Residential mobility of populations near UK power lines and implications for childhood leukaemia

Reanalysis of risks of childhood leukaemia with distance from overhead power lines in the UK

Our previous study of childhood leukaemia and distance to high-voltage overhead power lines in the UK has been included in an international pooled analysis. That pooled analysis used different distance categories to those we did, which has focussed attention on the effect of that choice. We re-analyse our previous subjects, using finer distance categories. In the 1960s and 1970s, when we principally found an elevated risk, the risk did not fall monotonically with distance from the power line but had a maximum at 100-200 m. This weakens the evidence that any elevated risks are related to magnetic fields, and slightly strengthens the evidence for a possible effect involving residential mobility or other socioeconomic factors.

Residential mobility and childhood leukemia

AIMS

Studies of environmental exposures and childhood leukemia studies do not usually account for residential mobility. Yet, in addition to being a potential risk factor, mobility can induce selection bias, confounding, or measurement error in such studies. Using data collected for California Powerline Study (CAPS), we attempt to disentangle the effect of mobility.

METHODS

We analyzed data from a population-based case-control study of childhood leukemia using cases who were born in California and diagnosed between 1988 and 2008 and birth certificate controls. We used stratified logistic regression, case-only analysis, and propensity-score adjustments to assess predictors of residential mobility between birth and diagnosis, and account for potential confounding due to residential mobility.

RESULTS

Children who moved tended to be older, lived in housing other than single-family homes, had younger mothers and fewer siblings, and were of lower socioeconomic status. Odds ratios for leukemia among non-movers living <50 meters (m) from a 200+ kilovolt line (OR: 1.62; 95% CI: 0.72-3.65) and for calculated fields ≥ 0.4 microTesla (OR: 1.71; 95% CI: 0.65-4.52) were slightly higher than previously reported overall results. Adjustments for propensity scores based on all variables predictive of mobility, including dwelling type, increased odds ratios for leukemia to 2.61 (95% CI: 1.76-3.86) for living < 50 m from a 200 + kilovolt line and to 1.98 (1.11-3.52) for calculated fields. Individual or propensity-score adjustments for all variables, except dwelling type, did not materially change the estimates of power line exposures on childhood leukemia.

CONCLUSION

The residential mobility of childhood leukemia cases varied by several sociodemographic characteristics, but not by the distance to the nearest power line or calculated magnetic fields. Mobility appears to be an unlikely explanation for the associations observed between power lines exposure and childhood leukemia.

Comparative analyses of studies of childhood leukemia and magnetic fields, radon and gamma radiation

In this paper we compare the findings of epidemiologic studies of childhood leukemia that examined at least two of ELF magnetic fields and/or distance to power lines, and exposure to radon and gamma radiation or distance to nuclear plants. Many of the methodologic aspects are common to studies of non-ionising (i.e. ELF-MF) and ionising radiation. A systematic search and review of studies with more than one exposure under study identified 33 key and 35 supplementary papers from ten countries that have been included in this review. Examining studies that have looked at several radiation exposures, and comparing similarities and differences for the different types of radiation, through the use of directed acyclic graphs, we evaluate to what extent bias, confounding and other methodological issues might be operating in these studies. We found some indication of bias, although results are not clear cut. There is little evidence that confounding has had a substantial influence on results. Influence of the residential mobility on the study conduct and interpretation is complex and can manifest as a selection bias, confounding, increased measurement error or could also be a potential risk factor. Other factors associated with distance to power lines and to nuclear power plants should be investigated. A more complete and consistent reporting of results in the future studies will allow for a more informative comparison across studies and integration of results.

Epidemiological study of power lines and childhood cancer in the UK: further analyses

We report further analyses from an epidemiological study of childhood cancer and residence at birth near high-voltage power lines in the UK. These results suggest that the elevated risks for childhood leukaemia that we previously found for overhead power lines may be higher for older age at diagnosis and for myeloid rather than lymphoid leukaemia. There are differences across regions of birth but not forming any obvious pattern. Our results suggest the decline in risk we previously reported from the 1960s to the 2000s is linked to calendar year of birth or of cancer occurrence rather than the age of the power lines concerned. Finally, we update our previous analysis of magnetic fields to include later subjects.

Residential mobility and associated factors in relation to the assessment of exposure to naturally occurring radiation in studies of childhood cancer

Migration, that is the study subjects moving from one residential address to another, is a complication for epidemiological studies where exposures to the agent of interest depend on place of residence [corrected]. In this paper we explore migration in cases from a large British case-control study of childhood cancer and natural background radiation. We find that 44% of cases had not moved house between birth and diagnosis, and about two-thirds were living within 2 km of their residence at birth. The estimated dose at the diagnosis address was strongly correlated with that at the birth address, suggesting that use of just the birth address in this case-control study does not lead to serious bias in risk estimates. We also review other individual-based studies of naturally occurring radiation, with particular emphasis on those from Great Britain. Interview-based case-control and cohort studies can potentially establish full residential histories for study subjects and make direct measurements of radiation levels in the dwellings in question. However, in practice, because of study size and difficulties in obtaining adequate response rates, interview-based studies generally do not use full residential histories, and a substantial proportion of dose estimates often derive from models rather than direct measurements. More seriously, problems of incomplete response may lead to bias, not just to loss of power. Record-based case-control studies, which do not require direct contact with study subjects, avoid such problems, but at the expense of having only model-based exposure estimates that use databases of measurements.

Validity of geographically modeled environmental exposure estimates

Geographic modeling is increasingly being used to estimate long-term environmental exposures in epidemiologic studies of chronic disease outcomes. However, without validation against measured environmental concentrations, personal exposure levels, or biologic doses, these models cannot be assumed a priori to be accurate. This article discusses three examples of epidemiologic associations involving exposures estimated using geographic modeling, and identifies important issues that affect geographically modeled exposure assessment in these areas. In air pollution epidemiology, geographic models of fine particulate matter levels have frequently been validated against measured environmental levels, but comparisons between ambient and personal exposure levels have shown only moderate correlations. Estimating exposure to magnetic fields by using geographically modeled distances is problematic because the error is larger at short distances, where field levels can vary substantially. Geographic models of environmental exposure to pesticides, including paraquat, have seldom been validated against environmental or personal levels, and validation studies have yielded inconsistent and typically modest results. In general, the exposure misclassification resulting from geographic models of environmental exposures can be differential and can result in bias away from the null even if non-differential. Therefore, geographic exposure models must be rigorously constructed and validated if they are to be relied upon to produce credible scientific results to inform epidemiologic research. To our knowledge, such models have not yet successfully predicted an association between an environmental exposure and a chronic disease outcome that has eventually been established as causal, and may not be capable of doing so in the absence of thorough validation.