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Spatial downscaling of multivariate disease risk.

David Payares-Garcia1, Frank Osei2, Jorge Mateu3

  • 1ITC Faculty Geo-Information Science and Earth Observation, University of Twente, Enschede, The Netherlands. d.e.payaresgarcia@utwente.nl.

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New Area-to-Area (ATA) and Area-to-Point (ATP) Poisson cokriging methods downscale disease risks from areal data. These methods improve spatial disease mapping and public health interventions by considering disease correlations and population differences.

Keywords:
CokrigingCountsDisease mappingDownscalingMultivariate

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Area of Science:

  • Spatial epidemiology
  • Geostatistics
  • Public health analytics

Background:

  • Accurate disease risk assessment requires downscaling areal health data to finer resolutions.
  • Understanding spatial disease patterns aids in identifying risk factors and developing targeted interventions.
  • Existing methods often struggle with population heterogeneity and varying spatial unit characteristics.

Purpose of the Study:

  • Introduce Area-to-Area (ATA) and Area-to-Point (ATP) Poisson cokriging for downscaling spatial disease risks.
  • Address challenges including inter-disease correlation, population heterogeneity, and spatial unit variability.
  • Enhance multivariate disease mapping for improved public health insights.

Main Methods:

  • Developed and applied ATA and ATP Poisson cokriging methodologies.
  • Incorporated correlation between multiple diseases.
  • Adjusted for population heterogeneity and varying spatial entity shapes/sizes.
  • Validated through simulation studies and application to COVID-19 and asthma data in Bogota, Colombia.

Main Results:

  • ATA and ATP Poisson cokriging outperformed univariate methods in simulations.
  • Achieved lower mean squared prediction errors and preserved small-scale spatial variations.
  • Revealed detailed disease hotspots/coldspots and refined COVID-19 risk estimates by leveraging asthma correlation.
  • Demonstrated improved small-area estimation and spatial pattern understanding.

Conclusions:

  • The proposed ATA and ATP Poisson cokriging methods offer significant advantages for downscaling spatial disease risks.
  • These methods enable more accurate risk assessment and enhanced understanding of multivariate disease patterns.
  • Provide valuable insights for targeted public health interventions and resource allocation.