Using Pointwise Mutual Information for Breast Cancer Health Disparities Research with SEER-Medicare Claims

Brian Egleston, Ashis Kumar Chanda, Tian Bai, Carolyn Fang, Richard Bleicher, Slobodan Vucetic

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Identification of procedures using International Classification of Diseases or Healthcare Common Procedure Coding System codes is challenging when conducting medical claims research. We demonstrate how Pointwise Mutual Information can be used to find associated codes. We apply the method to an investigation of racial differences in breast cancer outcomes. We used Surveillance Epidemiology and End Results (SEER) data linked to Medicare claims. We identified treatment using two methods. First, we used previously published definitions. Second, we augmented definitions using codes empirically identified by the Pointwise Mutual Information statistic. Similar to previous findings, we found that presentation differences between Black and White women closed much of the estimated survival curve gap. However, we found that survival disparities were completely eliminated with the augmented treatment definitions. We were able to control for a wider range of treatment patterns that might affect survival differences between Black and White women with breast cancer.

Original languageEnglish
Pages (from-to)43-59
Number of pages17
JournalMethodology
Volume19
Issue number1
DOIs
StatePublished - 2023

Keywords

  • SEER-Medicare claims
  • breast cancer
  • health disparities
  • machine learning
  • pointwise mutual information

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