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Online relative risks/rates estimation in spatial and spatio-temporal disease mapping.

Aritz Adin1, Tomás Goicoa1, María Dolores Ugarte1

  • 1Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Campus de Arrosadia, Pamplona 31006, Spain; InaMAT, Public University of Navarre, Campus de Arrosadia, Pamplona 31006, Spain.

Computer Methods and Programs in Biomedicine
|March 9, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces SSTCDapp, a user-friendly web tool for analyzing spatial and spatio-temporal health data. It simplifies complex statistical modeling for non-experts, enabling better disease mapping and risk assessment.

Keywords:
Areal dataDisease mappingR-INLAShinySmall areasSpatio-temporal models

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Geographic Information Systems (GIS)

Background:

  • Spatial and spatio-temporal analyses are vital for understanding disease incidence and mortality patterns.
  • Fitting complex spatial and spatio-temporal models is challenging for users without specialized expertise.
  • Existing software may have limitations in accessibility and ease of use for non-expert users.

Purpose of the Study:

  • To introduce SSTCDapp, an interactive and user-friendly web application for analyzing spatial and spatio-temporal mortality or incidence data.
  • To provide a tool that simplifies the fitting of complex statistical models for disease mapping.
  • To address key issues in spatial-temporal modeling, including identifiability, model selection, and sensitivity analyses.

Main Methods:

  • The SSTCDapp is developed using the R package shiny, offering an interactive web interface.
  • It employs the integrated nested Laplace approximation (INLA) technique for robust model fitting and inference.
  • The application supports a wide range of complex spatio-temporal models for smoothing incidence/mortality risks.

Main Results:

  • Demonstrates the application's utility through an analysis of Spanish spatio-temporal breast cancer data.
  • Offers diverse analytical options, including various model types and selection criteria.
  • Provides comprehensive graphical and numerical outputs for detailed data interpretation.

Conclusions:

  • SSTCDapp enables non-expert users to fit complex statistical models for disease mapping without local software installation.
  • Analyses are performed on a remote server, enhancing accessibility and computational power.
  • A desktop version is available for scenarios requiring local data processing and enhanced confidentiality.