From the Joint Departments of Epidemiology, Biostatistics, and Occupational Health, Faculty of Medicine, McGill University, Montréal, Québec, Canada.
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ABSTRACT |
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electromagnetic fields; occupational exposure; women
Abbreviations: ALL, acute lymphoblastic leukemia; ELF, extremely low frequency; EMF RAPID, Electric and Magnetic Fields Research and Public Information Dissemination
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INTRODUCTION |
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While residential exposures can, in some cases, equal the magnitude of certain occupational exposures, the occupational environment presents a greater opportunity for exposure. Extensive surveys of residences in Canada and the United States have found that 324 percent of homes have ELF magnetic fields in excess of 0.2 µT, depending on the configuration of wiring and grounding systems and on the extent of electric heating, among other factors (4, 5
). By comparison, the largest known published study of occupational exposures, covering 100 common jobs in Sweden, found that 40 percent of daily mean exposures to ELF magnetic fields exceeded a level of 0.2 µT (6
). Furthermore, magnetic field exposures in Europe are expected to be lower than those in North America because of higher European mains voltage (voltage at which electricity is used residentially or commercially) and hence lower currents.
In North America, assessment of occupational exposures to ELF magnetic fields has focused primarily on specialized groups of workers such as those in electric utilities (711
) and telephone utilities (12
), most often in men's jobs, and generally has used a job-exposure matrix approach in which exposures are assigned at the level of job title. Beyond these work environments, information on levels of exposure to ELF magnetic fields is very sparse. Although the Swedish exposure study (6
) provides exposure levels for many common occupations, the values cannot be transposed to North America directly because the power supply voltages are different. There are scattered reports of elevated exposures, such as among sewing machine operators (13
) and nurses in neonatal intensive care units (14
), but, for the vast majority of commonly held jobs in North America, exposures to ELF magnetic fields have not been documented.
A recent study of environmental and genetic risk factors and childhood acute lymphoblastic leukemia (ALL) (1517
), including parental occupation (18
), gathered self-reported information about the occupations of 491 parents of ALL cases and a similar number of healthy controls. This information included detailed descriptions of general and specific work environments, tasks and their durations, and electrical equipment used in the workplaces. By combining this information with published data on the intensities of ELF magnetic fields associated with these sources or work environments, this study provided a unique opportunity to semiquantitatively estimate ELF magnetic field exposures for a wide range of jobs. The results have the following key features: 1) they provide new information on expected levels of exposure in a range of jobs commonly and contemporarily held by women; 2) they are based on individually reported exposure data as opposed to job titles for groups of workersoften used to create job-exposure matriceswith the former mode expected to improve the precision of exposure estimates (10
, 19
, 20
); and 3
) they provide practical indications of exposures to ELF magnetic fields in the form of matrices by source of exposure, by work environment, and by job title.
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MATERIALS AND METHODS |
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Extraction of relevant data
The general and job-specific questionnaires were reviewed by one of the authors (J. E. D.), an industrial hygienist unaware of case-control status, to extract information on the following potential determinants of exposure: job title, work environment, magnetic field sources, duration of use or exposure, total hours of work per week, and type of work shift. The questionnaires included detailed descriptions of work tasks and work methods. These were reviewed for any information that would further help identify electrical equipment in the worker's vicinity, hours of use or of exposure per week, or type of work environment. Because the objective was to assign exposures based on ELF magnetic field sources (primarily electrical equipment), work environments, and duration of exposure, this information was noted and compiled into a list of 111 sources (table 1) and 59 work environments (table 2). When more than one source was reported, the one used most frequently was listed first, with up to two other sources listed in decreasing frequency of use. The list of job titles was standardized into a set of 61 titles (table 3).
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Assignment of ELF magnetic field intensity values
ELF magnetic field intensities for sources and work environments were obtained from the published literature, calculated from EMF RAPID data set number 004, or inferred by analogy with a documented value. The EMF RAPID database provided almost one third of the source intensity estimates and about one quarter of the environmental intensity estimates. Tables 1 and 2 provide the assigned values for sources and environments and the basis for each value.
Magnetic fields diminish rapidly with distance from small sources such as video display terminals, photocopiers, and typewriters. Since study subjects had not been asked about distances from sources, the expected typical distance between the worker and the source was assumed in assigning magnetic field levels using those cited in the literature. If a source was characterized by intermittent use (e.g., typewriters or sewing machines) and the published magnetic field values represented continuous use, field intensities were reduced to approximate average field levels over a common usage cycle. For video display terminals used before 1986, when magnetic field reduction techniques became more common, we assigned a value of 0.37 µT. Video display terminals used after 1986 were assigned a value of 0.25 µT.
Calculation of weekly time-weighted average exposures
For each job held by a subject, a weekly time-weighted average exposure was calculated by multiplying the ELF magnetic field intensity of each identified source by the weekly duration of use. Any remaining work time was multiplied by the background field level assigned to the specific work environment. The products of source and duration and of environment and duration were summed and were divided by the total weekly hours spent at work. For example, one store manager reported working 50 hours a week managing a pet-grooming salon. She mentioned using an electric razor for an average of 30 hours a week (assigned magnetic field value: 2 µT) and a cash register for an average of 10 hours a week (assigned magnetic field value: 0.33 µT). The remaining 10 hours were assigned the background magnetic field value for stores (0.2 µT). Thus, her weekly exposure was estimated as ((30 hours x 2) + (10 hours x 0.33) + (10 hours x 0.2))/50 hours = 1.31 µT.
For 54 percent of the 1,002 jobs (e.g., most waitresses and agricultural workers), local magnetic field sources were not expected to contribute substantially to exposure. In these instances, exposure was presumed to be similar to the background environmental level, and that level was assigned. For jobs in which local sources of magnetic fields were expected to substantially increase exposures above background levels from the work environment but in which the sources or their duration of use had not been reported in the questionnaire, we assigned the arithmetic mean value of the calculated weekly time-weighted average exposure for other workers with the same job title who had reported duration of use of local sources.
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RESULTS |
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Table 1 lists the frequency with which each source was reported or was identifiable; the minimum, maximum, and mean duration of use of the source or exposure to it; the ELF magnetic field value assigned to the source; and the basis for the estimate. Assigned ELF magnetic field values ranged from 0.1 to 4 µT. The 25th, 50th, and 75th cutpoints of the intensity range were 0.3, 0.5, and 0.94 µT, respectively. Sources for which the assigned ELF magnetic field intensities were less than 0.3 µT included electrocardiogram machines, floor polishing machines, hair dryers, milking machines, telephone switchboards, tractors, and video display terminals (after 1986). Those for which the assigned ELF magnetic field intensities were 0.30.5 µT included commercial aircraft (e.g., an arts representative traveling on business), cash registers, keypunch machines, microscopes, residential ovens (e.g., home care workers), photocopiers, posting machines, presses, saws, residential sewing machines, typewriters, and video display terminals (before 1986). Sources for which the assigned intensities were 0.50.94 µT included aquarium motors, deep fryers, heat-sealing plates and machines, light tables/viewers, overhead projectors, photocopiers, soldering guns, vacuum cleaners, warping machines, and weaving machines. The highest intensity sources (>0.94 µT) included band saws, electric compressors, electric razors, fluorescent viewing lamps, industrial sewing machines, paper shredders, and ultrasonic cleaners.
Work environments
Type of work environment was identifiable for all 1,002 jobs reviewed, which were classified into 59 categories chosen to reflect possible differences in ELF magnetic fields. The most common work environments were offices (28 percent), residences (10 percent), stores/supermarkets (9 percent), textile factories (7 percent), and restaurants (5 percent). Data on ELF magnetic field intensities associated with specific work environments were located for 22 of the 59 environments identified. For the remainder, magnetic field intensities were estimated by analogy with measured environments. Table 2 lists the frequency of each environment, the assigned ELF magnetic field value, and the basis for the estimate. By intensity, assigned ELF magnetic fields ranged from 0.01 µT (rural outdoors) to 0.32 µT (commercial kitchens). The 25th, 50th, and 75th cutpoints of the intensity range were 0.08, 0.1, and 0.16 µT, respectively. Environments in which the assigned ELF magnetic fields were less than 0.08 µT included electronics factories, nondairy farms, greenhouses, military barracks, outdoors (rural), and primary schools. Environments in which the fields were 0.080.16 µT included airports, banks, clinics/hospitals, dairy farms, day care facilities, factories (assembly and food products), hair salons, hotels, laundries, libraries, offices, outdoors (urban), and warehouses. Environments in which the fields were more than 0.16 µT included bakeries, textile or shoe factories, hospital intensive care units, operating rooms, cardiac or emergency areas, commercial and residential kitchens, residences, and supermarkets.
Job titles and estimated exposures
For the 1,002 jobs held by mothers of study subjects during pregnancy, the estimated time-weighted average exposure was 0.2 µT (minimum, 0.01; maximum, 1.45). Time-weighted average exposures in jobs exceeded levels of 0.2, 0.4, and 0.8 µT for 30, 6, and 3.5 percent of cases, respectively. The most prevalent job titles were office worker (18.8 percent), secretary (12.4 percent), teacher (6.1 percent), cashier (4.7 percent), nurse (4.4 percent), sewing machine operator (4.2 percent), waitress (4.2 percent), assembly worker (3.6 percent), textile machine operator and textile worker (3.3 percent), and bank clerk or teller (3.3 percent). When aggregated at the job category level (combining all work environments), the calculated time-weighted average exposures for the 61 categories of job titles ranged from 0.03 to 0.68 µT, with the 25th, 50th, and 75th percentiles as 0.135, 0.17, and 0.23 µT, respectively.
Jobs in the lowest quartile of exposure (<0.135 µT) included agricultural worker, bank clerk, horticultural worker, laundry worker, primary and secondary school teacher, and social worker. Jobs in which exposures were 0.1350.17 µT included assembly worker, barmaid, dental technician, nurse and hospital technician, and receptionist. Job categories in which exposures were 0.170.23 µT included bank teller, hairdresser, restaurant manager and cook, office worker, salesperson, waitress, and food products worker. The most highly exposed jobs (>0.23 µT) included bakery worker, cashier, cook and kitchen worker, electronics worker, residential and industrial sewing machine operator, and textile machine operator.
Time-weighted average exposures for each job category varied, depending on type of work environment (data not shown). When examined at the work environment level, the most highly exposed job categories were electronics worker in an assembly plant (0.70 µT) and sewing machine operator in a textile factory (0.68 µT) and a shoe factory (0.66 µT). Job categories covering the widest variety of work environments were office worker and secretary. For these jobs, exposures varied from 0.14 µT for a secretary in a clinic to 0.34 µT for a secretary in a secondary school.
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DISCUSSION |
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The only known published population-based job-exposure matrix (6) was developed from measurements on Swedish workers and includes exposure information on several of the jobs assessed in our study, notably clerical worker, cook, home worker, and nurse. However, these exposures cannot be directly transposed to North America, where electricity is supplied to the end user at a lower voltage than in Europe. The lower voltage is expected to result in higher electric currents and hence higher magnetic fields for equivalent jobs (24
). As a result, we did not use these data in deriving the estimates reported here.
The methods used thus far to estimate occupational exposures to ELF magnetic fields in epidemiologic studies cover a wide spectrum. At one extreme, job titles are classified into broad exposure categories (25) with or without supporting measurements. At the other, personal exposures of the study subjects are measured. When retrospective exposure assessments are required, job-exposure matrices are commonly used to provide more refined estimates of exposure for job titles beyond those obtained through broad classification. Although a job-exposure matrix can improve resolution of the exposure classification compared with a broad categorization of exposures, it assigns an identical exposure to all workers with the same job title. With the Gérin method of exposure assignment, workers are assigned to exposure categories by expert coders (chemists/industrial hygienists) who review detailed information on work activities and work environments (21
). The exposure estimation method described here is an extension of the Gérin method and is, to our knowledge, the first application to ELF magnetic fields. The work histories on which the estimation is based go beyond job title and job descriptions to focus on the main determinants of exposure: sources, duration of exposure, and work environments. The method requires expert judgment by an exposure coder, but the coder is not required to assign an exposure directly to a worker. Rather, he or she must be able to recognize and classify the person's work environment, the ELF magnetic field sources, and the duration of exposure to them. This information is then used to generate a weekly time-weighted average exposure based on documented or inferred values of ELF magnetic fields for each source or environment.
There are many potential sources of error when any exposure estimation method is used. For example, we assumed a single value of exposure intensity for each work environment or source; in reality, these vary between workplaces. We used self-reported information on exposure durations, which vary for each worker and according to perceptions of what constitutes use of or exposure to a machine. Overall, though, use of information specific to each person should have resulted in a less misclassified exposure assignment than if exposure had been assigned to the worker's job title by using a job-exposure matrix. On the other hand, using reported information on the occupational histories of the mothers of both cases and controls to create a job-exposure matrix could have created a validity problem if some jobs were held by mostly one of the two groups and there was a reporting bias for job title and job description (for example, if nurses were found only among the case group and they tended to overreport potential ELF exposure sources). On the basis of job distribution categories (e.g., blue collar, white collar) and industry distributions, we found no differences between the groups, and both distributions were comparable to those from population surveys of women in this province and in these age groups over this time period (26). Substantial bias in job description reporting for a large proportion of study subjects seems unlikely, although it was not measured.
Because duration of exposure is equally as important as intensity in estimating a person's exposure, its inclusion in the estimation process should have improved accuracy. For the duration estimates, we systematically used average weekly durations, which are expected to provide better estimates of the duration of long-term exposure than actual measurements of exposure duration over shorter periods (27). Furthermore, when intensity is estimated separately from duration, the exposure coder is required to judge exposure duration only, thus removing a subjective element from the process. When applied by a coder who has expert knowledge of the tasks involved in a job, sources of ELF fields, and specific exposure situations, this method is expected to increase the sensitivity of exposure assignments by identifying unexpected sources or environments in jobs that would have otherwise been considered as belonging to a low-exposure category. Similarly, the specificity of exposure assignments is expected to improve by identifying low exposures in jobs that could have been automatically considered, on the basis of job title alone, highly exposed.
The exposure estimation described in this paper focused on the key determinants of exposure for a person: the work environment and the sources within that environment. Stewart (28) has pointed out that deterministic-based modeling of exposures presents a key advantage for studies that rely on interview data. Once the principal determinants of exposure have been identified (e.g., type of source, duration of exposure), it would be necessary to ask questions on those determinants only, many of which could probably be answered by respondents. A campaign of personal measurements would therefore be very useful not only to validate the exposure estimates presented here but also to assess the performance of these and other exposure determinants in predicting exposures to ELF magnetic fields.
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NOTES |
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REFERENCES |
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