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The INTEROCC case-control study: Risk of meningioma and occupational exposure to selected combustion products, dusts and other chemical agents
Damien M. McElvenny1, Martie van Tongeren1,2a, Michelle C Turner3-6, Geza Benke7, Jordi Figuerola3-5, Sarah Fleming8, Martine Hours9, Laurel Kincl10, Daniel Krewski6,11, Dave McLean12, Marie-Elise Parent13, Lesley Richardson14, Brigitte Schlehofer15, Klaus Schlaefer15, Siegal Sadetzki16,17, Joachim Schüz18, Jack Siemiatycki14, Elisabeth Cardis3-5
1 Institute of Occupational Medicine, Edinburgh, UK2 Centre for Occupational and Environmental Health, Centre for Epidemiology, Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Medicine, Biology and Health, University of Manchester, UK3 Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain4 Universitat Pompeu Fabra (UPF), Barcelona, Spain5 CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain 6 McLaughlin Centre for Population Health Risk Assessment, University of Ottawa, Ottawa, Canada 7 Monash University, Melbourne, Australia8 University of Leeds, UK9 Unité Mixte de Recherche Epidémiologique Transport Travail Environnement Université Lyon 1/IFSTTAR, Université de Lyon, Lyon, France 10 Oregon State University, Corvallis, Oregon, USA11 Department of Epidemiology and Community Medicine, Faculty of Medicine, University of Ottawa, Ottawa, Canada 12 Massey University, Wellington, New Zealand13 INRS-Institut Armand-Frappier, Université du Québec, Laval, Canada14 University of Montreal Hospital Research Centre, Montreal15 Unit of Environmental Epidemiology, German Cancer Research Center, Heidelberg, Germany16 The Cancer & Radiation Epidemiology Unit, The Gertner Institute, Chaim Sheba Medical Center, Israel 17 Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel18 International Agency for Research on Cancer (IARC), Section of Environment and Radiation, Lyon, France
a Address for Correspondence:
Prof. Martie van TongerenCentre for Occupational and Environmental Health, Centre for Epidemiology, Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Medicine, Biology and HealthUniversity of ManchesterOxford Road M13 9PLUKEmail: [email protected]
(word count = 3,525)
Authors contribution:
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Study conception and design: MvT, EC; Acquisition of data: GB, SF, MH, DK, DMcL, MEP, LR, SS, KS, BS, JS, JS; Exposure assessment: MvT, GB, JF, LK, DMcL; Statistical analysis: MCT, JF; Drafting of manuscript: DMMcE; all authors participated in the interpretation of data and revision and approval of the manuscript.
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Abstract (n = 249)
Background
Little is known about occupational risk factors for meningioma.
Objectives
To study whether risk of meningioma is associated with several occupational exposures,
including selected combustion products, dusts and other chemical agents.
Methods
The INTEROCC was an international case-control study of brain cancer conducted in
seven countries. Data collection by interview included lifetime occupational histories. A
job exposure matrix was used to derive estimates of exposure for the 12 agents. Odds
ratios for ever versus never exposed and for exposure-response using duration of
exposure and cumulative exposure were derived using conditional logistic regression
stratified by sex, age group, country/region, adjusted for education.
Results
These analyses included 1,906 cases and 5,565 controls. For 11 of the 12 agents, no
excess risk was found for ever exposed. For ever exposure to oil mists, an elevated OR
of 1.57 (95% CI 1.10 to 2.22, 51 exposed cases) was found. Statistically significant
exposure-response relationships were observed with cumulative exposure (p-
trend=0.01) and duration of exposure (p-trend=0.04). Among women, there were also
significant trends for cumulative and duration of exposure to asbestos and excesses in
the highest exposure categories for formaldehyde.
Conclusions
Most agents examined did not provoke excess risks of meningioma. The main finding
from this study is that it is the first study to identify a statistical association between
exposure to oil mists and meningioma. This may be a chance finding or could be due to
confounding with iron exposure and further research is required to understand whether
the relationship is causal.
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What this paper adds:
Little is known about occupational risk factors for meningioma The INTEROCC study is the largest case control study of meningioma and
occupational risk factors, with data collected from 7 countries. Occupational exposure to mineral oil appeared to be associated with elevated risk
of meningioma. Among women, there was also some indication of exposure-response for asbestos
and some indication of excess risks from formaldehyde in the highest exposure categories.
No association was observed with other occupational substances investigated in this paper, which included combustion products, mineral and organic dusts and other chemical agents.
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Introduction
Meningioma is a type of brain tumour, usually benign, arising from the meningeal tissue
of the brain, with often serious and potentially fatal consequences 1. In the U.S.
meningioma accounts for a third of all primary brain and central nervous system tumours
and the age-adjusted incidence rate is about 7 per 100,000 person-years 1. The
incidence is rising in some countries, but remains stable in others 2. Differences in
cancer registration practices between countries mean that incidence rates differ
considerably between countries. Meningiomas exhibit a range of morphological
appearances, with the World Health Organisation (WHO) suggesting there are up to 15
histopathological variants 2. Five-year survival has been reported as 55% and three-year
survival at over 85% 2. The incidence rate increases rapidly with age and is twice as high
in females as in males 2.
The only established environmental risk factor for meningioma is exposure to ionizing
radiation, with some doubt as to the dose required to trigger excess risk 1. Results from
other epidemiological studies are somewhat sporadic. Results from the German
component of the INTEROCC study did not find an increased risk of meningioma for
occupations in the agricultural, construction, transport, chemical, electrical/electronic or
metal industries 3. In a different international case-control study, an increased risk of
meningioma was found in cooks 4. One US case-control study found elevated risks of
meningioma for auto body painters, designers and decorators, military occupations,
industrial production supervisors, teachers and managers. 5 Additional analyses found
inconsistent evidence for an association between the use of synthetic hair dye and
meningioma 6 and an association between meningioma and herbicide or insecticide
exposure among women, but not in men 7. A French population-based case-control study
concluded that meningioma may be caused by occupational or residential exposure to
electromagnetic fields 8 though the evidence in support of an association with
occupational exposure from the INTEROCC international study was somewhat weaker 9.
No association was found between mobile phone use and meningioma10 11. A Chinese
case-control study found significant associations for occupational exposure to some
metals such as lead, tin and cadmium. 12 We previously published results investigating
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link between meningioma and occupational exposures for a number of families of organic
solvents (aliphatic hydrocarbons, alicyclic hydrocarbons, aromatic hydrocarbons,
chlorinated hydrocarbons, and other organic solvents) or specific solvents (benzene,
gasoline, methylene chloride, perchloroethylene, trichloroethylene, 1,1,1-
trichloroethylene, and toluene), which failed to show significant associations between
meningioma and any of the solvents 13. The analyses of a number of metals failed to
show significant associations, apart from a positive borderline significant association for
iron exposure in women14. Because these tumours occur more often in women than in
men 1 and supported by some findings of an association between meningioma and
hormone replacement therapy 15 16, it has been suggested that hormones play a role in
the aetiology of meningioma. Use of oral contraception does not appear to influence the
risk 1. Asthma, hay fever and eczema are thought to be protective in relation to
meningioma risk 17.
Based on the INTEROCC study, the largest case-control study on occupational risk factors
for brain tumours, the present paper examines associations between the risk of
developing meningioma and occupational exposures to 12 agents, commonly present in
the occupational environments and for which there is some evidence that they may
cause damage to the brain 18: combustion fumes (diesel engine exhaust, gasoline engine
exhaust, bitumen fumes, benzo(a)pyrene and polycyclic aromatic hydrocarbons), mineral
and organic dusts (asbestos, quartz, animal dusts, wood dust) and some other agents
(formaldehyde, oil mist and sulphur dioxide).
Methods
Study Population
INTEROCC is a seven country population-based case-control study formed from the
parent INTERPHONE study 19. Incident cases of primary meningioma were recruited from
eleven study centres in Australia, Canada, France, Germany, Israel, New Zealand, and
the United Kingdom from the year 2000 to 2004 using a common protocol. The age
range of study subjects was defined as follows: in Germany, 30-69; in UK, 18-69; in Israel,
over 18; in all other countries, 30-59. Eligibility of cases was confirmed either
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histologically, or in approximately 25% of cases, through unequivocal diagnostic imaging.
Population control recruitment varied by country, but all controls were selected from
population registers and were either frequency- or individually-matched to cases in each
study centre by sex and year of birth (five year categories). To maximize statistical
power, we used all eligible controls from the INTEROCC study, including those collected
for the glioma cases. The reference date for controls was calculated as the interview
date minus the median difference between case diagnosis and interview date for each
study country. Written informed consent was obtained from all study participants prior
to the in-person interview. The study questionnaire solicited demographic, medical, and
lifetime occupational histories. In a small proportion of cases (5%), proxy interviews
were conducted where the case participant had died or could not be interviewed. Ethics
approval from all appropriate national and regional research ethics boards was obtained
including the Ethical Review Board of the International Agency for Research on Cancer
(IARC) for INTERPHONE and the Municipal Institute for Medical Investigation (IMIM)
Barcelona for INTEROCC.
Occupational Exposure Assessment
The exposure assessment methodology is described in more detail elsewhere 20. Briefly,
each job within the occupational histories was coded using International Standard
Classification of Occupations 1968 (ISCO68) 21 by a trained occupational hygienist. The
coding consistency between countries was tested before and after a comparison exercise
using a small subset of the job titles with any discrepancies discussed amongst the
coders. This exercise resulted in a moderate improvement in coding consistency 13.
A job exposure matrix was developed based on the Finnish Job Exposure Matrix (FINJEM) 22, which assigns estimates of the proportion of workers in the given occupation who are
considered exposed to the given agent and mean exposure levels of each agent for those
who are considered exposed. Exposure estimates (probability and level of exposure)
were obtained from the modified job exposure matrix (INTEROCC JEM) by: i) developing a
cross-walk between the Finnish occupational classification used in FINJEM to ISCO68; ii)
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splitting the early component of the entire 1945-2003 time period (1960-1984) into two
different time periods (1960-1973 and 1974-1984) to enable exposures to change over
this time period; iii) updating exposure estimates based on a comparison with data from
a study of occupational lung cancer risk in Montreal, Canada 23; and iv) peer-reviewing
the final updated estimates for generalisability to each of the seven study countries by
local occupational hygienists 20. For example, the internal calibration of the ISCO68
codes linked to ‘Machine and engine mechanics’ resulted in relatively high exposures to
aromatic hydrocarbons for the ISCO68 code ‘Office machine mechanic’, which were in
the same order of magnitude as mechanics of heavy and transport machinery. It was
decided that this was not realistic and exposure to aromatic hydrocarbons was reduced
for this occupational group.
Each job in each subject’s job history was linked to the INTEROCC JEM to infer possible
occupational exposures. The 12 agents under consideration in this paper were
subsequently evaluated for possible exposure in each job. Exposure to each agent was
defined as having had an occupation for at least one year where the estimated
probability of exposure (P) was 25% or more. Risk analyses were conducted as a
function of categorical indicators of ever/never exposed, quartiles of cumulative
exposure (calculated as the sum over all jobs of the product of duration of job and
concentration). Duration of exposure categories were chosen and use the same
categorisation for consistency, as other INTEROCC study analyses.
Statistical Analysis
Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for associations between
potential carcinogens of interest and meningioma were estimated using conditional
logistic regression stratified by sex, five-year age groups, country-region and adjusted for
education in all seven countries combined. Three exposure indices, decided upon a
priori, were used: (1) ever/never exposed; (2) lifetime cumulative exposure categorised
in quartiles of the distribution among exposed controls; and (3) total duration of
exposure in categories of 1-4 years, 5-14 years and 15+ years. The reference group
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consisted of those never exposed to the respective occupational exposure of interest.
Participants with a prevalence of exposure ≥ 5 but < 25% as well as with exposure
duration of less than one year were excluded from the analysis. Although we are aware
that meningioma potentially has a latency of 20 years or more1, the analyses presented
incorporate a 5-year lag for consistency with a previous analysis of the data investigating
possible associations with various metals14. Sensitivity analyses were conducted by
looking at lags of 1 and 10 years and using different cut points on the probability of
exposure scale for distinguishing exposed and unexposed (i.e. P>5% and P>50%), as
well as excluding proxy respondents and subjects aged over 69 years. Additionally,
adjustments for the potential confounders set out in Table 1 were also carried out. The
ever versus never analyses, carried out only for oil mist, were additionally adjusted for
individual metals and welding fumes to check for potential confounding.
Results
The analyses presented here included 1,906 cases of meningioma (80% of eligible cases)
and 5,565 controls (50% of eligible controls) (Table 1). The majority of cases (73%) and
controls (55%) were female. The mean (+ SD) age of participants included in this
analysis was 55.0 years (+ 11.8) for meningioma cases and 52.0 years (+ 11.5) for
controls. The countries providing the largest number of cases were Israel (39%),
Germany (20%), Australia (13%) and the UK (13%) and those providing the largest
number of controls were Germany (28%), the UK (20%) and Israel (18%). The majority of
cases (60%) and controls (54%) had no more than high school education. Cases tended
to have a slightly lower socioeconomic status score than controls and slightly fewer cases
than controls were current smokers and suffered from asthma, hay fever or eczema.
Table 2 shows some descriptive data for each of the 12 agents, separately for cases and
controls, and for males and females. For all agents except formaldehyde, the prevalence
of exposure was higher among controls than cases and for all agents except asbestos
and sulphur dioxide, the mean cumulative exposure was higher for controls than for
cases. The percentage prevalence of oil mist exposure for men and women combined
was 2.7% among cases and 2.8% among controls. Among the cases, the mean duration
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of exposure was longer in men than women for asbestos, quartz, wood dust,
benzo(a)pyrene, diesel engine exhaust, gasoline exhaust, oil mists, polycyclic aromatic
hydrocarbons and sulphur dioxide, but not for animal dusts or formaldehyde. Among the
cases the mean cumulative exposure was higher in men than women for asbestos,
quartz, wood dust, diesel engine exhaust, gasoline engine exhaust, formaldehyde and oil
mists, but not for animal dusts, benzo(a)pyrene or polycyclic aromatic hydrocarbons.
The mean duration of exposure to oil mists for men and women combined was 10.1 years
for cases and 12.3 years for controls; the mean cumulative exposure for cases was
1882.0 mg m-3-years for cases and 2058.1 mg m-3-years for controls.
The ORs for ever exposed to each agent are shown in Table 3. Only for oil mist exposure
there was some evidence of an elevated OR for meningioma among men and women
combined (OR 1.57; 95% CI: 1.10 to 2.22). In addition, the OR for asbestos exposure
among women was of borderline statistical significance (OR 1.50; 95% CI: 0.98-2.28).
Tables 4 and 5 present the results of the exposure-response analyses using duration of
exposure and cumulative exposure, respectively, for each agent. For duration of
exposure to oil mists there was an increased risk with increased exposure for men and
women combined, with an OR for 1-4 years of exposure of 1.57 (95% CI: 0.87 to 2.83),
for 5-14 years of 1.89 (1.11 to 3.23) and for 15+ years 1.17 (0.58 to 2.35)(p-trend =
0.04). The evidence was less strong in men (p-trend = 0.07) and not apparent in women
(p-trend = 0.27). For cumulative exposure to oil mists, there was evidence for an
increase in OR with increased exposure for both sexes combined with an OR 0.96 (95%
CI: 0.46 to 1.99) for 0-360.0 mg/m3-years, 1.99 (1.03 to 3.85) for 360.0-1312.5 mg/m3-
years, 2.23 (1.20 to 4.14) for 1312.4-3177.5 mg/m3-years and 1.27 (0.59 to 2.75) for
3177.5+ mg/m3-years (p-trend = 0.01). The p-values for the individual trends for men
and women were 0.04 and 0.07, respectively.
There was some evidence of an increasing trend with increased duration of exposure for
asbestos in women with the OR for 1-4 years of 1.16 (95% CI: 0.65 to 2.06), for 5-14
years of 1.67 (0.86 to 3.24) and for 15+ years of 8.17 (0.95 to 70.08) (p-trend = 0.02).
In addition, there was a significant trend for increasing cumulative exposure to asbestos
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in women with OR 0.95 (95% CI: 0.10 to 9.03) for 0-18.2 f/cm3-years, 1.18 (0.52 to 2.68)
for 18.2-59.1 f/cm3-years, 1.39 (0.70 to 2.75) for 59.1-181.8 f/cm3-years and 2.08 (1.00
to 4.34) for 181.8+ f/cm3-years (p-trend = 0.03).
An excess was seen in the highest duration of exposure category for formaldehyde of
15+ years for men and women combined (OR 1.64; 95% CI: 1.04 to 2.60) and for women
(OR 1.99; 95% CI 1.16 to 3.40), but not for men. An excess was also seen for women in
the highest cumulative exposure category for formaldehyde of 53.8+ ppm-years of (OR
1.88; 95% CI: 1.06 to 3.33) but not for men or men and women combined.
Sensitivity analyses were conducted for all agents included in this analyses to test the
effect of i) using different lag-periods (1 and 10 years), ii) different cut-offs for defining
exposures (i.e. P>5% and P>50%), and iii) excluding proxy respondents and subjects
aged over 69 years. Furthermore, the ever versus never analyses for oil mist were
additionally adjusted for individual metals and welding fumes to check for potential
confounding (for information on joint exposures for agents included in this paper as well
as metals and welding fume – see Supplementary Table). The metal that had the largest
impact on the odds ratio for oil mist was iron, the adjusted odds ratio being 1.38 (0.94 to
2.02) for men and women combined, 1.46 (0.92 to 2.31) for men and 1.13 (0.54 to 2.32)
for women. We additionally analysed the data excluding each country in turn; only the
removal of the cases and controls from Germany removed the statistical significance for
the odds ratio, and the p-value for an interaction by country was not statistically
significant (data not shown).
Discussion
This multi-national case-control study provides little evidence of associations between
meningioma and the following occupational exposures: animal dust, quartz, wood dust,
benzo(a)pyrene, bitumen, diesel engine exhaust, gasoline engine exhaust, polycyclic
aromatic hydrocarbons and sulphur dioxide. These results confirm existing evidence for
these substances in relation to lack of risk for meningioma 24-32.
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This study provided some evidence for an exposure response for asbestos in women, but
not in men. However, despite a suggestion to the contrary 31, there is little evidence to
link asbestos exposure with a rise in in the incidence of brain tumours 33 and so on
balance, especially in view of the number of significance tests carried out, we believe this
to be a chance finding.
This study shows an increased risk in relation to formaldehyde based mainly in women in
relation to duration of exposure more than 15 years, and highest cumulative exposure,
although neither of the trends is statistically significant. Studies of occupational
exposure to formaldehyde have generally not found an increased risk of brain cancer 34,
and is it interesting that the risk appears to be confined to women and not to men. This
finding may also be due to chance, but could merit further investigation.
In this study, an excess risk of meningioma was observed following occupational
exposure to oil mists in men (OR 1.58; 95% CI: 1.04 to 2.41) and in men and women
combined (OR 1.57; 95% CI: 1.10 to 2.22). There was also a significant exposure-
response relationship with increasing duration (p = 0.04) and cumulative exposure (p =
0.01) for men and women combined. However, it is worth noting that the risk was not
statistically elevated in either of the highest exposure categories. This could be due to
lack of power as there were only 11 and nine exposed cases respectively in these
exposure categories.
A similar pattern is seen in the data for men. The absence of a trend in risk in women
could be due to insufficient statistical power.
The most prevalent occupations with oil mist exposure were tool and die makers (38%),
machine tool operators (18%), lathe-setter operators (12%), metal-working machine
setter-operators (9%) and metal-working machine setters (6%). In a paper from the
same study looking at the same exposures in relation to glioma, no excess risk for
exposure to oil mists was observed35.
Mineral oils are chemical substances prepared from naturally occurring crude petroleum
oil and are obtained from further refinement of the residual fractions 36. Mineral oil mists
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are colourless oily liquid aerosols that form when high pressure fuel, lubricating or other
oil is sprayed through a narrow aperture or when leaked oil connects with a high
temperature surface, vaporizes and comes into contact with cooler air. This usually
happens when fluids interact with moving parts in machinery. The current National
Institute for Occupational Safety and Health Recommended Exposure Limit for oil mists is
5 mg/m3 8-hour time weighted average and 10 mg/m3 short-term exposure limit
(https://www.cdc.gov/niosh/npg/npgd0472.html, accessed December 2016). Human
exposure occurs primarily from direct contact with the skin or from inhalation of an
aerosol. There are a wide variety of mineral oil-containing products, including lubricants
and products intended for non-lubricating purposes and manufactured for different
applications. Lubricant products include engine oils, transmission fluids, and gear oils.
Non-lubricant products include agricultural spray oils, printing inks, and tyre oils. All such
oils contain a variety of additives such as antioxidants and detergents to improve
performance 36.
The major challenge in making an overall assessment of the carcinogenicity of mineral
oils lies in the diversity of processing, with incomplete information on the extent of
processing for specific industrial applications. They are typically used as part of a
complex mixture and are often additionally processed and combined with other agents.
The additional processing and combining with other agents makes attribution of risk
specifically to mineral oils difficult37. Nevertheless, IARC has determined that there is
sufficient evidence in humans for the carcinogenicity of untreated or mildly treated
mineral oils in relation to cancer of the skin (observed in the scrotum) although this is
thought due to the polycyclic aromatic hydrocarbons contained in the oils 3636363536363635.
No mention was made of a possible link between oil mist exposure with increased risk of
brain cancer or specifically meningioma in the 2012 IARC monograph 36. An earlier
review suggested an association between soluble metalworking fluids and brain cancer,
but did not separately consider meningioma 38.
Previously, we reported on the association between exposure to metals and welding
fumes and risk of meningioma 14. The major finding was an association of iron with
increased risk of developing meningioma. About 25% of those exposed to oil mist were
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also exposed to iron in this study. Additional adjustment for iron exposure reduced the
ORs for ever versus never exposed, in particular amongst women, which suggest that the
association between oil mist exposure and meningioma may be confounded by iron
exposure.
The INTEROCC study is the largest case-control study conducted which has investigated
associations between occupational exposures to the agents examined in this paper and
meningioma. However, the power to detect associations for some of the exposures with
a lower prevalence was limited, particularly for women.
As in all retrospective case-control studies of this type, there exists the possibility of bias
in the recall of job history information. The vast majority (98%) of occupational histories
were obtained from face-to-face interview, with the remaining being obtained from proxy
respondents. In a previous analysis from this study in relation to glioma for the same
agents, we found no association between oil mists and glioma 35; we might have
expected to see some excess if there was preferential recall of jobs involved exposure to
oil mists among the cases. However, we have no reason to believe that recall of jobs
associated with exposure to mineral oils would have been biased in any way. Although
job-exposure matrices in themselves have limitations, they remain the only feasible
approach to exposure assessment for studies of this size, containing over 35,000 jobs 39.
In this study, we used a job exposure matrix (INTEROCC JEM) that was based on the
FINJEM. The use of INTEROCC JEM resulted in a consistent exposure assessment across
the seven participating countries, although the approach is still limited by assigning a
single exposure estimate to all jobs with the same job title across the different countries.
However, any bias this will cause is likely toward the null 40.
Given the novelty of the association and the number of statistical tests carried out in this
study, the finding of the association between oil mists and increased risk of meningioma
must be interpreted with some caution and replicated in other studies.
Acknowledgements
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The authors would like to thank Rodrigo Villegas of ISGlobal (CREAL) for conducting
preliminary analyses of these data, and Avital Jarus-Hakak (Israel), Louise Nadon
(Canada), Hélène Tardy (France), Florence Samkange-Zeeb (Germany), and Anne
Sleeuwenhoek (UK), who coded the occupations or assisted in the data clean-up. We are
grateful to Mary McBride (Canada) and Drs Bruce Armstrong (Australia), Maria Blettner
(Germany), Alistair Woodward (New Zealand) and Patricia McKinney (UK) for the use of
the occupational data from their INTERPHONE study centres for the INTEROCC project.
Competing interest
MC Turner reports personal fees from ICF Incorporated, LLC, outside this work.
DK reports to serving as Chief Risk Scientist and CEO at Risk Sciences
International (http://www.risksciences.com), a Canadian company established in
2006 in partnership with the University of Ottawa conducting work in risk
assessment, management, and communication of health and environmental
risks and their broader impacts on both public and private interests. He also
holds an Industrial Research Chair in Risk Science under a peer-reviewed
university-industry partnership program administered by the Natural Sciences
and Engineering Research Council of Canada.
Funding
Michelle C Turner was funded by a Government of Canada Banting Postdoctoral
Fellowship. The INTEROCC study was funded by the National Institutes for Health (NIH)
Grant No. 1R01CA124759 (PI E Cardis). Coding of the French occupational data was in
part funded by AFSSET (Convention N° ST-2005-004). The INTERPHONE study was
supported by funding from the European Fifth Framework Program, ‘Quality of Life and
Management of Living Resources’ (contract 100 QLK4-CT-1999901563) and the
International Union against Cancer (UICC). The UICC received funds for this purpose from
the Mobile Manufacturers’ Forum and GSM Association. In Australia, funding was
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received from the Australian National Health and Medical Research Council (EME Grant
219129) with funds originally derived from mobile phone service license fees; a
University of Sydney Medical Foundation Program; the Cancer Council NSW and The
Cancer Council Victoria. In Canada funding was received from the Canadian Institutes of
Health Research (project MOP-42525); the Canada Research Chair programme; the
Guzzo-CRS Chair in Environment and Cancer; the Fonds de la recherche en santé du
Québec; the Canadian Institutes of Health Research (CIHR), the latter including partial
support from the Canadian Wireless Telecommunications Association; the NSERC Chair in
Risk Science at the University of Ottawa. In France, funding was received by l’Association
pour la Recherche sur le Cancer (ARC) (Contrat N85142) and three network operators
(Orange, SFR, Bouygues Telecom). In Germany, funding was received from the German
Mobile Phone Research Program (Deutsches Mobilfunkforschungsprogramm) of the
German Federal Ministry for the Environment, Nuclear Safety, and Nature Protection; the
Ministry for the Environment and Traffic of the state of Baden- Wuerttemberg; the
Ministry for the Environment of the state of North Rhine-Westphalia; the MAIFOR Program
(Mainzer Forschungsforderungsprogramm) of the University of Mainz. In New Zealand,
funding was provided by the Health Research Council, Hawkes Bay Medical Research
Foundation, the Wellington Medical Research Foundation, the Waikato Medical Research
Foundation and the Cancer Society of New Zealand. Additional funding for the UK study
was received from the Mobile Telecommunications, Health and Research (MTHR)
program, funding from the Health and Safety Executive, the Department of Health, the
UK Network Operators (O2, Orange, T-Mobile, Vodafone, ‘3’) and the Scottish Executive.
All industry funding was governed by contracts guaranteeing the complete scientific
independence of the investigators.
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10 INTERPHONE Study Group. Brain tumour risk in relation to mobile telephone use: results of the INTERPHONE international case–control study. International Journal of Epidemiology 2010;39:675-694.
11 Cardis E, Armstrong BK, Bowman JD et al. Risk of brain tumours in relation to estimated RF dose from mobile phones: results from five Interphone countries. Occupational and Environmental Medicine 2011;68:631-640.
12 Hu J, Little J, Xu T et al. Risk factors for meningioma in adults: A case-control study in northeast China. International Journal of Cancer 1999;83:299-304.
13 McLean D, Fleming S, Turner MC et al. Occupational solvent exposure and risk of meningioma: results from the INTEROCC multicentre case–control study. Occupational and Environmental Medicine 2014;71:253-258.
14 Sadetzki S, Chetrit A, Turner MC et al. Occupational exposure to metals and risk of meningioma: a multinational case-control study. Journal of Neuro-Oncology 2016;130:505-515.
15 Sadetzki S, Chetrit A, Freedman L, Stovall M, Modan B, Novikov I. Long-Term Follow-up for Brain Tumor Development after Childhood Exposure to Ionizing Radiation for Tinea Capitis. Radiation Research 2005;163:424-432.
16 Phillips LE, Longstreth Jr WT, Koepsell T, Custer BS, Kukull WA, van Belle G. Active and Passive Cigarette Smoking and Risk of Intracranial Meningioma. Neuroepidemiology 2005;24:117-122.
17 Wang P-f, Ji W-J, Zhang X-h, Li S-w, Yan C-X. Allergy reduces the risk of meningioma: a meta-analysis. Scientific Reports 2017;7:40333.
18 Calderón-Garcidueñas L, Azzarelli B, Acuna H et al. Air Pollution and Brain Damage. Toxicologic Pathology 2002;30:373-389.
19 Cardis E, Richardson L, Deltour I et al. The INTERPHONE study: design, epidemiological methods, and description of the study population. European Journal of Epidemiology 2007;22:647-664.
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20 van Tongeren M, Kincl L, Richardson L et al. Assessing Occupational Exposure to Chemicals in an International Epidemiological Study of Brain Tumours. Annals of Occupational Hygiene 2013;57:610-626.
21 ILO. International Standard Classification of Occupations, Revised Edition. Geneva: ILO, 1968.22 Kauppinen T, Toikkanen J, Pukkala E. From cross-tabulations to multipurpose exposure
information systems: A new job-exposure matrix. American Journal of Industrial Medicine 1998;33:409-417.
23 Lavoué J, Pintos J, Van Tongeren M et al. Comparison of exposure estimates in the Finnish job-exposure matrix FINJEM with a JEM derived from expert assessments performed in Montreal. Occupational and Environmental Medicine 2012.
24 Laakkonen A, Kyyrönen P, Kauppinen T, Pukkala EI. Occupational exposure to eight organic dusts and respiratory cancer among Finns. Occupational and Environmental Medicine 2006;63:726-733.
25 Benbrahim-Tallaa L, Baan RA, Grosse Y et al. Carcinogenicity of diesel-engine and gasoline-engine exhausts and some nitroarenes. The Lancet Oncology 2012;13:663-664.
26 Cogliano V, Grosse Y, Baan R, Straif K, Secretan B, Ghissassi FE. Advice on formaldehyde and glycol ethers. The Lancet Oncology 2004;5:528.
27 Gibbs GW, Labrèche F. Cancer Risks in Aluminum Reduction Plant Workers: A Review. Journal of Occupational and Environmental Medicine 2014;56:S40-S59.
28 Guha N, Staif K, Benbrahim-Tallaa L. The IARC Monographs on the carcinogenicity of crystalline silica. La Medicina del Lavoro 2011;102:310-20.
29 Lauby-Secretan B, Baan R, Grosse Y et al. Bitumens and bitumen emissions, and some heterocyclic polycyclic aromatic hydrocarbons. The Lancet Oncology 2011;12:1190-1191.
30 Meng Z, Qin G, Zhang B, Zhang J. [Inhalation of sulfur dioxide on the DNA damage of brain cells in mice]. Zhonghua Yu Fang Yi Xue Za Zhi 2002;36:370-3.
31 Pan SY, Ugnat AM, Mao Y. Occupational risk factors for brain cancer in Canada. J Occup Environ Med 2005;47:704-17.
32 Poulsen AH, Sørensen M, Andersen ZJ, Ketzel M, Raaschou-Nielsen O. Air pollution from traffic and risk for brain tumors: a nationwide study in Denmark. Cancer Causes & Control 2016;27:473-480.
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34 Hauptmann M, Stewart PA, Lubin JH et al. Mortality From Lymphohematopoietic Malignancies and Brain Cancer Among Embalmers Exposed to Formaldehyde. Journal of the National Cancer Institute 2009;101:1696-1708.
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37 Woskie SR, Virji MA, Hallock M, Smith TJ, Hammond SK. Summary of the findings from the exposure assessments for metalworking fluid mortality and morbidity studies. Applied Occupational and Environmental Hygiene 2003;18:855-64.
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-18-
40 Goldberg M, Hémon D. Occupational Epidemiology and Assessment of Exposure. International Journal of Epidemiology 1993;22:S5-S9.
-19-
Table 1: Description of selected characteristics of the study population.
Characteristic Cases ControlsN % N %
Total 1906 100 5565 100SexMales 507 26.6 2,484 44.6Females 1399 73.4 3,081 55.4Age (years)<40 182 9.5 902 16.240-49 448 23.5 1394 25.050-59 708 37.2 2019 36.360-69 370 19.4 959 17.470+ 198 10.4 291 5.2Mean age + SD 55.0+11.8 52.0+11.5CountryAustralia 254 13.3 666 12.0Canada 94 4.9 653 11.7France 145 7.6 472 8.5Germany 379 19.9 1,535 27.6Israel 737 38.7 987 17.7New Zealand 50 2.6 160 2.9UK 247 13.0 1,092 19.6EducationPrimary-Secondary 1,146 60.1 2,998 53.9Intermediate College 361 18.9 1,045 18.8Tertiary 392 20.6 1,511 27.2Unknown 7 6.4 11 0.2SIOPS (SES in quartiles)<35 550 28.9 1,369 24.635- 439 23.0 1,369 24.642.9- 417 21.9 1,369 24.652.1+ 421 22.1 1,368 24.6Unknown 79 4.1 90 1.6Smoking statusCurrent 486 25.5 1,501 27.0Ex 397 20.8 1,319 23.7Never 1,023 53.7 2,745 49.3Asthma/Hay Fever/EczemaNo 1,500 78.7 4,099 73.7Yes 400 21.0 1,464 26.3SIOPS, Standard International Occupational Prestige Scale; higher SIOP scores indicate higher SES.
Note some columns do not add to the total due to missing data.
-20-
Table 2: Prevalence and mean duration and mean cumulative exposures to substances in cases and controls All Men Women
cases controls cases controls cases controls
Agent1
N (%) Mean duration (years)
Mean cumulative exposure2
N (%) Mean duration (years)
Mean cumulative exposure2
N (%) Mean duration (years)
Mean cumulative exposure2
N Mean duration (years)
Mean cumulative exposure2
N Mean duration (years)
Mean cumulative exposure2
N Mean duration (years)
Mean cumulative exposure2
Animal dust(mg m-3)
78 (4.1)
13.0 16.9 225 (4.2) 13.3 18.5 42 (8.3)
12.0 16.8 146 (5.9)
14.1 20.9 36 (2.6)
14.2 17.1 79 (2.6) 10.9 14.2
Asbestos(f cm-3)
177 (9.3)
11.1 214.3 592 (10.6)
10.4 167.7 130 (25.6)
12.6 223.1 539 (21.7)
11.0 170.4 47 (3.4)
6.9 190.1 53 (1.7) 5.0 140.7
Quartz(mg m-3)
138 (7.2)
12.1 89.9 395 (7.1) 11.3 100.5 79 (15.6)
13.4 112.2 313 (12.6)
12.2 110.6 59 (4.2)
10.4 60.0 82 (2.7) 7.8 61.8
Wood dust(mg m-3)
28 (1.5)
13.2 613.9 137 (2.5) 13.3 746.1 21 (4.1)
15.6 805.7 120 (4.8)
14.1 803.5 7 (0.5)
5.7 38.3 17 (0.6) 7.8 340.8
Benzo(a)pyrene(µg m-3)
296 (15.5)
10.5 11.8 827 (14.9)
11.0 17.1 147 (29.0)
13.1 8.3 512 (20.6)
13.0 23.5 149 (10.7)
7.9 15.3 315 (11.2)
7.8 6.7
Bitumen(mg m-3)
0 (0.0)
- - 1 (0.0) 2.5 117.3 0 (0) - - 1 (0.0)
2.5 117.3 0 (0.0)
- - 0 (0.0) - -
Diesel engine exhaust(mg m-3)
89 (4.7)
11.9 94.7 397 (7.1) 12.0 104.0 77 (15.2)
12.5 104.9 360 (14.5)
12.8 112.1 12 (0.9)
7.6 29.2 37 (1.2) 4.9 25.4
Gasoline engine exhaust(mg m-3)
80 (4.2)
11.8 7826.6 391 (7.0) 12.0 8585.0 68 (13.4)
12.6 8818.5 355 (14.3)
12.7 9339.8 12 (0.9)
7.6 2206.2 36 (1.2) 4.9 1141.0
Formaldehyde(ppm)
116 (6.1)
11.0 53.2 278 (5.0) 8.0 65.5 25 (4.9)
9.5 115.5 103 (4.1)
9.0 131.8 91 (6.5)
11.5 36.1 175 (5.7) 7.3 26.5
Oil mist(mg m-3)
51 (2.6)
10.1 1882.0 138 (2.5) 12.6 2124.4 35 (6.9)
12.3 2423.1 112 (4.5)
13.8 2502.8 16 (1.1)
5.4 698.2 26 (0.8) 7.3 494.8
Polycyclic aromatic hydrocarbons(µg m-3)
299 (15.7)
10.6 183.5 837(20.1) 11.0 199.2 149 (29.4)
13.2 105.0 521 (21.0)
13.0 292.6 150 (10.7)
8.0 261.5 316(10.2) 7.8 44.80
Sulphur dioxide(ppm)
4 (0.2)
19.8 692.5 19(0.4) 13.6 624.7 2 (0.4)
21.1 170.0 18 (0.7)
13.0 687.3 2 (0.1)
18.5 1215.0 1 (0.0) 24.0 210.0
Notes:1 Exposure in an occupation with probability of exposure >25% for at least 1 year with 5-year lag.2 Cumulative exposure is expressed in the exposure units given for the agent times years (mg m-3 year for animal dust, quartz, wood dust, bitumen, diesel engine exhaust, gasoline engine exhaust, oil mist; µg m-3 year for Benzo(a)pyrene and polycyclic aromatic hydrocarbons; and ppm years for formaldehyde and sulphur dioxide N = number of cases or controls with information on the agent% = percentage exposedMean duration (among those exposed)Mean cumulative exposure (among those exposed)
-21-
Table 3: ORs for meningioma for ever versus never exposed (proportion 25% of more in JEM) with at least 1 year of exposure and 5-year lag.
All Men Women
Agent
Cases
N
Control
N OR# 95%CI
Cases
N
Control
N OR# 95%CI
Cases
N
Control
N OR# 95%CI
Animal dust 78 225 0.89 0.68 1.18 42 146 0.91 0.62 1.33 36 79 0.90 0.59 1.35
Asbestos 177 592 1.11 0.90 1.37 130 539 1.02 0.80 1.31 47 53 1.50 0.98 2.28
Quartz 138 395 1.11 0.88 1.38 79 313 1.01 0.75 1.35 59 82 1.31 0.92 1.88
Wood dust 28 137 0.85 0.55 1.31 21 120 0.78 0.48 1.28 7 17 1.19 0.48 2.97
Benzo(a)pyrene 296 827 1.02 0.81 1.30 147 512 0.97 0.64 1.46 149 315 1.07 0.79 1.44
Diesel engine exhaust 89 397 0.91 0.70 1.19 77 360 0.93 0.69 1.24 12 37 0.80 0.41 1.57
Gasoline engine exhaust 80 391 0.80 0.61 1.05 68 355 0.80 0.59 1.08 12 36 0.81 0.41 1.58
Formaldehyde 116 278 1.02 0.80 1.29 25 103 1.00 0.62 1.61 91 175 1.02 0.77 1.35
Oil mist 51 138 1.57 1.10 2.22 35 112 1.58 1.04 2.41 16 26 1.56 0.82 2.97
Polycyclic aromatic hydrocarbons 299 837 1.04 0.82 1.32 149 521 1.01 0.68 1.50 150 366 1.08 0.80 1.45
Sulphur dioxide 4 19 0.75 0.24 2.32 2 18 0.45 0.10 2.03 2 1 3.35 0.29 38.37
Note: The number of cases and control differs from Table 2 as there are some strata with controls only which are omitted from the analysis in Table 3
#ORs stratified by sex and 5-year age group and country-region, and adjusted for education in all 7 countries combined.
-22-
Table 4: ORs for lifetime duration of exposure in years (across all jobs with a probability of exposure of 25% or more), and a 5 year lag period
ALL MEN WOMENAgent Cases Cont. OR 95%CI p-trend Cases Cont. OR 95%CI p-trend Cases Cont. OR 95%CI p-valueAnimal dustNever exposed1-45-1415+
1817272823
4938847071
1.000.821.140.77
-0.520.710.47
-1.291.811.27 0.45
461141711
2053494750
1.001.021.160.60
-0.540.640.30
-1.932.131.20 0.38
1356131112
2885352321
1.000.671.111.10
-0.350.530.52
-1.282.342.28 0.96
AsbestosNever exposed1-45-1415+
1629646152
4236201230161
1.001.131.041.18
-0.830.750.83
-1.551.431.69 0.35
314434146
1399167212160
1.001.110.901.08
-0.760.620.74
-1.631.311.57 0.91
13152120
6
28573418
1
1.001.161.678.17
-0.650.860.95
-2.063.24
70.08 0.02QuartzNever exposed1-45-1415+
1758573843
4768160124111
1.001.190.941.18
-0.860.630.80
-1.661.391.74 0.47
421331927
1866119
9797
1.001.250.751.00
-0.810.440.62
-1.911.271.60 0.76
1337241916
2902412714
1.001.111.341.84
-0.650.720.87
-1.872.483.90 0.07
Wood dustNever exposed1-45-1415+
18781011
7
5056345746
1.001.140.910.57
-0.540.470.25
-2.401.791.30 0.26
486597
2084284943
1.000.850.910.62
-0.320.430.27
-2.271.911.44 0.27
1392520
2972683
1.001.960.940.00
-0.570.190.00
-6.774.61
- 0.82Benzo(a)pyreneNever exposed1-45-1415+
311113106
77
652313283251
1.000.961.091.03
-0.710.810.73
-1.301.491.45 0.70
53425451
173165165182
1.000.841.15
10.95
-0.500.710.58
-1.291.871.55 0.86
258715226
479148118
49
1.001.051.011.22
-0.710.670.71
-1.531.822.10 0.58
Diesel engine exhaustNever exposed1-45-1415+
1685332729
4474148114135
1.000.801.140.88
-0.540.730.57
-1.201.801.36 0.64
361262427
1615119109132
1.000.881.090.85
-0.550.670.54
-1.401.761.35 0.60
1325732
285929
53
1.000.601.711.27
-0.260.400.21
-1.407.367.84 0.92
Gasoline Engine ExhaustNever exposed1-45-1415+
1785312227
4693151108132
1.000.730.990.77
-0.480.600.49
-1.101.611.20 0.17
411241925
1758124102129
1.000.770.930.75
-0.480.550.47
-1.241.571.19 0.17
1324732
293427
63
1.000.621.541.22
-0.270.370.20
-1.456.417.59 0.89
FormaldehydeNever exposed1-45-1415+
1705443735
4649138
9050
1.000.781.011.64
-0.550.671.04
-1.121.522.60 0.26
4349
115
1893423922
1.000.931.150.90
-0.430.560.32
-1.982.362.48 0.98
1269352630
2756965128
-1.000.750.951.99
-0.500.581.16
-1.631.563.40 0.18
Oil MistNever exposed1-45-1415+
1806182211
4841425046
1.001.571.891.17
-0.871.110.58
-2.833.232.35 0.04
4349
1610
1914303943
1.001.502.031.23
-0.681.080.59
-3.293.812.55 0.07
1372961
29271211
3
1.001.681.620.85
-0.680.590.09
-4.144.468.18 0.27
Polycyclic Aromatic HydrocarbonsNever exposed1-4
313113107
79
669315288254
1.000.981.121.06
-0.720.820.75
-1.321.511.49
0.57 55425552
189167169185
1.000.861.210.98
-0.520.750.61
-1.421.941.58
0.75 258715227
480148119
49
1.001.051.001.28
-0.720.670.75
-1.541.512.19
0.50
-23-
5-1415+Sulphur dioxideNever exposed1-45-1415+
1900022
5164496
1.000.001.360.74
-0.000.270.14
--
6.823.87 0.75
503011
2176495
1.000.000.830.49
-0.000.100.05
--
6.814.40 0.40
1397011
2988001
1.00--
1.73
---
0.10
---
29.17 0.40
-24-
Table 5: ORs for lifetime cumulative exposure (across all jobs >1 year with probability of exposure of 25% or more) in quartiles of exposure according to the distribution in the controls, and a 5 year lag period
All Men WomenAgent
Cases Cont. OR 95%CI p-trend Cases Cont. OR 95%CI p-trend Cases Cont. OR 95%CI p-valueAnimal dustNever exposed0-44-8.58.5-26.526.5+
181713173018
493860525558
1.000.610.811.410.76
-0.330.460.880.43
-1.141.452.271.33 0.73
46179
129
205338283941
1.000.681.091.280.63
-0.290.480.690.29
-1.592.462.311.35 0.63
135668
139
288522241617
1.000.540.641.710.99
-0.210.280.800.43
-1.361.453.672.28 0.90
AsbestosNever exposed0-18.218.2-59.159.1-181.8181.8+
162925494162
4236138153148153
1.000.861.290.961.28
-0.540.900.660.90
-1.361.841.411.80 0.20
31424392443
1379134136129140
1.000.851.300.811.11
-0.530.870.500.74
-1.361.931.311.66 0.76
13151
101719
28574
171913
1.000.951.181.392.08
-0.100.520.701.00
-9.032.682.754.34 0.03
QuartzNever exposed0-12.112.1-37.537.5-98.298.2+
175835313537
4768103
9295
105
1.001.191.051.031.16
-0.790.670.680.76
-1.801.641.571.75 0.46
42117181925
186680677294
1.000.91
1.200.921.03
-0.520.680.530.63
-1.602.111.601.68 0.98
133810
320
289613
711
1.001.700.936.010.00
-0.710.230.530.00
-4.093.81
68.81- 1.00
Wood dustNever exposed0-99.899.8-351.5351.5-939.0939.0+
187810
585
505636323336
1.001.060.521.110.71
-0.510.200.500.27
-2.221.392.491.87 0.42
4864485
208429282934
1.000.630.481.320.78
-0.210.160.580.30
-1.841.442.982.05 0.54
13926100
29727442
1.002.280.800.000.00
-0.730.090.000.00
-7.187.40
-- 0.55
Benzo(a)pyreneNever exposed0-0.90.9-2.52.5-5.75.7+
31153907865
652199199212217
1.000.831.300.990.97
-0.581.940.710.68
-1.181.811.381.41 0.73
5328374042
172101111135165
1.000.851.250.950.89
-0.851.250.950.89
-1.512.141.581.49 1.00
25835533823
47998887752
1.000.801.361.011.23
-0.500.900.630.64
-1.282.071.601.97 0.45
Diesel engine exhaustNever exposed0-12.912-9-42.642.6-113.1113.1+
168515243020
4774989998
102
1.000.540.971.240.97
-0.310.600.790.58
-0.951.561.941.62 0.92
436111192720
1615768896
100
1.000.590.901.15
0.99
-0.300.530.720.59
-1.161.541.861.67 1.00
13244530
28592211
22
1.000.431.282.950.00
-0.150.440.480.00
-1.273.75
18.20- 0.99
Gasoline engine exhaustNever exposed0-612.7612.7-3271.83271.8-9738.59738.5+
178513282217
4693959799
100
1.000.491.050.860.83
-0.270.670.520.48
-0.891.651.411.44 0.29
4118
232017
1759728796
100
1.000.480.990.810.84
-0.230.600.480.48
-1.031.641.371.45 0.29
13745520
29342310
30
1.000.501.361.60
-
-0.190.450.26
-
-1.344.08
10.03- 0.99
FormaldehydeNever exposed0-7.3
17032920
464971
1.001.07
-0.68
-1.69
4345
189327
1.000.860.85
-0.32
-2.32
126924
275644
1.001.13
-0.67
-1.91
-25-
7.3-17.917.9-53.853.8+
3037
686970
0.680.911.46
0.410.580.95
1.151.43
2.25 0.53
36
11
131746
1.211.04
0.230.450.52
3.243.222.09 0.88
172426
555224
0.660.851.88
0.380.511.06
1.171.413.33 0.52
Oil mistNever exposed0-360.0360.0-1312.41312.4-3177.53177.5+
1806101517
9
484135323635
1.000.961.992.231.27
-0.461.031.200.59
-1.993.854.142.75 0.01
43439
149
191416283434
1.001.051.592.041.34
-0.290.711.040.62
-3.803.573.992.92 0.04
13727630
292719
421
1.000.913.694.29
-
-0.381.010.71
-
-2.22
13.5525.91
- 0.07Polycyclic aromatic hydrocarbonsNever exposed0-9.39.3-25.625.6-65.065.0+
31366968255
669205198211223
1.000.871.401.090.78
-0.611.020.790.53
-1.231.931.521.13 0.90
5529414237
189106107131177
1.000.871.441.150.72
-0.500.860.700.43
-1.532.411.911.20 0.51
25837554018
48099918046
1.000.861.401.040.95
-0.540.930.660.51
-1.362.111.641.75 0.59
Sulphur dioxideNever exposed0-91.991.9-230.0230.0-600.0600.0+
19001111
51645554
1.000.581.030.860.66
-0.060.100.090.07
-5.31
10.737.936.37 0.67
5030110
21765454
1.000.002.330.870.00
--
0.240.09
-
--
22.218.00
- 0.32
13971001
29880100
1.00-
0.000.00
-
----
---- 0.30
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Supplementary Table
Joint Exposures: Proportion of Participants with Exposure to Agent A co-exposed to Agent B
Agent A\ Agent B An
imal
Du
st
Asbe
stos
Qua
rtz
Woo
d du
st
Benz
o(a)
pyre
ne
Dies
el
engi
ne
exha
ust
Gaso
line
engi
ne
exha
ust
Form
alde
hyde
Oil
mist
Poly
cycl
ic
arom
atic
hydr
ocar
bons
Sulp
hur
diox
ide
Cadm
ium
Chro
miu
m Iron
Nic
kel
Lead
Wel
ding
fu
me
n 313 831 583 181 1391 531 511 409 206 1407 26 130 457 687 565 498 494Animal Dust - 4 8.1 5.5 3.7 7.2 7.4 2.2 2.9 3.8 3.8 6.9 4.8 3.9 4.6 4.8 3.6Asbestos 10.5 - 61.1 42.5 27.6 45.2 45.2 12.7 24.3 27.4 34.6 18.5 56.2 62.7 59.8 55.2 81.4Quartz 15 42.8 - 48.6 14.5 20.2 18.8 10.8 20.9 14.5 61.5 34.6 18.2 18.2 20 19.3 12.8Wood dust 3.2 9.3 15.1 - 2.7 6 5.5 5.4 3.9 2.7 11.5 4.6 3.9 3.9 3.9 2.6 3.6Benzo(a)pyrene 16.6 46.2 34.5 20.4 - 56.7 55.8 18.8 85 98.8 96.2 66.9 77.5 76 73.5 63.7 66.6Diesel engine exhaust 12.1 28.9 18.4 17.7 21.6 - 98.6 6.8 15.5 21.7 23.1 10.8 35.2 33.5 36.1 39.4 41.9Gasoline engine exhaust 12.1 27.8 16.5 15.5 20.5 94.9 - 5.9 14.6 20.5 11.5 7.7 35.2 33 35.9 38.4 41.9Formaldehyde 2.9 6.3 7.5 12.2 5.5 5.3 4.7 - 9.2 5.8 3.8 50 8.3 7.3 8 12.7 5.9Oil mist 1.9 6 7.4 4.4 12.6 6 5.9 4.6 - 12.4 0 10.8 34.8 24.9 28.7 5.2 7.9Polycyclic aromatic hydrocarbons 16.9 46.3 35 21 99.9 57.4 56.4 19.8 85 - 96.2 68.5 77.7 76.1 73.8 64.1 66.8Sulphur dioxide 0.3 1.1 2.7 1.7 1.8 1.1 0.6 0.2 0 1.8 - 8.5 2.4 2 1.9 3 0.4Cadmium 2.9 2.9 7.7 3.3 6.3 2.6 2 15.9 6.8 6.3 42.3 - 7.2 6.7 8 9 2.6Chromium 7 30.9 14.2 9.9 25.4 30.3 31.5 9.3 77.2 25.2 42.3 25.4 - 66.5 79.1 37.6 62.1Iron 8.6 51.9 21.4 14.9 37.5 43.3 44.4 12.2 83 37.2 53.8 35.4 100 - 96.1 61.6 100Nickel 8.3 40.7 19.4 12.2 29.8 38.4 39.7 11 78.6 29.6 42.3 34.6 97.8 79 - 49.8 80Lead 7.7 33.1 16.5 7.2 22.8 36.9 37.4 15.4 12.6 22.7 57.7 34.6 40.9 44.7 43.9 - 53.8Welding fume 5.8 48.4 10.8 9.9 23.7 39 40.5 7.1 18.9 23.5 7.7 10 67.2 71.9 69.9 53.4 -
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