Long-term air pollution exposure and cardio- respiratory mortality: a review
DOI: 10.1186/1476-069X-12-43
© Hoek et al.; licensee BioMed Central Ltd. 2013
Received: 19 December 2012
Accepted: 2 April 2013
Published: 28 May 2013
Abstract
Current day concentrations of ambient air pollution have been associated with a range of adverse health effects, particularly mortality and morbidity due to cardiovascular and respiratory diseases. In this review, we summarize the evidence from epidemiological studies on long-term exposure to fine and coarse particles, nitrogen dioxide (NO2) and elemental carbon on mortality from all-causes, cardiovascular disease and respiratory disease. We also summarize the findings on potentially susceptible subgroups across studies. We identified studies through a search in the databases Medline and Scopus and previous reviews until January 2013 and performed a meta-analysis if more than five studies were available for the same exposure metric.
There is a significant number of new studies on long-term air pollution exposure, covering a wider geographic area, including Asia. These recent studies support associations found in previous cohort studies on PM2.5. The pooled effect estimate expressed as excess risk per 10 μg/m3 increase in PM2.5 exposure was 6% (95% CI 4, 8%) for all-cause and 11% (95% CI 5, 16%) for cardiovascular mortality. Long-term exposure to PM2.5 was more associated with mortality from cardiovascular disease (particularly ischemic heart disease) than from non-malignant respiratory diseases (pooled estimate 3% (95% CI −6, 13%)). Significant heterogeneity in PM2.5 effect estimates was found across studies, likely related to differences in particle composition, infiltration of particles indoors, population characteristics and methodological differences in exposure assessment and confounder control. All-cause mortality was significantly associated with elemental carbon (pooled estimate per 1 μg/m3 6% (95% CI 5, 7%)) and NO2 (pooled estimate per 10 μg/m3 5% (95% CI 3, 8%)), both markers of combustion sources. There was little evidence for an association between long term coarse particulate matter exposure and mortality, possibly due to the small number of studies and limitations in exposure assessment. Across studies, there was little evidence for a stronger association among women compared to men. In subjects with lower education and obese subjects a larger effect estimate for mortality related to fine PM was found, though the evidence for differences related to education has been weakened in more recent studies.
Keywords
Air pollution Mortality Motorized traffic Cardiovascular Respiratory ParticlesReview
Background
There is growing evidence of mortality effects related to long-term exposure (i.e., exposures of a year or more) to ambient air pollution [1–3]. Cardiovascular effects of short- and long-term exposure to particulate matter air pollution focusing on PM2.5 have recently been comprehensively reviewed [4, 5]. Experimental and epidemiological studies in the recent decade have significantly increased our knowledge of mechanisms that could plausibly explain the associations observed in epidemiological studies between ambient air pollution and mortality [4].
Most studies have reported associations linked to particulate matter, often represented by the mass concentration of particles smaller than 10 μm (PM10) or 2.5 μm (PM2.5). In many urban areas, motorized traffic emissions are an important source of ambient particles and gaseous pollutants such as nitrogen oxides (NO2 and NO). Exposure contrasts related to traffic emissions are usually poorly represented by the concentration of PM10 or PM2.5, because of the high regional background concentration of these particle metrics from other sources [6, 7]. However, there are more specific markers for traffic related air pollution, which include elemental carbon and ultrafine particles number [7–10]. Janssen and co-workers recently demonstrated that health impact assessments of traffic-related pollutants based upon PM2.5 seriously underestimated the health risks compared to an assessment based upon elemental carbon [7]. There is also growing evidence of health effects related to ultrafine particles [8, 9]. Finally, the effects of coarse particles (the particle fraction between 2.5 and 10 μm) have attracted renewed attention [11]. Emission controls for road traffic have now substantially reduced tailpipe emissions, and therefore non-tailpipe emissions including engine crankcase emissions (combusted lubricating oil), road, tire and brake wear are becoming increasingly important. A recent study in the Netherlands found similar increases of concentrations in major roads compared to urban background for metals related to break and tire wear (Cu, Zn) as for soot and ultrafine particles which are due to tailpipe emissions [10] . In a review of the limited literature, coarse particles were associated with short-term effects on mortality and hospital admissions, but no evidence was found for long-term exposure effects [11]. The number of studies on long-term coarse particle exposure reviewed was small however at the time.
The aim of the current review is to evaluate the epidemiological evidence for cardiovascular and respiratory mortality effects of long-term exposure to fine particulate matter, including a meta-analysis. We focused on epidemiological studies of mortality, as experimental studies and mechanisms of effect have been discussed in detail previously [4]. The American Heart Association review [4] is updated with a significant number of new studies published in 2009 – 2012. We further include more pollutants in the review, specifically NO2, elemental carbon and coarse particles. We evaluated the findings on potentially susceptible subgroups across studies of PM2.5. In addition, we have included the studies on more specific cardiovascular causes of death, especially fatal myocardial infarction and stroke.
Methods
We performed a search in the databases Medline and Scopus with the search terms air pollution, cohort, and mortality until January 2013. We supplemented the search with studies included in the review by Brook and co-worker [4] and by browsing the reference lists of identified papers. In case more than five studies were identified, we performed a meta-analysis. We tested for heterogeneity of cohort-specific effect estimates and obtained combined effects estimates, using random effects methods of DerSimonian and Laird [12]. The I2 statistic was calculated as a measure of the degree of heterogeneity across studies [13]. I2 ranges from 0 to 100% and can be interpreted as the variability of study-specific effect estimates attributable to true between study effects. From some studies multiple papers were available such as the Six Cities study [14–16]. In the meta-analysis we used only the most recent paper, which had longer follow-up. We only included studies in the quantitative meta-analysis that directly provided PM2.5 exposure estimates. For NO2 we only included studies which accounted for intra-urban spatial variation using e.g. dispersion models, land use regression models or spatial interpolation. We used STATA version 10 (Stata Corp, College Station, Texas) for meta-analysis. Effect estimates are presented as excess risks expressed per 10 μg/m3 contrast in exposure, except elemental carbon for which risks were expressed per 1 μg/m3.
PM2.5 and all-cause and cardiovascular mortality
Summary of effect estimates (excess risk per 10 μg/m 3 ) from cohort studies on particulate matter (PM 10 or PM 2.5 ) and mortality from all causes and cardiovascular diseases
Study | Study population | Follow-up period | Pollutant | Conca(μg/m3) | Spatial scaleb | % change in risk (95% CI) in mortality associated with a 10 μg/m3increase PM | References | |
---|---|---|---|---|---|---|---|---|
All cause | Cardiovascular c | |||||||
Harvard six cities | 8111 adults in six US cities | 1976 - 1989 | PM2.5 | 18 (11–30) | City | 13(4, 23) | 18 (6, 32) | [15] |
Harvard six cities | 8096 adults in six US cities | 1979 -1998 | PM2.5 | 15 (10–22) | City | 16 (7, 26) | 28 (13,44) | [14] |
Harvard six cities | 8096 adults in six US cities | 1974 - 2009 | PM2.5 | 16 (11–24) | City | 14 (7, 22) | 26 (14, 40) | [16] |
American Cancer Society (ACS) study | 552, 800 adults from 51 US cities | 1982 - 1989 | PM2.5 | 18 (9–34) | City | 26 (8, 47) | NA | [17] |
ACS study | 500,000 adults from 51 US cities | 1982 -1998 | PM2.5 | 18 (4) | City | 6 (2, 11) | 9 (3, 16)c | [18] |
ACS sub-cohort study | 22,905 subjects in Los Angeles area | 1982 - 2000 | PM2.5 | (~9 – 27) | Zip code (Int) | 17 (5, 30) | 26 (1, 60)c | [19] |
German cohort | 4752 women in Ruhr area | 1985 – 2003 | PM10 | 44 (35–53) | Address (near) | 12 (−9, 37) | 52 (8, 114) | [20] |
German cohort | 4752 women in Ruhr and surrounding area | 1985 - 2008 | PM10 | 44 (35–53) | Address (near) | 22 (6, 41) | 61 (26, 104) | [21] |
Women’s Health Initiative Observational Study | 65,893 postmenopausal women from 36 US metropolitan areas | 1994-1998 | PM2.5 | 14 (3–28) | Zip code (near) | NA | 76 (25,147) | [22] |
Netherlands Cohort Study | 120, 852 subjects from Netherlands | 1987 -1996 | PM2.5 | 28 (23–37) | Address (LUR) | 6 (−3, 16) | 4 (−10, 21) | [23] |
Nurses’ Health Study | 66,250 women from the US north eastern metropolitan areas | 1992-2002 | PM10 | 22 (4) | Address (LUR) | 11 (1,23) | 35 (3, 77) | [24] |
Nurses’ Health Study | 66,250 women from the US north eastern metropolitan areas | 1992-2002 | PM2.5 | 14 (6–28) | Address (LUR) | 26 (2, 54) | NA | [25] |
Medicare national cohort | 13.2 million elderly Medicare recipients across the USA | 2000 - 2005 | PM2.5 | 13 (4) | Zip code (Mean) | 4 (3, 6)d | [26] | |
California teachers study | 45,000 female teachers | 2002 -2007 | PM2.5 | 18 (7–39) | Address (near) | 6 (−4, 16) | 19 (5, 36)c | [27] |
Swiss national cohort | National census data linked with mortality | 2000 - 2005 | PM10 | 19 (>40)e | Address (Disp) | NA | −1 (−3, 0) | [28] |
Health professionals follow-up study | 17,545 highly educated men in the midwestern and northeastern US | 1989 – 2003 | PM2.5 | 18 (3) | Address (LUR) | −14 (−28,2) | 3 (−17, 26) | [29] |
Vancouver cohort | 452,735 Vancouver residents 45–85 yr | 1999 – 2002 | PM2.5 | 4 (0 – 10) | Address (LUR) | NA | 7 (-14, 32) | [30] |
China nat. hypertension survey | 70,497 men and women | 1991 - 2000 | TSP | 289 (113–499) | City | 0.3 (0, 1) | 1 (0, 2) | [31] |
US trucking industry cohort | 53,814 men in the US trucking industry | 1985 -2000 | PM2.5 | 14 (4) | Address (near) | 10 (3, 18) | 5 (−7, 19) | [32] |
Chinese retrospective cohort study | 9,941 adults from five districts of Shenyang city | 1998 -2009 | PM10 | 154 (78–274)f | District (mean) | 53 (50, 56) | 55 (51, 60) | [33] |
Canadian national cohort | 2.1 million nonimmigrant Canadians . > 25 yr | 1991 - 2001 | PM2.5 | 9 (2 – 19) | Enumeration area, N = 45710 (satellite) | 10 (5, 15) | 15 (7, 24) | [34] |
New Zealand Census mortality study | 1.06 million adults in urban areas from 1996 census | 1996 -1999 | PM10 | 8 (0 – 19) | Census tract (Disp) | 7 (3, 10) | 6 (1, 11) | [35] |
California teachers study | 101,784 female teachers | 1997- 2005 | PM2.5 | 16 (3–28) | Address (Inter) | 1 (−5, 9) | 7 (−5, 19) | [36] |
Nippon data cohort | 7,250 adults > 30 yr throughout Japan | 1980 - 2004 | PM10 | <27 - > 43 | District (near) | −2 (−8, 4) | −10 (−19, 0) | [37] |
Rome longitudinal study | 1,265,058 adults from Rome | 2001 - 2010 | PM2.5 | 23 (7 – 32) | Address (DISP, 1 km grid) | 4 (3, 5) | 6 (4, 8) | [38] |
Effect modification
Effect modification of the effect (excess risk per 10 μg/m 3 ) of PM 2.5 on cardiovascular mortality
Subgroup | ACS [[18]]a | NLCS [[23]] | Harvard six city [[43]] | Nurses health [[24]] | WHI [[22]] | AHSMOG [[40]] | AHSMOG [[41]] |
---|---|---|---|---|---|---|---|
Sex | |||||||
Men | 5 (0, 11) | 3 (−5, 12)b | 33 (8, 63)a | NA | NA | 4 (−3,11) | −10 (−−24, 5) |
Women | 6 (0, 12) | 7 (0, 14) | 20 (−6, 53) | −3 (−9, 2) | 42 (6, 90) | ||
Smoking status | |||||||
Never | 6 (1, 12) | 13 (−4, 32) | 36 (2, 82) | 83 (20, 179) | 18 (−1, 40) | NA | NA |
Former | 5 (0, 11) | −4 (−17, 13) | 29 (−3, 72) | 22 (−18, 83) | 21 (1, 52) | ||
Current | 4 (−2, 11) | 3 (−10, 19) | 35 (94, 74) | −12 (−48, 48) | 68 (6, 166) | ||
Education | |||||||
Low | 11 (6, 18) | 20 (−10, 70)a | 45 (13, 85) | 40 (11, 75) | NA | NA | |
Medium | 6 (1, 13) | 2 (−16, 24) | 30 (−2,73) | 33 (14, 55) | |||
High | 1 (−3, 6) | −10 (−35, 20) | −3 (−29, 34) | 11 (−6, 31) | |||
Body mass index | |||||||
Non-Obese | NA | NA | NA | 8 (−24, 52) | −1 (−10, 29)c | NA | NA |
Obese | 99 (23, 222) | 35 (12, 64)c |
In two studies, PM2.5 effect estimates were substantially higher among subjects with high body mass index [22, 24].
It is likely that subject characteristics might explain part of the variability of air pollution effect estimates across studies where subgroup analyses are limited by power to detect differences. Hence, further research is required to study the effects of air pollution on women, smokers, obese participants, and diabetes mellitus with better measurement of the exposures. Gene-environment interactions have been shown for the (short-term) air pollution effects on inflammation markers [44, 45] Inflammation likely plays an important role in the mechanism of cardiovascular events [3, 4]. Gene-environment interactions have not yet been studied in the framework of mortality cohort studies.
Exposure issues
One of the important sources of variability of effect estimates between studies is likely related to exposure definition and misclassification. While the most important environmental predictor to consider is actual individual-level exposure to ambient particles, which presumably drives the health effects, most studies have used outdoor concentrations at sites distant to the participant’s precise location. The use of outdoor exposures leads to exposure misclassification. In the cohort studies, exposure has been characterized by the outdoor concentration at the city level based upon central site monitoring or the nearest monitor, or modeling at the individual address. Table 1 shows that the spatial scale of assessment and exposure assessment method varied significantly across studies, probably contributing to differences in effect estimates. Differences in pollution range across studies (Table 1) may have contributed as well. These exposure estimates do not take into account time activity patterns such as time spent in the home or in traffic and factors affecting infiltration of particles indoors. There is a large literature documenting the importance of air exchange rate on infiltration of particles indoors. Importantly, these factors may differ between homes within a study area and between study areas in different climates. In a study of short-term effects, PM10 effects on hospital admissions were larger in US cities with lower% of air conditioning, related to higher particle infiltration rates [46]. The impact of air conditioning use has not been investigated yet in the framework of cohort studies. In the Multiethnic study of Atherosclerosis Air study, indoor-outdoor measurements have been performed to adjust the exposure estimates [47, 48] and each participant provides time-activity information to weight exposures between time spent indoors and outdoors. Evidence for the importance of time activity patterns was obtained in the US truckers study, showing higher ambient PM2.5 effect estimates in the population excluding long-haul drivers who spend more time away from home [32]. Other factors could however also explain the higher effect estimated after excluding long-haul drivers. In the WHI study, effect estimates tended to be higher for subjects spending more than 30 minutes outdoors [22]. In a validation study in the Netherlands, the contrast of personal soot exposure for adults living on a major road compared to those living at a background location, was larger for those spending more time at home [49]. Because of the reliance on ambient exposure estimates, it is not surprising that some heterogeneity in effect estimates across studies is found.
Differences in particle composition or contributing sources very likely explain some of the heterogeneity in effect estimates, as was observed for short-term mortality and hospital admission studies of PM2.5 and PM10[50–53]. For a comprehensive review we refer to the recent evaluation made by the World Health Organization (http://www.euro.who.int/en/what-we-do/health-topics/environment-and-health/air-quality/publications/2013/review-of-evidence-on-health-aspects-of-air-pollution-revihaap). Particle composition effects have not been systematically investigated in cohort studies with the exception of the California teacher’s study [27]. In a recent review it was shown that on a per microgram per m3 basis, mortality effect estimates were about 10 times larger for EC than for PM2.5[7]. Hence, in locations with higher levels of primary combustion particles we could expect higher PM2.5 effects. In the next section, evidence on EC is further discussed.
A further important issue is for which period exposure is characterized. Air pollution data may not be available for the entire follow-up period. As an example in the ACS study, PM2.5 data were available at the start and end of follow-up [18]. When significant (often downward) trends in pollution occur with changing (often decreasing) spatial contrasts in the study, bias may occur in the estimated association between pollution and mortality. The follow-up study from the Harvard Six City study [14] and two studies in potentially at-risk populations [54, 55] suggested that the relevant exposure for cardiovascular effects may be the exposure in the past few years. These authors conclude that it does not take decades to bridge the gap between the short- and long-term exposure effect estimates, consistent with the effect of intervention studies showing reductions in mortality in the year after the intervention [54, 55]. These studies [54, 55] have made use of long-term temporal contrast within cities adjusting for secular trends. PM effect estimates were similar to the previously discussed studies exploiting spatial contrasts.
A further temporal issue in studies that use land use regression models for exposure assessment is that these models often are based upon current measurement campaigns and linked to health outcomes that occurred in the past. Three studies in the Netherlands, Rome (Italy) and Vancouver (Canada) have shown that for periods of about 10 years current LUR models predicted historic spatial contrasts well [56–58]. Even when concentrations have decreased over time, spatial contrasts often remain stable. Spatial contrasts may not be stable in areas with rapid economic development as indicated in one of the Chinese cohort studies in which the ranking of study areas changed during follow-up [33, 59]. Even when the ranking of subjects is not changed, the quantitative spatial contrast in a study area may have changed, e.g. because the difference between major roads and background locations has decreased in time. Changed spatial contrasts will affect the estimated slope of the mortality pollution association [18, 56]. Moving of subjects may further complicate the assessment.
An important question to address for the traffic pollution studies is potential confounding by road traffic noise, which has been shown to be related to cardiovascular disease including MI as well. A few studies have attempted to disentangle traffic-related air pollution and noise [60–62]. These studies found moderate correlations between air pollution and noise. The three studies differed somewhat in their findings of independent air pollution and noise effects. More work is needed in this area.
Coarse particles and elemental carbon
Summary of effect estimates (excess risk per 10 μg/m 3 ) from cohort studies on coarse particulate matter and elemental carbon (per 1 μg/m 3 ) and mortality from all causes and cardiovascular diseases
Study name | Study design | Follow-up period | Pollutant | Conca (μg/m3) | Spatial scaleb | % change in risk (95% CI) in mortality | References | |
---|---|---|---|---|---|---|---|---|
All causes | Cardiovascular c | |||||||
Coarse PM | ||||||||
ACS study | 500,000 adults 51 US cities | 1982 - 1998 | PM2.5–15 | 19 (6) | City | 1 (−2 3) | 2 (−2, 5)* | [18] |
AHSMOG study | 3769 California seventh-day Adventists | 1977 – 1992 | PM2.5–15 | 27 (4 – 44) | Address (Inter) | 5 (−8, 20) | NA | [63] |
Nurses’ Health Study | 66,250 women from US north eastern metropolitan areas | 1992- 2002 | PM2.5–10 | 8 (0 – 27) | Address (LUR) | 3 (−11, 18) | NA | [25] |
Health professionals follow-up study | 17,545 highly educated men in the midwestern and northeastern US | 1989 – 2003 | PM2.5–10 | 10 (3) | Address (LUR) | −10 (−22, 4) | 8 (−10, 29) | [29] |
EC | ||||||||
Netherlands Cohort Study | 120, 852 subjects from Netherlands | 1987 - 1996 | BSe | 17 (9–36) | Address (LUR) | 5 (0, 11) | 4 (−5, 13) | [23] |
ACS study (extended) | 500,000 adults 51 US cities | 1982 – 1998 | EC | IQR = 0.31 | City | 6 (1, 11) | 11 (3, 19) | [64] |
Worcester MI survivors | 3,895 MI patients | 1995 - 2005 | EC | 0.4 (0.1 – 0.9) | Address (LUR) | 2 (−7, 11)d | NA | [65] |
15 (3, 29) | ||||||||
Vancouver cohort | 452,735 Vancouver residents 45–85 yr | 1999 – 2002 | BC | 1.5 (0–5) | Address (LUR) | NA | 6 (3, 9) | [30] |
PAARC | 14,284 adults in 24 French areas | 1974 – 1998 | BS | 44 (18–77) | Address (near) | 7 (3, 10) | 5 (−2, 12) | [42] |
Veteran’s study | 70,000 male US veterans | 1997 – 2001 | EC | 0.6 (0.1 – 2.0) | County (mean) | 18 (5, 33) | NA | [66] |
California teachers study | 45,000 female teachers | 2002 -2007 | EC | 1.1 (0.2 – 2.4) | Address (near) | 3 (−11,19) | 11 (−9, 36) | [27] |
Two Scotch cohorts | 15, 402 and 7,028 adults from West-central and central Scotland | 1972 - 1998 1970 - 1998 | BS | 19 | LUR + temporal | 5 (1,9) | 7 (0, 13) | [67] |
Consistently, the summary estimate for PM10 was smaller than for PM2.5 with a summary effect estimate per 10 μg/m3 of 3.5% (95% CI 0.4%, 6.6%) with significant heterogeneity (I2 = 69%) of the studies included in Table 1, excluding the because of changing spatial patterns difficult to interpret Chinese retrospective study [33]. The PM10 analysis was added as several studies only report PM10.
Effect estimates for EC were very consistent across studies [23, 27, 30, 42, 64–67]. The random effects summary estimate for all-cause mortality per 1 μg/m3 EC was 6.1% (95% CI 4.9%, 7.3%), with highly non-significant heterogeneity of effect estimates (I2 = 0%). Most of the included studies assessed EC exposure at the city-scale [27, 64] which represents variation in city background but does not account for small-scale variation related to proximity to major roads. Many studies have documented significant intra-urban contrasts for EC, related to especially major roads [7]. Most likely EC and NO2 should be considered representatives of the complex mixture of traffic-related air pollution, rather than the only components causally associated with mortality.
Summary of cohort studies on NO 2 and mortality from all causes and cardiovascular diseases (excess risk per 10 μg/m 3 )
Study name | Study population | Follow-up period | Pollutant | Conca(μg/m3) | Spatial scaleb | % change in risk (95% CI) in mortality per 10 μg/m3 | References | |
---|---|---|---|---|---|---|---|---|
All causes | Cardiovascular | |||||||
Oslo cohort | 16,209 men in Oslo, Norway | 1972 – 1998 | NOx | 11 (1 – 168) | Address (DISP) | 8 (6,11) | NA | [68] |
Netherlands Cohort Study | 120, 852 subjects from Netherlands | 1987 -1996 | NO2 | 37 (15–67) | Address (LUR) | 8 (0, 16) | 7 (−6, 21) | [23] |
German cohort | 4752 women in Ruhr and surrounding area | 1985 – 2003 | NO2 | 39 (20 – 60) | Address (near) | 11 (1,21) | 36 (14, 63) | [20] |
German cohort | 4752 women in Ruhr and surrounding area | 1985 – 2008 | NO2 | 39 (20 – 60) | Address (near) | 11 (4,18) | 32 (18, 47) | [21] |
PAARC | 14,284 adults in 24 French areas | 1974 – 1998 | NO2 | 20 (12 – 32) | Address (near) | 14 (3, 25) | 27 (4, 56) | [42] |
China nat. hypertension survey | 70,497 men and women | 1991 - 2000 | NOx | 50 (20 – 122) | City | 2 (0, 3) | 2 (1, 4) | [31] |
Vancouver cohort | 452,735 Vancouver residents aged 45–85 yr | 1999 – 2002 | NO2 | 32 (15 – 58) | Address (LUR) | NA | 5 (1, 9) | [30] |
DCH | 52,061 adults in Copenhagen and Arhus | 1993 - 2009 | NO2 | 17 (11 – 60) | Address (DISP) | 8 (2, 13) | 15 (3,27) | [69] |
US trucking industry cohort | 53,814 men in the US trucking industry | 1985 -2000 | NO2 | 28 (14) | Address (LUR) | 5 (3, 7) | 4 (0, 8) | [32] |
Chinese retrospective cohort study | 9,941 adults from five districts of Shenyang city | 1998 -2009 | NO2 | 46 (18–78) | District (mean) | 145 (134, 158) | 146 (131, 163) | [33] |
Rome longitudinal study | 684,000 adults from Rome | 2001 - 2006 | NO2 | 45 (11) | Address (LUR) | 4 (3, 5) | NA | [56] |
California Teachers study | 101,784 female teachers | 1997 -2005 | NO2 | 67 (10 – 134) | Address (Inter) | −3 (−9, 4) | −2 (−12, 9) | [36] |
Shizuoka elderly cohort | 13,444 adults > 65 yr | 1999 - 2006 | NO2 | 25 (−19, 75) | Address (LUR) | 2 (−4, 8) | 15 (3, 28) | [70] |
Ontario tax cohort | 205, 440 adults in Toronto, Hamilton,Windsor | 1982 – 2004 | NO2 | 43 (8), 31 (6), 24 (5)c | Address (LUR) | NA | 8 (5, 11) | [71] |
Rome longitudinal study | 1,265,058 adults from Rome | 2001 - 2010 | NO2 | 44 (13–75) | Address (LUR) | 3 (2, 3) | 3 (2, 4) | [38] |
Specific cardiovascular causes of death
Summary of the studies on particulate matter and NO2 and mortality from specific cardiovascular diseases (excess risk per 10 μg/m3)
Study name | Pollutant | Conca(μg/m3) | Spatial scaleb | % change in risk (95% CI) in mortality associated with a 10 μg/m3increase | References | ||
---|---|---|---|---|---|---|---|
IHD mortality | M.I mortality | Cerebrovascular mortality | |||||
ACS study | PM2.5 | 17 (5) | City | 18 (14, 23) | NA | 2 (−5, 10) | [39] |
Oslo cohort | NOx | 11 (1 – 168) | Address (DISP) | 8 (3, 12) | NA | 4 (−6, 15) | [68] |
Women’s Health Initiative Study | PM2.5 | 14 (3–28) | Zip code 5 (near) | 76 (25,147) | NA | NA | [22] |
Netherlands Cohort Study | BS | 17 (9–36) | Address (LUR) | 1 (−17, 22) | NA | 39 (−1, 94) | [23] |
Nurses’ Health Study | PM10 | 22 (4) | Address (LUR) | 35 (3, 77) | NA | NA | [24] |
Nurses’ Health Study | PM2.5 | 14 (6–28) | Address (LUR) | NA | 102 (7, 278) | NA | [25] |
California teachers study | PM2.5 | 18 (7–39) | Address (near) | 55 (24, 93) | NA | NA | [27] |
Swiss national cohort | PM10 | 19 (>40)c | Address (Disp) | −1 (−3, 0) | NA | −1 (−2, 0) | [28] |
Health professionals follow-up study | PM2.5 | 18 (3) | Address (LUR) | −2 (−30, 35) | NA | NA | [29] |
Canadian national cohort | PM2.5 | 9 (2 – 19) | Enumeration area, N = 45710 (satellite) | 30 (18,43) | NA | 4 (−7, 16) | [34] |
Californian Teachers study | PM2.5 | 16 (3–28) | Address (Inter) | 20 (2, 41) | NA | 16 (−8, 46) | [36] |
Shizuoka elderly cohort | NO2 | 25 (−19, 75) | Address (LUR) | 27 (2, 58) | NA | 9 (−6, 27) | [70] |
Nippon data cohort | PM10 | <27 - > 43 | District (near) | −8 (−27, 17) | NA | −14 (−26,1) | [37] |
DCH | NO2 | 17 (11 – 60) | Address (Disp) | 7 (−9, 26) | NA | 6 (−14, 32) | [69] |
Ontario Tax cohort | NO2 | 43 (8), 31 (6), 24 (5)c | Address (LUR) | 9 (4, 14) | NA | −4 (−10, 5) | [71] |
Rome longitudinal study | PM2.5 | 23 (7 – 32) | Address (DISP, 1 km grid) | 10 (6, 13) | NA | 8 (4, 13) | [38] |
M.I. registry studies | |||||||
Stockholm | NO2 | 14 (3 – 32) | Address (DISP) | NA | 15 (−1, 33) | NA | [72] |
Rome residents | NO2 | (<30 - > 60) | Census block (LUR) | NA | 7 (2, 12) | NA | [73] |
Stockholm residents | NO2 | 12 (2 – 33) | Address (DISP) | NA | 8 (5, 11) | NA | [74] |
Fewer studies have evaluated cerebrovascular mortality. In the Dutch cohort study and in the Women’s Health Initiative Study, a strong association was found [22, 23]. In contrast, in the ACS study, the Norwegian cohort, and the Swiss national cohort study no association was found [28, 39, 68]. It is possible that poorer recording of cerebrovascular mortality on death certificates has contributed to these inconsistencies. There is also some evidence from ecological studies that air pollution may contribute to stroke mortality [78, 79].
Two studies have reported significant associations between particulate matter air pollution and dysrhythmia, heart failure and cardiac arrest combined [39, 60]. These results are based upon smaller numbers of events, and require large cohort studies for further verification. The results are consistent with several studies documenting significant associations between short-term PM or NO2 exposure and mortality due to heart failure and dysrhythmia and defibrillator discharges [4, 80].
Air pollution and respiratory mortality
Summary of the studies on air pollution and mortality from all respiratory disease (excess risk per 10 μg/m 3 )
Study Name | Pollutant | Conca(μg/m3) | Spatial scaleb | % change in risk (95% CI) in mortality per 10 μg/m3 | References |
---|---|---|---|---|---|
AHSMOG | PM10 | 51 (17) | Address (Inter) | 6 (−1, 15) | [40] |
ACS study | PM2.5 | 17 (5) | City | −8 (−14, -2) | [39] |
Oslo cohort | NOx | 11 (1 – 168) | Address (DISP) | 16 (6, 26) | [68] |
Harvard six cities | PM2.5 | 15 (10–22) | City | 8 (−21, 49) | [14] |
Netherlands Cohort Study | PM2.5 | 28 (23–37) | Address (LUR) | 7 (−25, 52) | [23] |
Netherlands Cohort Study | NO2 | 37 (15–67) | Address (LUR) | 12 (0, 26) | [23] |
California Teachers study | PM2.5 | 18 (7–39) | Address (near) | 3 (−20, 34) | [27] |
China national. hypertension survey | NOx | 50 (20 – 122) | City | 3 (0, 6) | [31] |
China national. hypertension survey | TSP | 289 (113 – 499) | City | 0.3 (−1,1) | [31] |
US truckers study | PM2.5 | 14 (4) | Address (near) | 20 (−9, 60) | [32] |
US truckers study | NO2 | 28 (14) | Address (LUR) | 15 (1,31) | [32] |
California Teachers study | PM2.5 | 16 (3–28) | Address (Inter) | 21 (−3, 52) | [36] |
New Zealand Census study | PM10 | 8 (0 – 19) | Census tract (Disp) | 14 (5, 23) | [35] |
Shenyang cohort study | PM10 | 154 (78 – 274) | District (mean) | 67 (60, 74) | [59] |
Shenyang cohort study | NO2 | 46 (18–78) | District (mean) | 197 (169, 227) | [59] |
Shizuoka elderly cohort | NO2 | 25 (−19, 75) | Address (LUR) | 19 (2, 38) | [70] |
Two Scotch cohorts | BS | 19 | LUR + temporal | 11 (−3, 28) | [67] |
Rome longitudinal study | PM2.5 | 23 (7 – 32) | Address (DISP, 1 km grid) | 3 (−3, 8) | [38] |
Conclusions
There is a significant number of new studies on long-term air pollution exposure, covering a wider geographic area, including Asia. These recent studies support associations found in previous cohort studies on PM2.5. The pooled effect estimate expressed as excess risk per 10 μg/m3 increase in PM2.5 exposure was 6% (95% CI 4, 8%) for all-cause and 11% (95% 5, 16%) for cardiovascular mortality. Long-term exposure to PM2.5 was more associated with mortality from cardiovascular disease (particularly ischemic heart disease) than from non-malignant respiratory diseases (pooled estimate 3% (95% CI −6, 13%)). Significant heterogeneity in PM2.5 effect estimates was found across studies, likely related to differences in particle composition, infiltration of particles indoors, population characteristics and methodological differences in exposure assessment and confounder control. All-cause mortality was significantly associated with elemental carbon (pooled estimate per 1 μg/m3 6% (95% CI 5, 7%)) and NO2 (pooled estimate per 10 μg/m3 5% (95% CI 3, 8%)), both markers of combustion sources. There was little evidence for an association between long term coarse particulate matter exposure and mortality, possibly due to the small number of studies and limitations in exposure assessment. Across studies, there was little evidence for stronger association among women compared to men. Subjects with lower education and obese subjects experienced larger mortality effect related to fine PM, though the evidence for differences related to education has been weakened in more recent studies.
Our review suggests several specific research questions. Research into the reasons for the heterogeneity of effect estimates would be extremely useful for health impact assessment. Better exposure assessment including spatially resolved outdoor exposures and more chemically speciated PM might in part be able to resolve the observed heterogeneity. Chemical speciation would allow assessing particles from different sources e.g. particles from combustion sources and non-tailpipe emissions separately, a question clearly relevant for air pollution control policy. Specific attention to motorized traffic emissions is important because (road) traffic is an important source of ambient air pollution. More work on coarse particles and at the other side of the particle size spectrum, ultrafine particles is needed. Ongoing new research in the USA in the Multi-Ethnic study of Atherosclerosis and Air pollution (MESA-AIR) and the European Study of Cohorts for Air Pollution Effects (ESCAPE) that use large cohorts and state-of the art spatially-resolved exposure methods will likely contribute significant new answers in the near future to these questions.
Abbreviations
- ACS:
-
American Cancer Society study
- BS:
-
Black Smoke
- BC:
-
Black Carbon
- CI:
-
Confidence interval
- EC:
-
Elemental Carbon
- NO2:
-
Nitrogen dioxide
- NOx:
-
Nitrogen oxides
- PM:
-
Particulate matter
- PM2.5:
-
Particles smaller than 2.5 μm
- PM10:
-
Particles smaller than 10 μm
- TSP:
-
Total suspended particles.
Declarations
Acknowledgments
The paper has been prepared in the framework of the projects ESCAPE and TRANSPHORM. The research leading to these results has received funding from the European Community’s Seventh Framework Program (FP7/2007-2011) under grant agreement number: 211250.
Authors’ Affiliations
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