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Estimating County Level Health Indicators Using Spatial Microsimulation.

Erich Seamon1, Mohamed Megheib1, Christopher J Williams2

  • 1Institute for Modeling, Collaboration, and Innovation (IMCI), University of Idaho, Moscow, Idaho, United States.

Population, Space and Place
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Summary
This summary is machine-generated.

Small area estimation using iterative proportional fitting (IPF) revealed geographic patterns in Idaho

Keywords:
DiabetesIdahoIterative Proportional FittingObesityOverweightSmall Area Estimates

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Area of Science:

  • Public Health
  • Biostatistics
  • Geographic Information Systems

Background:

  • Understanding health outcomes at fine spatial scales is crucial for targeted interventions.
  • Previous small area estimation methods may lack precision for specific health indicators.

Purpose of the Study:

  • To apply iterative proportional fitting (IPF) for small area estimation of health outcomes in Idaho.
  • To identify spatial clustering of obesity, overweight, and diabetes at the county level.

Main Methods:

  • Iterative proportional fitting (IPF) applied to 2019 Idaho Behavioral Risk Factor Surveillance System (BRFSS) data.
  • County-level American Community Survey (ACS) data used for constraints (age, race, sex, education).
  • Optimized modeling construction identified significant constraints and validated estimates internally and externally.

Main Results:

  • Externally validated model results showed strong correlations (0.79–0.85, p < .05) in populated counties.
  • Higher obesity and overweight prevalence observed in midsouth and southwestern Idaho.
  • Diabetes estimates clustered in central Idaho counties (Gooding, Lincoln, Minidoka, Jerome).

Conclusions:

  • IPF provides reliable county-level health outcome estimates for Idaho.
  • Identified geographic disparities in obesity, overweight, and diabetes prevalence.
  • Estimates align well with external sources, with wider intervals in rural areas.