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Disease Maps (disease + map)

Distribution by Scientific Domains
Mathematics and Statistics33%

Selected Abstracts

Bayesian Detection of Clusters and Discontinuities in Disease Maps

BIOMETRICS, Issue 1 2000
Leonhard Knorr-Held
Summary. An interesting epidemiological problem is the analysis of geographical variation in rates of disease incidence or mortality. One goal of such an analysis is to detect clusters of elevated (or lowered) risk in order to identify unknown risk factors regarding the disease. We propose a nonparametric Bayesian approach for the detection of such clusters based on Green's (1995, Biometrika82, 711,732) reversible jump MCMC methodology. The prior model assumes that geographical regions can be combined in clusters with constant relative risk within a cluster. The number of clusters, the location of the clusters, and the risk within each cluster is unknown. This specification can be seen as a change-point problem of variable dimension in irregular, discrete space. We illustrate our method through an analysis of oral cavity cancer mortality rates in Germany and compare the results with those obtained by the commonly used Bayesian disease mapping method of Besag, York, and Mollié (1991, Annals of the Institute of Statistical Mathematics, 43, 1,59). [source]

Secular trends, disease maps and ecological analyses of the incidence of childhood onset Type 1 diabetes in Northern Ireland, 1989,2003

DIABETIC MEDICINE, Issue 3 2007
C. R. Cardwell
Abstract Aims To investigate secular trends in the incidence of Type 1 diabetes in Northern Ireland over the period 1989,2003. To highlight geographical variations in the incidence of Type 1 diabetes by producing disease maps and to compare incidence rates by relevant area characteristics. Methods New cases of Type 1 diabetes in children aged 0,14 years in Northern Ireland were prospectively registered from 1989 to 2003. Standardized incidence rates were calculated and secular trends investigated. Bayesian methodology was used to produce maps of disease incidence using small geographical areas (582 electoral wards). Ecological analyses were conducted using Poisson regression to investigate incidence rates by area characteristics at a finer geographical subdivision (5022 census output areas). Results In Northern Ireland during 1989,2003, there were 1433 new cases, giving a directly standardized incidence rate of 24.7 per 100 000 person-years. This incidence rate increased by a mean of 4.2% per annum. Disease maps highlighted higher incidence rates in the predominately rural north-east of the province and lower incidence rates in the urban areas around Belfast in the east and Derry in the north-west of the province. Ecological analysis identified higher incidence in rural areas (P < 0.001), areas with low migration rates (P = 0.002), affluent areas (P < 0.0001), sparsely populated areas (P = 0.0001) and remote areas (P = 0.005). Conclusions In Northern Ireland the incidence of Type 1 diabetes is increasing. The observed higher incidence in rural, affluent, sparsely populated and remote areas may reflect a reduced or delayed exposure to infections in these areas. [source]

Empirical Bayes estimators and non-parametric mixture models for space and time,space disease mapping and surveillance

ENVIRONMETRICS, Issue 5 2003
Dankmar Böhning
Abstract The analysis of the geographic variation of disease and its representation on a map is an important topic in epidemiological research and in public health in general. Identification of spatial heterogeneity of relative risk using morbidity and mortality data is required. Frequently, interest is also in the analysis of space data with respect to time, where typically data are used which are aggregated in certain time windows like 5 or 10 years. The occurrence measure of interest is usually the standardized mortality (morbidity) ratio (SMR). It is well known that disease maps in space or in space and time should not solely be based upon the crude SMR but rather some smoothed version of it. This fact has led to a tremendous amount of theoretical developments in spatial methodology, in particular in the area of hierarchical modeling in connection with fully Bayesian estimation techniques like Markov chain Monte Carlo. It seems, however, that at the same time, where these theoretical developments took place, on the practical side only very few of these developments have found their way into daily practice of epidemiological work and surveillance routines. In this article we focus on developments that avoid the pitfalls of the crude SMR and simultaneously retain a simplicity and, at least approximately, the validity of more complex models. After an illustration of the typical pitfalls of the crude SMR the article is centered around three issues: (a) the separation of spatial random variation from spatial structural variation; (b) a simple mixture model for capturing spatial heterogeneity; (c) an extension of this model for capturing temporal information. The techniques are illustrated by numerous examples. Public domain software like Dismap is mentioned that enables easy mixture modeling in the context of disease mapping. Copyright © 2003 John Wiley & Sons, Ltd. [source]

Beyond Mule Kicks: The Poisson Distribution in Geographical Analysis

GEOGRAPHICAL ANALYSIS, Issue 2 2006
Daniel A. Griffith
The Poisson model, discovered nearly two centuries ago, is the basis for analyses of rare events. Its first applications included descriptions of deaths from mule kicks. More than half a century ago the Poisson model began being used in geographical analysis. Its initial descriptions of geographic distributions of points, disease maps, and spatial flows were accompanied by an assumption of independence. Today this unrealistic assumption is replaced by one allowing for the presence of spatial autocorrelation in georeferenced counts. Contemporary statistical theory has led to the creation of powerful Poisson-based modeling tools for geographically distributed count data. [source]