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Occupational exposures and non-Hodgkin's lymphoma: Canadian case-control study

  • Chandima P Karunanayake1,
  • Helen H McDuffie1,
  • James A Dosman1,
  • John J Spinelli2 and
  • Punam Pahwa1, 3Email author
Environmental Health20087:44

DOI: 10.1186/1476-069X-7-44

Received: 17 March 2008

Accepted: 07 August 2008

Published: 07 August 2008

Abstract

Background

The objective was to study the association between Non-Hodgkin's Lymphoma (NHL) and occupational exposures related to long held occupations among males in six provinces of Canada.

Methods

A population based case-control study was conducted from 1991 to 1994. Males with newly diagnosed NHL (ICD-10) were stratified by province of residence and age group. A total of 513 incident cases and 1506 population based controls were included in the analysis. Conditional logistic regression was conducted to fit statistical models.

Results

Based on conditional logistic regression modeling, the following factors independently increased the risk of NHL: farmer and machinist as long held occupations; constant exposure to diesel exhaust fumes; constant exposure to ionizing radiation (radium); and personal history of another cancer. Men who had worked for 20 years or more as farmer and machinist were the most likely to develop NHL.

Conclusion

An increased risk of developing NHL is associated with the following: long held occupations of faer and machinist; exposure to diesel fumes; and exposure to ionizing radiation (radium). The risk of NHL increased with the duration of employment as a farmer or machinist.

Background

Non-Hodgkin's Lymphoma (NHL) is a cancer of the lymphatic system [1, 2]. Even though NHL is a relatively rare disease, its incidence rates have been increasing worldwide for both men and women. The incidence rates in Canada, for both males and females were increased by about 50% between 1978 and the late 1990s. After the latter time, incidence rates have stabilized. Mortality rates of NHL have followed a similar pattern [3]. Age-standardized rates have increased faster among males than among females [15]. A number of factors, including inherited and acquired immunodeficiency states [6] as well as infectious, physical, and chemical agents have been associated with an increased risk for NHL [6, 7].

Epidemiological studies have reported positive associations between NHL and certain occupations including those of farmers [816], pesticide applicators [12, 1720], drivers [21, 22], and managers [23, 24]. Several studies have reported no association between development of NHL and the agricultural occupations (farmers, agricultural and forestry workers and pesticide applicators [2527]). Occupational exposures of a priori interest include pesticides [2833], dusts (metal, wood, paper [8], etc), paints [8, 35], diesel exhaust fumes [21, 22, 34, 35], cleaning fluids [8], cutting oils [36], and solvents [37, 38]. In this paper, we examined the association between NHL and (1) selected long term occupations, and (2) occupational exposures based on an individual's occupational history, and (3) duration of employment.

Methods

Details of the study design and methodology have been previously published [3941]. Briefly, we conducted a six province Canadian population based case-control study of men with an incident first diagnosis of NHL between 1991 to 1994; control subjects were frequency matched by age ± 2 years to be comparable with the age distribution of the entire case group (Soft Tissue Sarcoma (STS), Hodgkin's Disease (HD), NHL, and Multiple Myeloma (MM)) within each province of residence. The study had approximately three matched controls for each NHL case. Deceased subjects were ineligible as either cases or controls. All participating control subjects were used in the statistical analysis of each cancer site. Cases were identified from provincial cancer registries – except in Quebec where hospital records were used – and were coded using ICD-O 2nd edition except Quebec which used ICD-O 1st edition [42]. Malignant morphology codes 9591, 9642, 9670–9764, and 9823 were included. A reference pathologist reviewed the tumour tissue slides for 60% of the NHL cases, and confirmed NHL in all but 2% of cases. Cases not confirmed as NHL were eliminated. Control subjects were identified through provincial health insurance programs except in Ontario (telephone listing) and British Columbia (voter's lists), as generally described [3941].

The study design consisted of two stages: Stage 1 was a self-administered postal questionnaire; and Stage 2 was a detailed pesticide exposure information collected via telephone interview. With permission, we modified a pesticide exposure questionnaire developed by Hoar et al. [43] to create the study questionnaire. The results in this manuscript are based on the Stage 1 postal questionnaire only.

The postal questionnaire captured demographic details, personal medical history, lifetime occupational history and specific occupational exposures of interest. Occupational information included a list of all full time jobs held by the respondent for at least one year. For each job held, we collected information on job titles, business organization – whether service or industry – and duration of employment. A list of occupational exposures that have been epidemiologically linked to NHL or to one of the other three types of cancers which we studied simultaneously was grouped into dusts, coal products, printing products, paints, metals, pesticides, radiation and miscellaneous. Additional details of exposure to agricultural chemicals in broad classes i.e. herbicides, fertilizers etc, were obtained. Job titles and each industry's coding were provided by Statistics Canada [44].

Statistical analysis

Data were entered into a custom designed SPSS-data entry program. Results were presented as frequencies for categorical variables; mean, standard deviation (SD) for continuous variables for cases and controls were presented separately. We obtained information about the duration of employment (measured in years) for each individual. The occupations were selected for analysis if the occupant worked in a particular occupation at least for one year and at least 2% of cases for that occupational category. Based on that information, we derived two new variables called ever held occupations and long held occupations. Occupations were defined as ever held occupation if respondents worked at least for one year in that occupation. Occupations were defined as long held occupation if respondents worked for 10 years or more in that occupation. Duration of employment is the total of number of years in each long held occupation. A bivariate analysis was conducted to determine the association between each explanatory variable and the NHL outcome. Based on this model, building procedure explanatory variables with p < 0.20 were selected for the multivariate model. Statistically significant (p = 0.05) variables and important explanatory variables were considered for the final multivariate model adjusting for age and province of residence. Conditional logistic regression was used to compute adjusted odds ratios (OR) and 95% confidence intervals (95% CI).

Ethics

The letters of informed consent, questionnaires, and all other correspondence with study participants were approved by the relevant ethics agencies in each province. All of the information that could be used to identify study participants remained within each province of origin under the supervision of the provincial principal investigators.

Results

This study includes responses from 513 cases with NHL and 1506 control subjects. The mean age ± standard deviation (SD) of cases was 57.7 ± 14.0 years and, of the controls, 54.1 ± 16.0 years. More cases (n = 74, 14.4%) than controls (n = 87, 5.8%) had a personal history of cancer other than NHL (ORadj (95 % CI): 2.56 (1.81, 3.62)). There were no significant differences between NHL cases and controls with respect to their education level and to whether they ever lived or worked on a farm. Results are shown in Table 1.
Table 1

Characterization of study participants stratified by NHL case- control status: demographics and selected medical history

 

NHL (N = 513)

Controls (N = 1506)

ORb adj [P1](95% CI)

Demographics

   

   Mean age ± SD (years)

57.7 ± 14.0

54.1 ± 16.0

 

   Education Levela

   

University and Vocational

28 (6.6)

96 (5.5)

1.23 (0.81, 1.88)

University

94 (18.5)

310 (20.8)

1.08 (0.68, 1.70)

Vocational

111 (21.9)

358 (24.1)

1.06 (0.67, 1.70)

Elementary/High school

274 (54.0)

723 (48.6)

1.00

   Ever lived/worked on a farm

   

Yes n (%)

235 (45.8)

673 (44.7)

1.02 (0.82, 1.27)

No n (%)

278 (54.2)

833 (55.3)

1.00

Medical History

   

   Previous diagnosis of Cancer

   

Yes n (%)

74 (14.4)

87 (5.8)

2.56 (1.81, 3.62) c

No n (%)

439 (85.6)

1419 (94.2)

1.00

a 25 missing

b Adjusted for age (5 year groups) and province

c Statistically significant results are bold.

Table 2 shows the distribution of ever held occupations and long held occupations during a lifetime stratified by case-control status. None of the ever held occupations were statistically significant. The long held occupations (10 years or more) as farmer and machinist showed a significant risk increase for NHL. The adjusted odds ratios (ORadj) and 95% confidence intervals (95% CI) for a long held occupation during the lifetime as farmer and machinist were 1.54 (1.05, 2.27) and 2.21 (1.02, 4.79) respectively. Using four categories (no exposure, < 10 years, 10–20 years, and > 20 years), further models with years in these industries were used to investigate whether or not there is a dose-response relationship between the long held occupation as a farmer and a machinist and NHL (Table 3). A dose-response relationship between duration of exposure as farmer and incidence of NHL was observed. Those who worked as a farmer for more than 20 years were 1.5 times more likely to be diagnosed with NHL than non-exposed subjects. Similarly, we observed a dose-response relationship between duration of exposure as a machinist and incidence of NHL. Those who worked as a machinist for more than 20 years were 2.3 times more likely to be diagnosed with NHL than non-exposed subjects (Table 3).
Table 2

Adjusted odds ratio (OR) and 95% confidence interval (95% CI) for different occupations (job titles).

Job Title (code#)

NHL cases

n (%)

Controls

n (%)

OR adj a (95% CI)

Ever held Occupations

   

Accountant (1)

30 (5.8)

81 (5.4)

1.21 (0.77, 1.89)

Administrator (2)

11 (2.1)

52 (3.4)

0.58 (0.30, 1.15)

Carpenter (12)

21 (4.1)

55 (3.6)

1.06 (0.63, 1.79)

Clerk (17)

14 (2.7)

92 (6.1)

0.44 (0.24, 0.79)

Constructor (19)

14 (2.7)

78 (5.2)

0.51 (0.28, 0.93)

Driver (25)

55 (10.7)

133 (8.8)

1.29 (0.91, 1.82)

Electrician (26)

16 (3.1)

47 (3.1)

0.99 (0.54, 1.78)

Engineer (27)

13 (2.5)

68 (4.5)

0.54 (0.29, 1.02)

Factory worker (29)

13 (2.5)

46 (3.0)

1.14 (0.59, 2.17)

Foreman (30)

11 (2.1)

39 (2.6)

0.64 (0.32, 1.28)

Farmer (31, 33, 89)

86 (16.7)

230 (15.3)

1.14 (0.85, 1.54)

Armed forces (138)

28 (5.5)

92 (6.1)

0.76 (0.48, 1.18)

Janitor (41)

14 (2.7)

40 (2.7)

1.07 (0.57, 2.02)

Labourer (44)

31 (6.0)

99 (6.6)

0.86 (0.56, 1.33)

Lumberman (46)

17 (3.3)

38 (2.5)

1.12 (0.61, 2.03)

Machinist (47)

22 (4.3)

49 (3.2)

1.41 (0.83, 2.40)

Manager (48)

63 (12.3)

183 (12.1)

0.97 (0.70, 1.33)

Mechanic (49)

26 (5.1)

88 (5.8)

0.83 (0.52, 1.31)

Salesman (73)

44 (8.6)

127 (8.4)

1.06 (0.73, 1.53)

School Teacher (74)

31 (6.0)

88 (5.8)

0.96 (0.62, 1.48)

Welder (86)

13 (2.5)

33 (2.2)

1.25 (0.64, 2.44)

Office worker (97)

17 (3.3)

68 (4.5)

0.70 (0.40, 1.22)

Equipment hander (134)

14 (2.7)

37 (2.5)

1.34 (0.70, 2.56)

Long held Occupations

   

Accountant (1)

20 (3.9)

41 (2.7)

1.39 (0.79, 2.42)

Driver (25)

27 (5.3)

48 (3.2)

1.45 (0.88, 2.37)

Farmer (31, 33, 89)

50 (9.8)

106 (7.0)

1.54 (1.05, 2.27) c

Machinist (47)

12 (2.3)

16 (1.1)

2.21 (1.02, 4.79) c

Manager (48)

31 (6.0)

96 (6.4)

0.86 (0.56, 1.32)

Mechanic (49)

15 (2.9)

49 (2.2)

1.00 (0.99, 1.02)

# Statistics Canada. Standard occupational classification. Ottawa: Minister of Supply and Services, 1980.

a All odds ratios were adjusted for age and province of residence.

c Statistically significant results are bold.

Table 3

Duration of exposure as a farmer and machinist and risk of NHL

Duration (in years)

NHL (N = 513)

Control (N = 1506)

OR (95% CI)a

 

n (%)

n (%)

 

Job Title: Farmer

   

No exposure

427 (83.2)

1276 (84.7)

1.00

<10 years

36 (7.0)

124 (8.2)

0.84 (0.51, 1.41)

10–20 years

7 (1.4)

23 (1.5)

1.40 (0.57, 3.43)

> 20 years

43 (8.4)

83 (5.5)

1.55 (1.02, 2.36) c

Job Title: Machinist

   

No exposure

491 (95.7)

1457 (96.7)

1.00

<10 years

10 (1.9)

33 (2.2)

0.75 (0.30, 1.88)

10–20 years

2 (0.4)

4 (0.3)

1.77 (0.31, 10.22)

> 20 years

10 (1.9)

12 (0.8)

2.33 (1.00, 5.52) c

a all odds ratios were adjusted for age and province of residence.

c Statistically significant results are bold.

Of the 45 specific occupational exposures grouped into six classes (dusts, coal products, printing, paints, metals and miscellaneous), only exposure to diesel exhaust fumes showed an association with NHL (Table 4). Ever exposure to solvents and exposure to wood or paper dust were not associated with NHL. Ever exposure to ionizing radiation (radium) showed a significant association with the risk of NHL incidence (OR adj (95% CI): 3.26 (1.38, 7.73)).
Table 4

Adjusted odds ratio (OR) and 95% confidence interval (95% CI) for different occupational exposures.

 

NHL (N = 513)

Control (N = 1506)

 

Exposure

nb

%

nb

%

ORadj (95% CI)a

Dusts

     

   Cement dust

134

26.1

432

28.7

0.93 (0.73, 1.18)

   Fiberglass dust

102

19.9

319

21.2

1.02 (0.78, 1.33)

   Coal dust

63

12.3

149

9.9

1.19 (0.86, 1.66)

   Soil/field dust

142

27.7

375

24.9

1.26 (0.99, 1.61)

   Whey dust

12

2.3

38

2.5

0.89 (0.45, 1.77)

   Paper dust

68

13.3

180

11.9

1.22 (0.89, 1.67)

   Wood dust

143

27.9

445

29.5

0.95 (0.75, 1.20)

   Coke dust

10

1.9

58

3.8

0.53 (0.26, 1.06)

   Stone dust

55

10.7

173

11.5

0.99 (0.71, 1.40)

   Grain Dust

117

22.8

347

23.0

0.99 (0.76, 1.29)

   Sand

90

17.5

303

20.1

0.89 (0.67, 1.16)

   Cardboard dust

50

9.7

170

11.3

1.01 (0.71, 1.44)

   Metal dust

120

23.4

368

24.4

1.06 (0.82, 1.36)

Coal Products

     

   Pitch

17

3.3

38

2.5

1.24 (0.68, 2.25)

   Asphalt

46

8.9

142

9.4

0.96 (0.67, 1.38)

   Crude petroleum

30

5.8

84

5.6

1.00 (0.64, 1.57)

   Tar/tar products

53

10.3

143

9.5

1.20 (0.84, 1.69

Printing

     

   Printing inks

35

6.8

134

8.9

0.90 (0.60,1.36)

   Printing fluid

28

5.5

96

6.4

0.93 (0.59, 1.47)

Paints

     

   Paints, dyes

148

28.8

442

29.3

1.06 (0.84, 1.33)

Metals

     

   Arsenic

13

2.5

28

1.9

1.45 (0.72, 2.93)

   Nickel

29

5.6

85

5.6

1.11 (0.71, 1.74)

   Cadmium

20

3.9

55

3.6

1.07 (0.62, 1.84)

   Zinc

38

7.4

103

6.8

1.12 (0.75,1.67)

   Mercury

20

3.9

63

4.2

0.84 (0.49, 1.43)

   Chromium

24

4.7

58

3.8

1.33 (0.79, 2.22)

   Iron

40

7.8

100

6.6

1.18 (0.79, 1.77)

   Lead

65

12.7

182

12.1

1.03 (0.75, 1.42)

   Aluminum

71

13.8

220

14.6

1.03 (0.76, 1.40)

Miscellaneous

     

   Asbestos

76

14.8

237

15.7

0.91 (0.68, 1.21)

   Used motor oil

117

22.8

400

26.6

0.89 (0.69, 1.15)

   Diesel exhaust fumes

183

35.7

464

30.8

1.33 (1.06,1.67) c

   Cutting oils

74

14.4

277

18.4

0.81 (0.60, 1.08)

   Cleaning fluids

124

24.2

419

27.8

0.93 (0.72, 1.19)

   Preservatives

9

1.7

21

1.4

1.11 (0.49, 2.50)

   Chlorine

68

13.3

202

13.4

1.05 (0.77, 1.43)

   Hair permanent solutions

11

2.1

33

2.2

0.99 (0.48, 2.04)

   Sour gas

24

4.7

92

6.1

0.69 (0.42, 1.12)

   Wood smoke

121

23.6

371

24.6

0.95 (0.75, 1.22)

   Lubricants

152

29.6

477

31.7

0.99 (0.78, 1.25)

   Solvents

167

32.5

516

34.3

1.01 (0.80, 1.28)

   Ether

51

9.9

170

11.3

0.88 (0.62, 1.25)

   Mouldy grain/forage

61

11.9

176

11.7

1.09 (0.78, 1.53)

   Hair dyes

15

2.9

33

2.2

1.33 (0.69, 2.52)

   Cyanide

10

1.9

36

2.4

0.79 (0.38, 1.63)

Non-ionizing radiation

     

   Ultra Violet Light

44

8.6

151

10.0

1.06 (0.73, 1.55)

   Horticultural Grow lights

12

2.3

39

2.59

0.91 (0.46, 1.79)

   Unshielded microwaves

3

0.6

25

1.7

0.39 (0.11, 1.32)

Ionizing radiation

     

   Radium

12

2.34

12

0.80

3.26 (1.38, 7.73) c

   Uranium

12

2.34

18

1.20

2.10 (0.97, 4.56)

a all odds ratios were adjusted for age and province of residence.

b n and % are given for the "yes" responses.

c Statistically significant results are bold.

Table 5 shows the results of multivariate conditional logistic regression models for the long held jobs of farmer and machinist. The variables that remained statistically significantly associated with increased risk of NHL for long held job as a farmer were personal history of another cancer and exposure to ionizing radiation (radium). The variables for the long held job as a machinist associated with increased risk of NHL were personal history of another cancer, exposure to ionizing radiation (radium) and exposure to diesel. Duration of exposure for the long held jobs of farmer and machinist were borderline significant at 5% level (p = 0.08 and p = 0.059), but there was evidence of an increase risk of NHL with longer duration of exposure.
Table 5

Multivariate models of the important covariates associated with NHL for long held occupations.

Variable

Farmer

Machinist

 

OR (95% CI)a

OR (95% CI)a

Personal history of another cancer (yes)

2.60 (1.83, 3.69) c

2.57 (1.82, 3.65) c

Ever exposed to ionizing radiation (radium) (yes)

3.41 (1.44, 8.11) c

3.21 (1.34, 7.67) c

Ever exposed to diesel (yes)

1.23 (0.97, 1.56)

1.28 (1.02, 1.61) c

Duration (reference to no exposure)

  

<10 years

0.77 (0.45, 1.30)

0.73 (0.29, 1.86)

10–20 years

1.34 (0.54, 3.34)

1.87 (0.33, 10.57)

> 20 years

1.47 (0.95, 2.29)

2.34 (0.97, 5.68)

a all odds ratios were adjusted for age and province of residence.

c Statistically significant at 5% level results are bold.

Discussion

Our study investigated the association between NHL and several occupations and occupational exposures. The findings revealed that two long held occupations (10 years or more), farmer and machinist, were significantly associated with increased risk of developing NHL. One of the possible explanations is that farmers and drivers might be exposed to pesticides and engine exhaust and machinists might be exposed to solvents or engine exhaust at the work place. The increased risk of NHL for farmer and machinist seen in our study is consistent with the findings from other studies [816].

Pesticides including herbicides and insecticides have been associated with Non-Hodgkin's Lymphoma in studies of farmers, agricultural related workers, other pesticide applicators, manufacturing workers and other exposed populations [39, 45]. Grain handlers exposed to pesticides, grain dusts, and organic solvents were shown a five-fold risk of NHL [46]. Our study confirms that those who held the long held job title as a farmer (farmer, farm labourer and farm managers) had 1.5 times higher risk of being diagnosed with NHL than those who held a job title from the category of non-farmer.

Our results confirm previously reported associations of NHL and a personal history of cancer [47, 48]. Occupational exposure to dust (wood, paper, metal etc.), coal products, paints, metal, and printing are unlikely to increase the risk of NHL, as is evident from our analysis. In contrast, Kawachi et al [49] found a significant association between working with wood and NHL. In addition, Kogevinas et al [50] found an increased risk of Lymphomas in pulp and paper workers. Ever exposure to diesel exhaust fumes is likely to increase the risk of NHL, as is evident from our analysis. Our finding is agreement for diesel exhaust fumes with Baris et al [21] and Maizlish et al [34].

The mechanism of cancer induction by radiation suggested in our study is not clear. The most widely accepted hypothesis is that some of the ionizing events, which occur when radiation is absorbed in tissue, produce a change in the genes or chromosomes of one or more cells [51]. A case-referent study conducted to investigate the possible association between occupation and occupational exposures and risk of hematological malignancies showed that exposure to asbestos, hydrocarbons, fertilizer, radiation, pesticides and mineral oils were highly associated with hematological malignancies [10]. Another matched case-control study in the nuclear industry [52] found no significant excess of NHL at any radiation exposure level. Archer [51] stated that uranium mill workers appeared to have excess Lymphomas. In our study, any form of radiation exposure at work was considered. Exposure to ionizing radiation (radium) is significantly associated with increase risk of NHL, which suggests equivocal evidence of an association with NHL presented by Ron [53].

There are many potential sources of non-ionizing radiation to workers. One of them is ultraviolet (UV) radiation. There is suggestive evidence that exposure to ultraviolet (UV) light, an established cause of immune suppression, may increase the risk of NHL [5457]. The most recent epidemiologic literature suggests that there is no association or protective effect between exposure to sunlight and NHL [5863]. Our study did not find any association between exposure to ultraviolet (UV) light with NHL.

Solvents have been associated with NHL in a number of studies [6466], including studies of rubber workers [67], aircraft maintenance workers [68], and dry cleaners [69]. In particular, benzene exposure is common in above mention occupations and this may be due to its effects on the immune system [66]. Other occupations which might involve exposure to solvents or related chemicals and which are reported as being at increased risk of NHL include those of highway workers [34], petroleum refinery employees [7072], styrene workers [73], chemists [74, 75], and chemical manufacturers [76, 77]. We could not find any association between NHL and exposure to solvents, cleaning fluids, or preservatives.

A major strength of this study is the large number of cases and controls from residents of six Canadian provinces. Questions were designed to obtain a complete occupational history and extensive list of potential occupational exposures. A reference pathologist validated 84% of the NHL tumours.

There are, however, several limitations in this study. One of the limitations is the potential for recall bias and misclassification of pesticide exposures. Also, occupational exposures in this study were self-reported and this might also bias results. Due to budget constraints, the study was restricted to males. The response rates of 67.1% for cases and 48% for controls represent another potential limitation that could create misleading conclusions if the non-respondents differ significantly from the respondents with respect to the variables under investigation. We compared non-respondents to respondents using postal codes as an indicator of rural residence and did not find a rural bias among respondents. The most common reasons for not participating were death, change of address, and refusal for both cases and controls. Another limitation was the possibility of false-positive findings given the large number of jobs and exposures assessed.

Conclusion

Our results support previous findings of an association between NHL and specific job titles and occupational exposures. In our analysis, NHL was associated with personal history of cancer, exposure to diesel exhaust fumes, exposure to ionizing radiation (radium) and long held occupations such as farmer and machinist. Also, we have supportive evidence of increased risk of NHL with longer durations of exposure.

Abbreviations

NHL: 

Non-Hodgkin's Lymphoma

ICD: 

International Classification of Diseases

STS: 

Soft Tissue Sarcoma

HD: 

Hodgkin's Disease

MM: 

Multiple Myeloma.

Declarations

Acknowledgements

Special thanks go to the collaborators Drs. G. Theriault, J. McLaughlin, D. Robson, S. Fincham, L. Skinnider, D. White, T. To and Late N.W. Choi. Also, the authors are indebted to the following members of the Advisory Committee: Drs. G.B. Hill, A. Blair, L. Burmeister, H. Morrison, R. Gallagher, and D. White. We owe a debt of gratitude to the provincial coordinators across Canada and data managers for their meticulous attention to detail: T. Switzer, M. Gantefor, J. Welyklowa, J. Ediger, I. Fan, M. Ferron, E. Houle, S. de Freitas, K. Baerg, L. Lockinger, E. Hagel, P. Wang, G. Dequiang, J. Hu. We thank Drs. G. Theriault and N. Choi for supervising the collection of data in Quebec and Manitoba respectively; and to Dr. L. Skinnider for reviewing the pathological specimens. The study participants gave freely of their time and shared personal details with us and we sincerely thank each of them. Written consent for publication was obtained from the participants. This work was funded by Health Canada National Health Research Programs Directorate Grant 6608-1258, the British Columbia Health Research Foundation and Institute of Agricultural, Rural and Environmental Health, University of Saskatchewan.

Authors’ Affiliations

(1)
Canadian Centre for Health and Safety in Agriculture, University of Saskatchewan
(2)
Cancer Control Research, British Columbia Cancer Agency
(3)
Department of Community Health Epidemiology, University of Saskatchewan

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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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