Persistent Geographic Disparities despite Mortality Reductions: Applying Bayesian Models to Estimates Municipality-Level Mortality Rates in Finland

Eugenio Paglino, University of Helsinki

This study develops and employs a novel statistical modelling approach to estimate age-specific mortality rates and life expectancies at a granular spatio-temporal level for Finnish municipalities between 1990 and 2023. The model presented in the paper builds on previous methodological advances in statistical demography and addresses the challenges posed by small at-risk populations and event counts in small areas. I show that by employing a Bayesian hierarchical model with spatial and temporal smoothing, one can obtain stable and reliable estimates even for units with unstable rates and significant year-to-year variation. Descriptive findings reveal significant geographic disparities in mortality rates across Finland, despite overall improvements in life expectancy. Municipality-level estimates will help explore the underlying factors contributing to these disparities, including socioeconomic conditions and internal migration. Additionally, these estimates will allow me to examine the impact of the COVID-19 pandemic on mortality rates at the municipal level, providing insights into the spatial variation of excess deaths. The methods described in the paper are applicable in different contexts and can be used to advance existing research on the determinants of health disparities within and across countries.

Keywords: Small area estimation, Bayesian methods , Mortality and Longevity, Spatial Demography

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