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A Flexible Bayesian Model for Estimating Subnational Mortality.

Monica Alexander1, Emilio Zagheni2, Magali Barbieri3,4

  • 1Department of Demography, University of California, Berkeley, 2232 Piedmont Avenue, Berkeley, CA, 94720-2120, USA. monicaalexander@berkeley.edu.

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|October 12, 2017
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Summary
This summary is machine-generated.

This study introduces a Bayesian hierarchical model for accurate subnational mortality estimation, crucial for understanding health disparities. The model effectively addresses challenges posed by small populations, providing reliable health inequality insights.

Keywords:
Bayesian hierarchical modelFranceMortalityPrincipal componentsSubnational estimation

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Demography

Background:

  • Subnational mortality estimates are vital for studying health inequalities.
  • Small populations present challenges due to high stochastic variation in death counts, obscuring mortality levels.

Purpose of the Study:

  • To develop and validate a Bayesian hierarchical model for estimating subnational mortality.
  • To address the issue of high variability in small populations for clearer mortality level assessment.

Main Methods:

  • Utilized a Bayesian hierarchical model incorporating characteristic age patterns in mortality curves.
  • Employed principal components from reference mortality curves.
  • Pooled mortality rate information across geographic space and smoothed over time.

Main Results:

  • The model produced reasonable mortality estimates and uncertainty levels.
  • Validation was successful on both simulated U.S. county data and real French département data.

Conclusions:

  • The developed model offers a robust approach to subnational mortality estimation.
  • These estimates have direct applications in analyzing subregional health patterns and disparities.