Correspondence August 2015 | Volume 123 | Issue 8
Response to “Comment on ‘Background Ionizing Radiation and the Risk of Childhood Cancer: A Census-Based Nationwide Cohort Study’”
Ben D. Spycher,1 Martin Röösli,2,3 Matthias Egger,1 and Claudia E. Kuehni1
1Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland; 2Swiss Tropical and Public Health Institute, Basel, Switzerland; 3University of Basel, Basel, Switzerland
Citation: Spycher BD, Röösli M, Egger M, Kuehni CE. 2015. Response to “Comment on ‘Background Ionizing Radiation and the Risk of Childhood Cancer: A Census-Based Nationwide Cohort Study.’” Environ Health Perspect 123:A198–A199; http://dx.doi.org/10.1289/ehp.1509938R
Address correspondence to B. Spycher, University of Bern, Institute of Social and Preventive Medicine, Finkenhubelweg 11, CH-3012 Bern, Switzerland. E-mail: firstname.lastname@example.org
The authors declare they have no actual or potential competing financial interests.
Final Publication: 1 August 2015
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Background Ionizing Radiation and the Risk of Childhood Cancer: A Census-Based Nationwide Cohort Study
Comment on “Background Ionizing Radiation and the Risk of Childhood Cancer: A Census-Based Nationwide Cohort Study”
We thank Scott for his interest in our study on background ionizing radiation and the risk of childhood cancer. Scott claims that if all random and systematic errors in measurements had been addressed, our study would likely have found no association between levels of background ionizing radiation and childhood cancer risk. We acknowledge that errors often affect estimates of long-term exposures, but there are no obvious reasons why the sum of potential measurement errors in our study, if eliminated, should result in a null finding. In fact, there are reasons to the contrary.
Random error (or “statistical” error, in Scott’s terms) in exposure measurement would result in nondifferential misclassification and, therefore, would typically produce an underestimation, not an overestimation, of any effect (Keogh and White 2014). Differential misclassification of exposure, which could lead to under- or overestimation of the association, is unlikely given the design of the study and the geographical model used to estimate exposure. Confounding factors may not have been measured perfectly, but even imperfect measures should affect estimates of dose–response relationships if the factors are indeed confounders. Our estimates were virtually unchanged when including levels of traffic-related air pollution, electromagnetic fields from radio and TV transmitters or high-voltage power lines, and degree of urbanization and socioeconomic status of neighborhoods in the statistical model.
Scott argues that bias may have been introduced due to omission of some radiation sources, in particular exposure from medical procedures. We agree with Scott that ideally all radiation sources should be included in the study. However, in our study, bias due to omitted covariates is unlikely unless the excluded components of radiation dose were correlated with background radiation. It is difficult to see why exposure to medical radiation sources should correlate with other components of background radiation. Nevertheless, omission biases are not easily tractable in generalized linear models (Neuhaus and Jewell 1993) and certainly merit further investigation in this context.
Scott is mistaken in his assessment of our analyses for cumulative dose. The Cox proportional hazards model and the conditional logistic regression model in our nested sample relate cumulative doses with hazards, not with cumulative incidence. Hazards are instantaneous risks, and during model fitting comparisons are made only between children who are of the same age at the time the cases are diagnosed with cancer. In other words, only comparisons of doses accumulated over the same amount of time contribute to the estimation. In contrast to Scott’s assertion, the models can thus deal with the time-varying nature of the exposure and do not overestimate effects.
Keogh RH, White IR. 2014. A toolkit for measurement error correction, with a focus on nutritional epidemiology. Stat Med 33(12):2137–2155; doi: 10.1002/sim.6095.
Neuhaus JM, Jewell NP. 1993. A geometric approach to assess bias due to omitted covariates in generalized linear models. Biometrika 80(4):807–815; doi: 10.1093/biomet/80.4.807.
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