Small area estimation of incidence of cancer around a known source of exposure with fine resolution data.

Objectives: To describe the small area system developed in Finland. To illustrate the use of the system with analyses of incidence of lung cancer around an asbestos mine. To compare the performance of different spatial statistical models when applied to sparse data.Methods: In the small area system,...

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Publicado en:Occupational & Environmental Medicine Vol. 58; no. 5; pp. 315 - 321
Autores principales: Kokki E, Ranta J, Penttinen A, Pukkala E, Pekkanen J, Kokki, E, Ranta, J, Penttinen, A, Pukkala, E, Pekkanen, J
Formato: equations & formulas pictorial research tables/charts Journal Article
Publicado: BMJ Publishing Group May2001
Acceso en línea:Ver este registro en EBSCOhost
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      dt: May2001
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        atl: Small area estimation of incidence of cancer around a known source of exposure with fine resolution data.
      aug:
        au:
          Kokki E
          Ranta J
          Penttinen A
          Pukkala E
          Pekkanen J
          Kokki, E
          Ranta, J
          Penttinen, A
          Pukkala, E
          Pekkanen, J
        affil: Unit of Environmental Epidemiology, National Public Health Institute, PO Box 95, FIN-70701 Kuopio, Finland
      sug:
        subj:
          Asbestos Adverse Effects
          Lung Neoplasms Epidemiology
          Mining
          Adolescence
          Adult
          Aged
          Aged, 80 and Over
          Child
          Child, Preschool
          Comparative Studies
          Confidence Intervals
          Confounding
          Data Analysis Software
          Descriptive Statistics
          Finland
          Incidence
          Infant
          Infant, Newborn
          Life Style
          Lung Neoplasms Risk Factors
          Middle Age
          Models, Statistical
          Registries, Disease
          Relative Risk
          Socioeconomic Factors
          Funding Source
          Human
          Adolescent: 13-18 years
          Adult: 19-44 years
          Aged: 65+ years
          Aged, 80 & over
          Child: 6-12 years
          Child, Preschool: 2-5 years
          Infant: 1-23 months
          Infant, Newborn: birth-1 month
          Middle Aged: 45-64 years
      ab: Objectives: To describe the small area system developed in Finland. To illustrate the use of the system with analyses of incidence of lung cancer around an asbestos mine. To compare the performance of different spatial statistical models when applied to sparse data.Methods: In the small area system, cancer and population data are available by sex, age, and socioeconomic status in adjacent "pixels", squares of size 0.5 km x 0.5 km. The study area was partitioned into sub-areas based on estimated exposure. The original data at the pixel level were used in a spatial random field model. For comparison, standardised incidence ratios were estimated, and full bayesian and empirical bayesian models were fitted to aggregated data. Incidence of lung cancer around a former asbestos mine was used as an illustration.Results: The spatial random field model, which has been used in former small area studies, did not converge with present fine resolution data. The number of neighbouring pixels used in smoothing had to be enlarged, and informative distributions for hyperparameters were used to stabilise the unobserved random field. The ordered spatial random field model gave lower estimates than the Poisson model. When one of the three effects of area were fixed, the model gave similar estimates with a narrower interval than the Poisson model.Conclusions: The use of fine resolution data and socioeconomic status as a means of controlling for confounding related to lifestyle is useful when estimating risk of cancer around point sources. However, better statistical methods are needed for spatial modelling of fine resolution data.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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