Literature DB >> 11343370

Use of a density equalizing map projection in analysing childhood cancer in four California counties.

D W Merrill1.   

Abstract

In this study, 401 cases of childhood cancer in four California counties in 1980-1988 were analysed with the innovative methodology of density equalizing map projections. The data were originally collected and analysed by the California State Department of Health Services (DHS). In addition to the new analytic technique, the present analysis used population data more detailed and more accurate than those in the DHS analysis. The geographic boundaries of the 259 census tracts in the study area were adjusted according to population at risk so as to make population density everywhere constant; then the 401 case locations were plotted on the density equalized map. If risk is everywhere equal, the resulting distribution of cases should be uniform except for statistical variation. The metric used was a measure of the variability of the density of cases on the density equalized map. The same metric was calculated for independent samples of artificial cases, generated under the null hypothesis of equal risk. The slight geographic non-uniformity observed among the real cases is well within the limits of variation observed in the samples of artificial cases. In agreement with results published by DHS, we conclude that there is no evidence for geographic variation of risk among the cases studied. Subsets of the data, selected by age, sex, race, time period and cancer site, yielded similar negative results. Copyright 2001 John Wiley & Sons, Ltd.

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Year:  2001        PMID: 11343370     DOI: 10.1002/sim.686

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  4 in total

1.  From The Cover: Diffusion-based method for producing density-equalizing maps.

Authors:  Michael T Gastner; M E J Newman
Journal:  Proc Natl Acad Sci U S A       Date:  2004-05-10       Impact factor: 11.205

2.  Density-equalizing Euclidean minimum spanning trees for the detection of all disease cluster shapes.

Authors:  Shannon C Wieland; John S Brownstein; Bonnie Berger; Kenneth D Mandl
Journal:  Proc Natl Acad Sci U S A       Date:  2007-05-22       Impact factor: 11.205

3.  Use of density-equalizing cartograms to visualize trends and disparities in state-specific prevalence of obesity: 1996-2006.

Authors:  Brian Houle; James Holt; Cathleen Gillespie; David S Freedman; Michele Reyes
Journal:  Am J Public Health       Date:  2008-12-04       Impact factor: 9.308

4.  Visualizing statistical significance of disease clusters using cartograms.

Authors:  Barry J Kronenfeld; David W S Wong
Journal:  Int J Health Geogr       Date:  2017-05-15       Impact factor: 3.918

  4 in total

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