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Flexible Bayesian P-splines for smoothing age-specific spatio-temporal mortality patterns.

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Summary

This study introduces advanced age-space-time models for smoothing mortality rates. Results show that breast cancer mortality doesn't decrease for the oldest age groups in all Spanish provinces.

Keywords:
Breast cancer mortalitydisease mappingintegrated nested Laplace approximationssmoothingtime-space-age models

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Statistical Modeling

Background:

  • Breast cancer mortality rates have generally declined in Spain from 1985-2010.
  • However, localized trends and specific age group variations may persist.

Purpose of the Study:

  • To propose and evaluate age-space-time models for smoothing mortality rates.
  • To analyze Spanish breast cancer mortality data using advanced statistical techniques.
  • To investigate variations in mortality trends across different age groups and provinces.

Main Methods:

  • Development of one and two-dimensional P-spline models with B-spline bases.
  • Examination of fixed relative scale and scale-invariant two-dimensional penalties.
  • Application of integrated nested Laplace approximations (INLA) for Bayesian inference and computation.

Main Results:

  • The proposed models effectively smooth mortality rates, revealing nuanced patterns.
  • Analysis of Spanish breast cancer data indicates a general decline in mortality.
  • Crucially, mortality rates for the oldest age groups did not uniformly decrease across all provinces.

Conclusions:

  • Age-space-time models provide valuable insights into complex mortality patterns.
  • The findings highlight the need for province-specific and age-stratified analyses.
  • Despite overall declines, certain demographic segments may not benefit from observed mortality reductions.