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A new approach to investigating spatial variations of disease
Author(s) -
Choo Louise,
Walker Stephen G.
Publication year - 2008
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/j.1467-985x.2007.00503.x
Subject(s) - overdispersion , econometrics , statistics , poisson distribution , smoothing , spatial epidemiology , poisson regression , spatial dependence , bayesian probability , correlation , multivariate statistics , covariate , spatial correlation , spatial variability , variance (accounting) , count data , mathematics , medicine , epidemiology , environmental health , population , geometry , accounting , business
Summary. For rare diseases the observed disease count may exhibit extra Poisson variability, particularly in areas with low or sparse populations. Hence the variance of the estimates of disease risk, the standardized mortality ratios, may be highly unstable. This overdispersion must be taken into account otherwise subsequent maps based on standardized mortality ratios will be misleading and, rather than displaying the true spatial pattern of disease risk, the most extreme values will be highlighted. Neighbouring areas tend to exhibit spatial correlation as they may share more similarities than non‐neighbouring areas. The need to address overdispersion and spatial correlation has led to the proposal of Bayesian approaches for smoothing estimates of disease risk. We propose a new model for investigating the spatial variation of disease risks in conjunction with an alternative specification for estimates of disease risk in geographical areas—the multivariate Poisson–gamma model. The main advantages of this new model lie in its simplicity and ability to account naturally for overdispersion and spatial auto‐correlation. Exact expressions for important quantities such as expectations, variances and covariances can be easily derived.