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Catchment area analysis using bayesian regression modeling.

Aobo Wang1, David C Wheeler1

  • 1Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.

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This summary is machine-generated.

Defining a catchment area (CA) is crucial for cancer centers. This study developed diagnosis and diagnosis/treatment CAs for Massey Cancer Center, revealing distinct patient demographics within the defined areas.

Keywords:
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Area of Science:

  • Oncology
  • Biostatistics
  • Geographic epidemiology

Background:

  • A catchment area (CA) defines the patient population for a cancer center.
  • CA definition is essential for National Cancer Institute (NCI)-designated cancer center status.
  • Understanding CA demographics helps assess service needs and disparities.

Purpose of the Study:

  • To construct diagnosis and diagnosis/treatment catchment areas (CAs) for the Massey Cancer Center (MCC).
  • To analyze patient characteristics within the defined MCC CAs.
  • To identify potential disparities in cancer care access.

Main Methods:

  • Utilized Virginia state cancer registry data for diagnosis CAs.
  • Employed Bayesian hierarchical logistic and Poisson regression models.
  • Assessed spatial clustering of patients using exceedance probabilities for county random effects, adjusting for covariates.

Main Results:

  • Developed distinct diagnosis and diagnosis/treatment CAs for MCC.
  • Patients diagnosed at MCC within the CA were more likely to be minority, female, uninsured, or Medicaid recipients.
  • Identified demographic differences between patients inside and outside the MCC CAs.

Conclusions:

  • The study successfully defined MCC's catchment areas using advanced statistical methods.
  • The defined CAs highlight potential healthcare access and equity issues.
  • These findings can inform targeted interventions to improve cancer care delivery within the catchment area.