Cancer Causes Control (2014) 25:203–214 DOI 10.1007/s10552-013-0322-x
ORIGINAL PAPER
Occupational extremely low-frequency magnetic field exposure and selected cancer outcomes in a prospective Dutch cohort Tom Koeman • Piet A. van den Brandt • Pauline Slottje • Leo J. Schouten • R. Alexandra Goldbohm Hans Kromhout • Roel Vermeulen
•
Received: 15 July 2013 / Accepted: 6 November 2013 / Published online: 16 November 2013 Ó Springer Science+Business Media Dordrecht 2013
Abstract Purpose To investigate the association between exposure to occupational extremely low-frequency magnetic fields (ELF-MF) and the risk of a priori selected cancer outcomes within the prospective Netherlands Cohort Study. Methods 120,852 men and women aged 55–69 years at time of enrollment in 1986 were followed up (17.3 years) for incident lung, breast and brain cancer, and hematolymphoproliferative malignancies. Information on occupational history and potential confounders such as sex, age, smoking, alcohol use, and attained educational level were collected at baseline through a self-administered questionnaire. Occupational ELF-MF exposure was assigned with a job-exposure matrix. Using a case-cohort approach, associations with cancer incidence were analyzed with Cox regression stratified by sex, using three exposure metrics: (1) ever had a job with low or high exposure to ELF-MF versus background, (2) duration of exposure, and (3) cumulative exposure.
Results None of the exposure metrics showed an effect on incidence for lung, breast, and brain cancer, nor any of the assessed subtypes in men and women. Of the hemato-lymphoproliferative malignancies in men, ever high exposed to ELF-MF showed a significant association with acute myeloid leukemia (AML) [hazard ratio (HR) 2.15; 95 % confidence interval (CI) 1.06–4.35] and follicular lymphoma (FL) (HR 2.78; 95 % CI 1.00–5.77). Cumulative exposure to ELF-MF showed a significant, positive association with FL but not AML among men. Conclusions In this large prospective cohort study, we found some indications of an increased risk of AML and FL among men with occupational ELF-MF exposure. These findings warrant further investigation. Keywords Prospective cohort Breast cancer Extremely low-frequency magnetic fields Lung cancer Brain cancer Leukemia Non-Hodgkin lymphoma
Electronic supplementary material The online version of this article (doi:10.1007/s10552-013-0322-x) contains supplementary material, which is available to authorized users. T. Koeman (&) P. Slottje H. Kromhout R. Vermeulen Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, P.O. Box 80178, 3508 TD Utrecht, The Netherlands e-mail: [email protected] R. Vermeulen e-mail: [email protected]
R. A. Goldbohm Netherlands Organization for Applied Scientific Research (TNO), Wassenaarsweg 56, 2333 AL Leiden, The Netherlands R. Vermeulen Julius Centre for Public Health Sciences and Primary Care, University Medical Centre, P.O. Box 85500, 3508 GA Utrecht, The Netherlands
P. A. van den Brandt L. J. Schouten Department of Epidemiology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, PO Box 616, 6200 MD Maastricht, The Netherlands
123
204
Introduction The possible association between exposure to extremely low-frequency magnetic fields (ELF-MF) and cancer risk has been studied extensively in the past decades [1–6] and has been summarized in several reviews [7–10]. For various reasons, breast and brain cancer and hemato-lymphoproliferative malignancies have received more attention than other types of malignancies. Interest in breast cancer started based on a hypothesized suppression of nighttime melatonin production due to nighttime ELF-MF exposure, which in turn could increase breast cancer risk [8]. Interest in brain cancer grew after Lin et al. [11] reported possible associations between work in electrical jobs and increased brain cancer risk. Hemato-lymphoproliferative malignancies have become a topic of interest since childhood acute lymphoblastic leukemia (ALL) has been found to be consistently associated with environmental ELF-MF exposure [12–14], raising the question whether a similar association exists for adults [8]. In 2002, the international agency for research on cancer (IARC) has classified ELF-MF as possibly carcinogenic to humans (group 2B) [15]. This was mainly based on the above-mentioned association between environmental exposure to ELF-MF and childhood leukemia. To date, epidemiological studies on the possible associations between occupational ELF-MF exposure and cancer in adults have provided less than consistent results despite large occupational cohort studies among high-exposed utility workers [4–6]. Possible reasons for these inconsistencies could be related to methodological issues such as exposure misclassification, selection bias, and incomplete adjustment for relevant confounders. As it stands the question whether ELF-MF exposure is related to cancer risk, in particular breast and brain cancer and hemato-lymphoproliferative malignancies in adults, remains unanswered. We set out to study the possible association between occupational exposure to ELF-MF and selected cancer outcomes: brain and postmenopausal breast cancer, and hemato-lymphoproliferative malignancies. Lung cancer was added to the malignancies of primary interest as a negative control to identify potential methodological shortcomings (e.g., sampling bias, incomplete adjustment of smoking, and socioeconomic status).
Methods The Netherlands Cohort Study on diet and cancer (NLCS) consists of 120,852 subjects (58,279 males and 62,573 females) who were enrolled in 1986. Subjects were aged 55–69 years and living throughout the Netherlands at the time of enrollment [16]. Participants completed a
123
Cancer Causes Control (2014) 25:203–214
questionnaire on occupational history, dietary habits, and other potential risk factors for cancer at baseline. The first page, containing basic information on age, gender, smoking status, and medical history, was machine readable and entered for all subjects. After enrollment, the cohort was followed up for cause-specific mortality and cancer incidence. Following a case-cohort approach, person-years were estimated using a randomly drawn subcohort (n = 5,000). For emerging cases as well as for the subcohort, the remaining pages of the questionnaire were entered manually. Questionnaires were entered blind to case/subcohort status. Subjects who reported having prevalent cancer other than skin cancer at baseline were excluded, leaving 2,336 male and 2,438 female subcohort members. The NLCS was approved by the institutional review boards of the Netherlands Organization for Applied Scientific Research TNO (Zeist) and Maastricht University (Maastricht). Follow-up and case definition At the time of this study, data are available for 17.3 years of follow-up (from September 1986 to December 2003). Cases were obtained by record linkage with the Netherlands Cancer Registry and the Netherlands Pathological Registry. Incident cases of invasive lung, breast, and brain cancer and hemato-lymphoproliferative malignancies and their subtypes were defined according to the third edition of the International Classification of Diseases of Oncology (ICD-O-3; see Table 1). Breast cancer analyses were only performed on women for whom the age at menopause was known at baseline. Exposure assessment The baseline questionnaire contained questions on the occupational history up to enrollment in 1986. Subjects reported whether they ever had a paid job and, if so, supplied the job title, type of job, type and name of industry, and period of employment for up to five jobs held during their lifetime. If more than five jobs were reported, similar jobs were collapsed into a single job, and period of employment was summed. Based on the description, these jobs were originally coded in the Dutch Occupational Classification System (CBS-84) and subsequently translated to the International Standard Classification of Occupations 1988 (ISCO-88) [17]. Men in the subcohort reported 2.1 jobs on average, and women 1.6 jobs. About 6 % of men and 2 % of women in the subcohort reported five jobs or more. Exposure to ELF-MF was assigned using a recently further developed job-exposure matrix (JEM), assigning an ordinal ELF-MF exposure level to each job (i.e., background, low, and high exposure to ELF-MF) [18, 19]. Several other
Cancer Causes Control (2014) 25:203–214
205
Table 1 Cancer incidence case definitions and number of incident cases Cancer typea
Lung cancer
ICD-O-3 location code
Cell type
ICD-O-3 morphology code
C34.0–C34.9
Number of cases Men
Women
2,107
355
Large cell carcinomas
8012
350
61
Small cell carcinomas Squamous cell carcinomas
8040–8045 8050–8078
341 825
70 94
Adenocarcinomas Breast cancerb
8140–8147 C50.0–C50.9
453
104
NA
2,077 1,379
Ductal
8500–8508
NA
Lobular
8520–8521
NA
378
NA
815
ER? Brain cancer
C71
Astrocytic glioma Hemopoietic and lymphatic malignancies
9384, 9400–9401, 9410–9411, 9420–9421, 9424, 9440–9442, 9481
160
73
116
55
NA
9590–9999
Non-Hodgkin Lymphoma
NA
761
467
B-cell lineage
B-cell
9670–9671, 9673, 9675, 9678–9680, 9684, 9687, 9689, 9699, 9700–9702, 9705, 9708–9709, 9714–9719, 9727–9729, 9731–9734, 9748, 9760–9762, 9764, 9820, 9823, 9826–9827, 9831–9837, 9940 9590–9591, 9670–9671, 9673, 9675, 9678–9680, 9684, 9687, 9689, 9699, 9727–9729, 9731–9734, 9760–9762, 9764, 9823, 9826, 9832–9833, 9835–9836, 9940
708
440
Chronic lymphocytic leukemia
B-cell
9670, 9823
147
73
Diffuse B-cell lymphoma Follicular lymphoma
B-cell B-cell
9678–9680, 9684 9690–9691, 9695, 9698
160 49
87 45
Multiple myeloma
B-cell
150
9731–9734
192
Leukemia
9800–9948
155
79
Myeloid leukemia
9840, 9860–9931, 9945
148
76
Acute myeloid leukemia
9840, 9861–9862, 9864–9874, 9891–9931
108
55
NA not applicable a
Analyses performed on those cancer types with at least five ever high-exposed cases
b
Due to the low number of cases, breast cancer analyses were performed for women with information on age at menopause only
occupational exposures were explored as potential confounders: Exposure to asbestos, crystalline silica, diesel motor exhaust, chromium, nickel, and polycyclic aromatic hydrocarbons was assigned to each job through linkage with the DOMJEM [20, 21]. Exposure to total solvents in general and aromatic solvents in particular was assigned to each job through the ALOHA JEM [17, 22, 23] Both JEMs assign ordinal exposure levels (i.e., no, low and high exposure) to job codes (respectively, ISCO-68 [24] and ISCO-88 [25]). Subjects who indicated that they never had a paid job (housewives or other) were assigned to background exposure. ELF-MF exposure was analyzed using the following measures: (1) ever had a job with at maximum a background, a low or a high occupational ELF-MF exposure
(hereafter referred to as only low or ever high exposure); (2) duration in years of exposure to ELF-MF above background; and (3) cumulative exposure to occupational ELFMF. Cumulative exposure was calculated by assigning arbitrary weights to the exposure ratings reflecting the multiplicative nature of the exposure distribution (i.e., background 0, low 1, high 4). These weights per job were multiplied by duration of that job and summed over the entire job history. For potential confounding by chemical exposures as assessed by DOMJEM and ALOHA, cumulative exposure was calculated in the same way. Statistical analyses Cox proportional hazards models with age as the time axis were used to investigate the relationship between
123
206
occupational exposure to ELF-MF and cancer incidence. Person-years were estimated through the subcohort. Censoring occurred at the date of cancer diagnosis, death, or end of follow-up, whichever occurred first. Risk analyses were stratified by sex and performed only for the different cancer outcomes and their morphological subtypes if at least five cases ever had a job with a high exposure to ELFMF, and 95 % confidence intervals (CI) were calculated using the robust estimator of variance to account for the additional variance introduced by the case-cohort design of the study. Models were analyzed using STATA version 12.1 (Statacorp LP, College Station, Texas, USA). Ever had a job with background, only low or ever high exposure to ELF-MF was analyzed categorically, using background exposure as the reference category. Exposure duration was analyzed as a continuous variable. Cumulative exposure was analyzed categorically, using sex-specific cut points dividing exposure between background and tertiles of above background exposure. Trends for cumulative exposure were calculated over the medians of each category. First, a univariate analysis of ever had a job exposed to ELF-MF was performed. Second, each potential confounder was tested in a bivariate model. Covariates that lead to a 10 % change in the risk estimate of ever ELF-MF exposure in these analyses were subsequently evaluated in a multivariate model, and a backward, stepwise regression was performed retaining the ever ELF-MF exposure variable. Covariates with a p value \0.05 were retained in the final confounder model. This confounder model was used for all the adjusted analyses of all ELF-MF exposure metrics. The following covariates were considered for all cancer outcomes: smoking (current vs. former and ex-smokers, average number of cigarettes smoked daily, number of years smoking cigarettes), passive smoking by the partner (current, former, or nonsmoker), level of education as an indicator of socialeconomic status (primary, lower, secondary and medium, and higher vocational), body mass index (in kg/m2), alcohol consumption (g/day), vegetable, legume, fruit, fish and seafood, and meat consumption (each in g/day), and total energy intake (kcal/day). Specific occupational exposures were considered as confounders depending on the cancer outcome. For lung cancer analyses, potential confounding by cumulative occupational exposures to asbestos, silica, diesel motor exhaust, chromium, nickel, and polycyclic aromatic hydrocarbons was tested categorically (three tertiles of exposed vs. no exposure). Cumulative occupational exposure to total solvents and aromatic solvents was tested as a potential confounder in the analyses for hemato-lymphoproliferative malignancies [26]. In the breast cancer analyses, contraceptive use (never, ever), hormone replacement therapy (yes, no, don’t know),
123
Cancer Causes Control (2014) 25:203–214
age at menarche, age at menopause, and parity and age at first birth (nulliparous, 1–2 children, first child before 25; 1–2 children, first child after 25; three or more children, first child before 25; three or more children, first child after 25) were assessed for potential confounding. Lag time and sensitivity analyses All endpoints were analyzed with a 0- and 20-year lag. The 20-year lag was implemented to ensure a comparable exposure lag for all subjects independent of the end of the follow-up (max 17.3 years) by disregarding exposure for the 20 years before end of the follow-up (i.e., for subjects censored in 2003, job history up to 1983 was used, while for subjects censored in 1993, job history up to 1973 was used to determine the exposure metrics). Furthermore, three types of sensitivity analyses were performed. First, all subjects with missing exposure levels due to a job history that could not reliably be coded (*12 % of the cohort) were assigned to the background exposure group, whereas in the main analysis, these were removed from the analyses. Second, an analysis was performed where only subjects who ever had a paid job at baseline were included, i.e., housewives and unemployed were omitted from the analyses. Third, subjects still employed at baseline (32 % of men and 10 % of women) were excluded from the analysis.
Results The number of cases for the main cancer outcomes and their subtypes are shown in Table 1. For both cases and the subcohort, approximately 8 % of men and 1 % of women ever had a job with high occupational exposure to ELFMF. The remaining subjects were approximately evenly distributed between background and low exposure (see Table 2). The distribution of potential confounding covariates used in the analyses by cumulative exposure categories is shown in supplemental Table 1. Breast cancer in postmenopausal women Breast cancer showed no association with ever had a job with low or high ELF-MF exposure versus background exposure (adjusted HR only low: 1.07, 95 % CI 0.94–1.23; adjusted HR ever high: 1.24, 95 % CI 0.59–2.58). Duration of exposure and cumulative exposure to ELF-MF showed no monotonic association with breast cancer incidence (Table 3). Results did not change when the analyses were repeated for major subtypes of breast cancer (Ductal, estrogen receptor status; see supplemental Table 2).
Cancer Causes Control (2014) 25:203–214
207
Table 2 Distribution of occupational extremely low-frequency magnetic field exposure for subcohorta and cases Subcohorta
Lung cancer cases
Breast cancer cases
Brain cancer cases
Non-hodgkin lymphoma
Leukemias
n
%
n
%
n
%
n
%
n
%
n
%
1,027 930
48.3 43.7
1,221 1,093
46.9 42.0
NA NA
NA NA
74 69
46.3 43.1
359 352
46.3 45.4
65 74
41.9 47.7
169
7.9
288
11.1
NA
NA
17
10.6
64
8.3
16
10.3
Men Ever ELF exposed Background Low High Cumulative ELF exposure 1,027
48.3
1,221
46.9
NA
NA
74
46.3
359
46.3
65
41.9
[ 0–21.5 unit–years
Background
361
17.0
429
16.5
NA
NA
29
18.1
141
18.2
29
18.7
[ 21.5–38.1 unit-years
354
16.7
459
17.6
NA
NA
29
18.1
137
17.7
31
20.0
[ 38.1–204 unit-years
384
18.1
493
18.9
NA
NA
28
17.5
138
17.8
30
19.4
Women Ever ELF exposed Background
1,023
49.9
198
47.9
1,198
48.8
44
52.4
260
53.2
39
49.4
Low
1,011
49.3
209
50.6
1,238
50.4
40
47.6
225
46.0
40
50.6
High
16
0.8
6
1.5
19
0.8
0
0.0
4
0.8
0
0.0
Cumulative ELF exposure 1,023
49.9
198
47.9
1,198
48.8
44
52.4
260
53.2
39
49.4
[ 0–6.5 unit-years [ 6.5–11 unit-years
Background
339 341
16.5 16.6
73 67
17.7 16.2
478 368
19.5 15.0
13 16
15.5 19.0
82 77
16.8 15.7
9 19
11.4 24.1
[ 11–136 unit-years
347
16.9
75
18.2
411
16.7
11
13.1
70
14.3
12
15.2
NA not available a
Total number of people in the subcohort (n = 5,000)
Brain cancer There was no significant association between occupational ELF-MF exposure and brain cancer for any of the analyzed exposure metrics among men or women, although the risk for ever had a job with high exposure versus background exposure in men was elevated (HR 1.45, 95 % CI 0.83–2.52; see Table 3). Astrocytic gliomas also showed no association with ELF-MF exposure, with HR’s closer to unity compared to the analysis of total brain cancer (ever high exposure vs. background exposure in men HR 0.77, 95 % CI 0.34–1.71; see supplemental Table 3). Hemato-lymphoproliferative malignancies Among women, we did not observe any meaningful associations between ELF-MF and hemato-lymphoproliferative malignancies. In men, leukemia showed a nonsignificant association with ELF-MF exposure with an adjusted HR of 1.34 (95 % CI 0.94–1.92) and 1.66 (95 % CI 0.92–3.00) for only low and ever high exposure versus background, respectively, but no monotonic relationship was seen with cumulative ELF-MF exposure (see Table 3). This trend
seemed to be mainly driven by AML (Table 4, in men adjusted HRs ever low: 1.84, 95 % CI 1.18–2.85; ever high: 2.15, 95 % CI 1.06–4.35 compared to background). Associations between cumulative exposure and AML were also significantly increased for men, but the association was not monotonic, and there was no significant trend (HR first tertile of exposed: 1.98, 95 % CI 1.14–3.43; HR second tertile of exposed: 1.96, 95 % CI 1.12–3.43; HR third tertile of exposed: 1.73, 95 % CI 1.00–3.01; p for trend: 0.68; Table 4). Correcting the AML analyses for aromatic solvents did not change the HRs. Ever holding a job with low exposure went from 1.84 to 1.89 (95 % CI 1.19–3.02), and ever high exposure from 2.15 to 2.13 (95 % CI 0.97–4.65). Restricting the analyses to subjects who were nonexposed to aromatic solvents did not change the overall results. Similarly, adding total solvents to the model did not lead to meaningful differences in HRs (results not shown). Non-Hodgkin lymphoma (NHL) showed no indications for an increased risk for ever versus never holding a job with exposure above background levels in men (only lowexposed HR 1.07, 95 % CI 0.90–1.27; ever high-exposed HR 1.19, 95 % CI 0.86–1.64). Similarly, no associations
123
208
Cancer Causes Control (2014) 25:203–214
Table 3 Association between occupational extremely low-frequency magnetic fields exposure and lung, breast, brain cancer, and hematolymphoproliferative malignancies, stratified by sex Men
Women Cases
Personyears in sub cohort
Adjusted HR
95 % Confidence Interval
Cases
Personyears in sub cohort
Adjusted HR
95 % Confidence Interval
Breast cancerc Ever exposed
Duration of exposure Cumulative exposure
Background
1,068
13,360
Low
991
12,612
1.07
Reference
High
265
2,187
1.24
(0.59–2.58)
HR/10 years
1,252
15,877
0.96
(0.85–1.08)
Background
(0.94–1.23)
1,069
13,373
1st tertileb
393
4,828
1.28
Reference (1.06–1.56)
2nd tertileb
415
4,865
0.92
(0.75–1.12)
3rd tertileb
447
5,092
1.03
2,324
28,158
(0.85–1.25) p = 0.88
Brain cancer Ever exposed
Background
74
14,644
Reference
44
15,962
Low
69
13,474
1.01
(0.72–1.42)
40
15,892
High
17
2,335
1.45
(0.83–2.52)
0
253
Duration of exposure
HR/10 years
86
15,809
0.94
(0.81–1.09)
40
16,145
Cumulative exposure
Background
74
14,658
Reference
44
16,031
1st tertileb
29
5,183
1.11
(0.71–1.73)
13
5,242
0.92
(0.49–1.74)
2nd tertileb
29
5,126
1.14
(0.72 –1.78)
16
5,432
1.10
(0.61–1.98)
0.99
0.73
3rd tertileb
28
5,486
Test for trend
160
30,452
Background
359
14,576
Low
352
13,418
1.07
High
64
2,296
HR/10 years Background
416 360
1st tertileb
Reference 0.92
(0.60–1.43)
NC
NC
0.72
(0.50–1.05) Reference
(0.63–1.56)
11
5,402
p = 0.70
84
32,106
(0.38 - 1.44)
Reference
260
15,901
(0.90–1.27)
225
15,875
0.89
(0.73–1.08)
1.19
(0.86–1.64)
4
253
0.89
(0.29–2.76)
15,714 14,590
0.98
(0.91–1.06) Reference
229 260
16,128 15,971
1.00
(0.84–1.18) Reference
p = 0.27
Non-hodgkin lymphomad Ever exposed
Exposure duration Cumulative exposure
Reference
140
5,165
1.09
(0.87–1.38)
82
5,234
1.00
(0.75–1.32)
b
137
5,118
1.17
(0.92–1.48)
77
5,424
0.91
(0.68–1.21)
3rd tertileb
138
5,418
0.99
(0.79–1.26)
70
5,401
0.79
Test for trend
775
30,290
p = 0.62
489
32,029
p = 0.06
Background
51
13,197
Reference
39
14,899
Reference
Low
68
12,302
1.34
(0.94–1.92)
38
15,019
High
14
2,108
1.66
(0.92–3.00)
0
171
Exposure duration
HR/10 years
90
15,714
1.01
(0.87–1.17)
40
16,128
Cumulative exposure
Background
51
13,211
Reference
39
14,951
1st tertileb
2nd tertile
(0.59–1.06)
Leukemiae Ever exposed
NC
0.98
(0.70–1.37) Reference
28
4,783
1.35
(0.84 –2.16)
9
4,951
0.70
(0.33–1.48)
27
4,754
1.55
(0.98–2.46)
18
5,120
1.46
(0.83–2.56)
3rd tertileb
27
4,859
1.29
(0.81–2.05)
11
5,067
0.81
133
27,606
p = 0.15
77
30,089
Test for trend
123
(0.63–1.58)
NC
b
2nd tertile
Lung cancera Ever exposed
0.99
Background
(0.41–1.61) p = 0.74
1,068
13,360
Reference
190
15,407
Low
991
12,612
0.95
(0.81–1.11)
198
15,469
0.98
Reference (0.75–1.26)
High
265
2,187
1.33
(1.01–1.75)
6
220
1.19
(0.36–3.89)
Cancer Causes Control (2014) 25:203–214
209
Table 3 continued Men
Women Cases
Personyears in sub cohort
Adjusted HR
95 % Confidence Interval
Cases
Personyears in sub cohort
Adjusted HR
1.00
(0.92–1.08) Reference
215
16,181
0.99
190
15,476
95 % Confidence Interval
Duration of exposure
HR/10 years
1,382
15,753
Cumulative exposure
Background
1,069
13,373
1st tertileb
393
4,828
0.96
(0.78–1.19)
73
5,024
1.16
(0.81 –1.65)
2nd tertileb
415
4,865
1.05
(0.86–1.29)
61
5,344
0.97
(0.68–1.39)
3rd tertileb
447
5,092
1.00
(0.82–1.23)
70
5,253
0.86
2,324
28,158
394
31,096
Test for trend
p = 0.95
(0.81–1.21) Reference
(0.61–1.23) p = 0.23
HR hazard ratio, NC not calculated a
Corrected for smoking (current vs. noncurrent), number of cigarettes smoked per day, years of smoking cigarettes, attained level of education (primary vocational, lower vocational, secondary and medium vocational, higher vocational), and occupational asbestos exposure b Sex specific cut points; Men: first tertile: [0–21.5 unit-years, second tertile: [21.5–38.1 unit-years, third tertile: [38.1–204 unit-years; Women: first tertile: [0–6.5 unit-years, second tertile: [6.5–11 unit-years, third tertile: [11–136 unit-years c
Corrected for alcohol intake, body mass index, fruit intake, age at menarche, age at menopause, parity, age at first child, number of children, benign breast growth, and family history of breast cancer
d
Corrected for family history of hematological malignancies
e
Corrected for smoking status (current vs. noncurrent) and body mass index
were found for NHL with duration of exposure or cumulative exposure to ELF-MF (Table 3). The same held true for most subtypes of NHL (see supplemental Table 4) with the exception of follicular lymphoma (FL). For men, FL showed a positive trend in the ever versus never ELF-MF exposure (HR for ever low exposed 1.22, 95 % CI 0.66–2.24; HR for ever high exposed 2.40, 95 % CI 1.00–5.77), and in the analysis of cumulative exposure (HR first tertile of exposed: 1.14, 95 % CI 0.50–2.26; HR second tertile of exposed 1.20, 95 % CI 0.52–2.78; HR third tertile of exposed: 1.77, 95 % CI 0.87–3.60; p for trend: 0.03; Table 4). However, duration of occupational ELFMF exposure did not show a significant association with FL. Lung cancer Lung cancer incidence showed a borderline significant association between ever high exposure to ELF-MF and lung cancer incidence in men (HR 1.29, 95 % CI 0.99–1.70) and a nonsignificant association in women (HR 1.23, 95 % CI 0.37–4.07), see Table 3. Similar results were found with respect to the different subtypes of lung cancer. An association was found between ever high exposure to ELF-MF and large cell carcinoma and adenocarcinoma incidence in men (HR 1.70, 95 % CI 1.11–2.62 and HR 1.57, 95 % CI 1.08–2.29, respectively). For women, this association was observed for large cell carcinoma (HR 3.50, 95 % CI 0.60–20.40) but not for adenocarcinoma (HR 0.93, 95 % CI 0.11–7.84). Duration and cumulative
ELF-MF exposure did not show an association with lung cancer or any of its subtypes (see supplemental Table 5). 20-year lag and sensitivity analyses Repeating the analyses using a 20-year lag only slightly changed the risk estimates (results not shown). Similarly, defining all subjects with unknown job history as nonexposed, or restricting the analysis to only subjects who had a paid job, did not materially change the risk estimates (results not shown). For most cancer types, the analyses excluding subjects still employed at baseline only lead to negligible changes in HRs. However, excluding subjects still employed at baseline did influence the results for AML and FL in men. The trend observed for ever holding a job with a low or high exposure versus background disappeared in this analysis, although HRs were still increased (HR for ever holding a job with low exposure versus background changed from 1.84 to 2.27 (1.34–3.87), HR for ever high exposure versus background changed from 2.15 to 1.86 (0.73–4.72)). For FL, HRs increased, the observed positive trend remained (HR third tertile of cumulative exposure changed from 1.97 to 2.67 (1.21–5.91), and p for trend changed from 0.03 to 0.01).
Discussion The objective of this study was to determine possible associations between occupational exposure to ELF-MF
123
210
Cancer Causes Control (2014) 25:203–214
Table 4 Association between occupational extremely low-frequency magnetic fields exposure and incidence of subtypes of non-Hodgkin lymphoma and leukemia, stratified by sex Men
Women Cases
Personyears in sub cohort
Adjusted HR
95 % Confidence Interval
Cases
Personyears in sub cohort
Reference
23
15,901
Adjusted HR
95 % Confidence interval
Non-Hodgkin lymphoma: Follicular lymphomaa Ever exposed
Duration of exposure Cumulative exposure
Background
20
14,576
Low
22
13,418
1.23
(0.67–2.28)
21
15,875
0.94
High
7
2,296
2.78
(1.20–6.44)
1
253
2.68
(0.33–21.70)
HR/10 years
30
15,714
1.04
(0.81–1.35)
22
16,128
0.96
(0.59–1.58)
Background
20
14,590
Reference
23
15,971
first tertileb
8
5,165
1.14
(0.50–2.61)
6
5,234
0.82
(0.33–2.06)
second tertileb
Reference (0.51–1.71)
Reference
8
5,118
1.23
(0.54–2.80)
8
5,424
1.07
(0.47–2.43)
third tertileb
13
5,418
1.97
(0.98–3.95)
8
5,401
1.02
(0.45–2.31)
Test for trend
49
30,290
45
32,029
p = 0.03
p = 0.70
Non-hodgkin lymphoma: Chronic lymphocytic leukemia/Small lymphocytic lymphomaa Ever exposed
Background
68
14,576
Reference
41
15,901
Low
70
13,418
1.11
(0.78–1.58)
33
15,875
0.83
(0.52–1.32)
High
10
2,296
0.98
(0.49–1.97)
1
253
1.48
(0.19–11.75)
Duration of exposure
HR/10 years
80
15,714
0.94
(0.81–1.08)
34
16,128
0.91
Cumulative exposure
Background
68
14,590
Reference
41
15,971
first tertileb
30
5,165
1.24
(0.79–1.93)
12
5,234
0.93
(0.48–1.79)
second tertileb
26
5,118
1.17
(0.72–1.89)
11
5,424
0.82
(0.42–1.62)
third tertileb
24
5,418
0.91
(0.56–1.48)
11
5,401
0.79
148
30,290
p = 0.63
75
32,029
Test for trend
Reference
(0.62–1.33) Reference
(0.40–1.55) p = 0.68
Myeloid leukemiasb Ever exposed
Background
49
13,197
Reference
38
14,899
Low
64
12,302
1.31
(0.91–1.89)
36
15,019
Reference 0.97
(0.60–1.55)
NC
NC
High
13
2,108
1.61
(0.87–2.96)
0
171
Duration of exposure
HR/10 years
85
15,714
1.03
(0.89–1.20)
38
16,128
Cumulative exposure
Background
49
13,211
Reference
38
14,951
first tertileb
26
4,783
1.30
(0.80–2.11)
9
4,951
0.72
(0.34–1.52)
second tertileb
25
4,754
1.50
(0.94–2.42)
17
5,120
1.41
(0.79–2.51)
third tertileb
26
4,859
1.28
(0.80–2.06)
10
5,067
0.75
126
27,606
p = 0.18
74
30,089
Test for trend
0.98
(0.69–1.39) Reference
(0.37–1.55) p = 0.57
Acute myeloid leukemiac Ever exposed
Background
30
13,197
Reference
28
14,899
Low
52
12,302
1.84
(1.18–2.85)
25
15,019
High
10
2,108
2.15
(1.06–4.35)
0
171
Exposure duration
HR/10 years
69
15,714
0.97
(0.82–1.15)
27
16,128
Cumulative exposure
Background
30
13,211
Reference
28
14,951
first tertileb
23
4,783
1.98
(1.14–3.43)
5
4,951
0.53
(0.20–1.40)
second tertileb
19
4,754
1.96
(1.12–3.43)
14
5,120
1.57
(0.83–2.96)
1.73
6
5,067
0.61
53
30,089
b
third tertile
20
4,859
Test for trend
92
27,606
(1.00–3.01) p = 0.07
Reference 0.91
(0.53–1.57)
NC
NC
0.92
(0.61–1.38) Reference
(0.25–1.50) p = 0.40
HR hazard ratio, NC not calculated a
Corrected for family history of hematological malignancies
b
Sex specific cut points; Men: first tertile: [0–21.5 unit-years, 2nd
tertile: [21.5–38.1 unit-years, third tertile: [38.1–204 unit-years; Women: first tertile: [0–6.5 unit-years, second tertile: [6.5–11 unit-years, third tertile: [11–136 unit-years c
Corrected for family history of smoking status (current vs. noncurrent), number of cigarettes smoked and body mass index
123
Cancer Causes Control (2014) 25:203–214
and cancer incidence for selected cancer outcomes in a large population-based cohort. No clear evidence of such associations was observed for lung, breast, and brain cancer or their subtypes. Some indications of increased risks with occupational ELF-MF exposure were found for hematolymphoproliferative malignancies among men. Leukemia showed a possible association with occupational ELF-MF exposure which seemed to be driven mainly by the underlying association with AML. Furthermore, the NHLsubtype follicular lymphoma showed positive associations with ELF-MF exposure. Breast cancer Breast cancer mortality and incidence in relationship with ELF-MF exposure have been extensively studied in the past decades. A meta-analysis published in 2000 by Erren [9] estimated a small, increased risk for men as well as women. Among studies on occupational exposure to ELFMF using a job-exposure matrix, this risk was detected for women (pooled RR 1.09, 95 % CI 1.05–1.14), but not for men (pooled RR 1.00, 95 % CI 0.73–1.39). An IARC Working Group concluded, based on the evidence available at that time, that these studies were of insufficient quality to draw conclusions on the effects of ELF-MF on breast cancer [15]. Subsequently, the World Health Organization and a recent meta-analysis of case–control studies published between 2000 and 2009 concluded that there was no overall effect of ELF-MF on breast cancer incidence among women [7, 27]. This latter meta-analysis also showed no effect in subanalyses performed on studies on residential or occupational exposure, or on studies looking at receptor status of breast cancer. An update on a cohort study on Danish utility workers published in 2007 also found no increase in breast cancer mortality risk among women in the cohort (results for men were not shown)[1]. Our study, with more stringent correction for possible confounders such as age at menarche, age at menopause, pill use, HRT use and nulliparity, showed no association between occupational exposure to ELF-MF and breast cancer incidence nor estrogen receptor–positive breast cancer among postmenopausal women. This is in line with the more recent publications pointing toward an absence of an association between ELF-MF and breast cancer risk among women. Brain cancer IARC [15] and WHO [27] both concluded that the evidence for an effect of ELF-MF on brain cancer was inadequate. In a recent meta-analysis on brain cancer, Kheifets et al. [10] found a small, significant, positive association with occupational ELF-MF exposure (pooled RR 1.14,
211
95 % CI 1.07–1.22), which seemed to be driven by gliomas (pooled RR 1.18, 95 % CI 1.1–1.26). However, the studies on brain cancer and ELF-MF exposure were heterogeneous, with some studies reporting a small effect [28–30], while others report no effect [1, 31, 32]. Two studies looking at brain cancer and ELF-MF exposure were performed after the last meta-analysis, one industrial cohort study [33] and one population-based case–control study on environmental exposure to ELF-MF [34]. Both studies found no effects. We found no association between occupational ELF-MF and brain cancer incidence or astrocytic gliomas, offering no support for the hypothesis that ELFMF is associated with brain cancer incidence in general and glioma incidence in particular. Hemato-lymphoproliferative malignancies The association between ELF-MF and leukemia has been a topic of interest in both environmental and occupational epidemiology. The relationship was first observed for acute lymphoblastic leukemia (ALL) in children living close to a power line with a wiring configuration suspected to lead to high ELF-MF [12]. Subsequent studies that modeled and/or measured ELF-MF exposure have been remarkably consistent in detecting an association between exposure to ELF-MF from power lines and childhood ALL. However, there are concerns that the positive associations might be due to selection bias. On the other hand, if ELF-MF exposure and childhood leukemia would be causally related, exposure misclassification would likely attenuate the results. Lastly, the possibility of uncontrolled confounding is also always an issue in these types of studies, which might influence the observed risk in both directions [14, 15, 27]. These findings in children have spurred research into adult leukemia. Here, results have been less consistent. IARC [15] and WHO [27] both judged the evidence for an effect of ELF-MF on leukemia as inadequate. In a metaanalysis on adult leukemia in occupational epidemiological studies published in 2008, Kheifets et al. [10] observed a positive association between occupational ELF-MF exposure and leukemia, specifically AML and chronic lymphocytic leukemia (CLL). A case–control study performed in Brazil published after the meta-analyses observed increased leukemia mortality for subjects living close to power lines or living in an area with increased estimated magnetic fields [34]. A cohort study among electricity generation and transmission workers in the UK, also published after the meta-analysis, found no increased rate of leukemia for these workers when compared to national rates [33]. The authors of this study did observe an increased trend for workers with a longer work history in the utility industry and a decreasing trend for the time since workers ended working lending some support to a possible
123
212
association between ELF-MF exposure and leukemia risk. In our study, we observed an increased incidence of leukemia for men who ever had a job with a low or high exposure to ELF-MF compared to background exposure which seemed to be attributable to an increased incidence for AML. Although the analysis of cumulative exposure showed increased incidences for AML in all categories of ELF-MF exposure in men, we observed no significant trend in this increase. Furthermore, a trend was not observed when excluding the subjects who still worked at baseline. Therefore, our study only provides tentative support for the hypothesis that exposure to ELF-MF leads to an increased incidence of leukemia, and specifically AML. We also found a weak indication of a possible association between ELF-MF (ever/never and cumulative exposure) and FL. To our knowledge, estimates of such an association specifically for FL have not been reported to date. This may be because the numbers of cases of follicular lymphoma are generally low, or due to the fact that the overarching group of NHL generally showed no clear association with occupational exposure to ELF-MF and subtypes of NHL were not studied [1, 35, 36]. NHL is a diverse group of lymphopoietic malignancies that may have differing etiologies, and an analysis of this overarching group may miss associations of occupational or lifestyle exposures with particular NHL-subtypes [37–39]. A complicating factor in interpreting the existing literature is that the classification of lymphoma has undergone many changes over the years [40]. Further study into exposure to ELF-MF and NHL-subtypes is therefore warranted. A concern in previous studies has been the possibility of residual confounding [10]. We had the possibility of exploring and accounting for a large number of potential behavioral and demographic confounders. Very few of these showed a correlation with ELF-MF exposure or an association with NHL or leukemia. Furthermore, through the use of additional JEMs addressing chemical and biological exposures, the effects of other occupational exposures could be studied, most importantly occupational exposure to (aromatic) solvents. These results showed only a marginal change in risk estimates without changing the overall pattern. Lung cancer Our results indicate that there is probably no association between lung cancer incidence and occupational ELF-MF exposure, which is consistent with the literature [35, 41]. A single study among French–Canadian electrical workers did show a possible association between lung cancer and exposure to pulsed electromagnetic fields (PEMFs) [42], but this result was not replicated, and the methods by which the PEMFs were measured have been questioned [41].
123
Cancer Causes Control (2014) 25:203–214
We included lung cancer as a negative control to study the possible effect of methodological shortcomings such as unmeasured confounding. Unexpectedly, we found a borderline significant association between ever a job with high ELF-MF exposure and lung cancer incidence, in particular large cell carcinoma (men and women) and adenocarcinoma (men). We did not observe a significant association between duration of exposure or cumulative occupational ELF-MF exposure and lung cancer incidence. These observations may indicate residual confounding by smoking for the ever exposed analyses, but not for the analyses of duration and cumulative exposure. However, the associations were strongest for the two subtypes of cancer that are thought to have the weakest association with smoking [43]. Therefore, while confounding by smoking is unlikely as a cause for this result, residual confounding by other factors cannot be ruled out and the results of the analyses of ever had a job with a high exposure to ELF-MF should be interpreted with caution. ELF-MF exposure assessment Since the mechanism through which ELF-MF would cause cancer is unknown, it remains unclear what the most relevant measure of exposure would be. In this study, we obtained extensive occupational histories up to baseline reported by the participants and applied a semi-quantitative ELF-JEM incorporating intensity and probability of exposure to assess occupational exposure to ELF-MF[18]. Based on this, we calculated different measures of exposure to ELF-MF. While cumulative exposure is the most commonly used measure of exposure in epidemiological studies on the effects of ELF-MF, other measures have been proposed (e.g., intermittency of electromagnetic fields or percentage of time above a threshold [44], or other exposures such as pulsed electromagnetic fields [42]). In our study, we did not have the ability to look at other forms of ELF-MF exposure. The exposure assessment resulted in a detailed exposure characterization for the time up to baseline. The majority of the population (66 % of men and 82 % of women in the subcohort) was retired or otherwise had stopped working at baseline. Job information was not available for the 17.3year follow-up after baseline for the 32 % of men and 10 % of women who were still employed at baseline. Therefore, while we had complete job histories for a large part of the population, we could not look at recent (after baseline) occupational exposures. This may be important if ELF-MF works as a promoter, rather than an initiator of cancer. We did perform sensitivity analyses using a 20-year lag to ensure the lag time was identical for all subjects, but this did not change the results. Similarly, results for most cancer types were stable when performing a sensitivity
Cancer Causes Control (2014) 25:203–214
analysis excluding the subjects still working at baseline. Only the effects of ELF-MF on AML were less clear in these analyses. A drawback of many previous studies, as well as our analysis, was the fact that only a few women were employed in high-exposed jobs. This may have limited our ability to determine a possible effect among female workers, where a number of analyses had too few high-exposed women to perform an informative analysis. On the other hand, the low prevalence of breast cancer among men made an analysis of this group not feasible.
Conclusion
213
8.
9.
10.
11.
12. 13.
In this study, we investigated the association between occupational ELF-MF exposure and selected cancer outcomes. For lung, breast, and brain cancer, we found no evidence for an association. This largely concurs with previous studies, except for brain cancer for which the evidence remains mixed. We did observe associations between ELF-MF exposure and follicular lymphoma and acute myeloid leukemia in men, although AML did not show a clear exposure–response relationship. These results indicate that ELF-MF exposure may be related to certain subtypes of hemato-lymphoproliferative malignancies and warrant further investigation. Acknowledgments This work was supported by The Netherlands Organization for Health Research (ZonMW) within the program Electromagnetic Fields and Health Research under Grant Numbers 85200001 and 85800001.
References 1. Johansen C, Raaschou Nielsen O, Olsen JH, Schuz J (2007) Risk for leukaemia and brain and breast cancer among Danish utility workers: a second follow-up. Occup Environ Med 64:782–784 2. Milham S Jr (1985) Mortality in workers exposed to electromagnetic fields. Environ Health Perspect 62:297–300 3. Olsson AC, Gustavsson P, Kromhout H et al (2011) Exposure to diesel motor exhaust and lung cancer risk in a pooled analysis from case-control studies in Europe and Canada. Am J Respir Crit Care Med 183:941–948 4. Savitz DA, Loomis DP (1995) Magnetic field exposure in relation to leukemia and brain cancer mortality among electric utility workers. Am J Epidemiol 141:123–134 5. Deadman JE, Armstrong BG, Theriault G (1996) Exposure to 60-Hz magnetic and electric fields at a Canadian electric utility. Scand J Work Environ Health 22:415–424 6. Kheifets LI, Gilbert ES, Sussman SS et al (1999) Comparative analyses of the studies of magnetic fields and cancer in electric utility workers: studies from France, Canada, and the United States. Occup Environ Med 56:567–574 7. Chen C, Ma X, Zhong M, Yu Z (2010) Extremely low-frequency electromagnetic fields exposure and female breast cancer risk: a
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
meta-analysis based on 24,338 cases and 60,628 controls. Breast Cancer Res Treat 123:569–576 Ahlbom IC, Cardis E, Green A et al (2001) Review of the epidemiologic literature on EMF and Health. Environ Health Perspect 109(Suppl 6):911–933 Erren TC (2001) A meta-analysis of epidemiologic studies of electric and magnetic fields and breast cancer in women and men. Bioelectromagn Suppl 5:S105–S119 Kheifets L, Monroe J, Vergara X, Mezei G, Afifi AA (2008) Occupational electromagnetic fields and leukemia and brain cancer: an update to two meta-analyses. J Occup Environ Med 50:677–688 Lin RS, Dischinger PC, Conde J, Farrell KP (1985) Occupational exposure to electromagnetic fields and the occurrence of brain tumors. An analysis of possible associations. J Occup Med 27:413–419 Wertheimer N, Leeper E (1979) Electrical wiring configurations and childhood cancer. Am J Epidemiol 109:273–284 Feychting M, Ahlbom A (1995) Childhood leukemia and residential exposure to weak extremely low frequency magnetic fields. Environ Health Perspect 103(Suppl 2):59–62 Calvente I, Fernandez MF, Villalba J, Olea N, Nunez MI (2010) Exposure to electromagnetic fields (non-ionizing radiation) and its relationship with childhood leukemia: a systematic review. Sci Total Environ 408:3062–3069 IARC (2002) Non-ionizing radiation, Part 1, Static and extremely low-frequency (ELF) electric and magnetic fields. IARC monographs on the evaluation of carcinogenic risks to humans, volume 80. IARCPress, Lyon, France van den Brandt PA, Goldbohm RA, van ‘t Veer P, Volovics A, Hermus RJ, Sturmans F (1990) A large-scale prospective cohort study on diet and cancer in The Netherlands. J Clin Epidemiol 43:285–295 Koeman T, S M Offermans N, Christopher-de Vries Y, et al (2012) JEMs and incompatible occupational coding systems: effect of manual and automatic recoding of job codes on exposure assignment Ann Occup Hyg Koeman T, Slottje P, Kromhout H, et al (2013) Occupational exposure to extremely low-frequency magnetic fields and cardiovascular disease mortality in a prospective cohort study. Occup Environ Med Bowman JD, Touchstone JA, Yost MG (2007) A populationbased job exposure matrix for power-frequency magnetic fields. J Occup Environ Hyg 4:715–728 Offermans NS, Vermeulen R, Burdorf A et al (2012) Comparison of expert and job-exposure matrix-based retrospective exposure assessment of occupational carcinogens in The Netherlands Cohort study. Occup Environ Med 69:745–751 Peters S, Vermeulen R, Portengen L et al (2011) Modelling of occupational respirable crystalline silica exposure for quantitative exposure assessment in community-based case-control studies. J Environ Monit 13:3262–3268 de Vocht F, Zock JP, Kromhout H et al (2005) Comparison of self-reported occupational exposure with a job exposure matrix in an international community-based study on asthma. Am J Ind Med 47:434–442 Matheson MC, Benke G, Raven J et al (2005) Biological dust exposure in the workplace is a risk factor for chronic obstructive pulmonary disease. Thorax 60:645–651 International Labour Office (1968) International standard classification of occupations: ISCO-68. International Labour Office, Geneva International Labour Office (1990) International standard classification of occupations: ISCO-88. International Labour Office, Geneva
123
214 26. Dumas O, Smit LA, Pin I et al (2011) Do young adults with childhood asthma avoid occupational exposures at first hire? Eur Resp J 37:1043–1049 27. World Health Organization (2007) Extremely low frequency magnetic fields. environmental health criteria 238. WHO Press, Geneva 28. Baris D, Armstrong BG, Deadman J, Theriault G (1996) A mortality study of electrical utility workers in Quebec. Occup Environ Med 53:25–31 29. Floderus B, Stenlund C, Persson T (1999) Occupational magnetic field exposure and site-specific cancer incidence: a Swedish cohort study. Cancer Causes Control 10:323–332 30. Hakansson N, Floderus B, Gustavsson P, Johansen C, Olsen JH (2002) Cancer incidence and magnetic field exposure in industries using resistance welding in Sweden. Occup Environ Med 59:481–486 31. Cocco P, Dosemeci M, Heineman EF (1998) Occupational risk factors for cancer of the central nervous system: a case-control study on death certificates from 24 U.S. states. Am J Ind Med 33:247–255 32. Karipidis KK, Benke G, Sim MR, Yost M, Giles G (2007) Occupational exposure to low frequency magnetic fields and the risk of low grade and high grade glioma. Cancer Causes Control 18:305–313 33. Sorahan T (2012) Cancer incidence in UK electricity generation and transmission workers, 1973–2008. Occup Med 62:496–505 34. Marcilio I, Gouveia N, Pereira Filho ML, Kheifets L (2011) Adult mortality from leukemia, brain cancer, amyotrophic lateral sclerosis and magnetic fields from power lines: a case-control study in Brazil. Rev Bras Epidemiol 14:580–588 35. Johansen C, Olsen JH (1998) Risk of cancer among Danish utility workers–a nationwide cohort study. Am J Epidemiol 147:548–555
123
Cancer Causes Control (2014) 25:203–214 36. Verkasalo PK, Pukkala E, Kaprio J, Heikkila KV, Koskenvuo M (1996) Magnetic fields of high voltage power lines and risk of cancer in Finnish adults: nationwide cohort study. BMJ 313: 1047–1051 37. Gibson TM, Smedby KE, Skibola CF, et al (2012) Smoking, variation in N-acetyltransferase 1 (NAT1) and 2 (NAT2), and risk of non-Hodgkin lymphoma: a pooled analysis within the InterLymph consortium. Cancer Causes Control 38. Vlaanderen J, Lan Q, Kromhout H, Rothman N, Vermeulen R (2011) Occupational benzene exposure and the risk of lymphoma subtypes: a meta-analysis of cohort studies incorporating three study quality dimensions. Environ Health Perspect 119:159–167 39. Huh J (2012) Epidemiologic overview of malignant lymphoma. Korean J Hematol 47:92–104 40. Turner JJ, Morton LM, Linet MS et al (2010) InterLymph hierarchical classification of lymphoid neoplasms for epidemiologic research based on the WHO classification (2008): update and future directions. Blood 116:e90–e98 41. Savitz DA, Dufort V, Armstrong B, Theriault G (1997) Lung cancer in relation to employment in the electrical utility industry and exposure to magnetic fields. Occup Environ Med 54:396–402 42. Armstrong B, Theriault G, Guenel P, Deadman J, Goldberg M, Heroux P (1994) Association between exposure to pulsed electromagnetic fields and cancer in electric utility workers in Quebec, Canada, and France. Am J Epidemiol 140:805–820 43. de Groot P, Munden RF (2012) Lung cancer epidemiology, risk factors, and prevention. Radiol Clin North Am 50:863–876 44. Kheifets L, Bowman JD, Checkoway H et al (2009) Future needs of occupational epidemiology of extremely low frequency electric and magnetic fields: review and recommendations. Occup Environ Med 66:72–80
ORIGINAL PAPER
Occupational extremely low-frequency magnetic field exposure and selected cancer outcomes in a prospective Dutch cohort Tom Koeman • Piet A. van den Brandt • Pauline Slottje • Leo J. Schouten • R. Alexandra Goldbohm Hans Kromhout • Roel Vermeulen
•
Received: 15 July 2013 / Accepted: 6 November 2013 / Published online: 16 November 2013 Ó Springer Science+Business Media Dordrecht 2013
Abstract Purpose To investigate the association between exposure to occupational extremely low-frequency magnetic fields (ELF-MF) and the risk of a priori selected cancer outcomes within the prospective Netherlands Cohort Study. Methods 120,852 men and women aged 55–69 years at time of enrollment in 1986 were followed up (17.3 years) for incident lung, breast and brain cancer, and hematolymphoproliferative malignancies. Information on occupational history and potential confounders such as sex, age, smoking, alcohol use, and attained educational level were collected at baseline through a self-administered questionnaire. Occupational ELF-MF exposure was assigned with a job-exposure matrix. Using a case-cohort approach, associations with cancer incidence were analyzed with Cox regression stratified by sex, using three exposure metrics: (1) ever had a job with low or high exposure to ELF-MF versus background, (2) duration of exposure, and (3) cumulative exposure.
Results None of the exposure metrics showed an effect on incidence for lung, breast, and brain cancer, nor any of the assessed subtypes in men and women. Of the hemato-lymphoproliferative malignancies in men, ever high exposed to ELF-MF showed a significant association with acute myeloid leukemia (AML) [hazard ratio (HR) 2.15; 95 % confidence interval (CI) 1.06–4.35] and follicular lymphoma (FL) (HR 2.78; 95 % CI 1.00–5.77). Cumulative exposure to ELF-MF showed a significant, positive association with FL but not AML among men. Conclusions In this large prospective cohort study, we found some indications of an increased risk of AML and FL among men with occupational ELF-MF exposure. These findings warrant further investigation. Keywords Prospective cohort Breast cancer Extremely low-frequency magnetic fields Lung cancer Brain cancer Leukemia Non-Hodgkin lymphoma
Electronic supplementary material The online version of this article (doi:10.1007/s10552-013-0322-x) contains supplementary material, which is available to authorized users. T. Koeman (&) P. Slottje H. Kromhout R. Vermeulen Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, P.O. Box 80178, 3508 TD Utrecht, The Netherlands e-mail: [email protected] R. Vermeulen e-mail: [email protected]
R. A. Goldbohm Netherlands Organization for Applied Scientific Research (TNO), Wassenaarsweg 56, 2333 AL Leiden, The Netherlands R. Vermeulen Julius Centre for Public Health Sciences and Primary Care, University Medical Centre, P.O. Box 85500, 3508 GA Utrecht, The Netherlands
P. A. van den Brandt L. J. Schouten Department of Epidemiology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, PO Box 616, 6200 MD Maastricht, The Netherlands
123
204
Introduction The possible association between exposure to extremely low-frequency magnetic fields (ELF-MF) and cancer risk has been studied extensively in the past decades [1–6] and has been summarized in several reviews [7–10]. For various reasons, breast and brain cancer and hemato-lymphoproliferative malignancies have received more attention than other types of malignancies. Interest in breast cancer started based on a hypothesized suppression of nighttime melatonin production due to nighttime ELF-MF exposure, which in turn could increase breast cancer risk [8]. Interest in brain cancer grew after Lin et al. [11] reported possible associations between work in electrical jobs and increased brain cancer risk. Hemato-lymphoproliferative malignancies have become a topic of interest since childhood acute lymphoblastic leukemia (ALL) has been found to be consistently associated with environmental ELF-MF exposure [12–14], raising the question whether a similar association exists for adults [8]. In 2002, the international agency for research on cancer (IARC) has classified ELF-MF as possibly carcinogenic to humans (group 2B) [15]. This was mainly based on the above-mentioned association between environmental exposure to ELF-MF and childhood leukemia. To date, epidemiological studies on the possible associations between occupational ELF-MF exposure and cancer in adults have provided less than consistent results despite large occupational cohort studies among high-exposed utility workers [4–6]. Possible reasons for these inconsistencies could be related to methodological issues such as exposure misclassification, selection bias, and incomplete adjustment for relevant confounders. As it stands the question whether ELF-MF exposure is related to cancer risk, in particular breast and brain cancer and hemato-lymphoproliferative malignancies in adults, remains unanswered. We set out to study the possible association between occupational exposure to ELF-MF and selected cancer outcomes: brain and postmenopausal breast cancer, and hemato-lymphoproliferative malignancies. Lung cancer was added to the malignancies of primary interest as a negative control to identify potential methodological shortcomings (e.g., sampling bias, incomplete adjustment of smoking, and socioeconomic status).
Methods The Netherlands Cohort Study on diet and cancer (NLCS) consists of 120,852 subjects (58,279 males and 62,573 females) who were enrolled in 1986. Subjects were aged 55–69 years and living throughout the Netherlands at the time of enrollment [16]. Participants completed a
123
Cancer Causes Control (2014) 25:203–214
questionnaire on occupational history, dietary habits, and other potential risk factors for cancer at baseline. The first page, containing basic information on age, gender, smoking status, and medical history, was machine readable and entered for all subjects. After enrollment, the cohort was followed up for cause-specific mortality and cancer incidence. Following a case-cohort approach, person-years were estimated using a randomly drawn subcohort (n = 5,000). For emerging cases as well as for the subcohort, the remaining pages of the questionnaire were entered manually. Questionnaires were entered blind to case/subcohort status. Subjects who reported having prevalent cancer other than skin cancer at baseline were excluded, leaving 2,336 male and 2,438 female subcohort members. The NLCS was approved by the institutional review boards of the Netherlands Organization for Applied Scientific Research TNO (Zeist) and Maastricht University (Maastricht). Follow-up and case definition At the time of this study, data are available for 17.3 years of follow-up (from September 1986 to December 2003). Cases were obtained by record linkage with the Netherlands Cancer Registry and the Netherlands Pathological Registry. Incident cases of invasive lung, breast, and brain cancer and hemato-lymphoproliferative malignancies and their subtypes were defined according to the third edition of the International Classification of Diseases of Oncology (ICD-O-3; see Table 1). Breast cancer analyses were only performed on women for whom the age at menopause was known at baseline. Exposure assessment The baseline questionnaire contained questions on the occupational history up to enrollment in 1986. Subjects reported whether they ever had a paid job and, if so, supplied the job title, type of job, type and name of industry, and period of employment for up to five jobs held during their lifetime. If more than five jobs were reported, similar jobs were collapsed into a single job, and period of employment was summed. Based on the description, these jobs were originally coded in the Dutch Occupational Classification System (CBS-84) and subsequently translated to the International Standard Classification of Occupations 1988 (ISCO-88) [17]. Men in the subcohort reported 2.1 jobs on average, and women 1.6 jobs. About 6 % of men and 2 % of women in the subcohort reported five jobs or more. Exposure to ELF-MF was assigned using a recently further developed job-exposure matrix (JEM), assigning an ordinal ELF-MF exposure level to each job (i.e., background, low, and high exposure to ELF-MF) [18, 19]. Several other
Cancer Causes Control (2014) 25:203–214
205
Table 1 Cancer incidence case definitions and number of incident cases Cancer typea
Lung cancer
ICD-O-3 location code
Cell type
ICD-O-3 morphology code
C34.0–C34.9
Number of cases Men
Women
2,107
355
Large cell carcinomas
8012
350
61
Small cell carcinomas Squamous cell carcinomas
8040–8045 8050–8078
341 825
70 94
Adenocarcinomas Breast cancerb
8140–8147 C50.0–C50.9
453
104
NA
2,077 1,379
Ductal
8500–8508
NA
Lobular
8520–8521
NA
378
NA
815
ER? Brain cancer
C71
Astrocytic glioma Hemopoietic and lymphatic malignancies
9384, 9400–9401, 9410–9411, 9420–9421, 9424, 9440–9442, 9481
160
73
116
55
NA
9590–9999
Non-Hodgkin Lymphoma
NA
761
467
B-cell lineage
B-cell
9670–9671, 9673, 9675, 9678–9680, 9684, 9687, 9689, 9699, 9700–9702, 9705, 9708–9709, 9714–9719, 9727–9729, 9731–9734, 9748, 9760–9762, 9764, 9820, 9823, 9826–9827, 9831–9837, 9940 9590–9591, 9670–9671, 9673, 9675, 9678–9680, 9684, 9687, 9689, 9699, 9727–9729, 9731–9734, 9760–9762, 9764, 9823, 9826, 9832–9833, 9835–9836, 9940
708
440
Chronic lymphocytic leukemia
B-cell
9670, 9823
147
73
Diffuse B-cell lymphoma Follicular lymphoma
B-cell B-cell
9678–9680, 9684 9690–9691, 9695, 9698
160 49
87 45
Multiple myeloma
B-cell
150
9731–9734
192
Leukemia
9800–9948
155
79
Myeloid leukemia
9840, 9860–9931, 9945
148
76
Acute myeloid leukemia
9840, 9861–9862, 9864–9874, 9891–9931
108
55
NA not applicable a
Analyses performed on those cancer types with at least five ever high-exposed cases
b
Due to the low number of cases, breast cancer analyses were performed for women with information on age at menopause only
occupational exposures were explored as potential confounders: Exposure to asbestos, crystalline silica, diesel motor exhaust, chromium, nickel, and polycyclic aromatic hydrocarbons was assigned to each job through linkage with the DOMJEM [20, 21]. Exposure to total solvents in general and aromatic solvents in particular was assigned to each job through the ALOHA JEM [17, 22, 23] Both JEMs assign ordinal exposure levels (i.e., no, low and high exposure) to job codes (respectively, ISCO-68 [24] and ISCO-88 [25]). Subjects who indicated that they never had a paid job (housewives or other) were assigned to background exposure. ELF-MF exposure was analyzed using the following measures: (1) ever had a job with at maximum a background, a low or a high occupational ELF-MF exposure
(hereafter referred to as only low or ever high exposure); (2) duration in years of exposure to ELF-MF above background; and (3) cumulative exposure to occupational ELFMF. Cumulative exposure was calculated by assigning arbitrary weights to the exposure ratings reflecting the multiplicative nature of the exposure distribution (i.e., background 0, low 1, high 4). These weights per job were multiplied by duration of that job and summed over the entire job history. For potential confounding by chemical exposures as assessed by DOMJEM and ALOHA, cumulative exposure was calculated in the same way. Statistical analyses Cox proportional hazards models with age as the time axis were used to investigate the relationship between
123
206
occupational exposure to ELF-MF and cancer incidence. Person-years were estimated through the subcohort. Censoring occurred at the date of cancer diagnosis, death, or end of follow-up, whichever occurred first. Risk analyses were stratified by sex and performed only for the different cancer outcomes and their morphological subtypes if at least five cases ever had a job with a high exposure to ELFMF, and 95 % confidence intervals (CI) were calculated using the robust estimator of variance to account for the additional variance introduced by the case-cohort design of the study. Models were analyzed using STATA version 12.1 (Statacorp LP, College Station, Texas, USA). Ever had a job with background, only low or ever high exposure to ELF-MF was analyzed categorically, using background exposure as the reference category. Exposure duration was analyzed as a continuous variable. Cumulative exposure was analyzed categorically, using sex-specific cut points dividing exposure between background and tertiles of above background exposure. Trends for cumulative exposure were calculated over the medians of each category. First, a univariate analysis of ever had a job exposed to ELF-MF was performed. Second, each potential confounder was tested in a bivariate model. Covariates that lead to a 10 % change in the risk estimate of ever ELF-MF exposure in these analyses were subsequently evaluated in a multivariate model, and a backward, stepwise regression was performed retaining the ever ELF-MF exposure variable. Covariates with a p value \0.05 were retained in the final confounder model. This confounder model was used for all the adjusted analyses of all ELF-MF exposure metrics. The following covariates were considered for all cancer outcomes: smoking (current vs. former and ex-smokers, average number of cigarettes smoked daily, number of years smoking cigarettes), passive smoking by the partner (current, former, or nonsmoker), level of education as an indicator of socialeconomic status (primary, lower, secondary and medium, and higher vocational), body mass index (in kg/m2), alcohol consumption (g/day), vegetable, legume, fruit, fish and seafood, and meat consumption (each in g/day), and total energy intake (kcal/day). Specific occupational exposures were considered as confounders depending on the cancer outcome. For lung cancer analyses, potential confounding by cumulative occupational exposures to asbestos, silica, diesel motor exhaust, chromium, nickel, and polycyclic aromatic hydrocarbons was tested categorically (three tertiles of exposed vs. no exposure). Cumulative occupational exposure to total solvents and aromatic solvents was tested as a potential confounder in the analyses for hemato-lymphoproliferative malignancies [26]. In the breast cancer analyses, contraceptive use (never, ever), hormone replacement therapy (yes, no, don’t know),
123
Cancer Causes Control (2014) 25:203–214
age at menarche, age at menopause, and parity and age at first birth (nulliparous, 1–2 children, first child before 25; 1–2 children, first child after 25; three or more children, first child before 25; three or more children, first child after 25) were assessed for potential confounding. Lag time and sensitivity analyses All endpoints were analyzed with a 0- and 20-year lag. The 20-year lag was implemented to ensure a comparable exposure lag for all subjects independent of the end of the follow-up (max 17.3 years) by disregarding exposure for the 20 years before end of the follow-up (i.e., for subjects censored in 2003, job history up to 1983 was used, while for subjects censored in 1993, job history up to 1973 was used to determine the exposure metrics). Furthermore, three types of sensitivity analyses were performed. First, all subjects with missing exposure levels due to a job history that could not reliably be coded (*12 % of the cohort) were assigned to the background exposure group, whereas in the main analysis, these were removed from the analyses. Second, an analysis was performed where only subjects who ever had a paid job at baseline were included, i.e., housewives and unemployed were omitted from the analyses. Third, subjects still employed at baseline (32 % of men and 10 % of women) were excluded from the analysis.
Results The number of cases for the main cancer outcomes and their subtypes are shown in Table 1. For both cases and the subcohort, approximately 8 % of men and 1 % of women ever had a job with high occupational exposure to ELFMF. The remaining subjects were approximately evenly distributed between background and low exposure (see Table 2). The distribution of potential confounding covariates used in the analyses by cumulative exposure categories is shown in supplemental Table 1. Breast cancer in postmenopausal women Breast cancer showed no association with ever had a job with low or high ELF-MF exposure versus background exposure (adjusted HR only low: 1.07, 95 % CI 0.94–1.23; adjusted HR ever high: 1.24, 95 % CI 0.59–2.58). Duration of exposure and cumulative exposure to ELF-MF showed no monotonic association with breast cancer incidence (Table 3). Results did not change when the analyses were repeated for major subtypes of breast cancer (Ductal, estrogen receptor status; see supplemental Table 2).
Cancer Causes Control (2014) 25:203–214
207
Table 2 Distribution of occupational extremely low-frequency magnetic field exposure for subcohorta and cases Subcohorta
Lung cancer cases
Breast cancer cases
Brain cancer cases
Non-hodgkin lymphoma
Leukemias
n
%
n
%
n
%
n
%
n
%
n
%
1,027 930
48.3 43.7
1,221 1,093
46.9 42.0
NA NA
NA NA
74 69
46.3 43.1
359 352
46.3 45.4
65 74
41.9 47.7
169
7.9
288
11.1
NA
NA
17
10.6
64
8.3
16
10.3
Men Ever ELF exposed Background Low High Cumulative ELF exposure 1,027
48.3
1,221
46.9
NA
NA
74
46.3
359
46.3
65
41.9
[ 0–21.5 unit–years
Background
361
17.0
429
16.5
NA
NA
29
18.1
141
18.2
29
18.7
[ 21.5–38.1 unit-years
354
16.7
459
17.6
NA
NA
29
18.1
137
17.7
31
20.0
[ 38.1–204 unit-years
384
18.1
493
18.9
NA
NA
28
17.5
138
17.8
30
19.4
Women Ever ELF exposed Background
1,023
49.9
198
47.9
1,198
48.8
44
52.4
260
53.2
39
49.4
Low
1,011
49.3
209
50.6
1,238
50.4
40
47.6
225
46.0
40
50.6
High
16
0.8
6
1.5
19
0.8
0
0.0
4
0.8
0
0.0
Cumulative ELF exposure 1,023
49.9
198
47.9
1,198
48.8
44
52.4
260
53.2
39
49.4
[ 0–6.5 unit-years [ 6.5–11 unit-years
Background
339 341
16.5 16.6
73 67
17.7 16.2
478 368
19.5 15.0
13 16
15.5 19.0
82 77
16.8 15.7
9 19
11.4 24.1
[ 11–136 unit-years
347
16.9
75
18.2
411
16.7
11
13.1
70
14.3
12
15.2
NA not available a
Total number of people in the subcohort (n = 5,000)
Brain cancer There was no significant association between occupational ELF-MF exposure and brain cancer for any of the analyzed exposure metrics among men or women, although the risk for ever had a job with high exposure versus background exposure in men was elevated (HR 1.45, 95 % CI 0.83–2.52; see Table 3). Astrocytic gliomas also showed no association with ELF-MF exposure, with HR’s closer to unity compared to the analysis of total brain cancer (ever high exposure vs. background exposure in men HR 0.77, 95 % CI 0.34–1.71; see supplemental Table 3). Hemato-lymphoproliferative malignancies Among women, we did not observe any meaningful associations between ELF-MF and hemato-lymphoproliferative malignancies. In men, leukemia showed a nonsignificant association with ELF-MF exposure with an adjusted HR of 1.34 (95 % CI 0.94–1.92) and 1.66 (95 % CI 0.92–3.00) for only low and ever high exposure versus background, respectively, but no monotonic relationship was seen with cumulative ELF-MF exposure (see Table 3). This trend
seemed to be mainly driven by AML (Table 4, in men adjusted HRs ever low: 1.84, 95 % CI 1.18–2.85; ever high: 2.15, 95 % CI 1.06–4.35 compared to background). Associations between cumulative exposure and AML were also significantly increased for men, but the association was not monotonic, and there was no significant trend (HR first tertile of exposed: 1.98, 95 % CI 1.14–3.43; HR second tertile of exposed: 1.96, 95 % CI 1.12–3.43; HR third tertile of exposed: 1.73, 95 % CI 1.00–3.01; p for trend: 0.68; Table 4). Correcting the AML analyses for aromatic solvents did not change the HRs. Ever holding a job with low exposure went from 1.84 to 1.89 (95 % CI 1.19–3.02), and ever high exposure from 2.15 to 2.13 (95 % CI 0.97–4.65). Restricting the analyses to subjects who were nonexposed to aromatic solvents did not change the overall results. Similarly, adding total solvents to the model did not lead to meaningful differences in HRs (results not shown). Non-Hodgkin lymphoma (NHL) showed no indications for an increased risk for ever versus never holding a job with exposure above background levels in men (only lowexposed HR 1.07, 95 % CI 0.90–1.27; ever high-exposed HR 1.19, 95 % CI 0.86–1.64). Similarly, no associations
123
208
Cancer Causes Control (2014) 25:203–214
Table 3 Association between occupational extremely low-frequency magnetic fields exposure and lung, breast, brain cancer, and hematolymphoproliferative malignancies, stratified by sex Men
Women Cases
Personyears in sub cohort
Adjusted HR
95 % Confidence Interval
Cases
Personyears in sub cohort
Adjusted HR
95 % Confidence Interval
Breast cancerc Ever exposed
Duration of exposure Cumulative exposure
Background
1,068
13,360
Low
991
12,612
1.07
Reference
High
265
2,187
1.24
(0.59–2.58)
HR/10 years
1,252
15,877
0.96
(0.85–1.08)
Background
(0.94–1.23)
1,069
13,373
1st tertileb
393
4,828
1.28
Reference (1.06–1.56)
2nd tertileb
415
4,865
0.92
(0.75–1.12)
3rd tertileb
447
5,092
1.03
2,324
28,158
(0.85–1.25) p = 0.88
Brain cancer Ever exposed
Background
74
14,644
Reference
44
15,962
Low
69
13,474
1.01
(0.72–1.42)
40
15,892
High
17
2,335
1.45
(0.83–2.52)
0
253
Duration of exposure
HR/10 years
86
15,809
0.94
(0.81–1.09)
40
16,145
Cumulative exposure
Background
74
14,658
Reference
44
16,031
1st tertileb
29
5,183
1.11
(0.71–1.73)
13
5,242
0.92
(0.49–1.74)
2nd tertileb
29
5,126
1.14
(0.72 –1.78)
16
5,432
1.10
(0.61–1.98)
0.99
0.73
3rd tertileb
28
5,486
Test for trend
160
30,452
Background
359
14,576
Low
352
13,418
1.07
High
64
2,296
HR/10 years Background
416 360
1st tertileb
Reference 0.92
(0.60–1.43)
NC
NC
0.72
(0.50–1.05) Reference
(0.63–1.56)
11
5,402
p = 0.70
84
32,106
(0.38 - 1.44)
Reference
260
15,901
(0.90–1.27)
225
15,875
0.89
(0.73–1.08)
1.19
(0.86–1.64)
4
253
0.89
(0.29–2.76)
15,714 14,590
0.98
(0.91–1.06) Reference
229 260
16,128 15,971
1.00
(0.84–1.18) Reference
p = 0.27
Non-hodgkin lymphomad Ever exposed
Exposure duration Cumulative exposure
Reference
140
5,165
1.09
(0.87–1.38)
82
5,234
1.00
(0.75–1.32)
b
137
5,118
1.17
(0.92–1.48)
77
5,424
0.91
(0.68–1.21)
3rd tertileb
138
5,418
0.99
(0.79–1.26)
70
5,401
0.79
Test for trend
775
30,290
p = 0.62
489
32,029
p = 0.06
Background
51
13,197
Reference
39
14,899
Reference
Low
68
12,302
1.34
(0.94–1.92)
38
15,019
High
14
2,108
1.66
(0.92–3.00)
0
171
Exposure duration
HR/10 years
90
15,714
1.01
(0.87–1.17)
40
16,128
Cumulative exposure
Background
51
13,211
Reference
39
14,951
1st tertileb
2nd tertile
(0.59–1.06)
Leukemiae Ever exposed
NC
0.98
(0.70–1.37) Reference
28
4,783
1.35
(0.84 –2.16)
9
4,951
0.70
(0.33–1.48)
27
4,754
1.55
(0.98–2.46)
18
5,120
1.46
(0.83–2.56)
3rd tertileb
27
4,859
1.29
(0.81–2.05)
11
5,067
0.81
133
27,606
p = 0.15
77
30,089
Test for trend
123
(0.63–1.58)
NC
b
2nd tertile
Lung cancera Ever exposed
0.99
Background
(0.41–1.61) p = 0.74
1,068
13,360
Reference
190
15,407
Low
991
12,612
0.95
(0.81–1.11)
198
15,469
0.98
Reference (0.75–1.26)
High
265
2,187
1.33
(1.01–1.75)
6
220
1.19
(0.36–3.89)
Cancer Causes Control (2014) 25:203–214
209
Table 3 continued Men
Women Cases
Personyears in sub cohort
Adjusted HR
95 % Confidence Interval
Cases
Personyears in sub cohort
Adjusted HR
1.00
(0.92–1.08) Reference
215
16,181
0.99
190
15,476
95 % Confidence Interval
Duration of exposure
HR/10 years
1,382
15,753
Cumulative exposure
Background
1,069
13,373
1st tertileb
393
4,828
0.96
(0.78–1.19)
73
5,024
1.16
(0.81 –1.65)
2nd tertileb
415
4,865
1.05
(0.86–1.29)
61
5,344
0.97
(0.68–1.39)
3rd tertileb
447
5,092
1.00
(0.82–1.23)
70
5,253
0.86
2,324
28,158
394
31,096
Test for trend
p = 0.95
(0.81–1.21) Reference
(0.61–1.23) p = 0.23
HR hazard ratio, NC not calculated a
Corrected for smoking (current vs. noncurrent), number of cigarettes smoked per day, years of smoking cigarettes, attained level of education (primary vocational, lower vocational, secondary and medium vocational, higher vocational), and occupational asbestos exposure b Sex specific cut points; Men: first tertile: [0–21.5 unit-years, second tertile: [21.5–38.1 unit-years, third tertile: [38.1–204 unit-years; Women: first tertile: [0–6.5 unit-years, second tertile: [6.5–11 unit-years, third tertile: [11–136 unit-years c
Corrected for alcohol intake, body mass index, fruit intake, age at menarche, age at menopause, parity, age at first child, number of children, benign breast growth, and family history of breast cancer
d
Corrected for family history of hematological malignancies
e
Corrected for smoking status (current vs. noncurrent) and body mass index
were found for NHL with duration of exposure or cumulative exposure to ELF-MF (Table 3). The same held true for most subtypes of NHL (see supplemental Table 4) with the exception of follicular lymphoma (FL). For men, FL showed a positive trend in the ever versus never ELF-MF exposure (HR for ever low exposed 1.22, 95 % CI 0.66–2.24; HR for ever high exposed 2.40, 95 % CI 1.00–5.77), and in the analysis of cumulative exposure (HR first tertile of exposed: 1.14, 95 % CI 0.50–2.26; HR second tertile of exposed 1.20, 95 % CI 0.52–2.78; HR third tertile of exposed: 1.77, 95 % CI 0.87–3.60; p for trend: 0.03; Table 4). However, duration of occupational ELFMF exposure did not show a significant association with FL. Lung cancer Lung cancer incidence showed a borderline significant association between ever high exposure to ELF-MF and lung cancer incidence in men (HR 1.29, 95 % CI 0.99–1.70) and a nonsignificant association in women (HR 1.23, 95 % CI 0.37–4.07), see Table 3. Similar results were found with respect to the different subtypes of lung cancer. An association was found between ever high exposure to ELF-MF and large cell carcinoma and adenocarcinoma incidence in men (HR 1.70, 95 % CI 1.11–2.62 and HR 1.57, 95 % CI 1.08–2.29, respectively). For women, this association was observed for large cell carcinoma (HR 3.50, 95 % CI 0.60–20.40) but not for adenocarcinoma (HR 0.93, 95 % CI 0.11–7.84). Duration and cumulative
ELF-MF exposure did not show an association with lung cancer or any of its subtypes (see supplemental Table 5). 20-year lag and sensitivity analyses Repeating the analyses using a 20-year lag only slightly changed the risk estimates (results not shown). Similarly, defining all subjects with unknown job history as nonexposed, or restricting the analysis to only subjects who had a paid job, did not materially change the risk estimates (results not shown). For most cancer types, the analyses excluding subjects still employed at baseline only lead to negligible changes in HRs. However, excluding subjects still employed at baseline did influence the results for AML and FL in men. The trend observed for ever holding a job with a low or high exposure versus background disappeared in this analysis, although HRs were still increased (HR for ever holding a job with low exposure versus background changed from 1.84 to 2.27 (1.34–3.87), HR for ever high exposure versus background changed from 2.15 to 1.86 (0.73–4.72)). For FL, HRs increased, the observed positive trend remained (HR third tertile of cumulative exposure changed from 1.97 to 2.67 (1.21–5.91), and p for trend changed from 0.03 to 0.01).
Discussion The objective of this study was to determine possible associations between occupational exposure to ELF-MF
123
210
Cancer Causes Control (2014) 25:203–214
Table 4 Association between occupational extremely low-frequency magnetic fields exposure and incidence of subtypes of non-Hodgkin lymphoma and leukemia, stratified by sex Men
Women Cases
Personyears in sub cohort
Adjusted HR
95 % Confidence Interval
Cases
Personyears in sub cohort
Reference
23
15,901
Adjusted HR
95 % Confidence interval
Non-Hodgkin lymphoma: Follicular lymphomaa Ever exposed
Duration of exposure Cumulative exposure
Background
20
14,576
Low
22
13,418
1.23
(0.67–2.28)
21
15,875
0.94
High
7
2,296
2.78
(1.20–6.44)
1
253
2.68
(0.33–21.70)
HR/10 years
30
15,714
1.04
(0.81–1.35)
22
16,128
0.96
(0.59–1.58)
Background
20
14,590
Reference
23
15,971
first tertileb
8
5,165
1.14
(0.50–2.61)
6
5,234
0.82
(0.33–2.06)
second tertileb
Reference (0.51–1.71)
Reference
8
5,118
1.23
(0.54–2.80)
8
5,424
1.07
(0.47–2.43)
third tertileb
13
5,418
1.97
(0.98–3.95)
8
5,401
1.02
(0.45–2.31)
Test for trend
49
30,290
45
32,029
p = 0.03
p = 0.70
Non-hodgkin lymphoma: Chronic lymphocytic leukemia/Small lymphocytic lymphomaa Ever exposed
Background
68
14,576
Reference
41
15,901
Low
70
13,418
1.11
(0.78–1.58)
33
15,875
0.83
(0.52–1.32)
High
10
2,296
0.98
(0.49–1.97)
1
253
1.48
(0.19–11.75)
Duration of exposure
HR/10 years
80
15,714
0.94
(0.81–1.08)
34
16,128
0.91
Cumulative exposure
Background
68
14,590
Reference
41
15,971
first tertileb
30
5,165
1.24
(0.79–1.93)
12
5,234
0.93
(0.48–1.79)
second tertileb
26
5,118
1.17
(0.72–1.89)
11
5,424
0.82
(0.42–1.62)
third tertileb
24
5,418
0.91
(0.56–1.48)
11
5,401
0.79
148
30,290
p = 0.63
75
32,029
Test for trend
Reference
(0.62–1.33) Reference
(0.40–1.55) p = 0.68
Myeloid leukemiasb Ever exposed
Background
49
13,197
Reference
38
14,899
Low
64
12,302
1.31
(0.91–1.89)
36
15,019
Reference 0.97
(0.60–1.55)
NC
NC
High
13
2,108
1.61
(0.87–2.96)
0
171
Duration of exposure
HR/10 years
85
15,714
1.03
(0.89–1.20)
38
16,128
Cumulative exposure
Background
49
13,211
Reference
38
14,951
first tertileb
26
4,783
1.30
(0.80–2.11)
9
4,951
0.72
(0.34–1.52)
second tertileb
25
4,754
1.50
(0.94–2.42)
17
5,120
1.41
(0.79–2.51)
third tertileb
26
4,859
1.28
(0.80–2.06)
10
5,067
0.75
126
27,606
p = 0.18
74
30,089
Test for trend
0.98
(0.69–1.39) Reference
(0.37–1.55) p = 0.57
Acute myeloid leukemiac Ever exposed
Background
30
13,197
Reference
28
14,899
Low
52
12,302
1.84
(1.18–2.85)
25
15,019
High
10
2,108
2.15
(1.06–4.35)
0
171
Exposure duration
HR/10 years
69
15,714
0.97
(0.82–1.15)
27
16,128
Cumulative exposure
Background
30
13,211
Reference
28
14,951
first tertileb
23
4,783
1.98
(1.14–3.43)
5
4,951
0.53
(0.20–1.40)
second tertileb
19
4,754
1.96
(1.12–3.43)
14
5,120
1.57
(0.83–2.96)
1.73
6
5,067
0.61
53
30,089
b
third tertile
20
4,859
Test for trend
92
27,606
(1.00–3.01) p = 0.07
Reference 0.91
(0.53–1.57)
NC
NC
0.92
(0.61–1.38) Reference
(0.25–1.50) p = 0.40
HR hazard ratio, NC not calculated a
Corrected for family history of hematological malignancies
b
Sex specific cut points; Men: first tertile: [0–21.5 unit-years, 2nd
tertile: [21.5–38.1 unit-years, third tertile: [38.1–204 unit-years; Women: first tertile: [0–6.5 unit-years, second tertile: [6.5–11 unit-years, third tertile: [11–136 unit-years c
Corrected for family history of smoking status (current vs. noncurrent), number of cigarettes smoked and body mass index
123
Cancer Causes Control (2014) 25:203–214
and cancer incidence for selected cancer outcomes in a large population-based cohort. No clear evidence of such associations was observed for lung, breast, and brain cancer or their subtypes. Some indications of increased risks with occupational ELF-MF exposure were found for hematolymphoproliferative malignancies among men. Leukemia showed a possible association with occupational ELF-MF exposure which seemed to be driven mainly by the underlying association with AML. Furthermore, the NHLsubtype follicular lymphoma showed positive associations with ELF-MF exposure. Breast cancer Breast cancer mortality and incidence in relationship with ELF-MF exposure have been extensively studied in the past decades. A meta-analysis published in 2000 by Erren [9] estimated a small, increased risk for men as well as women. Among studies on occupational exposure to ELFMF using a job-exposure matrix, this risk was detected for women (pooled RR 1.09, 95 % CI 1.05–1.14), but not for men (pooled RR 1.00, 95 % CI 0.73–1.39). An IARC Working Group concluded, based on the evidence available at that time, that these studies were of insufficient quality to draw conclusions on the effects of ELF-MF on breast cancer [15]. Subsequently, the World Health Organization and a recent meta-analysis of case–control studies published between 2000 and 2009 concluded that there was no overall effect of ELF-MF on breast cancer incidence among women [7, 27]. This latter meta-analysis also showed no effect in subanalyses performed on studies on residential or occupational exposure, or on studies looking at receptor status of breast cancer. An update on a cohort study on Danish utility workers published in 2007 also found no increase in breast cancer mortality risk among women in the cohort (results for men were not shown)[1]. Our study, with more stringent correction for possible confounders such as age at menarche, age at menopause, pill use, HRT use and nulliparity, showed no association between occupational exposure to ELF-MF and breast cancer incidence nor estrogen receptor–positive breast cancer among postmenopausal women. This is in line with the more recent publications pointing toward an absence of an association between ELF-MF and breast cancer risk among women. Brain cancer IARC [15] and WHO [27] both concluded that the evidence for an effect of ELF-MF on brain cancer was inadequate. In a recent meta-analysis on brain cancer, Kheifets et al. [10] found a small, significant, positive association with occupational ELF-MF exposure (pooled RR 1.14,
211
95 % CI 1.07–1.22), which seemed to be driven by gliomas (pooled RR 1.18, 95 % CI 1.1–1.26). However, the studies on brain cancer and ELF-MF exposure were heterogeneous, with some studies reporting a small effect [28–30], while others report no effect [1, 31, 32]. Two studies looking at brain cancer and ELF-MF exposure were performed after the last meta-analysis, one industrial cohort study [33] and one population-based case–control study on environmental exposure to ELF-MF [34]. Both studies found no effects. We found no association between occupational ELF-MF and brain cancer incidence or astrocytic gliomas, offering no support for the hypothesis that ELFMF is associated with brain cancer incidence in general and glioma incidence in particular. Hemato-lymphoproliferative malignancies The association between ELF-MF and leukemia has been a topic of interest in both environmental and occupational epidemiology. The relationship was first observed for acute lymphoblastic leukemia (ALL) in children living close to a power line with a wiring configuration suspected to lead to high ELF-MF [12]. Subsequent studies that modeled and/or measured ELF-MF exposure have been remarkably consistent in detecting an association between exposure to ELF-MF from power lines and childhood ALL. However, there are concerns that the positive associations might be due to selection bias. On the other hand, if ELF-MF exposure and childhood leukemia would be causally related, exposure misclassification would likely attenuate the results. Lastly, the possibility of uncontrolled confounding is also always an issue in these types of studies, which might influence the observed risk in both directions [14, 15, 27]. These findings in children have spurred research into adult leukemia. Here, results have been less consistent. IARC [15] and WHO [27] both judged the evidence for an effect of ELF-MF on leukemia as inadequate. In a metaanalysis on adult leukemia in occupational epidemiological studies published in 2008, Kheifets et al. [10] observed a positive association between occupational ELF-MF exposure and leukemia, specifically AML and chronic lymphocytic leukemia (CLL). A case–control study performed in Brazil published after the meta-analyses observed increased leukemia mortality for subjects living close to power lines or living in an area with increased estimated magnetic fields [34]. A cohort study among electricity generation and transmission workers in the UK, also published after the meta-analysis, found no increased rate of leukemia for these workers when compared to national rates [33]. The authors of this study did observe an increased trend for workers with a longer work history in the utility industry and a decreasing trend for the time since workers ended working lending some support to a possible
123
212
association between ELF-MF exposure and leukemia risk. In our study, we observed an increased incidence of leukemia for men who ever had a job with a low or high exposure to ELF-MF compared to background exposure which seemed to be attributable to an increased incidence for AML. Although the analysis of cumulative exposure showed increased incidences for AML in all categories of ELF-MF exposure in men, we observed no significant trend in this increase. Furthermore, a trend was not observed when excluding the subjects who still worked at baseline. Therefore, our study only provides tentative support for the hypothesis that exposure to ELF-MF leads to an increased incidence of leukemia, and specifically AML. We also found a weak indication of a possible association between ELF-MF (ever/never and cumulative exposure) and FL. To our knowledge, estimates of such an association specifically for FL have not been reported to date. This may be because the numbers of cases of follicular lymphoma are generally low, or due to the fact that the overarching group of NHL generally showed no clear association with occupational exposure to ELF-MF and subtypes of NHL were not studied [1, 35, 36]. NHL is a diverse group of lymphopoietic malignancies that may have differing etiologies, and an analysis of this overarching group may miss associations of occupational or lifestyle exposures with particular NHL-subtypes [37–39]. A complicating factor in interpreting the existing literature is that the classification of lymphoma has undergone many changes over the years [40]. Further study into exposure to ELF-MF and NHL-subtypes is therefore warranted. A concern in previous studies has been the possibility of residual confounding [10]. We had the possibility of exploring and accounting for a large number of potential behavioral and demographic confounders. Very few of these showed a correlation with ELF-MF exposure or an association with NHL or leukemia. Furthermore, through the use of additional JEMs addressing chemical and biological exposures, the effects of other occupational exposures could be studied, most importantly occupational exposure to (aromatic) solvents. These results showed only a marginal change in risk estimates without changing the overall pattern. Lung cancer Our results indicate that there is probably no association between lung cancer incidence and occupational ELF-MF exposure, which is consistent with the literature [35, 41]. A single study among French–Canadian electrical workers did show a possible association between lung cancer and exposure to pulsed electromagnetic fields (PEMFs) [42], but this result was not replicated, and the methods by which the PEMFs were measured have been questioned [41].
123
Cancer Causes Control (2014) 25:203–214
We included lung cancer as a negative control to study the possible effect of methodological shortcomings such as unmeasured confounding. Unexpectedly, we found a borderline significant association between ever a job with high ELF-MF exposure and lung cancer incidence, in particular large cell carcinoma (men and women) and adenocarcinoma (men). We did not observe a significant association between duration of exposure or cumulative occupational ELF-MF exposure and lung cancer incidence. These observations may indicate residual confounding by smoking for the ever exposed analyses, but not for the analyses of duration and cumulative exposure. However, the associations were strongest for the two subtypes of cancer that are thought to have the weakest association with smoking [43]. Therefore, while confounding by smoking is unlikely as a cause for this result, residual confounding by other factors cannot be ruled out and the results of the analyses of ever had a job with a high exposure to ELF-MF should be interpreted with caution. ELF-MF exposure assessment Since the mechanism through which ELF-MF would cause cancer is unknown, it remains unclear what the most relevant measure of exposure would be. In this study, we obtained extensive occupational histories up to baseline reported by the participants and applied a semi-quantitative ELF-JEM incorporating intensity and probability of exposure to assess occupational exposure to ELF-MF[18]. Based on this, we calculated different measures of exposure to ELF-MF. While cumulative exposure is the most commonly used measure of exposure in epidemiological studies on the effects of ELF-MF, other measures have been proposed (e.g., intermittency of electromagnetic fields or percentage of time above a threshold [44], or other exposures such as pulsed electromagnetic fields [42]). In our study, we did not have the ability to look at other forms of ELF-MF exposure. The exposure assessment resulted in a detailed exposure characterization for the time up to baseline. The majority of the population (66 % of men and 82 % of women in the subcohort) was retired or otherwise had stopped working at baseline. Job information was not available for the 17.3year follow-up after baseline for the 32 % of men and 10 % of women who were still employed at baseline. Therefore, while we had complete job histories for a large part of the population, we could not look at recent (after baseline) occupational exposures. This may be important if ELF-MF works as a promoter, rather than an initiator of cancer. We did perform sensitivity analyses using a 20-year lag to ensure the lag time was identical for all subjects, but this did not change the results. Similarly, results for most cancer types were stable when performing a sensitivity
Cancer Causes Control (2014) 25:203–214
analysis excluding the subjects still working at baseline. Only the effects of ELF-MF on AML were less clear in these analyses. A drawback of many previous studies, as well as our analysis, was the fact that only a few women were employed in high-exposed jobs. This may have limited our ability to determine a possible effect among female workers, where a number of analyses had too few high-exposed women to perform an informative analysis. On the other hand, the low prevalence of breast cancer among men made an analysis of this group not feasible.
Conclusion
213
8.
9.
10.
11.
12. 13.
In this study, we investigated the association between occupational ELF-MF exposure and selected cancer outcomes. For lung, breast, and brain cancer, we found no evidence for an association. This largely concurs with previous studies, except for brain cancer for which the evidence remains mixed. We did observe associations between ELF-MF exposure and follicular lymphoma and acute myeloid leukemia in men, although AML did not show a clear exposure–response relationship. These results indicate that ELF-MF exposure may be related to certain subtypes of hemato-lymphoproliferative malignancies and warrant further investigation. Acknowledgments This work was supported by The Netherlands Organization for Health Research (ZonMW) within the program Electromagnetic Fields and Health Research under Grant Numbers 85200001 and 85800001.
References 1. Johansen C, Raaschou Nielsen O, Olsen JH, Schuz J (2007) Risk for leukaemia and brain and breast cancer among Danish utility workers: a second follow-up. Occup Environ Med 64:782–784 2. Milham S Jr (1985) Mortality in workers exposed to electromagnetic fields. Environ Health Perspect 62:297–300 3. Olsson AC, Gustavsson P, Kromhout H et al (2011) Exposure to diesel motor exhaust and lung cancer risk in a pooled analysis from case-control studies in Europe and Canada. Am J Respir Crit Care Med 183:941–948 4. Savitz DA, Loomis DP (1995) Magnetic field exposure in relation to leukemia and brain cancer mortality among electric utility workers. Am J Epidemiol 141:123–134 5. Deadman JE, Armstrong BG, Theriault G (1996) Exposure to 60-Hz magnetic and electric fields at a Canadian electric utility. Scand J Work Environ Health 22:415–424 6. Kheifets LI, Gilbert ES, Sussman SS et al (1999) Comparative analyses of the studies of magnetic fields and cancer in electric utility workers: studies from France, Canada, and the United States. Occup Environ Med 56:567–574 7. Chen C, Ma X, Zhong M, Yu Z (2010) Extremely low-frequency electromagnetic fields exposure and female breast cancer risk: a
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
meta-analysis based on 24,338 cases and 60,628 controls. Breast Cancer Res Treat 123:569–576 Ahlbom IC, Cardis E, Green A et al (2001) Review of the epidemiologic literature on EMF and Health. Environ Health Perspect 109(Suppl 6):911–933 Erren TC (2001) A meta-analysis of epidemiologic studies of electric and magnetic fields and breast cancer in women and men. Bioelectromagn Suppl 5:S105–S119 Kheifets L, Monroe J, Vergara X, Mezei G, Afifi AA (2008) Occupational electromagnetic fields and leukemia and brain cancer: an update to two meta-analyses. J Occup Environ Med 50:677–688 Lin RS, Dischinger PC, Conde J, Farrell KP (1985) Occupational exposure to electromagnetic fields and the occurrence of brain tumors. An analysis of possible associations. J Occup Med 27:413–419 Wertheimer N, Leeper E (1979) Electrical wiring configurations and childhood cancer. Am J Epidemiol 109:273–284 Feychting M, Ahlbom A (1995) Childhood leukemia and residential exposure to weak extremely low frequency magnetic fields. Environ Health Perspect 103(Suppl 2):59–62 Calvente I, Fernandez MF, Villalba J, Olea N, Nunez MI (2010) Exposure to electromagnetic fields (non-ionizing radiation) and its relationship with childhood leukemia: a systematic review. Sci Total Environ 408:3062–3069 IARC (2002) Non-ionizing radiation, Part 1, Static and extremely low-frequency (ELF) electric and magnetic fields. IARC monographs on the evaluation of carcinogenic risks to humans, volume 80. IARCPress, Lyon, France van den Brandt PA, Goldbohm RA, van ‘t Veer P, Volovics A, Hermus RJ, Sturmans F (1990) A large-scale prospective cohort study on diet and cancer in The Netherlands. J Clin Epidemiol 43:285–295 Koeman T, S M Offermans N, Christopher-de Vries Y, et al (2012) JEMs and incompatible occupational coding systems: effect of manual and automatic recoding of job codes on exposure assignment Ann Occup Hyg Koeman T, Slottje P, Kromhout H, et al (2013) Occupational exposure to extremely low-frequency magnetic fields and cardiovascular disease mortality in a prospective cohort study. Occup Environ Med Bowman JD, Touchstone JA, Yost MG (2007) A populationbased job exposure matrix for power-frequency magnetic fields. J Occup Environ Hyg 4:715–728 Offermans NS, Vermeulen R, Burdorf A et al (2012) Comparison of expert and job-exposure matrix-based retrospective exposure assessment of occupational carcinogens in The Netherlands Cohort study. Occup Environ Med 69:745–751 Peters S, Vermeulen R, Portengen L et al (2011) Modelling of occupational respirable crystalline silica exposure for quantitative exposure assessment in community-based case-control studies. J Environ Monit 13:3262–3268 de Vocht F, Zock JP, Kromhout H et al (2005) Comparison of self-reported occupational exposure with a job exposure matrix in an international community-based study on asthma. Am J Ind Med 47:434–442 Matheson MC, Benke G, Raven J et al (2005) Biological dust exposure in the workplace is a risk factor for chronic obstructive pulmonary disease. Thorax 60:645–651 International Labour Office (1968) International standard classification of occupations: ISCO-68. International Labour Office, Geneva International Labour Office (1990) International standard classification of occupations: ISCO-88. International Labour Office, Geneva
123
214 26. Dumas O, Smit LA, Pin I et al (2011) Do young adults with childhood asthma avoid occupational exposures at first hire? Eur Resp J 37:1043–1049 27. World Health Organization (2007) Extremely low frequency magnetic fields. environmental health criteria 238. WHO Press, Geneva 28. Baris D, Armstrong BG, Deadman J, Theriault G (1996) A mortality study of electrical utility workers in Quebec. Occup Environ Med 53:25–31 29. Floderus B, Stenlund C, Persson T (1999) Occupational magnetic field exposure and site-specific cancer incidence: a Swedish cohort study. Cancer Causes Control 10:323–332 30. Hakansson N, Floderus B, Gustavsson P, Johansen C, Olsen JH (2002) Cancer incidence and magnetic field exposure in industries using resistance welding in Sweden. Occup Environ Med 59:481–486 31. Cocco P, Dosemeci M, Heineman EF (1998) Occupational risk factors for cancer of the central nervous system: a case-control study on death certificates from 24 U.S. states. Am J Ind Med 33:247–255 32. Karipidis KK, Benke G, Sim MR, Yost M, Giles G (2007) Occupational exposure to low frequency magnetic fields and the risk of low grade and high grade glioma. Cancer Causes Control 18:305–313 33. Sorahan T (2012) Cancer incidence in UK electricity generation and transmission workers, 1973–2008. Occup Med 62:496–505 34. Marcilio I, Gouveia N, Pereira Filho ML, Kheifets L (2011) Adult mortality from leukemia, brain cancer, amyotrophic lateral sclerosis and magnetic fields from power lines: a case-control study in Brazil. Rev Bras Epidemiol 14:580–588 35. Johansen C, Olsen JH (1998) Risk of cancer among Danish utility workers–a nationwide cohort study. Am J Epidemiol 147:548–555
123
Cancer Causes Control (2014) 25:203–214 36. Verkasalo PK, Pukkala E, Kaprio J, Heikkila KV, Koskenvuo M (1996) Magnetic fields of high voltage power lines and risk of cancer in Finnish adults: nationwide cohort study. BMJ 313: 1047–1051 37. Gibson TM, Smedby KE, Skibola CF, et al (2012) Smoking, variation in N-acetyltransferase 1 (NAT1) and 2 (NAT2), and risk of non-Hodgkin lymphoma: a pooled analysis within the InterLymph consortium. Cancer Causes Control 38. Vlaanderen J, Lan Q, Kromhout H, Rothman N, Vermeulen R (2011) Occupational benzene exposure and the risk of lymphoma subtypes: a meta-analysis of cohort studies incorporating three study quality dimensions. Environ Health Perspect 119:159–167 39. Huh J (2012) Epidemiologic overview of malignant lymphoma. Korean J Hematol 47:92–104 40. Turner JJ, Morton LM, Linet MS et al (2010) InterLymph hierarchical classification of lymphoid neoplasms for epidemiologic research based on the WHO classification (2008): update and future directions. Blood 116:e90–e98 41. Savitz DA, Dufort V, Armstrong B, Theriault G (1997) Lung cancer in relation to employment in the electrical utility industry and exposure to magnetic fields. Occup Environ Med 54:396–402 42. Armstrong B, Theriault G, Guenel P, Deadman J, Goldberg M, Heroux P (1994) Association between exposure to pulsed electromagnetic fields and cancer in electric utility workers in Quebec, Canada, and France. Am J Epidemiol 140:805–820 43. de Groot P, Munden RF (2012) Lung cancer epidemiology, risk factors, and prevention. Radiol Clin North Am 50:863–876 44. Kheifets L, Bowman JD, Checkoway H et al (2009) Future needs of occupational epidemiology of extremely low frequency electric and magnetic fields: review and recommendations. Occup Environ Med 66:72–80
