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Geographical pattern of brain cancer incidence in the Navarre and Basque Country regions of Spain
  1. G López-Abente1,
  2. M Pollán1,
  3. E Ardanaz2,
  4. M Errezola3
  1. 1Environmental Cancer Epidemiology Unit, National Center for Epidemiology, Carlos III Institute of Health, Sinesio Delgado 6, 28029 Madrid, Spain
  2. 2Cancer Registry of Navarre, Navarre Public Health Institute, Pamplona, Spain
  3. 3Health Department, Basque Government, Vitoria, Spain
  1. Correspondence to:
 Dr G López-Abente, Area de Epidemiología Ambiental y Cáncer, Centro Nacional de Epidemiología, Instituto de Salud Carlos III, Sinesio Delgado, 6, 28029 Madrid, Spain; 


Background: The study of the distribution of brain cancer mortality in Spain shows a grouping of highest risk provinces corresponding to the autonomous regions of Navarre and the Basque Country.

Aim: To explore the possible existence of geographical patterns in these areas.

Methods: Municipal maps of brain cancer incidence were drawn up and the influence of land use related variables on the distribution of the disease duly analysed. Autoregressive conditional models were used to plot smoothed municipal maps. The influence of explanatory land use variables, ascertained by remote sensing, was assessed.

Results: The maps revealed that certain towns situated in the “Media” and “Cantábrica-Baja Montaña” districts of Navarre were areas of highest risk. Among the towns in question, those in the “Media” district lie very close to the city of Pamplona. However, the pattern of brain cancer incidence in Navarre and the Basque Country could not be conclusively said to be determined by any specific type of land cover and/or crop.

Conclusions: Results suggest a possible increase of risk linked to areas devoted to a high percentage of non-irrigated arable land.

  • brain neoplasm
  • cancer incidence
  • cancer mapping
  • remote sensing
  • small areas
  • Bayesian statistics
  • agriculture
  • spatial data analysis
  • CAR, conditional autoregressive
  • DIC, deviance information criterion
  • GLMM, generalised linear mixed model
  • SIR, standardised incidence ratio

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