Disease mapping method comparing the spatial distribution of a disease with a control disease

• Oana Petrof ^[1] ; Thomas Neyens ^[1] ; Maren Vranckx ^[1] ; Valerie Nuyts ^[2] ; Kristiaan Nackaerts ^[2] ; Benoit Nemery ^[2] ; Christel Faes ^[1]
  1. [1] University of Hasselt

     University of Hasselt

     Arrondissement Hasselt, Bélgica

     
  2. [2] KU Leuven

     KU Leuven

     Arrondissement Leuven, Bélgica

     
• Localización: Proceedings of the 35th International Workshop on Statistical Modelling : July 20-24, 2020 Bilbao, Basque Country, Spain / Itziar Irigoien Garbizu (ed. lit.), Dae-Jin Lee (ed. lit.), Joaquín Martínez Minaya (ed. lit.), María Xosé Rodríguez Álvarez (ed. lit.), 2020, ISBN 978-84-1319-267-3, págs. 185-188
• Idioma: inglés
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• Resumen

  • Traditional disease mapping models are based on relating the observed number of disease cases per spatially discrete area to an expected number of cases for that area. Expected numbers are calculated by internal standardisation, which requires both accurate population numbers and disease rates per age group. Con dentiality issues or the absence of high-quality information about the characteristics of a population-at-risk can hamper those calculations. Based on methods in point process analysis, we propose the use of a case-control approach in the context of lattice data, in which an unrelated spatially unstructured disease is used as a control disease. We apply our methods to a Belgian study of mesothelioma risk, where pancreatic cancer serves as the control disease. The analysis results are in close agreement with those coming from traditional disease mapping models based on internally standardised expected counts.