Mathematical Geosciences

, Volume 45, Issue 4, pp 437–452 | Cite as

Stochastic Simulation Model for the Spatial Characterization of Lung Cancer Mortality Risk and Study of Environmental Factors

  • Ana Rita Oliveira
  • Cristina Branquinho
  • Maria Pereira
  • Amílcar Soares
Article

Abstract

This paper presents a study in which the lung cancer risk in males was characterized based on a simulation model of mortality rates. Block sequential simulation of mortality rates, measured in counties of different sizes, was implemented and applied to a normal grid of continental Portugal with high spatial resolution. The uncertainty in the mortality rate measurements, directly related to differences in the population size of each county, was integrated in a block direct sequential simulation through Poisson kriging of local means and variances. Three age groups were examined: 50–59, 60–69, and 70–79 years. After the continuous geographic patterns of lung cancer risk were obtained, factors potentially associated with the main areas of risk were analyzed for southern Portugal. Thus, a defined class of land use and dry weather events, related to airborne particulate matter, were found to be associated with high-risk areas, resulting in high local spatial correlation patterns in all three age groups.

Keywords

Block sequential simulation Cancer risk Dry land Drought 

References

  1. Durão RM, Pereira MJ, Costa AC, Delgado J, del Barrio G, Soares A (2010) Spatial-temporal dynamics of precipitation extremes in southern Portugal: a geostatistical assessment study. Int J Climatol 30:1526–1537 Google Scholar
  2. Gomez-Hernandez J, Soares A, Froidevaux R (1999) GeoENVII—Geostatistics for environmental applications. Proceedings of the second European conference on geostatistics for environmental applications, Valencia, Spain, November 18–20, 1998. Kluwer Academic, Dordrecht, 562p Google Scholar
  3. Goovaerts P (2004) Simulation-based assessment of a geostatistical approach for estimation and mapping of the risk of cancer. In: Leuangthong O, Deutsch CV (eds) Geostatistics Banff. Kluwer Academic, Dordrecht, pp 787–796 Google Scholar
  4. Goovaerts P (2005) Geostatistical analysis of disease data: estimation of cancer mortality risk from empirical frequencies using Poisson kriging. Int J Health Geogr 4:31 CrossRefGoogle Scholar
  5. Goovaerts P (2006a) Geostatistical analysis of disease data: visualization and propagation of spatial uncertainty in cancer mortality risk using Poisson kriging and p-field simulation. Int J Health Geogr 5:7 CrossRefGoogle Scholar
  6. Goovaerts P (2006b) Geostatistical analysis of disease data: accounting for spatial support and population density in the isopleth mapping of cancer mortality risk using area-to-point Poisson kriging. Int J Health Geogr 5:52 CrossRefGoogle Scholar
  7. Goovaerts P (2008) Kriging and semivariogram deconvolution in the presence of irregular geographical units. Math Geosci 40:101–128 CrossRefGoogle Scholar
  8. Goovaerts P (2009) Medical geography: a promising field of application for geostatistics. Math Geosci 41:243–264 CrossRefGoogle Scholar
  9. Goovaerts P (2010) Combining areal and point data in geostatistical interpolation: applications to soil science and medical geography. Math Geosci 42:535–554 CrossRefGoogle Scholar
  10. Journel AG, Huigbreghts CJ (1978) Mining geostatistics. Academic Press, London Google Scholar
  11. Kyriakidis P (2004) A geostatistical framework for area-to-point spatial interpolation. Geogr Anal 36(3):259–289 Google Scholar
  12. Liu Y, Journel A (2009) A package for geostatistical integration of coarse and fine scale data. Comput Geosci 35:527–547 CrossRefGoogle Scholar
  13. Machado A, Nicolau R, Dias CM (2009) Consumo de tabaco na população Portuguesa: análise dos dados do. Inquérito Nacional de Saúde 2005/2006, Instituto Nacional de Saúde Doutor Ricardo Jorge, Departamento de Epidemiologia Google Scholar
  14. Monestiez P, Dubroca L, Bonnin E, Durbec J, Guinet C (2004) Comparison of model based geostatistical methods in ecology: application to fin whale spatial distribution in northwestern Mediterranean sea. In: Geostatistics Banff. Kluwer Academic, Dordrecht, pp 777–786 Google Scholar
  15. Monestiez P, Dubroca L, Bonnin E, Durbec J, Guinet C (2006) Geostatistical modeling of spatial distribution of Balaenoptera physalus in the Northwestern Mediterranean Sea from sparse count data and heterogenous observation efforts. Ecol Model 193:615–628 CrossRefGoogle Scholar
  16. Oliver MA, Webster R, Lajaunie C, Muir KR, Parkes SE, Cameron AH, Stevens MCG, Mann JR (1998) Binomial cokriging for estimating and mapping the risk of childhood cancer. IMA J Math Appl Med Biol 15:279–297 CrossRefGoogle Scholar
  17. Pyrcz MJ, Deutsch CV (2001) Two artifacts of probability field simulation. Math Geol 33(7):775–799 CrossRefGoogle Scholar
  18. Soares A (2001) Direct sequential simulation and cosimulation. Math Geol 33(8):911–926 CrossRefGoogle Scholar
  19. Soares A, Gomez-Hernandez J, Froidevaux R (1997) GeoENVI—Geostatistics for environmental applications. Proceedings of the first European conference on geostatistics for environmental applications, Lisbon, Portugal, 18–19 November 1996. Kluwer Academic, Dordrecht Google Scholar

Copyright information

© International Association for Mathematical Geosciences 2013

Authors and Affiliations

  • Ana Rita Oliveira
    • 1
  • Cristina Branquinho
    • 2
  • Maria Pereira
    • 1
  • Amílcar Soares
    • 1
  1. 1.CERENAInstituto Superior Técnico, Universidade Técnica de LisboaLisboaPortugal
  2. 2.Centro de Biologia AmbientalFaculdade de Ciências da Universidade de LisboaLisboaPortugal

Personalised recommendations