Visualizing statistical significance of disease clusters using cartograms

International Journal of Health Geographics
Barry J Kronenfeld, David W S Wong

Abstract

Health officials and epidemiological researchers often use maps of disease rates to identify potential disease clusters. Because these maps exaggerate the prominence of low-density districts and hide potential clusters in urban (high-density) areas, many researchers have used density-equalizing maps (cartograms) as a basis for epidemiological mapping. However, we do not have existing guidelines for visual assessment of statistical uncertainty. To address this shortcoming, we develop techniques for visual determination of statistical significance of clusters spanning one or more districts on a cartogram. We developed the techniques within a geovisual analytics framework that does not rely on automated significance testing, and can therefore facilitate visual analysis to detect clusters that automated techniques might miss. On a cartogram of the at-risk population, the statistical significance of a disease cluster is determinate from the rate, area and shape of the cluster under standard hypothesis testing scenarios. We develop formulae to determine, for a given rate, the area required for statistical significance of a priori and a posteriori designated regions under certain test assumptions. Uniquely, our approach enables dynami...Continue Reading

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Citations

Jan 22, 2019·Journal of Acquired Immune Deficiency Syndromes : JAIDS·Nguyen K TranNeal D Goldstein
Jun 7, 2019·American Journal of Epidemiology·Nguyen K Tran, Neal D Goldstein
May 27, 2021·Spatial Demography·Stephen A MatthewsDavid W S Wong

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Software Mentioned

Visual Basic
Cartogram Studio
Geovisual
Geovisual environment
SaTScan

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