Towards a Multidimensional Approach to Bayesian Disease Mapping

Details

• Personal Author:
  Banerjee S ; Botella-Rocamora P ; Martinez-Beneito MA
• Description:
  Multivariate disease mapping enriches traditional disease mapping studies by analysing several diseases jointly. This yields improved estimates of the geographical distribution of risk from the diseases by enabling borrowing of information across diseases. Beyond multivariate smoothing for several diseases, several other variables, such as sex, age group, race, time period, and so on, could also be jointly considered to derive multivariate estimates. The resulting multivariate structures should induce an appropriate covariance model for the data. In this paper, we introduce a formal framework for the analysis of multivariate data arising from the combination of more than two variables (geographical units and at least two more variables), what we have called Multidimensional Disease Mapping. We develop a theoretical framework containing both separable and nonseparable dependence structures and illustrate its performance on the study of real mortality data in Comunitat Valenciana (Spain). [Description provided by NIOSH]
• Subjects:
  Age Factors Demography Geographic Information Systems Mortality Occupational Health Pathology Race Factors Safety Sex Characteristics
• Keywords:
  Age Factors Analytical Models Analytical Processes Demographic Characteristics Disease Control Diseases Geographic Information Systems Mortality Data Pathogenesis Quantitative Analysis Racial Factors Sex Factors Statistical Analysis

  More +
• ISSN:
  1936-0975
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  California ; OSHA Region 9
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  239-259
• Volume:
  12
• Issue:
  1
• NIOSHTIC Number:
  nn:20051465
• Citation:
  Bayesian Anal 2017 Mar; 12(1):239-259
• Contact Point Address:
  Miguel A. Martinez-Beneito, Fundaci´on para el Fomento de la Investigaci´on Sanitaria y Biom´edica (FISABIO-Salud P´ublica), Valencia, Spain
• Email:
  martinez_mig@gva.es
• Federal Fiscal Year:
  2017
• Performing Organization:
  University of California, Los Angeles
• Peer Reviewed:
  True
• Start Date:
  20130901
• Source Full Name:
  Bayesian Analysis
• End Date:
  20170831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:0d8f5a4ffb23bb3b9d2591d2f34ecfd5160ffccd0c0a678f1af1f60813f691219a403b3510a7256b5c2f0c9e53179c0117441aa08392b7b6406ea4eb3b3932e3
• Download URL:
  https://stacks.cdc.gov/view/cdc/211409/cdc_211409_DS1.pdf
• File Type:
  [PDF - 1.27 MB ]