Objectives: Increased risks of central nervous system (CNS) tumors and leukemia associated with computed tomography (CT) exposure during childhood have been reported in recent epidemiological studies. However, no evidence of increased risks was suggested in a previous analysis of the French CT cohort. This study benefits from an updated cohort with a longer followup and a larger sample size of patients.Methods: The patients were followed from the date of their first CT (between 2000 and2011) until their date of cohort exit defined as the earliest among the following: 31st December 2016, date of death, date of first cancer diagnosis or date of their 18th birthday. Cancer incidence, vital status, cancer predisposing factors (PFs) and additional CT scans were collected via external national databases. Hazard ratios (HRs) associated to cumulative organ doses and sex were estimated from Cox models.Results: At the end of follow-up, mean cumulative doses were 27.7 and 10.3 mGy for the brain and the red bone marrow (RBM), respectively. In patients without PFs, an HR per 10 mGy of 1.05 (95% CI: 1.01 -1.09) for CNS tumors, 1.17 (95% CI: 1.09 -1.26) for leukemia and 0.96 (95% CI: 0.63 -1.45) for lymphoma was estimated. These estimates were not modified by the inclusion of CT scans performed outside the participating hospitals or after the inclusion period. Conclusions:This study shows statistically significant dose-response relationships for CNS tumors and leukemia for patients without PFs.

Background Mapping the spatial distribution of disease occurrence is a strategy to identify contextual factors that could be useful for public health policies. The purpose of this ecological study was to examine to which extent the socioeconomic deprivation and the urbanization level can explain gender difference of geographic distribution in stroke incidence in Pays de Brest, France between 2008 and 2013. Methods Stroke cases aged 60 years or more were extracted from the Brest stroke registry and combined at the census block level. Contextual socioeconomic, demographic, and geographic variables at the census block level come from the 2013 national census. We used spatial and non-spatial regression models to study the geographic correlation between socioeconomic deprivation, degree or urbanization and stroke incidence. We generated maps using spatial geographically weighted models, after longitude and latitude smoothing and adjustment for covariates. Results Stroke incidence was comparable in women and men (6.26 ± 3.5 vs 6.91 ± 3.3 per 1000 inhabitants-year, respectively). Results showed different patterns of the distribution of stroke risk and its association with deprivation or urbanisation across gender. For women, stroke incidence was spatially homogeneous over the entire study area, but was associated with deprivation level in urban census blocks: age adjusted risk ratio of high versus low deprivation = 1.24, [95%CI 1.04–1.46]. For men, three geographic clusters were identified. One located in the northern rural and deprived census blocks with a 9–14% increase in the risk of stroke. Two others clusters located in the south-eastern (mostly urban part) and south-western (suburban and rural part) with low deprivation level and associated with higher risk of stroke incidence between (3 and 8%) and (8.5 and 19%) respectively. There were no differences in profile of cardiovascular risk factors, stroke type and stroke severity between clusters, or when comparing clusters cases to the rest of the study population. Conclusions Understanding whether and how neighborhood and patient’s characteristics influence stroke risk may be useful for both epidemiological research and healthcare service planning.

Background: Stroke remains a devastating disease in Europe and geographic disparities persist. Mapping spatial distributions of disease occurrence can serve as a useful tool for identifying exposures of public health concern. The purpose of this study was to investigate geographic differences in relationship between socioeconomic, clinical, urban-rural factors and stroke incidence in Pays de Brest (Western France) between 2008 and 2013.Methods: We used cases and patient’s characteristics from the Brest stroke registry, and sociodemographic, urban –rural indicators constructed at the census blocks level. We generated maps using Poisson geographic weighted regression models, smoothing on longitude and latitude while adjusting for covariates. Results: Women living in more deprived census blocks evidenced a significantly higher age standardized stroke incidence risk 1.24, [95%CI 1.09-1.39] and 1.21, [95%CI 1.04-1.49], in rural and urban census blocks respectively. For men, three clusters of census blocks with high stroke incidence risk were detected, one in rural and deprived and two in urban and low deprived census bocks. Conclusions: Understand whether and how neighborhood and patient’s characteristics influence stroke risk, may be useful for both epidemiological research and health services planning.