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Elena Moltchanova 1 Mika Rytkönen 1 Anne Kousa 2 Olli Taskinen 1 Jaakko Tuomilehto 1

Geographical Variation of Noncommunicable Diseases and Environmental Risk Factors: Application of Bayesian Modeling and GIS. Elena Moltchanova 1 Mika Rytkönen 1 Anne Kousa 2 Olli Taskinen 1 Jaakko Tuomilehto 1 Marjatta Karvonen 1 for the SPAT Study Group

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Elena Moltchanova 1 Mika Rytkönen 1 Anne Kousa 2 Olli Taskinen 1 Jaakko Tuomilehto 1

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  1. Geographical Variation of Noncommunicable Diseases and Environmental Risk Factors: Application of Bayesian Modeling and GIS • Elena Moltchanova1 • Mika Rytkönen1 • Anne Kousa2 • Olli Taskinen1 • Jaakko Tuomilehto1 • Marjatta Karvonen1 for the SPAT Study Group 1 National Public Health Institute, Helsinki, Finland 2 Geographical Survey of Finland, Kuopio, Finland

  2. MODEL: The Data Yik = number of events in cell i age-group k Nik = population at risk in cell i age-group k Zi = other cell-specific covariates in cell i W = neighborhood matrix of the area such that wij = 1 if cells i and j are neighbors wij = 0 otherwise wii = 0 i i

  3. MODEL: The Relationships Likelihood: Yik ~ Poisson (ikNik) log(ik) =+0i+ k + Zi Priors: ln 0i ~ N ( ln 0-i,*mi)  ~ N (0,0.0001)  ~ N (0,0.0001)  ~ Gamma (0.001,0.001)

  4. MODEL: DAG

  5. MODEL: The Parameters α = overall average risk level β = age group effect on risk/incidence ξ = effect of cell-specific covariates on risk/incidence λi = geographical deviation from the mean at cell i for age group 0 τ = overall geographical precision (inverse variation)

  6. Application: AMI The occurrence of coronary heart disease (CHD) varies widely between different populations. In industrialized countries it is the greatest single cause of death. In Finland CHD mortality is higher than in most populations. The most important single disorder in cardiovascular disease is ishaemic heart disease including acute myocardial infarction (AMI). Earlier research has shown that the incidence of AMI varies widely within Finland. Although there has been a steady decrease in incidence during the last two decades, this difference still persists.

  7. AMI: Data AMI = Acute Myocardial Infarction (ICD9 410-414) Analysed population-at-risk: 35-74 year old men

  8. Results

  9. Observed age-standardized incidence of AMI among 35-74 year old men in Finland 1983, 1988, 1993

  10. Posterior mean incidence of AMI among 35-74 year old men in Finland in 1983, 1988, 1993

  11. Posterior probability of being a high-risk area of AMI incidence among 35-74 year old men in Finland in 1983, 1988, 1993

  12. Application: DM1 There is a striking variation in the incidence of childhood type 1 diabetes (DM1) between and within populations. Childhood type 1 diabetes (DM1) is of a particular importance in Finland, where the incidence is the highest in the world and still increasing. The aetiology of DM1 and the cause or causes of the increase in frequency are unknown. Geographical variations in DM1 can be interpreted as evidence of environmental and genetic factors in the aetiology of the disease.

  13. DM1: Data • 3649 cases from the period 1987-1996 • almost 100% ascertainment • 95% supplied with coordinates • population data available for the years 1987, 1989, 1991, 1993 • and 1995• Urban rural-rural status: 1. urban areas • 2. urban-adjacent rural areas, • 3. rural heartland areas • 4. remote/isolated areas

  14. Results Estimated effects of area rurality on the incidence of DM1 among 0-14 year olds in Finland. θij is the difference between the area types i and j, where 1= remote area , 2 = rural heartland, 3 = urban-adjacent rural area and 4 = urban area.

  15. Observed age-standardized incidence of DM1 among 0-14 year old children in Finland in 1987-1991 and 1992-1996

  16. Posterior mean incidence of DM1 among 0-14 year old children in Finland in 1987-1991 and 1992-1996

  17. Posterior probability of being a high-risk area of DM1 incidence among 0-14 year old children in Finland in 1987-1991 and 1992-1996

  18. Conclusions • Disease mapping is an important explorative and hypothesis- generating tool. • Continuous speedy progress due to GIS, Bayesian methodology and computer technology development. • Our study has produced an interesting and useful methodological framework & software needed for it’s implementation. • Future directions of our research include a more detailed exploration of socio-economic aspect, study of other similar diseases of complex aetiology e.g. Parkonsonism and further software development

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