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Zero-inflated multiscale models for aggregated small area health data.

Mehreteab Aregay1, Andrew B Lawson1, Christel Faes2

  • 1Department of Public Health, Medical University of South Carolina, Charleston SC USA.

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|January 17, 2018
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Summary

This study introduces zero-inflated multiscale models to accurately estimate disease risk across different geographical scales. The models address data aggregation effects and excessive zeros, ensuring consistent risk estimates.

Keywords:
Structural zerosmultiscale modelssampling zerosscaling effectszero inflated models

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Area of Science:

  • Spatial epidemiology
  • Biostatistics
  • Geographical information systems

Background:

  • Spatial data is often aggregated across multiple geographical levels (e.g., census tract, county, state).
  • Aggregating data can lead to information loss and mask disease incidence patterns, especially with excessive zeros at finer scales.
  • Ignoring zero-inflation and aggregation effects results in inconsistent spatial risk estimates.

Purpose of the Study:

  • To develop statistical models that jointly describe disease risk variations at multiple geographical scales.
  • To address the challenges of data aggregation and excessive zeros in spatial epidemiological studies.
  • To provide consistent and reliable disease risk estimates across different geographical resolutions.

Main Methods:

  • Utilized zero-inflated multiscale models to analyze spatial disease incidence.
  • Employed a zero-inflated convolution model for fine geographical levels with excessive zeros.
  • Applied a regular convolution model for smoothed data at coarser geographical levels.

Main Results:

  • The proposed models effectively handle excessive zeros and data aggregation effects.
  • Achieved consistent risk estimates at both fine and coarse geographical levels.
  • Demonstrated the utility of the models in scenarios with high percentages of structural zeros.

Conclusions:

  • Zero-inflated multiscale models offer a robust approach for spatial disease risk assessment.
  • These models ensure methodological consistency when analyzing data across various geographical scales.
  • The findings are crucial for accurate public health surveillance and resource allocation.