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

Authors

  • Brian L. Egleston Orcid
    Biostatistics and Bioinformatics Facility, Fox Chase Cancer Center, Temple University Health System, Philadelphia, PA, USA
  • Ashis Kumar Chanda Orcid
    Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA
  • Tian Bai Orcid
    Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA
  • Carolyn Y. Fang Orcid
    Cancer Prevention and Control, Fox Chase Cancer Center, Temple University Health System, Philadelphia, PA, USA
  • Richard J. Bleicher Orcid
    Department of Surgical Oncology, Fox Chase Cancer Center, Temple University Health System, Philadelphia, PA, USA
  • Slobodan Vucetic Orcid
    Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA

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.