Bayesian spatial modelling of childhood cancer incidence in Switzerland using exact point data: A nationwide study during 1985-2015.

Abstract

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. We investigated the spatial variation of childhood cancers in Switzerland using exact geocodes of place of residence. Methods We included 5,947 children diagnosed with cancer during 1985-2015 at age 0-15 from the Swiss Childhood Cancer Registry. We modelled cancer risk using log-Gaussian Cox processes and indirect standardization to adjust for age and year of diagnosis. We examined whether the modelled spatial variation of risk can be explained by ambient air concentration of NO 2 , natural background radiation, area-based socio-economic position (SEP), linguistic region, years of existing general cancer registration in the canton or degree of urbanization. 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 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 evidence of an association of background radiation and SEP with incidence of CNS tumours, (1.19;0.98-1.40) and (1.6;1-1.13) respectively. Conclusion Of the investigated diagnostic groups, childhood CNS tumours show the largest spatial variation in Switzerland. The selected covariates only partially explained the observed variation of CNS tumours suggesting that other environmental factors also play a role.

Metrics and barycenters for point pattern data

“…From biomedical imagery over geo-referrenced disease cases and positions of mobile phone users to climate change related space-time events, such as landslides, we have more and more complicated data available. See Samartsidis et al (2019), Konstantinoudis et al (2019), Chiaraviglio et al (2016), Lombardo et al (2018) for individual examples and the textbooks Diggle (2013), Baddeley et al (2015Baddeley et al ( ), B laszczyszyn et al (2018 for a broad overview of further applications. While a few decades ago data consisted typically of a single point pattern in a low dimensional Euclidean space, maybe with some low-dimensional mark information, we have nowadays often multiple observations of point patterns available that may live on more complicated spaces, e.g.…”