Of the included studies, four were conducted in Europe, and one each was conducted in Japan, Brazil and Australia. shows the numbers of cases and controls for each study, along with variables supplied by those studies. There was a total of 10
865 cases and 12
853 controls with exposure surrogates; however, total numbers in the high-exposure categories were small, even for this large data set.
presents the absolute numbers of subjects by case–control status, study and exposure level. The UK study provided by far the largest number of cases and controls, i.e., 89 and 75% however, influence on results is more dependent on the numbers in the high-exposure category, and thus Brazil with high numbers of exposed was expected to be the most influential. Overall, in the highest-exposure category (
T), there were 26 cases and 50 controls, 11 and 30 of them from the study in Brazil. Four studies (Germany, Italy1, Italy2 and Japan) provided histological type of leukaemia. Among subjects with data on type of leukaemia available, 86% were ALL cases. Numbers for other subtypes were too low to support additional analysis by subtype.
Absolute numbers of childhood leukaemia cases and controls by study and exposure level
summarises the main results. We present results for geometric means for long-term measurements (results for arithmetic means were similar) for each study adjusted for basic potential confounders, and separately for measured and calculated field studies, as well as combined results. A likelihood ratio test comparing models with and without random effects for exposure did not detect heterogeneity (P=0.201), supporting the pooling of studies.
Odds ratios (95% CI) for childhood leukaemia by exposure level with adjustment for age, sex and SES
In most individual studies and in the combined results, the risk increased with increase in exposure, although the estimates were imprecise. For calculated field studies, the number of subjects in high-exposure categories was often too small to provide reliable estimates. As Brazil was the most influential study in terms of the number of highly exposed subjects, and included only young and only ALL cases, we present results with and without Brazil. Influence analysis omitting one study at a time confirmed that Brazil was the most influential study (results not shown). Without Brazil, the summary odds ratio for
T is 1.56 (95% CI 0.78–3.10), which is close to the age, sex and study-adjusted summary OR of 1.68 (95% CI 1.23–2.31) obtained in the pooled analysis of Greenland (Greenland et al, 2000
), but less precise. In individual studies and in combined results, the number of observed cases
T was higher than the expected number obtained by modelling the probability of membership in exposure categories on the basis of the distribution of controls, including covariates.
For a more direct comparison of the current pooled results with those of Ahlbom et al
, we conducted an analysis using the same cutoff points. Our overall risk estimates, although compatible with previously reported estimates, are substantially lower (). This is particularly true for studies on measured fields, a result heavily influenced by the Brazilian study. The combined OR for
T with Brazil omitted was 2.02 (95% CI 0.87–4.69), whereas combined ORs when omitting other single studies ranged from 1.32 to 1.49. When the Brazilian study is excluded from the analysis, our point estimates are very close to the results of Ahlbom et al.
The same is true when a cutoff point
T is used, rather than
Table 4 Comparison of summary odds ratios in current pooled analysis update with pooled analysis of Ahlbom et al (2000); adjusted for age, sex, SES and study
Odds ratio estimates using categorical cutoff points and involving relatively small numbers of subjects are vulnerable to unstable results. To address this concern, we also calculated odds ratios using a moving window of exposure levels (). These results also suggested a possible trend of increasing risk with increase in exposure; however, the estimates were imprecise.
Figure 1 Odds ratios (95% CI) for moving window of exposure levels, adjusted for age, sex, SES and study. Reference level: <0.1μT.
An ordinary logistic regression analysis using exposure as a continuous linear predictor yielded OR=1.11 (95% CI 0.98–1.26) for each increase of 0.2μ
T, adjusting for age and sex. However, we prefer using a GAM, which is a more flexible modelling approach that provides a nonparametric estimate of the association between exposure and risk while controlling for potential confounders. presents the GAM nonparametric estimate of the trend in the log odds of being a case, with adjustment for study, age and sex. As a sensitivity analysis, we present results for a range of smoothing parameters, expressed as d.f., with models with more d.f. reflecting more fidelity to the data and models with fewer d.f. yielding more smoothing. Confidence limits widen as exposure increases, reflecting smaller number of subjects at high exposure levels. Although the curve suggests a positive exposure–response relationship, the width of the confidence bands indicates that a variety of exposure–response relationships, including no increase in risk, are compatible with the data.
presents sensitivity and subgroup analyses in which we examine whether results change with adjustments for potential confounders and to what extent results are limited to a particular subgroup. Not all potential confounders were available in all studies. Analyses adjusting for confounding were carried out on the subset of studies and subjects for which data on the confounder were available. Most adjustments did not make appreciable changes in odds ratio estimates. Risks were a little higher for ALL and for a younger age group, and a little lower for residences at birth, despite a suggestion from one study (Lowenthal et al, 2007
) that exposure at birth might carry particular risks. Neither an adjustment for mobility nor restriction to subjects who lived in a single residence before diagnosis changed the risk estimates appreciably. All confidence intervals included the null value.
Summary odds ratios (95% CI) for leukaemia by exposure level with adjustments for study and other potential confounders and within subgroups
In very early studies on magnetic field exposure, distance from power lines was used as a proxy for magnetic fields, but distance alone is a poor predictor of magnetic fields when a study involves lines of varying characteristics, as highlighted in a recent methodological paper (Maslanyj et al, 2009
). Draper et al, (2005)
found elevated risks at distances well beyond the point at which the magnetic fields from power lines would be elevated, but were unable to offer an explanation for this finding. Using the pooled data, we, similar to Draper et al (2005)
, evaluated the risk of childhood leukaemia as it relates to distance as an ‘exposure' in its own right and not as a substitute for magnetic fields.
The results for risk of childhood leukaemia as related to distance based on six studies (all except Germany) are shown in . Risk estimates increase with a decrease in distance, and the risk estimate for the closest band (
m) is the highest and relatively precise, but full exploration of how this effect occurs will require consideration of the different voltage lines involved and the effect of alternative reference levels.
Odds ratios (95% CIs) for childhood leukaemia and distance from nearest power line, adjusted for study, age, sex and SES