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Permanent link (DOI): https://doi.org/10.7939/R31J9795F

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Spatio-temporal modelling of disease mapping of rates Open Access

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Author or creator
Torabi, Mahmoud
Rosychuk, Rhonda J.
Additional contributors
Subject/Keyword
generalized estimating equation
random effects model
generalized linear mixed model
geographic epidemiology
seasonal effect
conditional autoregressive
Type of item
Journal Article (Published)
Language
English
Place
Time
Description
This paper studies generalized linear mixed models (GLMMs) for the analysis of geographic and temporal variability of disease rates. This class of models adopts spatially correlated random effects and random temporal components. Spatio-temporal models that use conditional autoregressive smoothing across the spatial dimension and autoregressive smoothing over the temporal dimension are developed. The model also accommodates the interaction between space and time. However, the effect of seasonal factors has not been previously addressed and in some applications (e.g., health conditions), these effects may not be negligible. The authors incorporate the seasonal effects of month and possibly year as part of the proposed model and estimate model parameters through generalized estimating equations. The model provides smoothed maps of disease risk and eliminates the instability of estimates in low-population areas while maintaining geographic resolution. They illustrate the approach using a monthly data set of the number of asthma presentations made by children to Emergency Departments (EDs) in the province of Alberta, Canada, during the period 2001–2004.
Date created
2010
DOI
doi:10.7939/R31J9795F
License information
Creative Commons Attribution-Non-Commercial-No Derivatives 3.0 Unported
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Citation for previous publication
Torabi M, Rosychuk RJ (2010) Spatio-temporal modeling of disease mapping of rates. The Canadian Journal of Statistics, 38(4),698-715.
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