)
0.93 (0.90, 0.95)
1.12 (1.07, 1.18)
1.02 (1.01, 1.03)
1.23 (1.00, 1.51)
1.03 (0.99, 1.07)
1.41 (1.14, 1.75)
1.01 (0.98, 1.04)
0.95 (0.89, 1.02)
1.04 (0.98, 1.10)
ES (95% CI)
11.34
7.01
4.69
7.34
2.62
11.14
6.72
8.18
0.91
1.53
11.28
11.74
6.62
5.78
3.11
100.00
6.73
31.07
6.23
32.14
23.84
100.00
Weight
%
1.9 1 1.1 1.2
21
eFigure 4 All-cause mortality - stratification by age range at cohort recruitment
NOTE: Weights are from random effects analysis
.
.
Adult
Crouse et al
Hart et al
Lipsett et al
Abbey et al
HEI
Turner et al
Carey et al
Beelen et al
Cesaroni et al
Fischer et al
Chen et al
Subtotal (I-squared = 87.6%, p = 0.000)
Restricted
Filleul et al
Bentayeb et al
Brunekreef et al
Yorifuji et al
Subtotal (I-squared = 78.9%, p = 0.003)
Study
2015b
2011
2011
1999
2000
2016
2013
2014b
2013
2015
2016
2005
2015
2009
2013
Year
CanCHEC
US trucking industry cohort
CTS
AHSMOG
Six Cities
ACS CPS-II
CPRD
ESCAPE
Rome longitudinal study
DUELS
Four northern Chinese cities
PAARC
Gazel
NLCS-AIR
Shizuoka elderly cohort
Cohort
Canada
USA
USA
USA
USA
USA
England
Europe
Italy
Netherlands
China
France
France
Netherlands
Japan
Setting
2,521,525
53,814
12,336
5,652
8,111
669,046
830,429
367,251
1,265,058
7,218,363
39,054
14,284
20,327
117,528
13,412
N
FM
M
F
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
Sex
25-89
15.3-84.9
>=30
27-95
25-74
>=30
40-89
All
>=30
>=30
23-89
25-59
35-50
55-69
65-84
Age
1.03 (1.03, 1.04)
1.05 (1.03, 1.08)
0.98 (0.95, 1.02)
1.00 (0.98, 1.02)
1.08 (1.02, 1.14)
1.02 (1.01, 1.03)
1.02 (1.00, 1.05)
1.01 (0.99, 1.03)
1.03 (1.02, 1.03)
1.03 (1.02, 1.03)
0.93 (0.90, 0.95)
1.02 (1.01, 1.03)
1.14 (1.03, 1.26)
1.07 (1.00, 1.15)
1.03 (1.00, 1.05)
1.12 (1.07, 1.18)
1.08 (1.02, 1.15)
ES (95% CI)
13.36
7.73
4.99
8.12
2.70
13.09
7.38
9.18
13.27
13.92
6.25
100.00
17.45
22.27
32.27
28.01
100.00
Weight
%
1.03 (1.03, 1.04)
1.05 (1.03, 1.08)
0.98 (0.95, 1.02)
1.00 (0.98, 1.02)
1.08 (1.02, 1.14)
1.02 (1.01, 1.03)
1.02 (1.00, 1.05)
1.01 (0.99, 1.03)
1.03 (1.02, 1.03)
1.03 (1.02, 1.03)
0.93 (0.90, 0.95)
1.02 (1.01, 1.03)
1.14 (1.03, 1.26)
1.07 (1.00, 1.15)
1.03 (1.00, 1.05)
1.12 (1.07, 1.18)
1.08 (1.02, 1.15)
ES (95% CI)
13.36
7.73
4.99
8.12
2.70
13.09
7.38
9.18
13.27
13.92
6.25
100.00
17.45
22.27
32.27
28.01
100.00
Weight
%
1.9 1 1.1 1.2
22
eFigure 5 Cardiovascular mortality
* indicates exclusion from meta-analysis. Refer to methods/results section of manuscript.
Chen et al
Crouse et al *
Crouse et al
Jerrett et al
Hart et al
Jerrett et al *
Lipsett et al
Turner et al
Zhang et al *
Raaschou-Nielsen et al *
Carey et al
Beelen et al
Bentayeb et al
Schikowski et al *
Cesaroni et al
Beelen et al *
Brunekreef et al
Fischer et al
Naess et al
Tseng et al
Yorifuji et al *
Yorifuji et al
Study
2013
2015a
2015b
2009
2011
2013
2011
2016
2011
2012
2013
2014a
2015
2007
2013
2009
2009
2015
2007
2015
2010
2013
Year
Ontario tax cohort
CanCHEC
CanCHEC
Toronto respiratory cohort
US trucking industry cohort
ACS CPS-II
CTS
ACS CPS-II
Shenyang cohort
DCH
CPRD
ESCAPE
Gazel
German cohort
Rome longitudinal study
NLCS-AIR
NLCS-AIR
DUELS
Oslo cohort
TCS
Shizuoka elderly cohort
Shizuoka elderly cohort
Cohort
Canada
Canada
Canada
Canada
USA
USA
USA
USA
China
Denmark
England
Europe
France
Germany
Italy
Netherlands
Netherlands
Netherlands
Norway
Taiwan
Japan
Japan
Setting
205,440
735,590
2,521,525
2,360
53,814
73,711
12,336
669,046
9,941
52,061
830,429
367,383
20,327
4,750
1,265,058
117,528
117,528
7,218,363
143,842
43,227
13,444
13,412
N
FM
FM
FM
FM
M
FM
F
FM
FM
FM
FM
FM
FM
F
FM
FM
FM
FM
FM
FM
FM
FM
Sex
35-85
25-89
25-89
60 (49 69)
15.3-84.9
>=30
>=30
>=30
35-103
50-64
40-89
All
35-50
50-59
>=30
55-69
55-69
>=30
51-90
41.3 (10.5)
65-84
65-84
Age
Indirect Smoking, No BMI
Indirect smoking and BMI
Indirect smoking and BMI
No BMI, Smoking
No BMI, Age
No BMI, Smoking
No BMI
No BMI
No BMI, Smoking
No BMI, Smoking
Adjustment
Confounder
1.08 (1.03, 1.12)
1.04 (1.02, 1.07)
1.03 (1.02, 1.04)
1.64 (1.13, 2.37)
1.04 (1.00, 1.09)
1.06 (1.01, 1.11)
0.99 (0.94, 1.05)
1.04 (1.03, 1.05)
2.46 (2.31, 2.62)
1.16 (1.03, 1.31)
1.00 (0.97, 1.03)
1.01 (0.97, 1.06)
1.00 (0.76, 1.30)
1.40 (1.14, 1.72)
1.03 (1.02, 1.04)
1.03 (0.98, 1.08)
1.02 (0.98, 1.07)
1.00 (0.99, 1.01)
1.02 (1.00, 1.05)
0.95 (0.75, 1.21)
1.15 (1.03, 1.28)
1.24 (1.15, 1.33)
ES (95% CI)
1.08 (1.03, 1.12)
1.04 (1.02, 1.07)
1.03 (1.02, 1.04)
1.64 (1.13, 2.37)
1.04 (1.00, 1.09)
1.06 (1.01, 1.11)
0.99 (0.94, 1.05)
1.04 (1.03, 1.05)
2.46 (2.31, 2.62)
1.16 (1.03, 1.31)
1.00 (0.97, 1.03)
1.01 (0.97, 1.06)
1.00 (0.76, 1.30)
1.40 (1.14, 1.72)
1.03 (1.02, 1.04)
1.03 (0.98, 1.08)
1.02 (0.98, 1.07)
1.00 (0.99, 1.01)
1.02 (1.00, 1.05)
0.95 (0.75, 1.21)
1.15 (1.03, 1.28)
1.24 (1.15, 1.33)
ES (95% CI)
1.9 1 1.11.2
23
eFigure 6a Cardiovascular mortality – fixed effects model
eFigure 6b Cardiovascular mortality – random effects model
Overall (I-squared = 83.4%, p = 0.000)
Yorifuji et al
Carey et al
Tseng et al
Hart et al
Brunekreef et al
Turner et al
Cesaroni et al
Beelen et al
Fischer et al
Lipsett et al
Jerrett et al
Crouse et al
Naess et al
Chen et al
Bentayeb et al
Study
2013
2013
2015
2011
2009
2016
2013
2014a
2015
2011
2009
2015b
2007
2013
2015
Year
Shizuoka elderly cohort
CPRD
TCS
US trucking industry cohort
NLCS-AIR
ACS CPS-II
Rome longitudinal study
ESCAPE
DUELS
CTS
Toronto respiratory cohort
CanCHEC
Oslo cohort
Ontario tax cohort
Gazel
Cohort
Japan
England
Taiwan
USA
Netherlands
USA
Italy
Europe
Netherlands
USA
Canada
Canada
Norway
Canada
France
Setting
13,412
830,429
43,227
53,814
117,528
669,046
1,265,058
367,383
7,218,363
12,336
2,360
2,521,525
143,842
205,440
20,327
N
1.03 (1.02, 1.03)
1.24 (1.15, 1.33)
1.00 (0.97, 1.03)
0.95 (0.75, 1.21)
1.04 (1.00, 1.09)
1.02 (0.98, 1.07)
1.04 (1.03, 1.05)
1.03 (1.02, 1.04)
1.01 (0.97, 1.06)
1.00 (0.99, 1.01)
0.99 (0.94, 1.05)
1.64 (1.13, 2.37)
1.03 (1.02, 1.04)
1.02 (1.00, 1.05)
1.08 (1.03, 1.12)
1.00 (0.76, 1.30)
ES (95% CI)
100.00
0.33
2.24
0.03
1.09
1.00
29.69
18.67
%
0.89
17.60
0.57
0.01
22.97
4.00
0.87
0.03
Weight
1.03 (1.02, 1.03)
1.24 (1.15, 1.33)
1.00 (0.97, 1.03)
0.95 (0.75, 1.21)
1.04 (1.00, 1.09)
1.02 (0.98, 1.07)
1.04 (1.03, 1.05)
1.03 (1.02, 1.04)
1.01 (0.97, 1.06)
1.00 (0.99, 1.01)
0.99 (0.94, 1.05)
1.64 (1.13, 2.37)
1.03 (1.02, 1.04)
1.02 (1.00, 1.05)
1.08 (1.03, 1.12)
1.00 (0.76, 1.30)
ES (95% CI)
100.00
0.33
2.24
0.03
1.09
1.00
29.69
18.67
%
0.89
17.60
0.57
0.01
22.97
4.00
0.87
0.03
Weight
1.9 1 1.11.2
NOTE: Weights are from random effects analysis
. (0.98, 1.08)with estimated predictive interval
Overall (I-squared = 83.4%, p = 0.000)
Tseng et al
Crouse et al
Turner et al
Yorifuji et al
Jerrett et al
Hart et al
Chen et al
Brunekreef et al
Naess et al
Cesaroni et al
Bentayeb et al
Beelen et al
Lipsett et al
Study
Carey et al
Fischer et al
2015
2015b
2016
2013
2009
2011
2013
2009
2007
2013
2015
2014a
2011
Year
2013
2015
TCS
CanCHEC
ACS CPS-II
Shizuoka elderly cohort
Toronto respiratory cohort
US trucking industry cohort
Ontario tax cohort
NLCS-AIR
Oslo cohort
Rome longitudinal study
Gazel
ESCAPE
CTS
Cohort
CPRD
DUELS
Taiwan
Canada
USA
Japan
Canada
USA
Canada
Netherlands
Norway
Italy
France
Europe
USA
Setting
England
Netherlands
43,227
2,521,525
669,046
13,412
2,360
53,814
205,440
117,528
143,842
1,265,058
20,327
367,383
12,336
N
830,429
7,218,363
1.03 (1.02, 1.05)
0.95 (0.75, 1.21)
1.03 (1.02, 1.04)
1.04 (1.03, 1.05)
1.24 (1.15, 1.33)
1.64 (1.13, 2.37)
1.04 (1.00, 1.09)
1.08 (1.03, 1.12)
1.02 (0.98, 1.07)
1.02 (1.00, 1.05)
1.03 (1.02, 1.04)
1.00 (0.76, 1.30)
1.01 (0.97, 1.06)
0.99 (0.94, 1.05)
ES (95% CI)
1.00 (0.97, 1.03)
1.00 (0.99, 1.01)
100.00
0.34
12.37
12.50
2.96
0.15
6.38
5.66
6.08
10.12
12.24
0.28
5.74
%
4.36
Weight
8.64
12.19
1.03 (1.02, 1.05)
0.95 (0.75, 1.21)
1.03 (1.02, 1.04)
1.04 (1.03, 1.05)
1.24 (1.15, 1.33)
1.64 (1.13, 2.37)
1.04 (1.00, 1.09)
1.08 (1.03, 1.12)
1.02 (0.98, 1.07)
1.02 (1.00, 1.05)
1.03 (1.02, 1.04)
1.00 (0.76, 1.30)
1.01 (0.97, 1.06)
0.99 (0.94, 1.05)
ES (95% CI)
1.00 (0.97, 1.03)
1.00 (0.99, 1.01)
100.00
0.34
12.37
12.50
2.96
0.15
6.38
5.66
6.08
10.12
12.24
0.28
5.74
%
4.36
Weight
8.64
12.19
1.9 1 1.11.2
24
eFigure 7 Cardiovascular mortality - stratification by pre-existing disease on recruitment
NOTE: Weights are from random effects analysis
.
.
General
Chen et al
Crouse et al
Hart et al
Lipsett et al
Turner et al
Carey et al
Beelen et al
Bentayeb et al
Cesaroni et al
Fischer et al
Brunekreef et al
Naess et al
Tseng et al
Yorifuji et al
Subtotal (I-squared = 83.3%, p = 0.000)
Preexisting
Jerrett et al
Subtotal (I-squared = .%, p = .)
Study
2013
2015b
2011
2011
2016
2013
2014a
2015
2013
2015
2009
2007
2015
2013
2009
Year
Ontario tax cohort
CanCHEC
US trucking industry cohort
CTS
ACS CPS-II
CPRD
ESCAPE
Gazel
Rome longitudinal study
DUELS
NLCS-AIR
Oslo cohort
TCS
Shizuoka elderly cohort
Toronto respiratory cohort
Cohort
Canada
Canada
USA
USA
USA
England
Europe
France
Italy
Netherlands
Netherlands
Norway
Taiwan
Japan
Canada
Setting
205,440
2,521,525
53,814
12,336
669,046
830,429
367,383
20,327
1,265,058
7,218,363
117,528
143,842
43,227
13,412
2,360
N
FM
FM
M
F
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
Sex
35-85
25-89
15.3-84.9
>=30
>=30
40-89
All
35-50
>=30
>=30
55-69
51-90
41.3 (10.5)
65-84
60 (49 69)
Age
1.08 (1.03, 1.12)
1.03 (1.02, 1.04)
1.04 (1.00, 1.09)
0.99 (0.94, 1.05)
1.04 (1.03, 1.05)
1.00 (0.97, 1.03)
1.01 (0.97, 1.06)
1.00 (0.76, 1.30)
1.03 (1.02, 1.04)
1.00 (0.99, 1.01)
1.02 (0.98, 1.07)
1.02 (1.00, 1.05)
0.95 (0.75, 1.21)
1.24 (1.15, 1.33)
1.03 (1.02, 1.04)
1.64 (1.13, 2.37)
1.64 (1.13, 2.37)
ES (95% CI)
5.54
12.59
6.27
4.23
12.74
8.60
5.62
0.27
12.45
12.40
5.97
10.16
0.32
2.85
100.00
100.00
100.00
Weight
%
1.08 (1.03, 1.12)
1.03 (1.02, 1.04)
1.04 (1.00, 1.09)
0.99 (0.94, 1.05)
1.04 (1.03, 1.05)
1.00 (0.97, 1.03)
1.01 (0.97, 1.06)
1.00 (0.76, 1.30)
1.03 (1.02, 1.04)
1.00 (0.99, 1.01)
1.02 (0.98, 1.07)
1.02 (1.00, 1.05)
0.95 (0.75, 1.21)
1.24 (1.15, 1.33)
1.03 (1.02, 1.04)
1.64 (1.13, 2.37)
1.64 (1.13, 2.37)
ES (95% CI)
5.54
12.59
6.27
4.23
12.74
8.60
5.62
0.27
12.45
12.40
5.97
10.16
0.32
2.85
100.00
100.00
100.00
Weight
%
1.7 .8 .9 1 1.11.21.3
25
eFigure 8 Cardiovascular mortality - stratification by age range at cohort recruitment
NOTE: Weights are from random effects analysis
.
.
Adult
Chen et al
Crouse et al
Hart et al
Lipsett et al
Turner et al
Carey et al
Beelen et al
Cesaroni et al
Fischer et al
Naess et al
Tseng et al
Subtotal (I-squared = 80.8%, p = 0.000)
Restricted
Bentayeb et al
Brunekreef et al
Yorifuji et al
Subtotal (I-squared = 90.2%, p = 0.000)
Study
2013
2015b
2011
2011
2016
2013
2014a
2013
2015
2007
2015
2015
2009
2013
Year
Ontario tax cohort
CanCHEC
US trucking industry cohort
CTS
ACS CPS-II
CPRD
ESCAPE
Rome longitudinal study
DUELS
Oslo cohort
TCS
Gazel
NLCS-AIR
Shizuoka elderly cohort
Cohort
Canada
Canada
USA
USA
USA
England
Europe
Italy
Netherlands
Norway
Taiwan
France
Netherlands
Japan
Setting
205,440
2,521,525
53,814
12,336
669,046
830,429
367,383
1,265,058
7,218,363
143,842
43,227
20,327
117,528
13,412
N
FM
FM
M
F
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
Sex
35-85
25-89
15.3-84.9
>=30
>=30
40-89
All
>=30
>=30
51-90
41.3 (10.5)
35-50
55-69
65-84
Age
1.08 (1.03, 1.12)
1.03 (1.02, 1.04)
1.04 (1.00, 1.09)
0.99 (0.94, 1.05)
1.04 (1.03, 1.05)
1.00 (0.97, 1.03)
1.01 (0.97, 1.06)
1.03 (1.02, 1.04)
1.00 (0.99, 1.01)
1.02 (1.00, 1.05)
0.95 (0.75, 1.21)
1.02 (1.01, 1.04)
1.00 (0.76, 1.30)
1.02 (0.98, 1.07)
1.24 (1.15, 1.33)
1.10 (0.93, 1.29)
ES (95% CI)
5.20
14.95
6.01
3.82
15.21
8.88
5.29
14.70
14.62
11.06
0.26
100.00
19.65
41.28
39.07
100.00
Weight
%
1.08 (1.03, 1.12)
1.03 (1.02, 1.04)
1.04 (1.00, 1.09)
0.99 (0.94, 1.05)
1.04 (1.03, 1.05)
1.00 (0.97, 1.03)
1.01 (0.97, 1.06)
1.03 (1.02, 1.04)
1.00 (0.99, 1.01)
1.02 (1.00, 1.05)
0.95 (0.75, 1.21)
1.02 (1.01, 1.04)
1.00 (0.76, 1.30)
1.02 (0.98, 1.07)
1.24 (1.15, 1.33)
1.10 (0.93, 1.29)
ES (95% CI)
5.20
14.95
6.01
3.82
15.21
8.88
5.29
14.70
14.62
11.06
0.26
100.00
19.65
41.28
39.07
100.00
Weight
%
1.7 .8 .9 1 1.1 1.2 1.3
26
eFigure 9 Cardiovascular mortality - stratification by level of adjustment for smoking and BMI
NOTE: Weights are from random effects analysis
.
.
Individual
Lipsett et al
Turner et al
Carey et al
Beelen et al
Tseng et al
Subtotal (I-squared = 68.0%, p = 0.014)
None/Indirect
Chen et al
Crouse et al
Hart et al
Cesaroni et al
Fischer et al
Naess et al
Subtotal (I-squared = 82.4%, p = 0.000)
Study
2011
2016
2013
2014a
2015
2013
2015b
2011
2013
2015
2007
Year
CTS
ACS CPS-II
CPRD
ESCAPE
TCS
Ontario tax cohort
CanCHEC
US trucking industry cohort
Rome longitudinal study
DUELS
Oslo cohort
Cohort
USA
USA
England
Europe
Taiwan
Canada
Canada
USA
Italy
Netherlands
Norway
Setting
12,336
669,046
830,429
367,383
43,227
205,440
2,521,525
53,814
1,265,058
7,218,363
143,842
N
F
FM
FM
FM
FM
FM
FM
M
FM
FM
FM
Sex
>=30
>=30
40-89
All
41.3 (10.5)
35-85
25-89
15.3-84.9
>=30
>=30
51-90
Age
0.99 (0.94, 1.05)
1.04 (1.03, 1.05)
1.00 (0.97, 1.03)
1.01 (0.97, 1.06)
0.95 (0.75, 1.21)
1.01 (0.99, 1.04)
1.08 (1.03, 1.12)
1.03 (1.02, 1.04)
1.04 (1.00, 1.09)
1.03 (1.02, 1.04)
1.00 (0.99, 1.01)
1.02 (1.00, 1.05)
1.03 (1.01, 1.04)
ES (95% CI)
15.41
36.27
27.39
19.57
1.35
100.00
7.52
22.74
8.74
22.33
22.20
16.48
100.00
Weight
%
0.99 (0.94, 1.05)
1.04 (1.03, 1.05)
1.00 (0.97, 1.03)
1.01 (0.97, 1.06)
0.95 (0.75, 1.21)
1.01 (0.99, 1.04)
1.08 (1.03, 1.12)
1.03 (1.02, 1.04)
1.04 (1.00, 1.09)
1.03 (1.02, 1.04)
1.00 (0.99, 1.01)
1.02 (1.00, 1.05)
1.03 (1.01, 1.04)
ES (95% CI)
15.41
36.27
27.39
19.57
1.35
100.00
7.52
22.74
8.74
22.33
22.20
16.48
100.00
Weight
%
1.7 .8 .9 1 1.1 1.2
27
eFigure 10 Cardiovascular mortality - stratification by spatial resolution of NO2 concentration estimates
NOTE: Weights are from random effects analysis
.
.
LUR Address
Chen et al
Crouse et al
Hart et al
Turner et al
Beelen et al
Cesaroni et al
Fischer et al
Subtotal (I-squared = 87.1%, p = 0.000)
Area
Lipsett et al
Carey et al
Naess et al
Tseng et al
Subtotal (I-squared = 0.0%, p = 0.429)
Study
2013
2015b
2011
2016
2014a
2013
2015
2011
2013
2007
2015
Year
Ontario tax cohort
CanCHEC
US trucking industry cohort
ACS CPS-II
ESCAPE
Rome longitudinal study
DUELS
CTS
CPRD
Oslo cohort
TCS
Cohort
Canada
Canada
USA
USA
Europe
Italy
Netherlands
USA
England
Norway
Taiwan
Setting
205,440
2,521,525
53,814
669,046
367,383
1,265,058
7,218,363
12,336
830,429
143,842
43,227
N
FM
FM
M
FM
FM
FM
FM
F
FM
FM
FM
Sex
35-85
25-89
15.3-84.9
>=30
All
>=30
>=30
>=30
40-89
51-90
41.3 (10.5)
Age
1.08 (1.03, 1.12)
1.03 (1.02, 1.04)
1.04 (1.00, 1.09)
1.04 (1.03, 1.05)
1.01 (0.97, 1.06)
1.03 (1.02, 1.04)
1.00 (0.99, 1.01)
1.03 (1.01, 1.04)
0.99 (0.94, 1.05)
1.00 (0.97, 1.03)
1.02 (1.00, 1.05)
0.95 (0.75, 1.21)
1.01 (1.00, 1.03)
ES (95% CI)
7.06
19.50
8.15
19.81
7.18
19.20
19.10
100.00
8.34
32.81
58.41
0.44
100.00
Weight
%
1.08 (1.03, 1.12)
1.03 (1.02, 1.04)
1.04 (1.00, 1.09)
1.04 (1.03, 1.05)
1.01 (0.97, 1.06)
1.03 (1.02, 1.04)
1.00 (0.99, 1.01)
1.03 (1.01, 1.04)
0.99 (0.94, 1.05)
1.00 (0.97, 1.03)
1.02 (1.00, 1.05)
0.95 (0.75, 1.21)
1.01 (1.00, 1.03)
ES (95% CI)
7.06
19.50
8.15
19.81
7.18
19.20
19.10
100.00
8.34
32.81
58.41
0.44
100.00
Weight
%
1.7 .8 .9 1 1.1 1.2 1.3
28
eFigure 11 CHD mortality
* indicates exclusion from meta-analysis. Refer to methods/results sections of manuscript.
Chen et al
Crouse et al *
Crouse et al
Gan et al
Chen et al
Hart et al
Jerrett et al *
Krewski et al *
Lipsett et al
Turner et al
Raaschou-Nielsen et al *
Carey et al
Beelen et al
Cesaroni et al
Beelen et al
Yorifuji et al *
Yorifuji et al
Study
2013
2015a
2015b
2011
2005
2011
2013
2009
2011
2016
2012
2013
2014a
2013
2009
2010
2013
Year
Ontario tax cohort
CanCHEC
CanCHEC
Vancouver Cohort
AHSMOG
US trucking industry cohort
ACS CPS-II
ACS CPS-II
CTS
ACS CPS-II
DCH
CPRD
ESCAPE
Rome longitudinal study
NLCS-AIR
Shizuoka elderly cohort
Shizuoka elderly cohort
Cohort
Canada
Canada
Canada
Canada
USA
USA
USA
USA
USA
USA
Denmark
England
Europe
Italy
Netherlands
Japan
Japan
Setting
205,440
735,590
2,521,525
452,735
3,239
53,814
73,711
406,917
12,336
669,046
52,061
830,429
367,383
1,265,058
117,528
13,444
13,412
N
FM
FM
FM
FM
FM
M
FM
FM
F
FM
FM
FM
FM
FM
FM
FM
FM
Sex
35-85
25-89
25-89
45-85
27-95
15.3-84.9
>=30
>=30
>=30
>=30
50-64
40-89
All
>=30
55-69
65-84
65-84
Age
Indirect Smoking, No BMI
Indirect smoking and BMI
Indirect smoking and BMI
No BMI, Smoking
No BMI, Smoking
No BMI, Smoking
No BMI
Adjustment
Confounder
1.09 (1.02, 1.15)
1.05 (1.02, 1.09)
1.04 (1.03, 1.05)
1.05 (1.01, 1.09)
1.09 (1.00, 1.17)
1.00 (0.95, 1.06)
1.09 (1.02, 1.16)
1.02 (1.01, 1.03)
1.04 (0.96, 1.12)
1.07 (1.06, 1.09)
1.08 (0.89, 1.31)
0.99 (0.95, 1.04)
1.00 (0.91, 1.09)
1.05 (1.04, 1.07)
0.99 (0.93, 1.06)
1.27 (1.02, 1.58)
1.29 (1.12, 1.48)
ES (95% CI)
1.09 (1.02, 1.15)
1.05 (1.02, 1.09)
1.04 (1.03, 1.05)
1.05 (1.01, 1.09)
1.09 (1.00, 1.17)
1.00 (0.95, 1.06)
1.09 (1.02, 1.16)
1.02 (1.01, 1.03)
1.04 (0.96, 1.12)
1.07 (1.06, 1.09)
1.08 (0.89, 1.31)
0.99 (0.95, 1.04)
1.00 (0.91, 1.09)
1.05 (1.04, 1.07)
0.99 (0.93, 1.06)
1.27 (1.02, 1.58)
1.29 (1.12, 1.48)
ES (95% CI)
1.9 1 1.1 1.2
29
eFigure 12a CHD mortality - fixed effects model
eFigure 12b CHD mortality - random effects model
Overall (I-squared = 67.3%, p = 0.000)
Hart et al
Crouse et al
Chen et al
Cesaroni et al
Study
Beelen et al
Turner et al
Chen et al
Yorifuji et al
Gan et al
Carey et al
Lipsett et al
Beelen et al
2011
2015b
2013
2013
Year
2009
2016
2005
2013
2011
2013
2011
2014a
US trucking industry cohort
CanCHEC
Ontario tax cohort
Rome longitudinal study
Cohort
NLCS-AIR
ACS CPS-II
AHSMOG
Shizuoka elderly cohort
Vancouver Cohort
CPRD
CTS
ESCAPE
USA
Canada
Canada
Italy
Setting
Netherlands
USA
USA
Japan
Canada
England
USA
Europe
53,814
2,521,525
205,440
1,265,058
N
117,528
669,046
3,239
13,412
452,735
830,429
12,336
367,383
1.05 (1.04, 1.06)
1.00 (0.95, 1.06)
1.04 (1.03, 1.05)
1.09 (1.02, 1.15)
1.05 (1.04, 1.07)
ES (95% CI)
0.99 (0.93, 1.06)
1.07 (1.06, 1.09)
1.09 (1.00, 1.17)
1.29 (1.12, 1.48)
1.05 (1.01, 1.09)
0.99 (0.95, 1.04)
1.04 (0.96, 1.12)
1.00 (0.91, 1.09)
100.00
1.88
39.77
1.37
23.49
Weight
1.14
24.72
0.78
0.25
3.04
2.19
0.78
0.59
%
1.05 (1.04, 1.06)
1.00 (0.95, 1.06)
1.04 (1.03, 1.05)
1.09 (1.02, 1.15)
1.05 (1.04, 1.07)
ES (95% CI)
0.99 (0.93, 1.06)
1.07 (1.06, 1.09)
1.09 (1.00, 1.17)
1.29 (1.12, 1.48)
1.05 (1.01, 1.09)
0.99 (0.95, 1.04)
1.04 (0.96, 1.12)
1.00 (0.91, 1.09)
100.00
1.88
39.77
1.37
23.49
Weight
1.14
24.72
0.78
0.25
3.04
2.19
0.78
0.59
%
1.9 1 1.1 1.2
NOTE: Weights are from random effects analysis
. (1.00, 1.10)with estimated predictive interval
Overall (I-squared = 67.3%, p = 0.000)
Carey et al
Study
Turner et al
Yorifuji et al
Chen et al
Beelen et al
Cesaroni et al
Chen et al
Gan et al
Hart et al
Beelen et al
Crouse et al
Lipsett et al
2013
Year
2016
2013
2013
2009
2013
2005
2011
2011
2014a
2015b
2011
CPRD
Cohort
ACS CPS-II
Shizuoka elderly cohort
Ontario tax cohort
NLCS-AIR
Rome longitudinal study
AHSMOG
Vancouver Cohort
US trucking industry cohort
ESCAPE
CanCHEC
CTS
England
Setting
USA
Japan
Canada
Netherlands
Italy
USA
Canada
USA
Europe
Canada
USA
830,429
N
669,046
13,412
205,440
117,528
1,265,058
3,239
452,735
53,814
367,383
2,521,525
12,336
1.05 (1.03, 1.06)
0.99 (0.95, 1.04)
ES (95% CI)
1.07 (1.06, 1.09)
1.29 (1.12, 1.48)
1.09 (1.02, 1.15)
0.99 (0.93, 1.06)
1.05 (1.04, 1.07)
1.09 (1.00, 1.17)
1.05 (1.01, 1.09)
1.00 (0.95, 1.06)
1.00 (0.91, 1.09)
1.04 (1.03, 1.05)
1.04 (0.96, 1.12)
100.00
7.92
Weight
17.22
1.41
5.84
5.13
17.11
3.83
%
9.50
7.20
3.05
17.99
3.80
1.05 (1.03, 1.06)
0.99 (0.95, 1.04)
ES (95% CI)
1.07 (1.06, 1.09)
1.29 (1.12, 1.48)
1.09 (1.02, 1.15)
0.99 (0.93, 1.06)
1.05 (1.04, 1.07)
1.09 (1.00, 1.17)
1.05 (1.01, 1.09)
1.00 (0.95, 1.06)
1.00 (0.91, 1.09)
1.04 (1.03, 1.05)
1.04 (0.96, 1.12)
100.00
7.92
Weight
17.22
1.41
5.84
5.13
17.11
3.83
%
9.50
7.20
3.05
17.99
3.80
1.9 1 1.1 1.2
30
eFigure 13 CHD mortality - stratification by age range at cohort recruitment
NOTE: Weights are from random effects analysis
.
.
Adult
Gan et al
Chen et al
Crouse et al
Hart et al
Lipsett et al
Chen et al
Turner et al
Carey et al
Beelen et al
Cesaroni et al
Subtotal (I-squared = 59.4%, p = 0.008)
Restricted
Beelen et al
Yorifuji et al
Subtotal (I-squared = 91.2%, p = 0.001)
Study
2011
2013
2015b
2011
2011
2005
2016
2013
2014a
2013
2009
2013
Year
Vancouver Cohort
Ontario tax cohort
CanCHEC
US trucking industry cohort
CTS
AHSMOG
ACS CPS-II
CPRD
ESCAPE
Rome longitudinal study
NLCS-AIR
Shizuoka elderly cohort
Cohort
Canada
Canada
Canada
USA
USA
USA
USA
England
Europe
Italy
Netherlands
Japan
Setting
452,735
205,440
2,521,525
53,814
12,336
3,239
669,046
830,429
367,383
1,265,058
117,528
13,412
N
FM
FM
FM
M
F
FM
FM
FM
FM
FM
FM
FM
Sex
45-85
35-85
25-89
15.3-84.9
>=30
27-95
>=30
40-89
All
>=30
55-69
65-84
Age
1.05 (1.01, 1.09)
1.09 (1.02, 1.15)
1.04 (1.03, 1.05)
1.00 (0.95, 1.06)
1.04 (0.96, 1.12)
1.09 (1.00, 1.17)
1.07 (1.06, 1.09)
0.99 (0.95, 1.04)
1.00 (0.91, 1.09)
1.05 (1.04, 1.07)
1.05 (1.03, 1.06)
0.99 (0.93, 1.06)
1.29 (1.12, 1.48)
1.12 (0.87, 1.45)
ES (95% CI)
9.09
5.10
22.11
6.51
3.17
3.19
20.62
7.28
2.50
20.43
100.00
52.82
47.18
100.00
Weight
%
1.05 (1.01, 1.09)
1.09 (1.02, 1.15)
1.04 (1.03, 1.05)
1.00 (0.95, 1.06)
1.04 (0.96, 1.12)
1.09 (1.00, 1.17)
1.07 (1.06, 1.09)
0.99 (0.95, 1.04)
1.00 (0.91, 1.09)
1.05 (1.04, 1.07)
1.05 (1.03, 1.06)
0.99 (0.93, 1.06)
1.29 (1.12, 1.48)
1.12 (0.87, 1.45)
ES (95% CI)
9.09
5.10
22.11
6.51
3.17
3.19
20.62
7.28
2.50
20.43
100.00
52.82
47.18
100.00
Weight
%
1.8 1 1.2 1.4
31
eFigure 14 CHD mortality - stratification by level of adjustment for smoking and BMI
NOTE: Weights are from random effects analysis
.
.
Individual
Lipsett et al
Chen et al
Turner et al
Carey et al
Beelen et al
Subtotal (I-squared = 67.7%, p = 0.015)
None/Indirect
Gan et al
Chen et al
Crouse et al
Hart et al
Cesaroni et al
Subtotal (I-squared = 13.4%, p = 0.329)
Study
2011
2005
2016
2013
2014a
2011
2013
2015b
2011
2013
Year
CTS
AHSMOG
ACS CPS-II
CPRD
ESCAPE
Vancouver Cohort
Ontario tax cohort
CanCHEC
US trucking industry cohort
Rome longitudinal study
Cohort
USA
USA
USA
England
Europe
Canada
Canada
Canada
USA
Italy
Setting
12,336
3,239
669,046
830,429
367,383
452,735
205,440
2,521,525
53,814
1,265,058
N
F
FM
FM
FM
FM
FM
FM
FM
M
FM
Sex
>=30
27-95
>=30
40-89
All
45-85
35-85
25-89
15.3-84.9
>=30
Age
1.04 (0.96, 1.12)
1.09 (1.00, 1.17)
1.07 (1.06, 1.09)
0.99 (0.95, 1.04)
1.00 (0.91, 1.09)
1.04 (1.00, 1.08)
1.05 (1.01, 1.09)
1.09 (1.02, 1.15)
1.04 (1.03, 1.05)
1.00 (0.95, 1.06)
1.05 (1.04, 1.07)
1.04 (1.03, 1.06)
ES (95% CI)
15.31
15.38
32.51
23.67
13.13
100.00
6.12
2.82
51.23
3.84
35.99
100.00
Weight
%
1.04 (0.96, 1.12)
1.09 (1.00, 1.17)
1.07 (1.06, 1.09)
0.99 (0.95, 1.04)
1.00 (0.91, 1.09)
1.04 (1.00, 1.08)
1.05 (1.01, 1.09)
1.09 (1.02, 1.15)
1.04 (1.03, 1.05)
1.00 (0.95, 1.06)
1.05 (1.04, 1.07)
1.04 (1.03, 1.06)
ES (95% CI)
15.31
15.38
32.51
23.67
13.13
100.00
6.12
2.82
51.23
3.84
35.99
100.00
Weight
%
1.9 1 1.1 1.2
32
eFigure 15 CHD mortality - stratification by spatial resolution of NO2 concentration estimates
NOTE: Weights are from random effects analysis
.
.
LUR Address
Gan et al
Chen et al
Crouse et al
Hart et al
Turner et al
Beelen et al
Cesaroni et al
Subtotal (I-squared = 61.1%, p = 0.017)
Area
Lipsett et al
Chen et al
Carey et al
Subtotal (I-squared = 49.5%, p = 0.138)
Study
2011
2013
2015b
2011
2016
2014a
2013
2011
2005
2013
Year
Vancouver Cohort
Ontario tax cohort
CanCHEC
US trucking industry cohort
ACS CPS-II
ESCAPE
Rome longitudinal study
CTS
AHSMOG
CPRD
Cohort
Canada
Canada
Canada
USA
USA
Europe
Italy
USA
USA
England
Setting
452,735
205,440
2,521,525
53,814
669,046
367,383
1,265,058
12,336
3,239
830,429
N
FM
FM
FM
M
FM
FM
FM
F
FM
FM
Sex
45-85
35-85
25-89
15.3-84.9
>=30
All
>=30
>=30
27-95
40-89
Age
1.05 (1.01, 1.09)
1.09 (1.02, 1.15)
1.04 (1.03, 1.05)
1.00 (0.95, 1.06)
1.07 (1.06, 1.09)
1.00 (0.91, 1.09)
1.05 (1.04, 1.07)
1.05 (1.03, 1.07)
1.04 (0.96, 1.12)
1.09 (1.00, 1.17)
0.99 (0.95, 1.04)
1.03 (0.97, 1.09)
ES (95% CI)
9.61
5.18
26.93
6.70
24.68
2.48
24.40
100.00
27.68
27.80
44.51
100.00
Weight
%
1.05 (1.01, 1.09)
1.09 (1.02, 1.15)
1.04 (1.03, 1.05)
1.00 (0.95, 1.06)
1.07 (1.06, 1.09)
1.00 (0.91, 1.09)
1.05 (1.04, 1.07)
1.05 (1.03, 1.07)
1.04 (0.96, 1.12)
1.09 (1.00, 1.17)
0.99 (0.95, 1.04)
1.03 (0.97, 1.09)
ES (95% CI)
9.61
5.18
26.93
6.70
24.68
2.48
24.40
100.00
27.68
27.80
44.51
100.00
Weight
%
1.9 1 1.1 1.2
33
eFigure 16 Cerebrovascular mortality
* indicates exclusion from meta-analysis. Refer to methods/results sections of manuscript.
Chen et al
Crouse et al *
Crouse et al
Lipsett et al
Turner et al
Zhang et al *
Raaschou-Nielsen et al *
Beelen et al
Cesaroni et al
Beelen et al
Yorifuji et al *
Yorifuji et al
Study
2013
2015a
2015b
2011
2016
2011
2012
2014a
2013
2009
2010
2013
Year
Ontario tax cohort
CanCHEC
CanCHEC
CTS
ACS CPS-II
Shenyang cohort
DCH
ESCAPE
Rome longitudinal study
NLCS-AIR
Shizuoka elderly cohort
Shizuoka elderly cohort
Cohort
Canada
Canada
Canada
USA
USA
China
Denmark
Europe
Italy
Netherlands
Japan
Japan
Setting
205,440
735,590
2,521,525
12,336
669,046
9,941
52,061
367,251
1,265,058
117,528
13,444
13,412
N
FM
FM
FM
F
FM
FM
FM
FM
FM
FM
FM
FM
Sex
35-85
25-89
25-89
>=30
>=30
35-103
50-64
All
>=30
55-69
65-84
65-84
Age
Indirect Smoking, No BMI
Indirect smoking and BMI
Indirect smoking and BMI
No BMI, Smoking
No BMI
Adjustment
Confounder
0.96 (0.89, 1.04)
1.01 (0.96, 1.07)
1.00 (0.99, 1.02)
0.93 (0.83, 1.03)
1.00 (0.98, 1.01)
2.44 (2.27, 2.62)
1.09 (0.83, 1.43)
1.01 (0.93, 1.10)
1.01 (0.99, 1.03)
1.15 (1.02, 1.29)
1.09 (0.94, 1.27)
1.19 (1.06, 1.34)
ES (95% CI)
0.96 (0.89, 1.04)
1.01 (0.96, 1.07)
1.00 (0.99, 1.02)
0.93 (0.83, 1.03)
1.00 (0.98, 1.01)
2.44 (2.27, 2.62)
1.09 (0.83, 1.43)
1.01 (0.93, 1.10)
1.01 (0.99, 1.03)
1.15 (1.02, 1.29)
1.09 (0.94, 1.27)
1.19 (1.06, 1.34)
ES (95% CI)
1.9 1 1.1 1.2
34
eFigure 17a Cerebrovascular mortality - fixed effects model
eFigure 17b Cerebrovascular mortality - random effects model
Overall (I-squared = 61.5%, p = 0.011)
Lipsett et al
Chen et al
Yorifuji et al
Crouse et al
Beelen et al
Turner et al
Beelen et al
Study
Cesaroni et al
2011
2013
2013
2015b
2014a
2016
2009
Year
2013
CTS
Ontario tax cohort
Shizuoka elderly cohort
CanCHEC
ESCAPE
ACS CPS-II
NLCS-AIR
Cohort
Rome longitudinal study
USA
Canada
Japan
Canada
Europe
USA
Netherlands
Setting
Italy
12,336
205,440
13,412
2,521,525
367,251
669,046
117,528
N
1,265,058
1.00 (0.99, 1.02)
0.93 (0.83, 1.03)
0.96 (0.89, 1.04)
1.19 (1.06, 1.34)
1.00 (0.99, 1.02)
1.01 (0.93, 1.10)
1.00 (0.98, 1.01)
1.15 (1.02, 1.29)
ES (95% CI)
1.01 (0.99, 1.03)
100.00
0.99
1.88
0.83
33.67
1.62
31.07
0.88
Weight
29.07
%
1.00 (0.99, 1.02)
0.93 (0.83, 1.03)
0.96 (0.89, 1.04)
1.19 (1.06, 1.34)
1.00 (0.99, 1.02)
1.01 (0.93, 1.10)
1.00 (0.98, 1.01)
1.15 (1.02, 1.29)
ES (95% CI)
1.01 (0.99, 1.03)
100.00
0.99
1.88
0.83
33.67
1.62
31.07
0.88
Weight
29.07
%
1.9 1 1.1 1.2
NOTE: Weights are from random effects analysis
. (0.95, 1.07)with estimated predictive interval
Overall (I-squared = 61.5%, p = 0.011)
Beelen et al
Study
Turner et al
Cesaroni et al
Yorifuji et al
Crouse et al
Beelen et al
Lipsett et al
Chen et al
2014a
Year
2016
2013
2013
2015b
2009
2011
2013
ESCAPE
Cohort
ACS CPS-II
Rome longitudinal study
Shizuoka elderly cohort
CanCHEC
NLCS-AIR
CTS
Ontario tax cohort
Europe
Setting
USA
Italy
Japan
Canada
Netherlands
USA
Canada
367,251
N
669,046
1,265,058
13,412
2,521,525
117,528
12,336
205,440
1.01 (0.98, 1.03)
1.01 (0.93, 1.10)
ES (95% CI)
1.00 (0.98, 1.01)
1.01 (0.99, 1.03)
1.19 (1.06, 1.34)
1.00 (0.99, 1.02)
1.15 (1.02, 1.29)
0.93 (0.83, 1.03)
0.96 (0.89, 1.04)
100.00
6.18
Weight
25.17
24.88
3.52
25.50
3.70
4.11
6.94
%
1.01 (0.98, 1.03)
1.01 (0.93, 1.10)
ES (95% CI)
1.00 (0.98, 1.01)
1.01 (0.99, 1.03)
1.19 (1.06, 1.34)
1.00 (0.99, 1.02)
1.15 (1.02, 1.29)
0.93 (0.83, 1.03)
0.96 (0.89, 1.04)
100.00
6.18
Weight
25.17
24.88
3.52
25.50
3.70
4.11
6.94
%
1.9 1 1.1 1.2
35
eFigure 18 Cerebrovascular mortality - stratification by age range at cohort recruitment
NOTE: Weights are from random effects analysis
.
.
Adult
Chen et al
Crouse et al
Lipsett et al
Turner et al
Beelen et al
Cesaroni et al
Subtotal (I-squared = 0.0%, p = 0.457)
Restricted
Beelen et al
Yorifuji et al
Subtotal (I-squared = 0.0%, p = 0.659)
Study
2013
2015b
2011
2016
2014a
2013
2009
2013
Year
Ontario tax cohort
CanCHEC
CTS
ACS CPS-II
ESCAPE
Rome longitudinal study
NLCS-AIR
Shizuoka elderly cohort
Cohort
Canada
Canada
USA
USA
Europe
Italy
Netherlands
Japan
Setting
205,440
2,521,525
12,336
669,046
367,251
1,265,058
117,528
13,412
N
FM
FM
F
FM
FM
FM
FM
FM
Sex
35-85
25-89
>=30
>=30
All
>=30
55-69
65-84
Age
0.96 (0.89, 1.04)
1.00 (0.99, 1.02)
0.93 (0.83, 1.03)
1.00 (0.98, 1.01)
1.01 (0.93, 1.10)
1.01 (0.99, 1.03)
1.00 (0.99, 1.01)
1.15 (1.02, 1.29)
1.19 (1.06, 1.34)
1.17 (1.08, 1.27)
ES (95% CI)
1.91
34.26
1.01
31.61
1.65
29.57
100.00
51.38
48.62
100.00
Weight
%
0.96 (0.89, 1.04)
1.00 (0.99, 1.02)
0.93 (0.83, 1.03)
1.00 (0.98, 1.01)
1.01 (0.93, 1.10)
1.01 (0.99, 1.03)
1.00 (0.99, 1.01)
1.15 (1.02, 1.29)
1.19 (1.06, 1.34)
1.17 (1.08, 1.27)
ES (95% CI)
1.91
34.26
1.01
31.61
1.65
29.57
100.00
51.38
48.62
100.00
Weight
%
1.9 1 1.1 1.2
36
eFigure 19 Cerebrovascular mortality - stratification by level of adjustment for smoking and BMI
NOTE: Weights are from random effects analysis
.
.
Individual
Lipsett et al
Turner et al
Beelen et al
Subtotal (I-squared = 0.0%, p = 0.391)
None/Indirect
Chen et al
Crouse et al
Cesaroni et al
Subtotal (I-squared = 0.0%, p = 0.433)
Study
2011
2016
2014a
2013
2015b
2013
Year
CTS
ACS CPS-II
ESCAPE
Ontario tax cohort
CanCHEC
Rome longitudinal study
Cohort
USA
USA
Europe
Canada
Canada
Italy
Setting
12,336
669,046
367,251
205,440
2,521,525
1,265,058
N
F
FM
FM
FM
FM
FM
Sex
>=30
>=30
All
35-85
25-89
>=30
Age
0.93 (0.83, 1.03)
1.00 (0.98, 1.01)
1.01 (0.93, 1.10)
0.99 (0.98, 1.01)
0.96 (0.89, 1.04)
1.00 (0.99, 1.02)
1.01 (0.99, 1.03)
1.01 (0.99, 1.02)
ES (95% CI)
2.94
92.25
4.80
100.00
2.90
52.11
44.99
100.00
Weight
%
0.93 (0.83, 1.03)
1.00 (0.98, 1.01)
1.01 (0.93, 1.10)
0.99 (0.98, 1.01)
0.96 (0.89, 1.04)
1.00 (0.99, 1.02)
1.01 (0.99, 1.03)
1.01 (0.99, 1.02)
ES (95% CI)
2.94
92.25
4.80
100.00
2.90
52.11
44.99
100.00
Weight
%
1.9.9 1 1.1 1.2
37
eFigure 20 Cerebrovascular mortality - stratification by spatial resolution of NO2 concentration
NOTE: Weights are from random effects analysis
.
.
LUR Address
Chen et al
Crouse et al
Turner et al
Beelen et al
Cesaroni et al
Subtotal (I-squared = 0.0%, p = 0.638)
Area
Lipsett et al
Subtotal (I-squared = .%, p = .)
Study
2013
2015b
2016
2014a
2013
2011
Year
Ontario tax cohort
CanCHEC
ACS CPS-II
ESCAPE
Rome longitudinal study
CTS
Cohort
Canada
Canada
USA
Europe
Italy
USA
Setting
205,440
2,521,525
669,046
367,251
1,265,058
12,336
N
FM
FM
FM
FM
FM
F
Sex
35-85
25-89
>=30
All
>=30
>=30
Age
0.96 (0.89, 1.04)
1.00 (0.99, 1.02)
1.00 (0.98, 1.01)
1.01 (0.93, 1.10)
1.01 (0.99, 1.03)
1.00 (0.99, 1.01)
0.93 (0.83, 1.03)
0.93 (0.83, 1.03)
ES (95% CI)
1.93
34.60
31.93
1.66
29.87
100.00
100.00
100.00
Weight
%
0.96 (0.89, 1.04)
1.00 (0.99, 1.02)
1.00 (0.98, 1.01)
1.01 (0.93, 1.10)
1.01 (0.99, 1.03)
1.00 (0.99, 1.01)
0.93 (0.83, 1.03)
0.93 (0.83, 1.03)
ES (95% CI)
1.93
34.60
31.93
1.66
29.87
100.00
100.00
100.00
Weight
%
1.9 1 1.1 1.2
38
eFigure 21 Respiratory mortality
* indicates exclusion from meta-analysis. Refer to methods/results sections of manuscript.
NOTE: Weights are from random effects analysis
Crouse et al *
Crouse et al
Jerrett et al
Abbey et al
Hart et al
Jerrett et al *
Lipsett et al
Turner et al
Dong et al *
Carey et al
Dimakopoulou K
Heinrich et al *
Cesaroni et al
Brunekreef et al
Fischer et al
Katanoda et al
Yorifuji et al *
Yorifuji et al
Study
2015a
2015b
2009
1999
2011
2013
2011
2016
2012
2013
2014
2013
2013
2009
2015
2011
2010
2013
Year
CanCHEC
CanCHEC
Toronto respiratory cohort
AHSMOG
US trucking industry cohort
ACS CPS-II
CTS
ACS CPS-II
Shenyang cohort
CPRD
ESCAPE
German cohort
Rome longitudinal study
NLCS-AIR
DUELS
3 Japanease Prefectures
Shizuoka elderly cohort
Shizuoka elderly cohort
Cohort
Canada
Canada
Canada
USA
USA
USA
USA
USA
China
England
Europe
Germany
Italy
Netherlands
Netherlands
Japan
Japan
Japan
Setting
735,590
2,521,525
2,360
5,652
53,814
73,711
12,336
669,046
9,941
830,429
307,553
4,752
1,265,058
117,528
7,218,363
63,520
13,444
13,412
N
FM
FM
FM
FM
M
FM
F
FM
FM
FM
FM
F
FM
FM
FM
FM
FM
FM
Sex
25-89
25-89
60 (49 69)
27-95
15.3-84.9
>=30
>=30
>=30
35-103
40-89
All
50-59
>=30
55-69
>=30
>=40
65-84
65-84
Age
Indirect smoking and BMI
Indirect smoking and BMI
No BMI, Smoking
No BMI, Age(?)
No BMI, Smoking
No BMI
No BMI, Smoking
No BMI
Adjustment
Confounder
1.04 (1.00, 1.09)
1.02 (1.00, 1.04)
1.08 (0.64, 1.84)
0.98 (0.93, 1.03)
1.04 (0.95, 1.14)
1.00 (0.91, 1.10)
0.96 (0.86, 1.08)
1.02 (1.00, 1.04)
2.97 (2.69, 3.27)
1.08 (1.04, 1.13)
0.97 (0.89, 1.05)
1.08 (0.81, 1.44)
1.03 (1.00, 1.06)
1.11 (1.00, 1.23)
1.02 (1.01, 1.03)
1.08 (1.06, 1.10)
1.19 (1.02, 1.38)
1.19 (1.06, 1.34)
ES (95% CI)
1.04 (1.00, 1.09)
1.02 (1.00, 1.04)
1.08 (0.64, 1.84)
0.98 (0.93, 1.03)
1.04 (0.95, 1.14)
1.00 (0.91, 1.10)
0.96 (0.86, 1.08)
1.02 (1.00, 1.04)
2.97 (2.69, 3.27)
1.08 (1.04, 1.13)
0.97 (0.89, 1.05)
1.08 (0.81, 1.44)
1.03 (1.00, 1.06)
1.11 (1.00, 1.23)
1.02 (1.01, 1.03)
1.08 (1.06, 1.10)
1.19 (1.02, 1.38)
1.19 (1.06, 1.34)
ES (95% CI)
1.9 1 1.11.2
39
eFigure 22a Respiratory mortality - fixed effects model
eFigure 22b Respiratory mortality - random effects model
Overall (I-squared = 75.5%, p = 0.000)
Katanoda et al
Carey et al
Fischer et al
Turner et al
Brunekreef et al
Abbey et al
Dimakopoulou K
Cesaroni et al
Lipsett et al
Hart et al
Yorifuji et al
Study
Jerrett et al
Crouse et al
2011
2013
2015
2016
2009
1999
2014
2013
2011
2011
2013
Year
2009
2015b
3 Japanease Prefectures
CPRD
DUELS
ACS CPS-II
NLCS-AIR
AHSMOG
ESCAPE
Rome longitudinal study
CTS
US trucking industry cohort
Shizuoka elderly cohort
Cohort
Toronto respiratory cohort
CanCHEC
Japan
England
Netherlands
USA
Netherlands
USA
Europe
Italy
USA
USA
Japan
Setting
Canada
Canada
63,520
830,429
7,218,363
669,046
117,528
5,652
307,553
1,265,058
12,336
53,814
13,412
N
2,360
2,521,525
FM
FM
FM
FM
FM
FM
FM
FM
F
M
FM
Sex
FM
FM
>=40
40-89
>=30
>=30
55-69
27-95
All
>=30
>=30
15.3-84.9
65-84
Age
60 (49 69)
25-89
No BMI
No BMI, Smoking
No BMI
No BMI, Smoking
No BMI, Smoking
Adjustment
Indirect smoking and BMI
Confounder
1.03 (1.02, 1.04)
1.08 (1.06, 1.10)
1.08 (1.04, 1.13)
1.02 (1.01, 1.03)
1.02 (1.00, 1.04)
1.11 (1.00, 1.23)
0.98 (0.93, 1.03)
0.97 (0.89, 1.05)
1.03 (1.00, 1.06)
0.96 (0.86, 1.08)
1.04 (0.95, 1.14)
1.19 (1.06, 1.34)
ES (95% CI)
1.08 (0.64, 1.84)
1.02 (1.00, 1.04)
100.00
10.88
2.54
48.73
10.96
0.43
2.03
0.69
5.52
0.38
0.59
0.34
Weight
0.02
16.89
%
1.03 (1.02, 1.04)
1.08 (1.06, 1.10)
1.08 (1.04, 1.13)
1.02 (1.01, 1.03)
1.02 (1.00, 1.04)
1.11 (1.00, 1.23)
0.98 (0.93, 1.03)
0.97 (0.89, 1.05)
1.03 (1.00, 1.06)
0.96 (0.86, 1.08)
1.04 (0.95, 1.14)
1.19 (1.06, 1.34)
ES (95% CI)
1.08 (0.64, 1.84)
1.02 (1.00, 1.04)
100.00
10.88
2.54
48.73
10.96
0.43
2.03
0.69
5.52
0.38
0.59
0.34
Weight
0.02
16.89
%
1.9 1 1.1 1.2
NOTE: Weights are from random effects analysis
. (0.97, 1.10)with estimated predictive interval
Overall (I-squared = 75.5%, p = 0.000)
Cesaroni et al
Hart et al
Study
Abbey et al
Yorifuji et al
Carey et al
Katanoda et al
Jerrett et al
Lipsett et al
Turner et al
Fischer et al
Brunekreef et al
Crouse et al
Dimakopoulou K
2013
2011
Year
1999
2013
2013
2011
2009
2011
2016
2015
2009
2015b
2014
Rome longitudinal study
US trucking industry cohort
Cohort
AHSMOG
Shizuoka elderly cohort
CPRD
3 Japanease Prefectures
Toronto respiratory cohort
CTS
ACS CPS-II
DUELS
NLCS-AIR
CanCHEC
ESCAPE
Italy
USA
Setting
USA
Japan
England
Japan
Canada
USA
USA
Netherlands
Netherlands
Canada
Europe
1,265,058
53,814
N
5,652
13,412
830,429
63,520
2,360
12,336
669,046
7,218,363
117,528
2,521,525
307,553
FM
M
Sex
FM
FM
FM
FM
FM
F
FM
FM
FM
FM
FM
>=30
15.3-84.9
Age
27-95
65-84
40-89
>=40
60 (49 69)
>=30
>=30
>=30
55-69
25-89
All
No BMI, Smoking
No BMI, Smoking
Adjustment
No BMI
Confounder
No BMI, Smoking
No BMI
Indirect smoking and BMI
1.03 (1.01, 1.05)
1.03 (1.00, 1.06)
1.04 (0.95, 1.14)
ES (95% CI)
0.98 (0.93, 1.03)
1.19 (1.06, 1.34)
1.08 (1.04, 1.13)
1.08 (1.06, 1.10)
1.08 (0.64, 1.84)
0.96 (0.86, 1.08)
1.02 (1.00, 1.04)
1.02 (1.01, 1.03)
1.11 (1.00, 1.23)
1.02 (1.00, 1.04)
0.97 (0.89, 1.05)
100.00
11.60
3.67
Weight
8.06
2.37
8.92
13.28
0.13
2.62
13.30
%
15.02
2.87
14.03
4.13
1.03 (1.01, 1.05)
1.03 (1.00, 1.06)
1.04 (0.95, 1.14)
ES (95% CI)
0.98 (0.93, 1.03)
1.19 (1.06, 1.34)
1.08 (1.04, 1.13)
1.08 (1.06, 1.10)
1.08 (0.64, 1.84)
0.96 (0.86, 1.08)
1.02 (1.00, 1.04)
1.02 (1.01, 1.03)
1.11 (1.00, 1.23)
1.02 (1.00, 1.04)
0.97 (0.89, 1.05)
100.00
11.60
3.67
Weight
8.06
2.37
8.92
13.28
0.13
2.62
13.30
%
15.02
2.87
14.03
4.13
1.9 1 1.1 1.2
40
eFigure 23 Respiratory mortality - stratification by pre-existing disease
NOTE: Weights are from random effects analysis
.
.
General
Crouse et al
Hart et al
Lipsett et al
Abbey et al
Turner et al
Carey et al
Dimakopoulou K
Cesaroni et al
Fischer et al
Brunekreef et al
Yorifuji et al
Katanoda et al
Subtotal (I-squared = 77.5%, p = 0.000)
Preexisting
Jerrett et al
Subtotal (I-squared = .%, p = .)
Study
2015b
2011
2011
1999
2016
2013
2014
2013
2015
2009
2013
2011
2009
Year
CanCHEC
US trucking industry cohort
CTS
AHSMOG
ACS CPS-II
CPRD
ESCAPE
Rome longitudinal study
DUELS
NLCS-AIR
Shizuoka elderly cohort
3 Japanease Prefectures
Toronto respiratory cohort
Cohort
Canada
USA
USA
USA
USA
England
Europe
Italy
Netherlands
Netherlands
Japan
Japan
Canada
Setting
2,521,525
53,814
12,336
5,652
669,046
830,429
307,553
1,265,058
7,218,363
117,528
13,412
63,520
2,360
N
FM
M
F
FM
FM
FM
FM
FM
FM
FM
FM
FM
FM
Sex
25-89
15.3-84.9
>=30
27-95
>=30
40-89
All
>=30
>=30
55-69
65-84
>=40
60 (49 69)
Age
1.02 (1.00, 1.04)
1.04 (0.95, 1.14)
0.96 (0.86, 1.08)
0.98 (0.93, 1.03)
1.02 (1.00, 1.04)
1.08 (1.04, 1.13)
0.97 (0.89, 1.05)
1.03 (1.00, 1.06)
1.02 (1.01, 1.03)
1.11 (1.00, 1.23)
1.19 (1.06, 1.34)
1.08 (1.06, 1.10)
1.03 (1.01, 1.05)
1.08 (0.64, 1.84)
1.08 (0.64, 1.84)
ES (95% CI)
13.98
3.72
2.66
8.11
13.27
8.96
4.18
11.61
14.95
2.92
2.41
13.25
100.00
100.00
100.00
Weight
%
1.02 (1.00, 1.04)
1.04 (0.95, 1.14)
0.96 (0.86, 1.08)
0.98 (0.93, 1.03)
1.02 (1.00, 1.04)
1.08 (1.04, 1.13)
0.97 (0.89, 1.05)
1.03 (1.00, 1.06)
1.02 (1.01, 1.03)
1.11 (1.00, 1.23)
1.19 (1.06, 1.34)
1.08 (1.06, 1.10)
1.03 (1.01, 1.05)
1.08 (0.64, 1.84)
1.08 (0.64, 1.84)
ES (95% CI)
13.98
3.72
2.66
8.11
13.27
8.96
4.18
11.61
14.95
2.92
2.41
13.25
100.00
100.00
100.00
Weight
%
1.7 .8 .9 1 1.1 1.2 1.3
41
eFigure 24 Respiratory mortality - stratification by age range at cohort recruitment
NOTE: Weights are from random effects analysis
.
.
Adult
Crouse et al
Hart et al
Lipsett et al
Abbey et al
Turner et al
Carey et al
Dimakopoulou K
Cesaroni et al
Fischer et al
Katanoda et al
Subtotal (I-squared = 77.9%, p = 0.000)
Restricted
Brunekreef et al
Yorifuji et al
Subtotal (I-squared = 0.0%, p = 0.391)
Study
2015b
2011
2011
1999
2016
2013
2014
2013
2015
2011
2009
2013
Year
CanCHEC
US trucking industry cohort
CTS
AHSMOG
ACS CPS-II
CPRD
ESCAPE
Rome longitudinal study
DUELS
3 Japanease Prefectures
NLCS-AIR
Shizuoka elderly cohort
Cohort
Canada
USA
USA
USA
USA
England
Europe
Italy
Netherlands
Japan
Netherlands
Japan
Setting
2,521,525
53,814
12,336
5,652
669,046
830,429
307,553
1,265,058
7,218,363
63,520
117,528
13,412
N
FM
M
F
FM
FM
FM
FM
FM
FM
FM
FM
FM
Sex
25-89
15.3-84.9
>=30
27-95
>=30
40-89
All
>=30
>=30
>=40
55-69
65-84
Age
1.02 (1.00, 1.04)
1.04 (0.95, 1.14)
0.96 (0.86, 1.08)
0.98 (0.93, 1.03)
1.02 (1.00, 1.04)
1.08 (1.04, 1.13)
0.97 (0.89, 1.05)
1.03 (1.00, 1.06)
1.02 (1.01, 1.03)
1.08 (1.06, 1.10)
1.03 (1.01, 1.05)
1.11 (1.00, 1.23)
1.19 (1.06, 1.34)
1.15 (1.06, 1.24)
ES (95% CI)
15.15
3.62
2.56
8.24
14.27
9.20
4.08
12.26
16.38
14.25
100.00
55.80
44.20
100.00
Weight
%
1.02 (1.00, 1.04)
1.04 (0.95, 1.14)
0.96 (0.86, 1.08)
0.98 (0.93, 1.03)
1.02 (1.00, 1.04)
1.08 (1.04, 1.13)
0.97 (0.89, 1.05)
1.03 (1.00, 1.06)
1.02 (1.01, 1.03)
1.08 (1.06, 1.10)
1.03 (1.01, 1.05)
1.11 (1.00, 1.23)
1.19 (1.06, 1.34)
1.15 (1.06, 1.24)
ES (95% CI)
15.15
3.62
2.56
8.24
14.27
9.20
4.08
12.26
16.38
14.25
100.00
55.80
44.20
100.00
Weight
%
1.9 1 1.1 1.2 1.3
42
eFigure 25 Respiratory mortality - stratification by level of adjustment for smoking and BMI
NOTE: Weights are from random effects analysis
.
.
Individual
Lipsett et al
Abbey et al
Turner et al
Carey et al
Dimakopoulou K
Subtotal (I-squared = 69.4%, p = 0.011)
None/Indirect
Crouse et al
Hart et al
Cesaroni et al
Fischer et al
Katanoda et al
Subtotal (I-squared = 84.9%, p = 0.000)
Study
2011
1999
2016
2013
2014
2015b
2011
2013
2015
2011
Year
CTS
AHSMOG
ACS CPS-II
CPRD
ESCAPE
CanCHEC
US trucking industry cohort
Rome longitudinal study
DUELS
3 Japanease Prefectures
Cohort
USA
USA
USA
England
Europe
Canada
USA
Italy
Netherlands
Japan
Setting
12,336
5,652
669,046
830,429
307,553
2,521,525
53,814
1,265,058
7,218,363
63,520
N
F
FM
FM
FM
FM
FM
M
FM
FM
FM
Sex
>=30
27-95
>=30
40-89
All
25-89
15.3-84.9
>=30
>=30
>=40
Age
0.96 (0.86, 1.08)
0.98 (0.93, 1.03)
1.02 (1.00, 1.04)
1.08 (1.04, 1.13)
0.97 (0.89, 1.05)
1.01 (0.97, 1.05)
1.02 (1.00, 1.04)
1.04 (0.95, 1.14)
1.03 (1.00, 1.06)
1.02 (1.01, 1.03)
1.08 (1.06, 1.10)
1.04 (1.01, 1.06)
ES (95% CI)
9.45
22.44
30.36
23.97
13.77
100.00
24.56
5.91
19.90
26.52
23.11
100.00
Weight
%
0.96 (0.86, 1.08)
0.98 (0.93, 1.03)
1.02 (1.00, 1.04)
1.08 (1.04, 1.13)
0.97 (0.89, 1.05)
1.01 (0.97, 1.05)
1.02 (1.00, 1.04)
1.04 (0.95, 1.14)
1.03 (1.00, 1.06)
1.02 (1.01, 1.03)
1.08 (1.06, 1.10)
1.04 (1.01, 1.06)
ES (95% CI)
9.45
22.44
30.36
23.97
13.77
100.00
24.56
5.91
19.90
26.52
23.11
100.00
Weight
%
1.9 1 1.1
43
eFigure 26 Respiratory mortality - stratification by spatial resolution of NO2 concentration
NOTE: Weights are from random effects analysis
.
.
LUR Address
Crouse et al
Hart et al
Turner et al
Dimakopoulou K
Cesaroni et al
Fischer et al
Subtotal (I-squared = 0.0%, p = 0.826)
Area
Lipsett et al
Abbey et al
Carey et al
Katanoda et al
Subtotal (I-squared = 83.2%, p = 0.000)
Study
2015b
2011
2016
2014
2013
2015
2011
1999
2013
2011
Year
CanCHEC
US trucking industry cohort
ACS CPS-II
ESCAPE
Rome longitudinal study
DUELS
CTS
AHSMOG
CPRD
3 Japanease Prefectures
Cohort
Canada
USA
USA
Europe
Italy
Netherlands
USA
USA
England
Japan
Setting
2,521,525
53,814
669,046
307,553
1,265,058
7,218,363
12,336
5,652
830,429
63,520
N
FM
M
FM
FM
FM
FM
F
FM
FM
FM
Sex
25-89
15.3-84.9
>=30
All
>=30
>=30
>=30
27-95
40-89
>=40
Age
1.02 (1.00, 1.04)
1.04 (0.95, 1.14)
1.02 (1.00, 1.04)
0.97 (0.89, 1.05)
1.03 (1.00, 1.06)
1.02 (1.01, 1.03)
1.02 (1.01, 1.03)
0.96 (0.86, 1.08)
0.98 (0.93, 1.03)
1.08 (1.04, 1.13)
1.08 (1.06, 1.10)
1.04 (0.98, 1.10)
ES (95% CI)
20.26
0.70
13.15
0.82
6.62
58.45
100.00
14.18
26.50
27.61
31.71
100.00
Weight
%
1.02 (1.00, 1.04)
1.04 (0.95, 1.14)
1.02 (1.00, 1.04)
0.97 (0.89, 1.05)
1.03 (1.00, 1.06)
1.02 (1.01, 1.03)
1.02 (1.01, 1.03)
0.96 (0.86, 1.08)
0.98 (0.93, 1.03)
1.08 (1.04, 1.13)
1.08 (1.06, 1.10)
1.04 (0.98, 1.10)
ES (95% CI)
20.26
0.70
13.15
0.82
6.62
58.45
100.00
14.18
26.50
27.61
31.71
100.00
Weight
%
1.9 1 1.1
44
eFigure 27 COPD mortality
* indicates exclusion from meta-analysis. Refer to methods/results sections of manuscript.
Crouse et al
Gan et al
Hart et al
Turner et al
Carey et al
Naess et al
Katanoda et al
Yorifuji et al *
Yorifuji et al
Study
2015b
2013
2011
2016
2013
2007
2011
2010
2013
Year
CanCHEC
Vancouver Cohort
US trucking industry cohort
ACS CPS-II
CPRD
Oslo cohort
3 Japanease Prefectures
Shizuoka elderly cohort
Shizuoka elderly cohort
Cohort
Canada
Canada
USA
USA
England
Norway
Japan
Japan
Japan
Setting
2,521,525
467,994
53,814
669,046
830,429
143,842
63,520
13,444
13,412
N
FM
FM
M
FM
FM
FM
FM
FM
FM
Sex
25-89
45-85
15.3-84.9
>=30
40-89
51-90
>=40
65-84
65-84
Age
Indirect smoking and BMI
No BMI, Smoking
No BMI, Smoking
No BMI, Smoking
No BMI
Adjustment
Confounder
1.03 (1.01, 1.06)
1.05 (0.95, 1.15)
0.99 (0.88, 1.10)
1.00 (0.98, 1.03)
1.07 (0.99, 1.14)
1.05 (1.01, 1.09)
1.02 (0.96, 1.08)
1.11 (0.78, 1.57)
0.98 (0.75, 1.29)
ES (95% CI)
1.03 (1.01, 1.06)
1.05 (0.95, 1.15)
0.99 (0.88, 1.10)
1.00 (0.98, 1.03)
1.07 (0.99, 1.14)
1.05 (1.01, 1.09)
1.02 (0.96, 1.08)
1.11 (0.78, 1.57)
0.98 (0.75, 1.29)
ES (95% CI)
1.9 1 1.1 1.2
45
eFigure 28a COPD mortality - fixed effects model
eFigure 28b COPD mortality - random effects model
Overall (I-squared = 0.0%, p = 0.492)
Gan et al
Katanoda et al
Study
Carey et al
Yorifuji et al
Hart et al
Turner et al
Crouse et al
Naess et al
2013
2011
Year
2013
2013
2011
2016
2015b
2007
Vancouver Cohort
3 Japanease Prefectures
Cohort
CPRD
Shizuoka elderly cohort
US trucking industry cohort
ACS CPS-II
CanCHEC
Oslo cohort
Canada
Japan
Setting
England
Japan
USA
USA
Canada
Norway
467,994
63,520
N
830,429
13,412
53,814
669,046
2,521,525
143,842
FM
FM
Sex
FM
FM
M
FM
FM
FM
45-85
>=40
Age
40-89
65-84
15.3-84.9
>=30
25-89
51-90
No BMI, Smoking
No BMI
Adjustment
No BMI, Smoking
Confounder
Indirect smoking and BMI
No BMI, Smoking
1.03 (1.01, 1.04)
1.05 (0.95, 1.15)
1.02 (0.96, 1.08)
ES (95% CI)
1.07 (0.99, 1.14)
0.98 (0.75, 1.29)
0.99 (0.88, 1.10)
1.00 (0.98, 1.03)
1.03 (1.01, 1.06)
1.05 (1.01, 1.09)
100.00
2.18
6.43
Weight
4.18
0.28
1.71
%
30.62
39.72
14.88
1.03 (1.01, 1.04)
1.05 (0.95, 1.15)
1.02 (0.96, 1.08)
ES (95% CI)
1.07 (0.99, 1.14)
0.98 (0.75, 1.29)
0.99 (0.88, 1.10)
1.00 (0.98, 1.03)
1.03 (1.01, 1.06)
1.05 (1.01, 1.09)
100.00
2.18
6.43
Weight
4.18
0.28
1.71
%
30.62
39.72
14.88
1.9 1 1.1 1.2
NOTE: Weights are from random effects analysis
. (1.01, 1.05)with estimated predictive interval
Overall (I-squared = 0.0%, p = 0.492)
Hart et al
Crouse et al
Katanoda et al
Gan et al
Turner et al
Study
Yorifuji et al
Carey et al
Naess et al
2011
2015b
2011
2013
2016
Year
2013
2013
2007
US trucking industry cohort
CanCHEC
3 Japanease Prefectures
Vancouver Cohort
ACS CPS-II
Cohort
Shizuoka elderly cohort
CPRD
Oslo cohort
USA
Canada
Japan
Canada
USA
Setting
Japan
England
Norway
53,814
2,521,525
63,520
467,994
669,046
N
13,412
830,429
143,842
M
FM
FM
FM
FM
Sex
FM
FM
FM
15.3-84.9
25-89
>=40
45-85
>=30
Age
65-84
40-89
51-90
No BMI, Smoking
Indirect smoking and BMI
No BMI
No BMI, Smoking
Adjustment
No BMI, Smoking
Confounder
1.03 (1.01, 1.04)
0.99 (0.88, 1.10)
1.03 (1.01, 1.06)
1.02 (0.96, 1.08)
1.05 (0.95, 1.15)
1.00 (0.98, 1.03)
ES (95% CI)
0.98 (0.75, 1.29)
1.07 (0.99, 1.14)
1.05 (1.01, 1.09)
100.00
1.71
39.72
6.43
2.18
30.62
Weight
0.28
4.18
14.88
%
1.03 (1.01, 1.04)
0.99 (0.88, 1.10)
1.03 (1.01, 1.06)
1.02 (0.96, 1.08)
1.05 (0.95, 1.15)
1.00 (0.98, 1.03)
ES (95% CI)
0.98 (0.75, 1.29)
1.07 (0.99, 1.14)
1.05 (1.01, 1.09)
100.00
1.71
39.72
6.43
2.18
30.62
Weight
0.28
4.18
14.88
%
1.9 1 1.1 1.2
46
eFigure 29 COPD mortality - stratification by age range at cohort recruitment
NOTE: Weights are from random effects analysis
.
.
Adult
Gan et al
Crouse et al
Hart et al
Turner et al
Carey et al
Naess et al
Katanoda et al
Subtotal (I-squared = 4.8%, p = 0.390)
Restricted
Yorifuji et al
Subtotal (I-squared = .%, p = .)
Study
2013
2015b
2011
2016
2013
2007
2011
2013
Year
Vancouver Cohort
CanCHEC
US trucking industry cohort
ACS CPS-II
CPRD
Oslo cohort
3 Japanease Prefectures
Shizuoka elderly cohort
Cohort
Canada
Canada
USA
USA
England
Norway
Japan
Japan
Setting
467,994
2,521,525
53,814
669,046
830,429
143,842
63,520
13,412
N
FM
FM
M
FM
FM
FM
FM
FM
Sex
45-85
25-89
15.3-84.9
>=30
40-89
51-90
>=40
65-84
Age
1.05 (0.95, 1.15)
1.03 (1.01, 1.06)
0.99 (0.88, 1.10)
1.00 (0.98, 1.03)
1.07 (0.99, 1.14)
1.05 (1.01, 1.09)
1.02 (0.96, 1.08)
1.03 (1.01, 1.04)
0.98 (0.75, 1.29)
0.98 (0.75, 1.29)
ES (95% CI)
2.42
38.07
1.90
30.35
4.60
15.66
7.01
100.00
100.00
100.00
Weight
%
1.05 (0.95, 1.15)
1.03 (1.01, 1.06)
0.99 (0.88, 1.10)
1.00 (0.98, 1.03)
1.07 (0.99, 1.14)
1.05 (1.01, 1.09)
1.02 (0.96, 1.08)
1.03 (1.01, 1.04)
0.98 (0.75, 1.29)
0.98 (0.75, 1.29)
ES (95% CI)
2.42
38.07
1.90
30.35
4.60
15.66
7.01
100.00
100.00
100.00
Weight
%
1.8 .9 1 1.1 1.2
47
eFigure 30 COPD mortality - stratification by level of adjustment for smoking and BMI
NOTE: Weights are from random effects analysis
.
.
Individual
Turner et al
Carey et al
Subtotal (I-squared = 56.8%, p = 0.128)
None/Indirect
Gan et al
Crouse et al
Hart et al
Naess et al
Katanoda et al
Subtotal (I-squared = 0.0%, p = 0.764)
Study
2016
2013
2013
2015b
2011
2007
2011
Year
ACS CPS-II
CPRD
Vancouver Cohort
CanCHEC
US trucking industry cohort
Oslo cohort
3 Japanease Prefectures
Cohort
USA
England
Canada
Canada
USA
Norway
Japan
Setting
669,046
830,429
467,994
2,521,525
53,814
143,842
63,520
N
FM
FM
FM
FM
M
FM
FM
Sex
>=30
40-89
45-85
25-89
15.3-84.9
51-90
>=40
Age
1.00 (0.98, 1.03)
1.07 (0.99, 1.14)
1.02 (0.97, 1.08)
1.05 (0.95, 1.15)
1.03 (1.01, 1.06)
0.99 (0.88, 1.10)
1.05 (1.01, 1.09)
1.02 (0.96, 1.08)
1.03 (1.02, 1.05)
ES (95% CI)
66.39
33.61
100.00
3.36
61.18
2.64
22.92
9.91
100.00
Weight
%
1.00 (0.98, 1.03)
1.07 (0.99, 1.14)
1.02 (0.97, 1.08)
1.05 (0.95, 1.15)
1.03 (1.01, 1.06)
0.99 (0.88, 1.10)
1.05 (1.01, 1.09)
1.02 (0.96, 1.08)
1.03 (1.02, 1.05)
ES (95% CI)
66.39
33.61
100.00
3.36
61.18
2.64
22.92
9.91
100.00
Weight
%
1.9 1 1.1
48
eFigure 31 COPD mortality - stratification by spatial resolution of NO2 concentration
NOTE: Weights are from random effects analysis
.
.
Area
Carey et al
Naess et al
Katanoda et al
Subtotal (I-squared = 0.0%, p = 0.524)
LUR Address
Gan et al
Crouse et al
Hart et al
Turner et al
Subtotal (I-squared = 9.5%, p = 0.346)
Study
2013
2007
2011
2013
2015b
2011
2016
Year
CPRD
Oslo cohort
3 Japanease Prefectures
Vancouver Cohort
CanCHEC
US trucking industry cohort
ACS CPS-II
Cohort
England
Norway
Japan
Canada
Canada
USA
USA
Setting
830,429
143,842
63,520
467,994
2,521,525
53,814
669,046
N
FM
FM
FM
FM
FM
M
FM
Sex
40-89
51-90
>=40
45-85
25-89
15.3-84.9
>=30
Age
1.07 (0.99, 1.14)
1.05 (1.01, 1.09)
1.02 (0.96, 1.08)
1.04 (1.01, 1.07)
1.05 (0.95, 1.15)
1.03 (1.01, 1.06)
0.99 (0.88, 1.10)
1.00 (0.98, 1.03)
1.02 (1.00, 1.04)
ES (95% CI)
16.40
58.37
25.23
100.00
3.64
51.51
2.87
41.98
100.00
Weight
%
1.07 (0.99, 1.14)
1.05 (1.01, 1.09)
1.02 (0.96, 1.08)
1.04 (1.01, 1.07)
1.05 (0.95, 1.15)
1.03 (1.01, 1.06)
0.99 (0.88, 1.10)
1.00 (0.98, 1.03)
1.02 (1.00, 1.04)
ES (95% CI)
16.40
58.37
25.23
100.00
3.64
51.51
2.87
41.98
100.00
Weight
%
1.9 1 1.1
49
eFigure 32 Pneumonia mortality
Turner et al
Carey et al
Yorifuji et al
Yorifuji et al
Katanoda et al
Study
2016
2013
2013
2010
2011
Year
ACS CPS-II
CPRD
Shizuoka elderly cohort
Shizuoka elderly cohort
3 Japanease Prefectures
Cohort
USA
England
Japan
Japan
Japan
Setting
669,046
830,429
13,412
13,444
63,520
N
FM
FM
FM
FM
FM
Sex
>=30
40-89
65-84
65-84
>=40
Age
1.06 (1.02, 1.09)
1.08 (1.03, 1.15)
1.15 (0.98, 1.34)
1.18 (0.96, 1.45)
1.08 (1.06, 1.10)
ES (95% CI)
1.06 (1.02, 1.09)
1.08 (1.03, 1.15)
1.15 (0.98, 1.34)
1.18 (0.96, 1.45)
1.08 (1.06, 1.10)
ES (95% CI)
11 1.1 1.2 1.3
50
eFigure 33 Lung cancer mortality
* indicates exclusion from meta-analysis. Refer to methods/results sections of manuscript.
NOTE: Weights are from random effects analysis
Crouse et al
HEI *
Krewski et al *
Jerrett et al *
Turner et al
Abbey et al
Lipsett et al
HEI
Hart et al
Chen et al
Carey et al
Filleul et al
Heinrich et al
Cesaroni et al
Fischer et al
Brunekreef et al
Naess et al
Katanoda et al
Yorifuji et al *
Yorifuji et al
Study
2015b
2000
2009
2013
2016
1999
2011
2000
2011
2016
2013
2005
2013
2013
2015
2009
2007
2011
2010
2013
Year
CanCHEC
ACS CPS-II
ACS CPS-II
ACS CPS-II
ACS CPS-II
AHSMOG
CTS
Six Cities
US trucking industry cohort
Four northern Chinese cities
CPRD
PAARC
German cohort
Rome longitudinal study
DUELS
NLCS-AIR
Oslo cohort
3 Japanease Prefectures
Shizuoka elderly cohort
Shizuoka elderly cohort
Cohort
Canada
USA
USA
USA
USA
USA
USA
USA
USA
China
England
France
Germany
Italy
Netherlands
Netherlands
Norway
Japan
Japan
Japan
Setting
2,521,525
552,138
406,917
73,711
669,046
5,652
12,336
8,111
53,814
39,054
830,429
14,284
4,752
1,265,058
7,218,363
117,528
143,842
63,520
13,444
13,412
N
FM
FM
FM
FM
FM
FM
F
FM
M
FM
FM
FM
F
FM
FM
FM
FM
FM
FM
FM
Sex
25-89
>=30
>=30
>=30
>=30
27-95
>=30
25-74
15.3-84.9
23-89
40-89
25-59
50-59
>=30
>=30
55-69
51-90
>=40
65-84
65-84
Age
Indirect smoking and BMI
No BMI, Smoking
No BMI, Age(?)
No BMI, Smoking
No BMI, Smoking
No BMI
No BMI, Smoking
No BMI
Adjustment
Confounder
1.04 (1.02, 1.06)
0.99 (0.97, 1.01)
1.00 (0.98, 1.01)
1.15 (1.03, 1.28)
1.00 (0.97, 1.02)
1.23 (1.06, 1.42)
1.00 (0.86, 1.16)
1.05 (0.86, 1.27)
1.04 (0.98, 1.10)
0.90 (0.82, 0.98)
1.06 (1.00, 1.11)
1.48 (1.06, 2.07)
1.27 (0.95, 1.69)
1.04 (1.02, 1.07)
1.10 (1.09, 1.11)
0.97 (0.90, 1.05)
1.08 (1.03, 1.12)
1.08 (1.03, 1.12)
0.95 (0.78, 1.16)
1.20 (1.03, 1.40)
ES (95% CI)
1.04 (1.02, 1.06)
0.99 (0.97, 1.01)
1.00 (0.98, 1.01)
1.15 (1.03, 1.28)
1.00 (0.97, 1.02)
1.23 (1.06, 1.42)
1.00 (0.86, 1.16)
1.05 (0.86, 1.27)
1.04 (0.98, 1.10)
0.90 (0.82, 0.98)
1.06 (1.00, 1.11)
1.48 (1.06, 2.07)
1.27 (0.95, 1.69)
1.04 (1.02, 1.07)
1.10 (1.09, 1.11)
0.97 (0.90, 1.05)
1.08 (1.03, 1.12)
1.08 (1.03, 1.12)
0.95 (0.78, 1.16)
1.20 (1.03, 1.40)
ES (95% CI)
1.9 1 1.1 1.2
51
eFigure 34a Lung cancer mortality - fixed effects model
eFigure 34b Lung cancer mortality - random effects model
Overall (I-squared = 88.1%, p = 0.000)
Katanoda et al
Brunekreef et al
Yorifuji et al
Carey et al
Turner et al
Lipsett et al
Naess et al
Filleul et al
Fischer et al
HEI
Hart et al
Abbey et al
Study
Chen et al
Heinrich et al
Crouse et al
Cesaroni et al
2011
2009
2013
2013
2016
2011
2007
2005
2015
2000
2011
1999
Year
2016
2013
2015b
2013
3 Japanease Prefectures
NLCS-AIR
Shizuoka elderly cohort
CPRD
ACS CPS-II
CTS
Oslo cohort
PAARC
DUELS
Six Cities
US trucking industry cohort
AHSMOG
Cohort
Four northern Chinese cities
German cohort
CanCHEC
Rome longitudinal study
Japan
Netherlands
Japan
England
USA
USA
Norway
France
Netherlands
USA
USA
USA
Setting
China
Germany
Canada
Italy
63,520
117,528
13,412
830,429
669,046
12,336
143,842
14,284
7,218,363
8,111
53,814
5,652
N
39,054
4,752
2,521,525
1,265,058
FM
FM
FM
FM
FM
F
FM
FM
FM
FM
M
FM
Sex
FM
F
FM
FM
>=40
55-69
65-84
40-89
>=30
>=30
51-90
25-59
>=30
25-74
15.3-84.9
27-95
Age
23-89
50-59
25-89
>=30
No BMI
No BMI
Confounder
No BMI, Smoking
No BMI, Smoking
No BMI, Smoking
Adjustment
No BMI, Age(?)
Indirect smoking and BMI
No BMI, Smoking
1.07 (1.06, 1.08)
1.08 (1.03, 1.12)
0.97 (0.90, 1.05)
1.20 (1.03, 1.40)
1.06 (1.00, 1.11)
1.00 (0.97, 1.02)
1.00 (0.86, 1.16)
1.08 (1.03, 1.12)
1.48 (1.06, 2.07)
1.10 (1.09, 1.11)
1.05 (0.86, 1.27)
1.04 (0.98, 1.10)
1.23 (1.06, 1.42)
ES (95% CI)
0.90 (0.82, 0.98)
1.27 (0.95, 1.69)
1.04 (1.02, 1.06)
1.04 (1.02, 1.07)
100.00
2.72
0.78
0.20
%
1.69
10.18
0.22
3.06
0.04
57.57
0.13
1.37
0.23
Weight
0.59
0.06
12.86
8.31
1.07 (1.06, 1.08)
1.08 (1.03, 1.12)
0.97 (0.90, 1.05)
1.20 (1.03, 1.40)
1.06 (1.00, 1.11)
1.00 (0.97, 1.02)
1.00 (0.86, 1.16)
1.08 (1.03, 1.12)
1.48 (1.06, 2.07)
1.10 (1.09, 1.11)
1.05 (0.86, 1.27)
1.04 (0.98, 1.10)
1.23 (1.06, 1.42)
ES (95% CI)
0.90 (0.82, 0.98)
1.27 (0.95, 1.69)
1.04 (1.02, 1.06)
1.04 (1.02, 1.07)
100.00
2.72
0.78
0.20
%
1.69
10.18
0.22
3.06
0.04
57.57
0.13
1.37
0.23
Weight
0.59
0.06
12.86
8.31
1.9 1 1.1 1.2
NOTE: Weights are from random effects analysis
. (0.94, 1.17)with estimated predictive interval
Overall (I-squared = 88.1%, p = 0.000)
Yorifuji et al
Abbey et al
Crouse et al
Hart et al
Fischer et al
Lipsett et al
Chen et al
HEI
Carey et al
Naess et al
Cesaroni et al
Study
Katanoda et al
Heinrich et al
Brunekreef et al
Turner et al
Filleul et al
2013
1999
2015b
2011
2015
2011
2016
2000
2013
2007
2013
Year
2011
2013
2009
2016
2005
Shizuoka elderly cohort
AHSMOG
CanCHEC
US trucking industry cohort
DUELS
CTS
Four northern Chinese cities
Six Cities
CPRD
Oslo cohort
Rome longitudinal study
Cohort
3 Japanease Prefectures
German cohort
NLCS-AIR
ACS CPS-II
PAARC
Japan
USA
Canada
USA
Netherlands
USA
China
USA
England
Norway
Italy
Setting
Japan
Germany
Netherlands
USA
France
13,412
5,652
2,521,525
53,814
7,218,363
12,336
39,054
8,111
830,429
143,842
1,265,058
N
63,520
4,752
117,528
669,046
14,284
FM
FM
FM
M
FM
F
FM
FM
FM
FM
FM
Sex
FM
F
FM
FM
FM
65-84
27-95
25-89
15.3-84.9
>=30
>=30
23-89
25-74
40-89
51-90
>=30
Age
>=40
50-59
55-69
>=30
25-59
Indirect smoking and BMI
No BMI, Smoking
No BMI, Smoking
No BMI, Smoking
No BMI, Smoking
Adjustment
No BMI
No BMI, Age(?)
No BMI
Confounder
1.05 (1.02, 1.08)
1.20 (1.03, 1.40)
1.23 (1.06, 1.42)
1.04 (1.02, 1.06)
1.04 (0.98, 1.10)
1.10 (1.09, 1.11)
1.00 (0.86, 1.16)
0.90 (0.82, 0.98)
1.05 (0.86, 1.27)
1.06 (1.00, 1.11)
1.08 (1.03, 1.12)
1.04 (1.02, 1.07)
ES (95% CI)
1.08 (1.03, 1.12)
1.27 (0.95, 1.69)
0.97 (0.90, 1.05)
1.00 (0.97, 1.02)
1.48 (1.06, 2.07)
100.00
2.85
3.10
10.39
7.68
10.75
3.00
5.55
2.00
8.12
9.15
10.16
Weight
8.98
0.99
6.26
10.28
0.75
%
1.05 (1.02, 1.08)
1.20 (1.03, 1.40)
1.23 (1.06, 1.42)
1.04 (1.02, 1.06)
1.04 (0.98, 1.10)
1.10 (1.09, 1.11)
1.00 (0.86, 1.16)
0.90 (0.82, 0.98)
1.05 (0.86, 1.27)
1.06 (1.00, 1.11)
1.08 (1.03, 1.12)
1.04 (1.02, 1.07)
ES (95% CI)
1.08 (1.03, 1.12)
1.27 (0.95, 1.69)
0.97 (0.90, 1.05)
1.00 (0.97, 1.02)
1.48 (1.06, 2.07)
100.00
2.85
3.10
10.39
7.68
10.75
3.00
5.55
2.00
8.12
9.15
10.16
Weight
8.98
0.99
6.26
10.28
0.75
%
1.9 1 1.1 1.2
52
eFigure 35 Lung cancer mortality - stratification by age range at cohort recruitment
NOTE: Weights are from random effects analysis
.
.
Adult
Crouse et al
Hart et al
Lipsett et al
Abbey et al
HEI
Turner et al
Chen et al
Carey et al
Heinrich et al
Cesaroni et al
Fischer et al
Naess et al
Katanoda et al
Subtotal (I-squared = 89.5%, p = 0.000)
Restricted
Filleul et al
Brunekreef et al
Yorifuji et al
Subtotal (I-squared = 81.1%, p = 0.005)
Study
2015b
2011
2011
1999
2000
2016
2016
2013
2013
2013
2015
2007
2011
2005
2009
2013
Year
CanCHEC
US trucking industry cohort
CTS
AHSMOG
Six Cities
ACS CPS-II
Four northern Chinese cities
CPRD
German cohort
Rome longitudinal study
DUELS
Oslo cohort
3 Japanease Prefectures
PAARC
NLCS-AIR
Shizuoka elderly cohort
Cohort
Canada
USA
USA
USA
USA
USA
China
England
Germany
Italy
Netherlands
Norway
Japan
France
Netherlands
Japan
Setting
2,521,525
53,814
12,336
5,652
8,111
669,046
39,054
830,429
4,752
1,265,058
7,218,363
143,842
63,520
14,284
117,528
13,412
N
FM
M
F
FM
FM
FM
FM
FM
F
FM
FM
FM
FM
FM
FM
FM
Sex
25-89
15.3-84.9
>=30
27-95
25-74
>=30
23-89
40-89
50-59
>=30
>=30
51-90
>=40
25-59
55-69
65-84
Age
1.04 (1.02, 1.06)
1.04 (0.98, 1.10)
1.00 (0.86, 1.16)
1.23 (1.06, 1.42)
1.05 (0.86, 1.27)
1.00 (0.97, 1.02)
0.90 (0.82, 0.98)
1.06 (1.00, 1.11)
1.27 (0.95, 1.69)
1.04 (1.02, 1.07)
1.10 (1.09, 1.11)
1.08 (1.03, 1.12)
1.08 (1.03, 1.12)
1.05 (1.02, 1.08)
1.48 (1.06, 2.07)
0.97 (0.90, 1.05)
1.20 (1.03, 1.40)
1.15 (0.92, 1.42)
ES (95% CI)
11.65
8.47
3.22
3.33
2.14
11.51
6.04
8.98
1.05
11.37
12.07
10.18
9.98
100.00
21.45
42.12
36.43
100.00
Weight
%
1.04 (1.02, 1.06)
1.04 (0.98, 1.10)
1.00 (0.86, 1.16)
1.23 (1.06, 1.42)
1.05 (0.86, 1.27)
1.00 (0.97, 1.02)
0.90 (0.82, 0.98)
1.06 (1.00, 1.11)
1.27 (0.95, 1.69)
1.04 (1.02, 1.07)
1.10 (1.09, 1.11)
1.08 (1.03, 1.12)
1.08 (1.03, 1.12)
1.05 (1.02, 1.08)
1.48 (1.06, 2.07)
0.97 (0.90, 1.05)
1.20 (1.03, 1.40)
1.15 (0.92, 1.42)
ES (95% CI)
11.65
8.47
3.22
3.33
2.14
11.51
6.04
8.98
1.05
11.37
12.07
10.18
9.98
100.00
21.45
42.12
36.43
100.00
Weight
%
1.8 1 1.2 1.4 1.6
53
eFigure 36 Lung cancer mortality - stratification by level of adjustment for smoking and BMI
NOTE: Weights are from random effects analysis
.
.
Individual
Lipsett et al
Abbey et al
HEI
Turner et al
Chen et al
Carey et al
Subtotal (I-squared = 71.5%, p = 0.004)
None/Indirect
Crouse et al
Hart et al
Heinrich et al
Cesaroni et al
Fischer et al
Naess et al
Katanoda et al
Subtotal (I-squared = 86.0%, p = 0.000)
Study
2011
1999
2000
2016
2016
2013
2015b
2011
2013
2013
2015
2007
2011
Year
CTS
AHSMOG
Six Cities
ACS CPS-II
Four northern Chinese cities
CPRD
CanCHEC
US trucking industry cohort
German cohort
Rome longitudinal study
DUELS
Oslo cohort
3 Japanease Prefectures
Cohort
USA
USA
USA
USA
China
England
Canada
USA
Germany
Italy
Netherlands
Norway
Japan
Setting
12,336
5,652
8,111
669,046
39,054
830,429
2,521,525
53,814
4,752
1,265,058
7,218,363
143,842
63,520
N
F
FM
FM
FM
FM
FM
FM
M
F
FM
FM
FM
FM
Sex
>=30
27-95
25-74
>=30
23-89
40-89
25-89
15.3-84.9
50-59
>=30
>=30
51-90
>=40
Age
1.00 (0.86, 1.16)
1.23 (1.06, 1.42)
1.05 (0.86, 1.27)
1.00 (0.97, 1.02)
0.90 (0.82, 0.98)
1.06 (1.00, 1.11)
1.02 (0.96, 1.08)
1.04 (1.02, 1.06)
1.04 (0.98, 1.10)
1.27 (0.95, 1.69)
1.04 (1.02, 1.07)
1.10 (1.09, 1.11)
1.08 (1.03, 1.12)
1.08 (1.03, 1.12)
1.06 (1.03, 1.10)
ES (95% CI)
10.71
11.01
7.43
28.64
18.02
24.20
100.00
19.23
11.20
0.95
18.37
20.61
15.08
14.57
100.00
Weight
%
1.00 (0.86, 1.16)
1.23 (1.06, 1.42)
1.05 (0.86, 1.27)
1.00 (0.97, 1.02)
0.90 (0.82, 0.98)
1.06 (1.00, 1.11)
1.02 (0.96, 1.08)
1.04 (1.02, 1.06)
1.04 (0.98, 1.10)
1.27 (0.95, 1.69)
1.04 (1.02, 1.07)
1.10 (1.09, 1.11)
1.08 (1.03, 1.12)
1.08 (1.03, 1.12)
1.06 (1.03, 1.10)
ES (95% CI)
10.71
11.01
7.43
28.64
18.02
24.20
100.00
19.23
11.20
0.95
18.37
20.61
15.08
14.57
100.00
Weight
%
1.8 1 1.2 1.4
54
eFigure 37 Lung cancer mortality - stratification by spatial resolution of NO2 concentration
NOTE: Weights are from random effects analysis
.
.
Area
Lipsett et al
Abbey et al
HEI
Chen et al
Carey et al
Heinrich et al
Naess et al
Katanoda et al
Subtotal (I-squared = 65.3%, p = 0.005)
LUR Address
Crouse et al
Hart et al
Turner et al
Cesaroni et al
Fischer et al
Subtotal (I-squared = 95.7%, p = 0.000)
Study
2011
1999
2000
2016
2013
2013
2007
2011
2015b
2011
2016
2013
2015
Year
CTS
AHSMOG
Six Cities
Four northern Chinese cities
CPRD
German cohort
Oslo cohort
3 Japanease Prefectures
CanCHEC
US trucking industry cohort
ACS CPS-II
Rome longitudinal study
DUELS
Cohort
USA
USA
USA
China
England
Germany
Norway
Japan
Canada
USA
USA
Italy
Netherlands
Setting
12,336
5,652
8,111
39,054
830,429
4,752
143,842
63,520
2,521,525
53,814
669,046
1,265,058
7,218,363
N
F
FM
FM
FM
FM
F
FM
FM
FM
M
FM
FM
FM
Sex
>=30
27-95
25-74
23-89
40-89
50-59
51-90
>=40
25-89
15.3-84.9
>=30
>=30
>=30
Age
1.00 (0.86, 1.16)
1.23 (1.06, 1.42)
1.05 (0.86, 1.27)
0.90 (0.82, 0.98)
1.06 (1.00, 1.11)
1.27 (0.95, 1.69)
1.08 (1.03, 1.12)
1.08 (1.03, 1.12)
1.05 (1.00, 1.11)
1.04 (1.02, 1.06)
1.04 (0.98, 1.10)
1.00 (0.97, 1.02)
1.04 (1.02, 1.07)
1.10 (1.09, 1.11)
1.04 (1.00, 1.09)
ES (95% CI)
7.78
8.02
5.27
13.83
19.50
2.64
21.66
21.30
100.00
21.08
15.66
20.86
20.62
21.79
100.00
Weight
%
1.00 (0.86, 1.16)
1.23 (1.06, 1.42)
1.05 (0.86, 1.27)
0.90 (0.82, 0.98)
1.06 (1.00, 1.11)
1.27 (0.95, 1.69)
1.08 (1.03, 1.12)
1.08 (1.03, 1.12)
1.05 (1.00, 1.11)
1.04 (1.02, 1.06)
1.04 (0.98, 1.10)
1.00 (0.97, 1.02)
1.04 (1.02, 1.07)
1.10 (1.09, 1.11)
1.04 (1.00, 1.09)
ES (95% CI)
7.78
8.02
5.27
13.83
19.50
2.64
21.66
21.30
100.00
21.08
15.66
20.86
20.62
21.79
100.00
Weight
%
1.8 1 1.2 1.4
55
eFigure 38 Diabetes associated mortality – random effects model
NOTE: Weights are from random effects analysis
. (0.75, 1.44)with estimated predictive interval
Overall (I-squared = 38.2%, p = 0.198)
Study
Raaschou-Nielsen et al
Turner et al
Crouse et al
Year
2013
2016
2015b
Cohort
DCH
ACS CPS-II
CanCHEC
Setting
Denmark
USA
Canada
N
52,061
669,046
2,521,525
Sex
FM
FM
FM
Age
50-64
>=30
25-89
1.04 (1.00, 1.07)
ES (95% CI)
1.31 (0.98, 1.76)
1.05 (1.01, 1.09)
1.03 (1.00, 1.05)
100.00
Weight
1.35
%
40.83
57.82
1.04 (1.00, 1.07)
ES (95% CI)
1.31 (0.98, 1.76)
1.05 (1.01, 1.09)
1.03 (1.00, 1.05)
100.00
Weight
1.35
%
40.83
57.82
1.8 1 1.2 1.4