Use of density equalizing map projections (DEMP) in the analysis of childhood cancer in four California counties

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In studying geographic disease distributions, one normally compares rates of arbitrarily defined geographic subareas (e.g. census tracts), thereby sacrificing the geographic detail of the original data. The sparser the data, the larger the subareas must be in order to calculate stable rates. This dilemma is avoided with the technique of Density Equalizing Map Projections (DEMP). Boundaries of geographic subregions are adjusted to equalize population density over the entire study area. Case locations plotted on the transformed map should have a uniform distribution if the underlying disease rates are constant. The density equalized map portrays both individual cases and rates, and ... continued below

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18 p.

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Merrill, D.W. Selvin, S.; Close, E.R. & Holmes, H.H. April 1, 1995.

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In studying geographic disease distributions, one normally compares rates of arbitrarily defined geographic subareas (e.g. census tracts), thereby sacrificing the geographic detail of the original data. The sparser the data, the larger the subareas must be in order to calculate stable rates. This dilemma is avoided with the technique of Density Equalizing Map Projections (DEMP). Boundaries of geographic subregions are adjusted to equalize population density over the entire study area. Case locations plotted on the transformed map should have a uniform distribution if the underlying disease rates are constant. The density equalized map portrays both individual cases and rates, and can be understood by untrained observers. Simple statistical techniques can be used to test the uniformity of the transformed map. This report describes application of the DEMP technique to a sizeable `real-world` data set: 401 childhood cancer cases occurring between 1980 and 1988 in four California counties. In an earlier analysis of the same data, the California Department of Health Services (DHS) calculated rates for 101 communities and found no significant geographic variability. The DDS 1980--88 population estimates are no longer available, so in this analysis 1980 Census data were used; geographic units were 262 census tracts. A k`th nearest neighbor analysis, corrected for boundary effects and for within-tract variability, provides strong evidence for geographic nonuniformity in tract rates ({rho} < l0{sup {minus}4}). No such effect is observed for artificial cases generated under the assumption of constant rates. Pending reanalysis with 1980-88 population estimates, no epidemiologic conclusions can be drawn at this time.

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18 p.

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OSTI as DE95012388

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  • 1995 CDC/ATSDR symposium on statistical methods: small area statistics in public health: design, analysis graphic and spatial methods, Atlanta, GA (United States), 25-26 Jan 1995

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  • Other: DE95012388
  • Report No.: LBL--36630-Rev.
  • Report No.: CONF-950168--1-Rev.
  • Grant Number: AC03-76SF00098
  • DOI: 10.2172/10117532 | External Link
  • Office of Scientific & Technical Information Report Number: 73024
  • Archival Resource Key: ark:/67531/metadc708180

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  • April 1, 1995

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  • Sept. 12, 2015, 6:31 a.m.

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  • April 5, 2016, 11:07 a.m.

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Merrill, D.W. Selvin, S.; Close, E.R. & Holmes, H.H. Use of density equalizing map projections (DEMP) in the analysis of childhood cancer in four California counties, article, April 1, 1995; California. (digital.library.unt.edu/ark:/67531/metadc708180/: accessed December 12, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.