Residential Exposure to Natural Background Radiation and Risk of Childhood Acute Leukemia in France, 1990–2009

Demoury, Claire; Marquant, Fabienne; Ielsch, Géraldine; Debayle, Christophe; Coste, Astrid; Olivier, Laurent; Guillevic, Jérôme; Laurier, Dominique; Hémon, Denis; Clavel, Jacqueline

doi:10.1289/ehp296

Cited by 45 publications

(61 citation statements)

References 37 publications

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“…Kollerud et al (31) found an adjusted HR of 0.93 (95% CI 0.76, 1.13) per 100 Bq/m 3 increment. Also, the analyses from United-Kingdom and France did not report increased risks related to higher indoor radon concentrations (27,28). The British study reported an RR of 1.03 (95% CI 0.96-1.11) for every 1 mSv increase in cumulative red bone marrow dose as the French study reported and standardized incidence ratio of 1.01 (95% CI 0.91-1.12) for an increase of 100 Bq/m 3 in the indoor radon concentration.…”

Section: Integration Of the Findings With Previous Studiesmentioning

confidence: 89%

“…The potential dose pathway for the association, in addition to the exposure of the red bone marrow, has been suggested to be through the exposure of lymphocytes within the tracheobronchial epithelium (23). Studies in Norway, France, the UK, and Switzerland showed no association, but a Danish case-control study with complete residential histories and a statistical model with 40% r 2 reported an elevated risk (24)(25)(26)(27)(28). In these studies, exposure estimates were derived from model-based predictions of radon exposure (29)(30)(31).…”

Section: Health Effects Of Indoor Radonmentioning

confidence: 99%

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Predicting residential radon concentrations in Finland: Model development, validation, and application to childhood leukemia

Nikkilä¹,

Arvela²,

Mehtonen³

et al. 2019

Scand J Work Environ Health

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Indoor radon prediction models were created based on ~80 000 measurements and modern machine learning methods were used in modelling. The performance of the models was comparable to the previously published ones. We observed a non-significant risk of childhood leukemia from indoor radon. However, the modelling involves some uncertainties.Nikkilä A, Arvela H, Mehtonen J, Raitanen J, Heinäniemi M, Auvinen A. Predicting residential radon concentrations in Finland: Model development, validation, and application to childhood leukemia.Objectives Inhaled radon gas is a known alpha-emitting carcinogen linked especially to lung cancer. Studies on higher concentrations of indoor radon and childhood leukemia have conflicting but largely negative results. In this study, we aimed to create a sophisticated statistical model to predict indoor radon concentrations and apply it to a Finnish childhood leukemia case-control dataset.Methods Prediction was based on ~80 000 indoor radon measurements, which were linked to national registries for potential indoor radon predictors based on the literature. In modelling, we used classical methods, random forests and deep neural networks. We had 1093 cases and 3279 controls from a nationwide case-control study. We estimated odds ratio (OR) for childhood leukemia using conditional logistic regression adjusted for potential confounders. ResultsThe r 2 of the final log-linear model was 0.21 for houses and 0.20 for apartments. Using random forest method, we were able to obtain slightly better fit for both houses (r 2 = 0.28) and apartments (r 2 = 0.23). In a risk analysis based on the case-control data with log-linear model, we observed a non-significant (P=0.54) increase with predicted radon concentrations [OR for the 2 nd quartile 1.08, 95% confidence interval (CI) 0.77-1.50, OR 1.10 with 95% CI 0.79-1.53 for the 3 rd , and 1.29 with 95% CI 0.93-1.77 for the highest quartile].Conclusions Our modelling and the previously published models performed similarly but involves major uncertainties, and the results should be interpreted with caution. We observed a slight non-significant increase in risk of childhood leukemia related to higher average indoor radon concentrations.

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Section: Integration Of the Findings With Previous Studiesmentioning

confidence: 89%

Section: Health Effects Of Indoor Radonmentioning

confidence: 99%

Predicting residential radon concentrations in Finland: Model development, validation, and application to childhood leukemia

Nikkilä¹,

Arvela²,

Mehtonen³

et al. 2019

Scand J Work Environ Health

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“…Previous studies investigating childhood cancer risks in relation to background ionising radiation showed mixed results [22,[41][42][43][44]. While two studies reported associations between childhood leukaemia and gamma radiation [22,41], others found no evidence of an association [42][43][44].…”

Section: Comparison Of Our Study With Other Studies On Environmental mentioning

confidence: 94%

Bayesian spatial modelling of childhood cancer incidence in Switzerland using exact point data: a nationwide study during 1985–2015

et al. 2020

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Background: The aetiology of most childhood cancers is largely unknown. Spatially varying environmental factors such as traffic-related air pollution, background radiation and agricultural pesticides might contribute to the development of childhood cancer. This study is the first investigation of the spatial disease mapping of childhood cancers using exact geocodes of place of residence. Methods: We included 5947 children diagnosed with cancer in Switzerland during 1985-2015 at 0-15 years of age from the Swiss Childhood Cancer Registry. We modelled cancer risk using log-Gaussian Cox processes and indirect standardisation to adjust for age and year of diagnosis. We examined whether the spatial variation of risk can be explained by modelled ambient air concentration of NO 2 , modelled exposure to background ionising radiation, areabased socioeconomic position (SEP), linguistic region, duration in years of general cancer registration in the canton or degree of urbanisation. Results: For all childhood cancers combined, the posterior median relative risk (RR), compared to the national level, varied by location from 0.83 to 1.13 (min to max). Corresponding ranges were 0.96 to 1.09 for leukaemia, 0.90 to 1.13 for lymphoma, and 0.82 to 1.23 for central nervous system (CNS) tumours. The covariates considered explained 72% of the observed spatial variation for all cancers, 81% for leukaemia, 82% for lymphoma and 64% for CNS tumours. There was weak evidence of an association of CNS tumour incidence with modelled exposure to background ionising radiation (RR per SD difference 1.17; 0.98-1.40) and with SEP (1.6; 1.00-1.13). Conclusion: Of the investigated diagnostic groups, childhood CNS tumours showed the largest spatial variation. The selected covariates only partially explained the observed variation of CNS tumours suggesting that other environmental factors also play a role.

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“…A French study of natural background radiation and childhood cancer (Demoury et al, 2017) made use of dose estimates by Warnery et al . Like the GB study, Warnery and co-workers also had a large set of indoor measurements made in 17,404 dental surgeries and veterinary clinics.…”

Section: Other Approachesmentioning

confidence: 99%

Modelling the bimodal distribution of indoor gamma-ray dose-rates in Great Britain

Kendall

Chernyavskiy

Appleton

et al. 2018

Radiat Environ Biophys

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Gamma radiation from naturally occurring sources (including directly ionizing cosmic-rays) is a major component of background radiation. An understanding of the magnitude and variation of doses from these sources is important, and the ability to predict them is required for epidemiological studies. In the present paper, indoor measurements of naturally occurring gamma-rays at representative locations in Great Britain are summarized. It is shown that, although the individual measurement data appear unimodal, the distribution of gamma-ray dose-rates when averaged over relatively small areas, which probably better represents the underlying distribution with inter-house variation reduced, appears bimodal. The dose-rate distributions predicted by three empirical and geostatistical models are also bimodal and compatible with the distributions of the areally averaged dose-rates. The distribution of indoor gamma-ray dose-rates in the UK is compared with those in other countries, which also tend to appear bimodal (or possibly multimodal). The variation of indoor gamma-ray dose-rates with geology, socio-economic status of the area, building type, and period of construction are explored. The factors affecting indoor dose-rates from background gamma radiation are complex and frequently intertwined, but geology, period of construction, and socio-economic status are influential; the first is potentially most influential, perhaps, because it can be used as a general proxy for local building materials. Various statistical models are tested for predicting indoor gamma-ray dose-rates at unmeasured locations. Significant improvements over previous modelling are reported. The dose-rate estimates generated by these models reflect the imputed underlying distribution of dose-rates and provide acceptable predictions at geographical locations without measurements.

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Residential Exposure to Natural Background Radiation and Risk of Childhood Acute Leukemia in France, 1990–2009

Cited by 45 publications

References 37 publications

Predicting residential radon concentrations in Finland: Model development, validation, and application to childhood leukemia

Predicting residential radon concentrations in Finland: Model development, validation, and application to childhood leukemia

Bayesian spatial modelling of childhood cancer incidence in Switzerland using exact point data: a nationwide study during 1985–2015

Modelling the bimodal distribution of indoor gamma-ray dose-rates in Great Britain

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