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Probabilistic small area risk assessment using GIS‐based data: a case study on Finnish childhood diabetes
Author(s) -
Ranta J.,
Penttinen A.
Publication year - 2000
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/1097-0258(20000915/30)19:17/18<2345::aid-sim574>3.0.co;2-g
Subject(s) - population , bayesian probability , hierarchical database model , statistical model , probabilistic logic , statistics , pixel , computer science , geography , data mining , medicine , mathematics , artificial intelligence , environmental health
A Bayesian hierarchical spatial model is constructed to describe the regional incidence of insulin dependent diabetes mellitus (IDDM) among the under 15‐year‐olds in Finland. The model exploits aggregated pixel‐wise locations for both the cases and the population at risk. Typically such data arise from combining geographic information systems (GIS) with large databases. The dates of diagnosis and locations of the cases are observed from 1987 to 1996. The population at risk counts are available for every second year during the same period. A hierarchical model is suggested for the pixel wise case counts, including a population model to account for the uncertainty of the population at risk over the years. The model is applied in the construction of disease maps (aggregated 100 km 2 pixels), and spatial posterior predictive distributions are computed to study whether there can be found a statistically exceptional number of cases in a small area of interest. Copyright © 2000 John Wiley & Sons, Ltd.