Eur J Epidemiol (2014) 29:517–525 DOI 10.1007/s10654-014-9933-0
OCCUPATIONAL EPIDEMIOLOGY
Myocardial infarction and occupational exposure to motor exhaust: a population-based case–control study in Sweden Anna Ilar • Marie Lewne´ • Nils Plato • Johan Hallqvist • Magnus Alderling Carolina Bigert • Christer Hogstedt • Per Gustavsson
•
Received: 19 January 2014 / Accepted: 17 June 2014 / Published online: 1 July 2014 Ó Springer Science+Business Media Dordrecht 2014
Abstract There is a well-established association between particulate urban air pollution and cardiovascular disease, but few studies have investigated the risk associated with occupational exposure to particles from motor exhaust. This study investigated the risk of myocardial infarction (MI) after occupational exposure to motor exhaust, using elemental carbon (EC) as a marker of exposure. A population-based case–control study of first-time non-lethal MI was conducted among Swedish citizens in ages 45–70 living in Stockholm County 1992–1994, including 1,643 cases and 2,235 controls. Working histories and data on potential confounders were collected by questionnaire and medical examination. The exposure to EC was assessed through a job-exposure matrix. Odds ratios (ORs) and corresponding 95 % confidence intervals (CIs) were estimated by unconditional logistic regression. We investigated various exposure metrics: intensity, cumulative exposure and years since exposure. There was an exposure–response relation between the highest average exposure intensity during the work history and the risk of MI
A. Ilar (&) M. Lewne´ N. Plato C. Bigert C. Hogstedt P. Gustavsson Institute of Environmental Medicine, Karolinska Institutet, Box 210, SE-171 77 Stockholm, Sweden e-mail:
[email protected] M. Lewne´ e-mail:
[email protected] N. Plato e-mail:
[email protected] C. Bigert e-mail:
[email protected] C. Hogstedt e-mail:
[email protected]
when adjusting for smoking and alcohol drinking (p for trend 0.034), with an OR of 1.30 (95 % CI 0.99–1.71) in the highest tertile of exposure compared to the unexposed. An exposure– response pattern was observed in the analysis of years since exposure cessation among formerly exposed. Additional adjustments for markers of the metabolic syndrome reduced ORs and trends to non-significant levels, although this might be an over-adjustment since the metabolic syndrome may be part of the causal pathway. Occupational exposure to motor exhaust was associated with a moderately increased risk of MI. Keywords Myocardial infarction Occupational diseases Occupational exposure Particulate matter Vehicle emissions Gasoline Abbreviations CO Carbon monoxide CI Confidence interval EC Elemental carbon JEM Job-exposure matrix M. Lewne´ N. Plato M. Alderling C. Bigert P. Gustavsson Centre for Occupational and Environmental Medicine, Stockholm County Council, Box 210, SE-171 77 Stockholm, Sweden e-mail:
[email protected] J. Hallqvist Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden e-mail:
[email protected] J. Hallqvist Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
P. Gustavsson e-mail:
[email protected]
123
518
MI OR SHEEP
A. Ilar et al.
Myocardial infarction Odds ratio Stockholm Heart Epidemiology Program
Background Ischemic heart disease is the leading cause of death in Sweden, with myocardial infarction (MI) being the most common cause among both men and women [1]. Wellestablished risk factors for MI include hypertension, diabetes, tobacco use, high cholesterol levels, obesity, psychosocial factors and physical inactivity [2, 3]. Moderate alcohol consumption is believed to be protective, with a Jor U-shaped dose–response curve [4–6]. There is also a well-established association between particulate urban air pollution and MI [7–10]. The main mechanisms linking particulate matter exposure to cardiovascular morbidity involve an elevated risk of pulmonary inflammation, cellular oxidative stress, increased coagulation and cardiac arrhythmia [7–10]. Motor exhaust from diesel or gasoline fuelled vehicles is a major component of urban air pollution [11, 12]. The core of the particles in motor exhaust is elemental carbon (EC) to which organic compounds like polycyclic aromatic hydrocarbons attach, whereas the gas phase is a mixture of a very large number of chemical compounds including oxides of nitrogen (NOX), carbon dioxide and carbon monoxide (CO) [13]. There is a lack of studies relating quantitative exposure data on motor exhaust exposure to the risk of MI. We have earlier reported the findings from Stockholm Heart Epidemiology Program (SHEEP) investigating the risk of MI in relation to occupational motor exhaust exposure, using CO as exposure marker. The study showed no consistent association between exposure to motor exhaust and the risk of MI [14]. However, CO is a poor indicator for motor exhaust, especially for diesel fuelled vehicles [15, 16]. In view of potential misclassification in the earlier study, an occupational hygiene program was initiated to improve the exposure classification. The program included exposure measurements [17], modeling of historical exposures and development of a job-exposure matrix (JEM) using EC as a marker of diesel or gasoline exhaust. EC is currently considered to be the most suitable marker for diesel exhaust [18, 19]. We here report the findings when applying the new exposure estimates to the SHEEP study.
Methods Study design and study base SHEEP is a population-based case–control study investigating risk factors for first-time cases of MI [20]. The study
123
base comprised Swedish citizens in ages 45–70 living in Stockholm County during 1992–1994. During this time period, Stockholm County had a population of approximately 475,000 citizens aged 45–70 [21]. Detailed information on subjects and methods have been published earlier [14, 20], but are summarized briefly here. First-time, surviving cases of MI were identified from: (1) the intensive or coronary care units at the 10 emergency hospitals in Stockholm County, (2) a computerized hospital discharge register for Stockholm County and (3) from death certificates from the National Register of Death Causes at Statistics Sweden. The study was limited to surviving cases in order to obtain work history information from the cases themselves. One control per case was randomly selected from the Stockholm County population through a computerized register after stratification for age, sex, hospital catchment area and year of inclusion in the study. A new control was selected if the first one did not respond to the questionnaire. Therefore some cases have more than one control due to late responses. Life-time working histories and data on tobacco smoking and a number of other potential risk factors for MI were collected by mailed questionnaires and completed by telephone interviews. All participants underwent a medical examination where nurses collected blood samples and examined blood pressure, weight, height and hip and waist circumference. The response rate was 83 % among cases and 75 % among controls (Table 1). Ethical approval of this study was granted by the ethics committee of Karolinska Institutet. Development of exposure estimates to motor exhaust The work histories of the participants were coded using the job title coding scheme of NYK-83 [22]. Two industrial hygienists developed a job exposure matrix (JEM) using EC to quantify the occupational exposure to motor exhaust. 104 occupations in the SHEEP study were considered to be associated with motor exhaust exposure. The matrix specified the annual average exposure to EC in these 104 occupations per calendar year, based on historical exposure data and a measurement program including sampling of workers [17]. In the JEM, relative changes in exposure over time were assessed for three main professional groups: city bus drivers represented all types of professional drivers (40 occupations), bus garage workers represented occupations exposed to motor exhaust indoors (30 occupations) and construction machine operators represented occupations exposed to motor exhaust outdoors (34 occupations). For the main professional groups historical exposure measurement curves were developed representing historical changes in exposure levels 1947–1992. To account for
Myocardial infarction and motor exhaust
519
Table 1 Number of identified cases and controls and response rates among men and women in the SHEEP study All
Men
Cases n (%)
Controls n (%)
Cases n (%)
Women Controls n (%)
Cases n (%)
Controls n (%)
Identified from study base
1,643
2,235
1,105
1,464
538
771
Responding to questionnaire
1,359 (83)
1,683 (75)
952 (86)
1,137 (78)
407 (76)
546 (71)
Sufficient data on occupational exposures and confoundersa
1,335 (81)
1,658 (74)
937 (85)
1,120 (77)
398 (74)
538 (70)
SHEEP Stockholm Heart Epidemiology Program a
Sex, age group, hospital catchment area, year of enrollment, diabetes mellitus, physical inactivity at leisure time, smoking, hypertension, alcohol drinking and overweight
variation in exposure between all the other occupations, an adjustment factor was assigned to each of the 40, 30 and 34 different occupations represented by the main professional groups. If the exposure assessed to be higher for the individual occupation than for the main professional occupation the adjustment factor was [1, and if the exposure seemed to be lower the factor was \1. Five industrial hygienists’ decisions on these adjustment factors were made by consensus. The adjustment factors were linked to the three historical curves to form the JEM (city bus drivers, bus garage workers and construction machine operators). Thus, the JEM was specific for 104 occupations but relative changes in exposure over time were assumed to be similar for all occupations in each of the three main professional groups. The JEM was applied to the study subjects’ work histories in a case-by-case procedure in order to make full use of the additional data on work tasks and work conditions, e.g. part-time work, that was present in the questionnaires. The industrial hygienists doing this were blinded with regard to the participant’s case–control status.
Statistical analysis Age was categorized into five groups (45–50, 51–55, 56–60, 61–65, 66–70). Tobacco smoking habits were classified as never smoker, former smoker or current smokers smoking 1–10, 11–20, or[20 g of tobacco/day. Participants who had quit smoking within 2 years before inclusion in the study were classified as current smokers. Alcohol drinking habits were categorized in two categories considering the daily average consumption as 0 or more than 0 gm of alcohol. Subjects were classified as physically inactive at leisure time if they at the most were engaged in occasional walks during their spare time. Overweight was defined as a body mass index greater than 27. Participants with either diet- or drugtreated diabetes were coded as diabetics. We used the original classification of hypertension from the SHEEP study; a
diastolic blood pressure greater than 100 mm Hg or systolic blood pressure exceeding 180 mm Hg or use of antihypertensive drugs by the time of the health examination. Unconditional logistic regression analysis was used to estimate odds ratios (ORs) and 95 % confidence intervals (CIs) of first-time MI. The highest average intensity of exposure to EC (lg EC/m3) during at least 1 year of work, cumulative exposure to EC (lg-year/m3) and years since last exposure to EC were analyzed. Cumulative exposure to EC was calculated as the sum of the products of exposure duration and exposure intensity summarized over all work periods in the participant’s work history. Categories for highest average exposure intensity and cumulative exposure were based on tertiles among the exposed controls and years since last exposure to EC was based on tertiles among formerly exposed controls. We examined if the risk of MI from highest average exposure intensity was modified by years since exposure cessation by presenting risk estimates of MI from all possible combinations of classes of intensity and years since exposure cessation. All independent variables, matching variables and potential confounders were included as indicator variables in the regression. We also conducted trend tests where the main independent variable was treated as a continuous variable and participants were given the mean value of their exposure class. Never exposed and currently exposed participants were excluded in the trend test for years since last exposure. All tests for trend were 2-tailed, with statistical significance set at a .05 level. All analyses were adjusted for the stratification variables (sex, age group, year of enrollment and hospital catchment area). Potential confounders were added to the model in a two-step procedure, first adding those representing the lifestyle associated habits: smoking and alcohol intake (Adjusted model 1). In a second step, factors were added which are associated with the metabolic syndrome: physical inactivity at leisure time, overweight, diabetes and hypertension (Adjusted model 2). The reason to separate
123
520
A. Ilar et al.
Table 2 Baseline characteristics of analyzed 1,335 cases and 1,658 controls in the SHEEP study Characteristic
All
Men
Women
Cases n (%)
Controls n (%)
Cases n (%)
Controls n (%)
Cases n (%)
Controls n (%)
1,335
1,658
937
1,120
398
538
45–50 51–55
205 (15) 199 (15)
249 (15) 230 (14)
169 (18) 155 (17)
194 (17) 182 (16)
36 (9) 44 (11)
55 (10) 48 (9)
56–60
282 (21)
341 (21)
207 (22)
247 (22)
75 (19)
94 (17)
61–65
326 (24)
412 (25)
221 (24)
265 (24)
105 (26)
147 (27)
66–70
323 (24)
426 (26)
185 (20)
232 (21)
138 (35)
194 (36)
1992
501 (38)
600 (36)
408 (44)
477 (43)
93 (23)
123 (23)
1993
672 (50)
843 (51)
518 (55)
624 (56)
154 (39)
219 (41)
1994
162 (12)
215 (13)
11 (1)
19 (2)
151 (38)
196 (36)
Never
327 (24)
664 (40)
195 (21)
395 (35)
132 (33)
269 (50)
Former
333 (25)
516 (31)
271 (29)
386 (34)
62 (16)
130 (24)
Current 1–10
78 (6)
98 (6)
45 (5)
52 (5)
33 (8)
46 (9)
Current 11–20
412 (31)
280 (17)
264 (28)
199 (18)
148 (37)
81 (15)
Current [20
185 (14)
100 (6)
162 (17)
88 (8)
23 (6)
12 (2)
Total number Age group
Inclusion year
Tobacco smoking (gm/day)
Alcohol intake (gm/day) 0 [0 Physically inactive at leisure timea Overweight
a
95 (7)
94 (6)
35 (4)
51 (5)
60 (15)
43 (8)
1,240 (93)
1,564 (94)
902 (96)
1,069 (95)
338 (85)
495 (92)
621 (47)
586 (35)
398 (42)
368 (33)
223 (56)
218 (41) 179 (33)
586 (44)
512 (31)
407 (43)
333 (30)
179 (45)
Diabetesa
182 (14)
82 (5)
116 (12)
62 (6)
66 (17)
20 (4)
Hypertensiona
428 (32)
397 (24)
275 (29)
257 (23)
153 (38)
140 (26)
Ever
393 (29)
403 (24)
373 (40)
390 (35)
20 (5)
13 (2)
Never
942 (71)
1,255 (76)
564 (60)
730 (65)
378 (95)
525 (98)
25th percentile
21
19
22
19
10
5
Medan
34
31
36
31
21
12
75th percentile
70
50
72
50
25
37
History of occupational exposure to motor exhaust
Percentiles of concentration of motor exhaust (lg EC/m3) among exposed participants
SHEEP Stockholm Heart Epidemiology Program, EC elemental carbon Restricted to participants with data on sex, age group, hospital catchment area, year of enrollment, diabetes mellitus, physical inactivity at leisure time, smoking, hypertension, alcohol drinking and overweight a
See statistical analysis for definitions of physical inactivity at leisure time, overweight, diabetes and hypertension
potential confounders in two groups was that while it is evident that smoking and alcohol are potential true confounders it is not clear to what extent the metabolic syndrome may be part of the pathway by which exposure to particles increases the risk of MI [23–25], and adjusting for them might obscure a true effect of the exposure. While
123
this is not possible to judge from the data, we present estimates both with and without adjustment for markers of the metabolic syndrome. Analyses were conducted using the Stata software package, version 13 for Windows (Stata Corporation, College Station, TX, USA).
Myocardial infarction and motor exhaust
Results Between 1992 and 1994, 1,643 non-lethal cases and 2,235 controls were identified for the study. Exclusion of nonresponders and participants with incomplete data on the main exposures and confounders resulted in 1,335 cases and 1,658 controls available for the final analysis (Table 1). Table 2 provides information on characteristics of the analyzed participants. As expected, there were higher proportions of smokers, never drinkers, physically inactive, overweight, diabetics and individuals with hypertension among cases compared to controls. The proportion of participants ever occupationally exposed to motor exhaust was 29 % among cases and 24 % among controls and the concentrations of motor exhaust exposure (lg EC/m3) appeared slightly higher among cases than controls (Table 2). Ever exposure to motor exhaust at work was associated with an increased crude OR (adjusted for design variables only), cf. Table 3. Adjustment for smoking and alcohol habits (Adjusted model 1) and for markers of the metabolic syndrome (Adjusted model 2) attenuated the OR for ever exposed participants to a nonsignificant level, with ever exposed having an OR of 1.09 (95 % CI 0.91–1.32) in the fully adjusted model. However, in a gender-restricted analysis, women ever occupationally exposed to motor exhaust had an OR of 2.24 (95 % CI 1.03–4.88) in Adjusted model 2, whereas men had an OR of 1.06 (95 % CI 0.87–1.29) (data not shown). We found exposure–response relations in all three models of MI according to the highest average intensity of exposure to motor exhaust (Table 3). Among all participants, the crude OR increased significantly with exposure intensity. Adjustment for smoking and alcohol habits attenuated the estimates, but showed a significant trend in ORs (p 0.034), which was non-significant in Adjusted model 2 (p 0.139). Table 3 additionally shows the ORs of non-lethal MI according to the highest average intensity of exposure to motor exhaust subdivided by years since exposure cessation. There was a dose–response relation between intensity levels and risk of MI in the crude model in all three groups of formerly exposed, which was still present in the adjusted models among participants with the longest (C27 years) and the least (1–11 years) time since exposure cessation. There was also a tendency of a trend in Adjusted model 1 among participants with 1–11 years since exposure cessation (p 0.057). Among participants currently exposed to motor exhaust, there was no indication of an increased risk of MI with increasing exposure intensity. By looking at the ever exposed participants in each stratum of time since exposure cessation, we can observe the association between years since last exposure to motor exhaust and risk of MI independently of intensity level.
521
The ORs decreased with increasing time since exposure cessation among the formerly exposed in all three models. In Adjusted model 1, the risk of MI was 1.37 (95 % CI 1.01–1.87) in the lowest tertile of years since exposure to motor exhaust (1–11 years) compared to the unexposed. The p for trend among formerly exposed was 0.092 in the crude model, 0.113 in Adjusted model 1 and 0.263 in Adjusted model 2 (data not shown). Table 4 provides the OR of developing MI subdivided by cumulative exposure expressed as lg-year/m3of EC. Crude ORs increased statistically significantly with cumulative exposure (p 0.006). After adjusting for smoking and alcohol habits (adjusted model 1) as well as diabetes, physical inactivity at leisure time, hypertension and overweight (adjusted model 2), there was no evident exposure– response pattern or statistically significant trend tests associated with increasing cumulative exposure.
Discussion This population-based case–control study has explored the risk of MI from occupational exposure to EC as a quantitative marker of motor exhaust. We found indications of an association between occupational exposure to motor exhaust, using EC as a marker of exposure, and the risk of MI. The strongest association (in terms of ORs and trend in ORs) was detected in the analysis of exposure intensity (Table 3). An exposure–response relation was found between highest average intensity of exposure to motor exhaust and risk of MI when adjusting for the variables used in matching of controls (‘‘crude OR’’). Adjustment for smoking and alcohol drinking decreased the risk estimates, but the trend test for highest intensity of exposure was significant. The association between exposure intensity and MI appeared to be stronger when restricting the exposed participants to only include those with no more than 1–11 years since cessation of motor exhaust exposure. There was an exposure–response relation among formerly exposed participants and risk of MI, independently of intensity level, but trend tests in all three models were non-significant. Currently exposed participants appeared to have no increased risk of MI with increasing intensity level, i.e. probably a healthy worker effect where the risk of MI from motor exhaust exposure is being masked among those that remain workers due to a healthier lifestyle overall. Another potential explanation is that workers developing symptoms of coronary insufficiency, preceding an MI, may stop working and therefore do not belong to the group of currently exposed participants. There was no clear association between cumulative exposure and risk of MI in any of the adjusted models, possibly suggesting that exposure duration does not have
123
522
A. Ilar et al.
Table 3 ORs of non-lethal myocardial infarction according to the highest average intensity of exposure to motor exhaust during at least 1 year of work subdivided by years since exposure cessation lg EC/m3
Crude modela OR (95 % CI)
Adjusted 1b OR (95 % CI)
Adjusted 2c OR (95 % CI)
Mean exposure in class (lg EC/m3)
No. of cases/ controls
Unexposed
0
942/1,255
1.00
1.00
1.00
Ever exposed [0–21.9
45.1 12.6
393/403 103/135
1.28 (1.08–1.53) 1.01 (0.77–1.33)
1.15 (0.96–1.38) 0.94 (0.71–1.25)
1.09 (0.91–1.32) 0.93 (0.69–1.25)
All
22.0–42.0
31.6
148/137
1.42 (1.10–1.83)
1.22 (0.94–1.59)
1.14 (0.87–1.50)
[42.0
87.5
142/131
1.43 (1.10–1.87)
1.30 (0.99–1.71)
1.21 (0.91–1.59)
1,335/1,658
P = 0.002
P = 0.034
P = 0.139
942/1,255
1.00
1.00
1.00
Test for trend Currently exposed Unexposed
0
Ever exposed
46.8
123/129
1.24 (0.94–1.63)
1.15 (0.87–1.53)
1.09 (0.81–1.45)
[0–21.9
13.8
31/37
1.13 (0.69–1.86)
1.01 (0.60–1.68)
1.00 (0.59–1.68)
22.0–42.0
31.9
51/43
1.56 (1.02–2.38)
1.35 (0.87–2.10)
1.29 (0.82–2.02)
[42.0
87.2
41/49
1.10 (0.71–1.69)
1.11 (0.70–1.74)
1.01 (0.64–1.60)
1,065/1,384
P = 0.320
P = 0.429
P = 0.739
Test for trend 1–11 years since exposure cessation Unexposed
0
942/1,255
1.00
1.00
1.00
Ever exposed
48.3
109/92
1.58 (1.17–2.12)
1.37 (1.01–1.87)
1.25 (0.91–1.72)
[0–21.9 22.0–42.0
12.0 33.6
23/24 42/34
1.25 (0.70–2.24) 1.62 (1.01–2.57)
1.16 (0.63–2.12) 1.30 (0.80–2.12)
1.07 (0.58–1.98) 1.17 (0.71–1.94)
[42.0
84.5
Test for trend
44/34
1.71 (1.07–2.72)
1.50 (0.93–2.43)
1.38 (0.84–2.27)
1,051/1,347
P = 0.006
P = 0.057
P = 0.162 1.00
12–26 years since exposure cessation Unexposed
0
942/1,255
1.00
1.00
Ever exposed
50.1
93/93
1.31 (0.97–1.78)
1.15 (0.84–1.58)
1.08 (0.78–1.50)
[0–21.9
11.1
23/34
0.90 (0.52–1.54)
0.88 (0.50–1.55)
0.85 (0.48–1.52)
22.0–42.0
30.0
29/25
1.47 (0.85–2.54)
1.30 (0.74–2.28)
1.17 (0.65–2.09)
[42.0
94.3
41/34
1.56 (0.98–2.50)
1.25 (0.76–2.04)
1.17 (0.71–1.92)
1,035/1,348
P = 0.038
P = 0.303
P = 0.495
942/1,255
1.00
1.00
1.00
Test for trend C27 years since exposure cessation Unexposed Ever exposed
32.3
68/89
1.01 (0.72–1.41)
0.91 (0.65–1.29)
0.93 (0.66–1.33)
[0–21.9
13.1
26/40
0.82 (0.49–1.35)
0.75 (0.44–1.27)
0.78 (0.46–1.35)
22.0–42.0
30.0
26/35
0.94 (0.56–1.59)
0.83 (0.48–1.43)
0.85 (0.49–1.46)
[42.0 Test for trend
79.1
16/14 1,010/1,344
1.46 (0.70–3.02) P = 0.516
1.34 (0.63–2.84) P = 0.849
1.31 (0.61–2.84) P = 0.849
Bold values indicate statistical significance (95 % confidence intervals do not include 1, or P\0.05 for the trend tests) CI confidence interval, EC elemental carbon, OR odds ratio a
Adjusted for sex, age group, hospital catchment area and year of enrollment
b
Adjusted for sex, age group, hospital catchment area, year of enrollment, smoking and alcohol drinking
c
Adjusted for sex, age group, hospital catchment area, year of enrollment, smoking, alcohol drinking, diabetes mellitus, physical inactivity at leisure time, hypertension and overweight
the same impact on the effect estimates as intensity have. But the results from this study still should be regarded as a stronger indication of an association between occupational motor exhaust exposure and MI than previous findings from the SHEEP study, which showed no consistent
123
association between exposure intensity to CO and risk of MI [14]. A US cohort investigating occupational exposure to respirable EC at non-metal mining facilities found a standardized mortality ratio of 0.99 (95 % CI 0.91–1.07) for ischemic heart disease among ever exposed workers
Myocardial infarction and motor exhaust
523
Table 4 ORs of non-lethal myocardial infarction subdivided by cumulative exposure to motor exhaust at work EC lg-year/m3
Mean exposure in class (lg-year/m3)
No. of cases/ controls
Crude modela OR (95 % CI)
Adjusted 1b OR (95 % CI)
Adjusted 2c OR (95 % CI)
Unexposed
0
942/1,255
1.00
1.00
1.00
[ 0–202
88
110/135
1.08 (0.82–1.41)
0.97 (0.73–1.29)
0.97 (0.73–1.30)
[ 202–710
419
142/135
1.38 (1.06–1.79)
1.25 (0.96–1.63)
1.17 (0.89–1.54)
1,452
141/133
1.40 (1.08–1.83)
1.24 (0.95–1.63)
1.14 (0.86–1.50)
1,335/1,658
P = 0.006
P = 0.075
P = 0.287
[ 710 Test for trend
Bold values indicate statistical significance (95 % confidence intervals do not include 1, or P\0.05 for the trend tests) CI confidence interval, EC elemental carbon, OR odds ratio a
Adjusted for sex, age group, hospital catchment area and year of enrollment
b
Adjusted for sex, age group, hospital catchment area, year of enrollment, smoking and alcohol drinking
c
Adjusted for sex, age group, hospital catchment area, year of enrollment, diabetes mellitus, physical inactivity at leisure time, smoking, hypertension, alcohol drinking and overweight
compared to never exposed workers [27]. No exposure– response relation was reported. The OR among ever exposed women was statistically significant in the fully adjusted model, indicating that women who had ever been exposed to motor exhaust in their profession had a more than twofold risk of MI compared to unexposed women. Only 33 % of cases identified in the study base were women, since women develop MI later in life than men [26]. We explored the possibility that sex was an effect modifier of the association between ever exposure to motor exhaust and MI with the Mantel–Haenszel test of homogeneity, and could not detect a significant difference between men and women (p 0.1444). Still, the difference in ORs between sexes suggests that future studies with larger proportion of motor exhaust exposed women ought to be performed to explore possible genderdifferences in the association between motor exhaust exposure and MI. A benefit of using the SHEEP study is that the internal validity is considered high, as reported earlier [20, 28], with high participation proportion and identical diagnostic criteria for all cases. Another strength is the detailed occupational information on work tasks and duration of work tasks, which was used to assess historical and current occupational exposure to EC. Only surviving cases were included in this study in order to obtain exposure histories from the study subjects themselves. An advantage with using a JEM, compared to self-assessed exposure, is that the exposure assessment is based on expert rating rather than on self-reports, making it less susceptible to recall bias [29]. A weakness of using a JEM is the risk of a non-differential misclassification of exposure to EC when assigning level of intensity, which could bias the ORs towards the null. Nonetheless, since this JEM included detailed
occupational information on work tasks and duration of work tasks the exposure assessment is believed to be more precise than if only work title had been included. Further, the measurement program used to assess exposure to EC included samples from only 71 workers [17], which adds to the misclassification. However, this misclassification is likely to be non-differential and thus tends to attenuate the true ORs. EC is a more specific marker of motor exhaust exposure, than e.g. CO and NO2, because it represents a large part of the particulate mass, and motor exhaust, especially diesel exhaust, is its main source [18]. While EC has served as a marker of diesel exhaust in previous studies [27, 30], it is unclear whether it is a reliable marker of gasoline exhaust. Gasoline vehicles emit less EC than diesel vehicles do [15, 31]. There is a paucity of adequate markers of gasoline exhaust, and it is difficult to determine whether our findings are due to diesel or gasoline exposure. When adjusting for diabetes, physical inactivity at leisure time, hypertension and overweight (Adjusted model 2), a majority of the point estimates decreased slightly compared to the smoking and alcohol adjusted model (Adjusted model 1) in all tables. However, as the metabolic syndrome may be part of the causal chain between exposure to particles and air pollution exposure and MI, adjusting for them may result in over-adjustment and give misleading results. This was indicated in a study showing that particles with an aerodynamic diameter less than 2.5 mm (PM2.5) and diesel exhaust were associated with elevated blood pressure [23], and studies have shown an association between particulate matter or traffic related air pollution and diabetes mellitus [24, 25]. If the metabolic syndrome is part of the pathway through which particulate air pollution causes MI, adjusting for markers of it will cause ORs to be biased towards unity. Thus, we consider
123
524
A. Ilar et al.
the estimates from the Adjusted model 1 more reliable than the fully adjusted model. Since the information on certain confounders was self-reported retrospectively there is always a risk of recall bias, which may result in incomplete adjustment for confounding. In conclusion, exposure intensity was associated with the magnitude of risk with an exposure–response relation and a significant trend test in Adjusted model 1, all supporting that occupational exposure to motor exhaust increases the risk of MI. The association between intensity and risk of MI appeared to be stronger when restricting the exposed participants to only include those with no more than 1–11 years since exposure cessation. There was an exposure–response pattern between years since exposure cessation, when intensity level was not considered, but trend tests in all three models were non-significant. Adjustments for components of the metabolic syndrome reduced most ORs, but this could be an over-adjustment as the metabolic syndrome might be a link in the causal chain between exhaust exposure and MI. The combined results from the reported analyses indicate that EC might be a useful indicator for motor exhaust exposure and that occupational exposure to motor exhaust is associated with a moderately increased risk of MI.
10.
11.
12.
13.
14.
15. 16.
17.
18. Acknowledgments This work was supported by the Swedish Council for Working Life and Social Research (FAS) [93-0541]. 19. Conflict of interest of interest.
The authors declare that they have no conflict 20.
References 21. 1. National Board of Health and Welfare. Causes of death 2010. (In Swedish). Stockholm, Sweden: National Board of Health and Welfare; 2011. 2. Castelli WP. Epidemiology of coronary heart disease: the Framingham study. Am J Med. 1984;76(2A):4–12. 3. Yusuf S, Hawken S, Ounpuu S, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case–control study. Lancet. 2004;364:937–52. 4. Marmot M, Brunner E. Alcohol and cardiovascular disease: the status of the U shaped curve. BMJ. 1991;303:565–8. 5. White IR, Altmann DR, Nanchahal K. Alcohol consumption and mortality: modelling risks for men and women at different ages. BMJ. 2002;325:191–8. 6. Corrao G, Rubbiati L, Bagnardi V, Zambon A, Poikolainen K. Alcohol and coronary heart disease: a meta-analysis. Addiction. 2000;95:1505–23. 7. Seaton A, MacNee W, Donaldson K, Godden D. Particulate air pollution and acute health effects. Lancet. 1995;345:176–8. 8. Mills NL, Donaldson K, Hadoke PW, et al. Adverse cardiovascular effects of air pollution. Nat Clin Pract Cardiovasc Med. 2009;6:36–44. 9. Brook RD, Rajagopalan S, Pope CA, et al. Particulate matter air pollution and cardiovascular disease: an update to the scientific
123
22.
23. 24.
25.
26.
statement from the American Heart Association. Circulation. 2010;121:2331–78. Mustafic H, Jabre P, Caussin C, et al. Main air pollutants and myocardial infarction: a systematic review and meta-analysis. JAMA. 2012;307:713–21. European Environment Agency (EEA). Air quality in Europe— 2012 report. Report no. 4/2012. Copenhagen, Denmark: EEA; 2012. Omstedt G, Andersson S, Bergstro¨m R. Present and future air quality in Sweden. Estimates of NO2, PM10 and PM2.5 in Swedish traffic environments for future scenarios with reduced European emissions (in Swedish). SMHI Meteorol. 2010;140: 1–56. International Agency for Research on Cancer. IARC monographs on the evaluation of carcinogenic risks to humans. Diesel and gasoline engine exhausts and some nitroarenes. IARC Monogr Eval Carcinog Risks Hum 1989;46:1–458. Gustavsson P, Plato N, Hallqvist J, et al. A population-based case-referent study of myocardial infarction and occupational exposure to motor exhaust, other combustion products, organic solvents, lead, and dynamite. Stockholm Heart Epidemiology Program (SHEEP) Study Group. Epidemiology. 2001;12:222–8. WHO. Diesel fuel and exhaust emissions—environmental health criteria 171. Geneva: World Health Organization; 1996. Grumet J, Levin R, Marin A. Heavy-duty engine emissions in the Northeast. Washington, DC: Northeast States for Coordinated Air Use Management; 1997. Lewne´ M, Plato N, Gustavsson P. Exposure to particles, elemental carbon and nitrogen dioxide in workers exposed to motor exhaust. Ann Occup Hyg. 2007;51:693–701. Birch ME, Cary RA. Elemental carbon-based method for monitoring occupational exposures to particulate diesel exhaust. Aerosol Sci Technol. 1996;25:221–41. Birch ME, Noll JD. Submicrometer elemental carbon as a selective measure of diesel particulate matter in coal mines. J Environ Monit. 2004;6:799–806. Reuterwall C, Hallqvist J, Ahlbom A, et al. Higher relative, but lower absolute risks of myocardial infarction in women than in men: analysis of some major risk factors in the SHEEP study. The SHEEP Study Group. J Intern Med. 1999;246:161–74. Statistical database: Statistics Sweden. Population by county, marital status, age and sex. Year 1968-2012. Stockholm, Sweden: Statistics Sweden; 2013. http://www.ssd.scb.se/databaser/makro/ Visavar.asp?yp=tansss&xu=C9233001&huvudtabell=Befolkning Ny&deltabell=L1&deltabellnamn=Population?by?county% 2C?marital?status%2C?age?and?sex%2E??Year& omradekod=BE&omradetext=Population&preskat=O&innehall= Folkmangd&starttid=1968&stopptid=2012&Prodid= BE0101&fromSok=&Fromwhere=S&lang=2&langdb=2. Accessed 19 Jan 2014. The National Labour Market Board. Nordic Classification of Occupations. (In Swedish). Stockholm, Sweden: The National Labour Market Board; 1983. Brook RD, Rajagopalan S. Particulate matter, air pollution, and blood pressure. J Am Soc Hypertens. 2009;3:332–50. Coogan PF, White LF, Jerrett M, et al. Air pollution and incidence of hypertension and diabetes mellitus in black women living in Los Angeles. Circulation. 2012;125:767–72. Andersen ZJ, Raaschou-Nielsen O, Ketzel M, et al. Diabetes incidence and long-term exposure to air pollution: a cohort study. Diabetes Care. 2012;35:92–8. Hange D, Sigurdsson JA, Bjo¨rkelund C, Lapidus L, Bengtsson C. Main causes of death among Swedish women born 1914 and 1918: 32-year follow-up of the Population Study of Women in Gothenburg. Int J Gen Med. 2012;5:597–601.
Myocardial infarction and motor exhaust 27. Attfield MD, Schleiff PL, Lubin JH, et al. The diesel exhaust in miners study: a cohort mortality study with emphasis on lung cancer. J Natl Cancer Inst. 2012;104:869–83. 28. Hammar N, Linnersjo¨ A, Gustavsson A, Hallqvist J, Reuterwall C, Sandberg, E. Myocardial Infarction in Stockholm County 1980–1995. Report no. 2/1998. Stockholm, Sweden: Unit of Epidemiology, Community Medicine, Stockholm. County Council; 1998 (In Swedish). 29. McGuire V, Nelson LM, Koepsell TD, et al. Assessment of occupational exposures in community-based case–control studies. Annu Rev Public Health. 1998;19:35–53.
525 30. Pronk A, Coble J, Stewart PA. Occupational exposure to diesel engine exhaust: a literature review. J Expo Sci Environ Epidemiol. 2009;19:443–57. 31. Zaebst DD, Clapp DE, Blade LM, et al. Quantitative determination of trucking industry workers’ exposures to diesel exhaust particles. Am Ind Hyg Assoc J. 1991;52:529–41.
123
