We searched the published literature through PubMed, as well as references of identified papers, and conducted an informal survey of epidemiologists involved in ELF-MF research to identify relevant studies on residential ELF-MF exposure and childhood brain tumors. To be included, studies had to provide data for children, provide separate data for cancers of the brain or central nervous system, and provide measured or calculated values for residential exposure to ELF-MFs.
We identified 16 studies published between 1979 and 2010, of which 10 could be included in the pooled analyses (). Four studies (2
) were not included because they did not have measurements or calculated fields for childhood brain tumors. One study (12
) was not included because it used dwellings rather than persons as units of analysis, included only outside spot measurements, and overlapped with another study (13
). A sixth study (14
) was not included because its cases were a small subset of those from another study (15
). Appendix Table 1 summarizes the methods and findings of the studies that did not meet our inclusion criteria. Two included studies had a large overlap, with perhaps 90% of the cases in the United Kingdom Childhood Cancer Study (UKCCS) (16
) also being included in the Kroll et al. (15
) study, but they differed in terms of type of exposure surrogates and timing of exposure. To maintain independence of observations, we included only 1 of these studies in any given analysis. Investigators in the UKCCS (16
) used a 2-phase measurement strategy, in which 48-hour measurements were conducted when either a shorter measurement (108 minutes) or a characteristic of the residence indicated that ELF-MF exposure was elevated. In this pooled analysis, we used only second-phase measurements, which were taken for all subjects (with their matched cases or controls) who had potential sources of high exposure (>0.1 μT) in the first phase.
Characteristics of Studies Included in a Pooled Analysis of Childhood Brain Tumors and Extremely Low-Frequency Magnetic Field Exposure, 1960–2001
Investigators utilized stratified sampling of controls in all of the studies, although the stratification variables were not the same in all studies. In Finland, the authors of the original publication reported findings from a cohort study (17
), but in preparation for this pooled analysis, a control group was selected and the data were evaluated using a matched case-control design with 6 additional years of follow-up. For some studies, the same controls were used for both brain tumors and leukemia cases in the original publications, and we maintained the same approach in these instances. Since we wanted to use as many of the cases and controls as possible to increase the flexibility of the analysis, we ignored the matching and instead included adjustment for age at diagnosis, gender, and study.
To make the data as consistent as possible across studies, we limited the age of diagnosis to 0–15 years inclusive and converted all measurements to microteslas. One Finnish patient with 3 tumors was included only once. One Japanese patient with cavernous angioma and 2 corresponding controls were excluded. However, germ-cell tumors and corresponding controls were included in the analysis, although these tumors were not consistently included in all individual studies.
We focused on ELF-MFs present in the general area of the home; that is, we excluded exposure occurring in schools, data on which would have been available for only 1 study (UKCCS) (16
), and did not include short-duration exposures close to appliances. In all studies, investigators took long-term measurements or spot measurements and/or calculated the strengths of ELF-MFs. Only 2 of the included studies, both from the United States (18
), had information on wire codes, but they also had measurements. We did not use wire codes in the analyses. With regard to long-term measurements, measurements were taken for 24 hours in 2 studies (18
), for 48 hours (for highly exposed subjects) in 1 study (16
), and for a 1-week period in 1 study (21
); these measurements are referred to as “long-term measurements” throughout this paper. Arithmetic mean values rather than geometric means were used, since these were available for all studies.
Our analyses included separate analyses for long-term measurements, calculated fields, and spot measurements. Investigators had collected data from the child's birth home, the home in which the child had lived longest prior to diagnosis, the latest home in which the child had lived prior to diagnosis that was near a power line, and/or the home in which the child was living at diagnosis. For subjects with data from more than 1 home, we used the following hierarchy to select a single exposure proxy for the analysis: Diagnosis home was used if data were available; if not, then the latest home lived in before diagnosis; if not, then the home lived in the longest; and if not, then birth home. We also performed separate analyses for diagnosis homes, longest-lived-in homes, and birth homes. For these analyses, for subjects with more than 1 type of exposure proxy, we used long-term measurements if they were available; if not, then we used calculated fields; and if neither measure was available, then we used spot measurements. In addition, in an attempt to maximize the available sample size, we conducted a “best measure” pooled analysis in which we selected a single best exposure measurement for each subject using both the exposure metric hierarchy (long-term over calculated fields over spot) and the home hierarchy (diagnosis over latest over longest-lived-in over birth). The choice between selection of type of exposure metric before residence and selection of residence before type of exposure metric was immaterial, since both approaches yielded identical measurements for each subject.
Additional potential confounders for which data were available included type of dwelling, mobility, urbanization, socioeconomic status, and exposure to traffic exhaust. The number, type, and coding of potential confounders differed among the studies (see ). We examined socioeconomic status (standardized to a 3-level ordinal variable), urbanization (dichotomized as urban/rural), dwelling type (dichotomized as single-family/multiple-unit), and mobility (dichotomized as number of residences before diagnosis: 1/>1).
An analysis plan that included hypotheses, hierarchy of measurements, and cutpoints was developed and agreed upon prior to analysis. The data were analyzed using both ordinary logistic regression, with fixed intercepts to adjust for study, and mixed-effects logistic regression, with random intercepts and exposure effect coefficients for study. Separate intercepts were used for East Germany and West Germany. Ordinary and mixed-effects logistic regression gave similar results; we present results for ordinary logistic regression.
We conducted an analysis using best measures with continuous exposure as a linear predictor, reporting results as the odds ratio for an increase of 0.2 μT. We also used this analysis for a likelihood ratio test of homogeneity of effects across studies, in which models with and without study-specific coefficients for exposure were compared. In addition, we estimated the trend in the log odds of being a case using a generalized additive model (22
), using a nonparametric curve (natural cubic smoothing spline with interior and boundary knots at the unique values of exposure) to estimate the risk associated with exposure, while controlling for study, age, and gender. The amount of smoothing is determined by the degrees of freedom (df), with higher df corresponding to less smoothing. For this analysis, we transformed exposures using an inverse cubic transformation to reduce the influence of outliers at high exposure levels. These analyses were conducted using the gam package in R, version 2.9.2 (23
In other analyses, we used increasing exposure categories of 0.1–<0.2 μT, 0.2–<0.4 μT, and ≥0.4 μT to examine the exposure-response relation, with a reference category of <0.1 μT. Single cutpoints at 0.3 μT and 0.4 μT were also explored. We also obtained odds ratios using a moving window of exposure. These analyses used exposure categories of 0.1–<0.2 μT, 0.15–<0.25 μT, 0.20–<0.30 μT, 0.25–<0.35 μT, ≥0.30 μT, ≥0.35 μT, and ≥0.40 μT, with a reference category of <0.1 μT, and results were adjusted for age, gender, and study. Treating age at diagnosis as continuous or categorical gave similar results; results obtained with age included as a continuous variable are presented. The influence of individual studies was examined by omitting 1 study at a time. These analyses were conducted using Stata 10 (24