Constructing geographic areas for cancer data analysis: A case study on late-stage breast cancer risk in Illinois

Fahui Wang, Diansheng Guo, Sara McLafferty

Research output: Contribution to journalArticlepeer-review

Abstract

Analysis of cancer data, particularly with a small geographic unit, often suffers from the small population (numbers) problem, which causes unstable rate estimates and data suppression in sparsely populated areas. This research proposes a regionalization approach to mitigate the problem by constructing larger areas in Geographic Information Systems (GIS) that are more coherent than geopolitical areas or arbitrary zip code area and census units in terms of attribute and spatial closeness. The method is applied to analysis of late-stage breast cancer risks in Illinois in 2000. Cancer rates in these newly-constructed areas have sufficiently large base population, and are thus more reliable and also conform to a normal distribution. This permits direct mapping, exploratory spatial data analysis, and even simple OLS regression. The method can be used to effectively mitigate the small population problem commonly encountered in analysis of public health data.

Original languageEnglish (US)
Pages (from-to)1-11
Number of pages11
JournalApplied Geography
Volume35
Issue number1-2
DOIs
StatePublished - 2012

Keywords

  • Cancer data analysis
  • Late-stage cancer
  • REDCAPc
  • Regionalization
  • Small numbers problem
  • Small population problem

ASJC Scopus subject areas

  • Forestry
  • Geography, Planning and Development
  • General Environmental Science
  • Tourism, Leisure and Hospitality Management

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