1 Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC.
2 Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA.
3 Etiology Program, Cancer Research Center of Hawaii, University of Hawaii, Honolulu, HI.
4 EM Factors, Richland, WA.
Received for publication November 1, 2002; accepted for publication June 3, 2003.
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ABSTRACT |
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breast neoplasms; electricity; electromagnetic fields; environmental exposure
Abbreviations: Abbreviations: CI, confidence interval; SEER, Surveillance, Epidemiology, and End Results.
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INTRODUCTION |
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The epidemiologic data on the relation between residential exposure to magnetic fields and female breast cancer are limited and inconclusive (6). Several studies had only self-reported data on one source of exposure, electric blanket use (710)a measure that is subject to recall bias. Eight studies (1118) used an objective but indirect measure of exposure based on distance to power distribution and/or transmission lines. Among these studies, two included fewer than 32 cases (11, 13), and two large studies (14, 16) were conducted in Scandinavian countries, where population exposure is likely to be lower than in the United States because primary and secondary distribution lines are buried. The only published study with direct measurements was a case-control study from Seattle, Washington, in which no effect was seen (18).
To address the possible association between breast cancer incidence and residential magnetic field exposure, we conducted a nested case-control study of breast cancer within a multiethnic cohort of African Americans, Latinas, and Caucasians in Los Angeles County, California. Because the case-control study was nested within the cohort study, we were able to evaluate the potential distortion of effects due to selection bias. Selection bias is difficult to assess in case-control studies when the base population is not well defined, as occurs with the use of random digit dialing for control selection. We assessed magnetic field exposure by means of two objective methodsdirect measurement of magnetic fields in the bedroom over a period of 7 days and an indirect measure of exposure known as wiring configuration coding, which has previously been shown to correlate with measured magnetic fields as well as with childhood leukemia risk in Los Angeles County and elsewhere in North America (1924).
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MATERIALS AND METHODS |
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For the nested case-control study of magnetic field exposure in relation to breast cancer risk, we ascertained incident cases of breast cancer by computer linkage of the cohort with the Surveillance, Epidemiology, and End Results (SEER) cancer registry in Los Angeles County and with the statewide cancer registry for California. A total of 751 case women without prior breast cancer were identified through August 1999. These cases were diagnosed from June 1993 to January 1999. From cohort members without a history of breast cancer at baseline, we randomly sampled 702 controls, using approximate frequency matching on self-reported ethnicity in three categoriesAfrican American, Latina, and Caucasian. Data on estrogen receptor and progesterone receptor status at breast cancer diagnosis were obtained through linkage to the SEER registry and were available for 403 cases. The study was approved by the Institutional Review Board at the Keck School of Medicine of the University of Southern California. Subjects provided written informed consent.
Data collection
Data collection for the nested case-control study began in June 1995 and concluded in January 2001. We obtained data on traditional risk factors for breast cancer from the baseline cohort questionnaire completed by all potential subjects. Our assessment of magnetic field exposure included an indirect assessment known as wiring configuration coding, measurement of magnetic fields in the subjects bedroom over a 7-day period, and questionnaire data. We invited each eligible subject to undergo an in-home interview on factors relevant to magnetic field exposure. The reference date for the interview was the date of diagnosis for cases. For controls, at the beginning of each 6-month period, we set a reference date that was the average of the diagnosis dates for cases that had most recently been identified via linkage to the SEER registry. In this manner, we ensured that cases and controls interviewed at any point during the study had comparable reference dates. The reference period was the 10 years prior to the reference date. The questionnaire included sections on residential history, usual time of sleep, occupation, and use of appliances over the reference period. Measurements were made in the residence currently occupied by the subject if this residence had also been occupied on the reference date. Subjects who refused an in-home visit were offered a telephone questionnaire. Subjects who could not be reached or who refused both in-person and telephone interviews were mailed an abbreviated questionnaire with questions on residential history. The median time from diagnosis to interview was 17 months for cases (interquartile range, 1425 months).
For more than 99 percent of subjects, we were able to assess magnetic fields by means of a surrogate measure known as wiring configuration coding (26), henceforth referred to as wire coding. We showed previously that wire code was associated with measured magnetic fields in Los Angeles (20, 23). Wire code has also been shown to be a useful surrogate measure for magnetic fields elsewhere in North America (27, 28). We attempted to perform wire coding on all homes that the subject had inhabited in Los Angeles County during the 10 years prior to the reference date. For subjects we could not contact, we used the address from the baseline cohort questionnaire.
To determine the wire code for a subjects home, field technicians first created a drawing of the house and all overhead electric power wiring within 150 feet (46 m) of any of its exterior walls. The technician then recorded, on a coding sheet, the distances to the nearest transmission lines, thick and thin three-phase primary distribution lines, and spun, open, and first-span secondary distribution lines. The technician also recorded whether there was electric power wiring of any type, including single-phase, two-phase, and "end-pole" primary lines, located within 150 feet of the home. These data were entered into a computer and were assigned wire codes using the Wertheimer-Leeper system (26, 29), as modified by Savitz et al. (21) and Kleinerman et al. (30). Homes were thus classified into the following five categories: very low current configuration, underground wiring (defined as having no overhead electric power wiring within 150 feet), ordinary low current configuration, ordinary high current configuration, and very high current configuration. A total of 872 case addresses and 803 control addresses for the 743 cases and 699 controls were wire-coded. Prior to assignment of final codes, each map and its coding sheet were reviewed by one of the investigators (W. T. K.), who had developed the wire coding protocol and instructed the field technicians in its use.
Over the course of the study, two technicians performed the wire coding blinded as to the case or control status of the resident of the home. For reinforcement of adherence to the protocol, every 69 months the two technicians were required to code 2025 homes independently; they then compared their maps and coding sheets and reached a consensus code if there were any discrepancies. So that we could evaluate the degree of concordance between the two technicians, they independently mapped 56 homes without consultation. The weighted kappa statistic was 0.78 (95 percent confidence interval (CI): 0.65, 0.91). The disagreements in coding were minorthree homes varied from "ordinary low current configuration" to "ordinary high," four homes varied from "underground" to "very low," one home varied from "ordinary high" to "very high," and two homes varied from "very low" to "ordinary low."
Magnetic fields were measured in the subjects bedroom using an Emdex II meter (Enertech Consultants, Campbell, California). Residential magnetic fields typically consist of 60-Hz fundamental field components along with smaller harmonic components at frequencies of 180 Hz, 300 Hz, 420 Hz, 600 Hz, etc. (31). The Emdex II meter uses filters to measure both the fundamental and harmonic components in a bandwidth extending from 40 Hz to 800 Hz and to separately measure only the harmonic components in a bandwidth extending from 100 Hz to 800 Hz. The former measurement is of the broadband field and the latter measurement is of the harmonic field. For our purposes, the meter was programmed to take and retain in its memory a sample every 120 seconds during a 7-day period (total number of samples = 5,040). The meter was placed on a bedside stand, on the floor under the bed, or next to the bed at a location where the measurement was within 0.05 µT of the field strength on the surface of the bed where the upper third of the subjects body would normally lie.
We checked the proper operation and approximate calibration of our Emdex meters weekly by placing the meters in the known magnetic field produced by a simple rectangular coil (Electric Field Measurements Company, West Stockbridge, Massachusetts). The absolute calibration of each meter was performed by the manufacturer (Enertech Consultants) every 69 months throughout the data collection period.
The primary magnetic field measurement metric was the nighttime mean, because we knew that subjects would be in the bedroom during this period. Each subjects questionnaire responses regarding the usual times of going to bed and rising were used to determine the nighttime period. We elicited separate responses for weekends and weekdays. Our metrics of nighttime magnetic field measurements were the overall mean for bedtime hours over the 7-day period for both the harmonic and broadband magnetic fields, the proportion of time above 0.4 µT, and a rate-of-change metric proposed by Wilson et al. (32) for assessment of short-term variability in the magnetic field.
Statistical methods
We estimated odds ratios and their 95 percent confidence intervals using unconditional logistic regression (SAS, version 8.00; SAS Institute, Inc., Cary, North Carolina). In all models, results were adjusted for self-reported ethnicity (African American, Latina, or Caucasian) because of the frequency matching. Age was included as a continuous variable; inclusion of categorical age did not materially alter the odds ratio estimates given that the age distributions of cases and controls were so similar. We considered confounding by established risk factors for breast cancer, including age at menopause/menopausal status, use of hormone replacement therapy, age at first birth, parity, history of breast cancer in a mother or full sister, alcohol intake, body mass index, and vigorous physical activity. For a woman using hormone replacement therapy before her reported age at the last menstrual period, we set her age of menopause as the year in which she began hormone replacement therapy (excluding use of progestin alone), with the rationale that she started hormone replacement therapy because of menopausal symptoms (33). We also considered potential confounding by season of measurement. We included all factors that were associated with increased or decreased risk of breast cancer at the p = 0.10 level of significance. The following variables met this criterion: age at menarche, parity, age at first birth, age at menopause/menopausal status, current use of hormone replacement therapy, alcohol intake, and history of breast cancer in a mother or sister. Although there was minimal confounding by these factors, we present the multivariate-adjusted results along with the results from the age- and ethnicity-adjusted models.
For the wire coding, we conducted the analysis in two ways. We first considered the wire code for the house occupied at the end of the reference period. We ordered the five categories according to the mean broadband nighttime magnetic field values measured in the subjects homes, as follows: very low current configuration (0.06 µT), underground wiring (0.07 µT), ordinary low current configuration (0.10 µT), ordinary high current configuration (0.11 µT), and very high current configuration (0.13 µT). Median fields by wire code category were 0.04 µT for a very low current configuration and for underground wiring, 0.05 µT for the ordinary low and ordinary high current configurations, and 0.10 µT for the very high current configuration. To quantify the risk associated with wire codes at multiple residences over a period of 10 years prior to the reference date, we also created variables that represented the number of years spent at each wire code level over the past 10 years. The parameter estimates associated with each of the variables are the odds ratios for someone who spent the entire 10 years with that wire code. Furthermore, the odds ratios for multiple residences are then the time-weighted combinations of the wire code odds ratios for each residence.
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RESULTS |
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DISCUSSION |
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This study was unique among studies of breast cancer and magnetic fields in that a majority of our subjects came from two underrepresented groups, African Americans and Latinas. There was no evidence of an association in either of these groups. Our population was somewhat more highly exposed than the subjects in the previous study with direct magnetic field measurements (18). For measured fields, our top quartile was 0.089 µT or higher, as compared with 0.073 µT or higher in the Seattle study (18). In addition, 52 percent of our controls resided in homes in the two highest-exposure wire code categories, as compared with 23 percent in the Seattle study. This enhanced our power to find associations with measured fields and wire codes.
Our results are in agreement with those of the only other published study that evaluated breast cancer risk with direct magnetic field measurements (18). The results are also in agreement with those of previous studies of breast cancer incidence that used indirect estimates of magnetic field strength based on distance to power lines (1117).
In a study from Sweden in which exposure was based on fields calculated from wire configurations, no increased risk was observed overall (16). However, a markedly increased risk of breast cancer of borderline statistical significance (odds ratio = 7.4, 95 percent CI: 1.0, 178.1) was seen within the subgroup of 27 case women under age 50 years with estrogen receptor-positive breast tumors. In that registry-based study, age less than 50 years provided a surrogate measure for premenopausal status. Our cohort was an older cohort with few premenopausal case women, including only 19 who were also estrogen receptor-positive. We did not find any association in this small group. However, the study by Davis et al. (18) included 129 premenopausal case women who were positive for both estrogen and progesterone receptors, and it found no evidence of an association with magnetic fields in this group. Among postmenopausal women, we found no evidence of effect modification by estrogen receptor status, which is consistent with the findings of the two previous studies (16, 18).
A potential limitation of this study was the relatively low level of participation in the 7-day magnetic field measurements. However, we were able to assess evidence of selection bias, because we had additional data on virtually all of the subjects who did not allow these measurementsboth wire code and information on known breast cancer risk factors from the baseline cohort questionnaire. The distribution of wire codes was virtually identical between subjects with 7-day measurements and those without them. Among the 286 controls with magnetic field measurements, the percentage of homes by wire code was as follows: 11 percent very low, 10 percent underground, 29 percent ordinary low, 36 percent ordinary high, and 14 percent very high. Among the 413 controls without measurements, these percentages were 10 percent very low, 9 percent underground, 26 percent ordinary low, 38 percent ordinary high, and 15 percent very high. In addition, the subset of subjects who allowed 7-day measurements was quite similar to the overall study group with respect to risk factors for breast cancer (table 2). The availability of these additional data on the entire study population suggests that our results for 7-day measurements in relation to breast cancer were not appreciably affected by selection bias.
We measured magnetic fields over a 7-day period. This provided a more stable assessment of residential exposures than has been present in previous studies of residential magnetic fields and risk of breast cancer or other cancers. The only previous study of breast cancer included 48 hours of weekday measurements (18). Theoretically, exposure misclassification is a potential issue, because home measurements of magnetic fields do not completely capture personal exposure. In general, residential exposures are much lower than exposures documented in some occupational settings, including those of electrical workers, welders, and train conductors (35). However, few women hold these jobs entailing high levels of exposure (36). Another limitation of this study, as well as of the previous one (18), was that we assessed magnetic field exposure during the 10 years prior to diagnosis. The relevant etiologic period may be earlier in life, perhaps in childhood or adolescence.
It is possible that there are population subgroups with special sensitivity to the effects of magnetic fields. For example, a recent study in the Seattle area found that higher nighttime magnetic field measurements were weakly associated with lower urinary melatonin levels primarily among women who were taking several medications that may interfere with melatonin production, such as beta blockers, calcium channel blockers, and psychotropic medications, and only at times of the year with the fewest hours of darkness (4). We did not have data on the use of these medications. However, even among these medication users, magnetic fields were only weakly associated with melatonin levels. Thus, it seems very unlikely that we would have found an effect of magnetic fields on breast cancer risk among women using medications that interfere with melatonin, a subset we cannot identify. Although melatonin has been hypothesized to influence breast cancer risk, there are few data supporting this hypothesis, and none in humans. Given the available data, it does not seem likely that minor variation in melatonin levels that might be caused by exposure to magnetic fields among women using certain medications would have any strong effect on breast cancer risk. In an experimental study using the MCF-7 breast cancer cell line, administration of melatonin did not inhibit estradiol-induced proliferation (37). In another study, melatonin inhibited oxidative damage in MCF-7 cells only at pharmacologic concentrations; no effect was seen at physiologic levels (38).
Our results tend to rule out modest effects of exposure to residential magnetic fields, at levels experienced by the vast majority of the population, on breast cancer risk. Given the distribution of exposure and the sample size, the study had 80 percent power to detect a 10 percent increase in risk and 98 percent power to detect a 15 percent increase in risk per 0.1 µT for the nighttime bedroom broadband magnetic field measurement at the p = 0.05 level of significance. Given the distribution of bedroom magnetic fields, with 0.2 µT corresponding to the 90th percentile and 0.4 µT being rare, even if there were a true 1015 percent increase in risk per 0.1-µT increase in magnetic fields, only an extremely small proportion of breast cancers would be due to magnetic field exposure. Our study would have 90 percent power to detect relative risks of 3 or 4 for effects at 0.3 µT or 0.4 µT, respectively. Within the distribution of bedroom magnetic fields among controls, 0.2 µT corresponds to the 90th percentile, and approximately 3 percent of controls have fields above 0.4 µT. Thus, even if there were a true 1015 percent increase in risk per 0.1-µT increase in magnetic fields or there was an effect at very high exposures, only an extremely small proportion of breast cancers could be explained.
There has been some controversy recently regarding the use of wiring configuration codes in epidemiologic studies (34). Wire codes are not a perfect surrogate measure for magnetic fields and thus will introduce some misclassification of magnetic field exposure (28). The use of wire codes dates to the childhood leukemia study by Wertheimer and Leeper in Denver, Colorado (26). In subsequent studies, associations with childhood leukemia were found in Denver, Los Angeles, Canada, and Mexico but not in all locations in North America (19, 24, 27). In Los Angeles, the relation between wire code and leukemia persisted after adjustment for measured fields, and wire code was more strongly associated with leukemia risk than it was in a more complex prediction model using additional data from local utilities (22). However, the wire code that we used is not applicable to studies in all locations, because wiring practices differ (27). It has no relevance in Europe, where power distribution lines are generally buried and only the rarer high-voltage transmission lines are above ground. In contrast, many people in the United States live in close proximity to overhead primary distribution lines, especially in Los Angeles, where 15 percent of our subjects were classified in the highest wire code category (very high current configuration). Furthermore, wire codes in Los Angeles, as in other locations in North America, have a modest correlation with magnetic fields and distinguish between magnetic field exposures in the lowest categories (very low and underground) and those in the highest category (very high current configuration), thus providing a useful surrogate measure when magnetic field measurements cannot be made (27, 28). Because of the correlation with magnetic fields and the previous association with childhood leukemia risk in Los Angeles, which persists despite attempts to assess selection bias and confounding by other factors such as traffic density (39, 40), wire code is an important exposure metric in this Los Angeles study. The absence of an association between breast cancer risk and wire code in Los Angeles is a pertinent negative finding, especially given the absence of selection bias in our nested case-control design.
It has been suggested that the previous findings for childhood leukemia and wire code in several US locations, which persisted after adjustment for magnetic fields (19), might reflect a parameter of magnetic fields that has not been measured in previous studies, such as the harmonic magnetic field, which correlates with wire code (31). Therefore, we measured harmonic magnetic fields in this study, but we found no association with breast cancer risk. These results are in agreement with those reported by Davis et al. (18).
We believe that the most compelling interpretation of our results is that residential magnetic field exposures experienced by the vast majority of US women do not play an etiologic role in breast cancer. The findings of this study, along with those of the previous study with magnetic field measurements (18), should provide some reassurance to the public regarding this ubiquitous low-level exposure.
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ACKNOWLEDGMENTS |
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The authors acknowledge the field staff of the study and Misty Fewel, who was the initial project coordinator.
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NOTES |
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REFERENCES |
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