Bayesian Models for Detecting Difference Boundaries in Areal Data

Details

• Personal Author:
  Banerjee S ; Hanson TA ; Li P ; McBean AM
• Description:
  With increasing accessibility to Geographical Information Systems (GIS) software, researchers and administrators in public health routinely encounter areal data compiled as aggregates over areal regions, such as counts or rates across counties in a state. Spatial models for areal data attempt to deliver smoothed maps by accounting for high variability in certain regions. Subsequently, inferential interest is focused upon formally identifying the "difference edges" or " difference boundaries" on the map that delineate adjacent regions with vastly disparate outcomes, perhaps caused by latent risk factors. We propose nonparametric Bayesian models for areal data that can formally identify boundaries between disparate neighbors. After elucidating these models and their estimation methods, we resort to simulation experiments to assess their effectiveness, and subsequently analyze Pneumonia and Influenza hospitalization maps from the SEER-Medicare program in Minnesota, where we detect and report highly disparate neighboring counties. [Description provided by NIOSH]
• Subjects:
  Epidemiology Geographic Information Systems Occupational Health Public Health Safety
• Keywords:
  Author Keywords: Areal Data Conditional Autoregressive Model Difference Boundary Dirichlet Process Exposure Assessment Geographic Information Systems Mathematical Models Models Public Health Statistical Analysis Stick-breaking Process Wombling

  More +
• ISSN:
  1017-0405
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  California ; Minnesota ; OSHA Region 5 ; OSHA Region 9
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  385-402
• Volume:
  25
• Issue:
  1
• NIOSHTIC Number:
  nn:20058665
• Citation:
  Stat Sin 2015 Jan; 25(1):385-402
• Contact Point Address:
  Pei Li, Medtronic Incorporated, 710 Medtronic Pkwy NE, Minneapolis, MN 55432, USA
• Email:
  pei.li@medtronic.com
• Federal Fiscal Year:
  2015
• Performing Organization:
  University of California, Los Angeles
• Peer Reviewed:
  True
• Start Date:
  20130901
• Source Full Name:
  Statistica Sinica
• End Date:
  20170831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:7d8f12d7bc9f1df59c9ae39609cbefc95a2afbb46998a458a021ab4be9d6b8fbbc2124b372167b06d7ed943fcbd4a0685d2a89b383843bdd0960f303cf647a9d
• Download URL:
  https://stacks.cdc.gov/view/cdc/221704/cdc_221704_DS1.pdf
• File Type:
  [PDF - 235.14 KB ]