Geographical inequalities in mortality by age and gender in Italy, 2002-2019: insights from a spatial extension of the Lee-Carter model

2025-10-07

AI Summary

This study quantifies and models geographic inequality in age- and sex-specific mortality across 107 Italian provinces from 2002 to 2019, addressing sparse small-area mortality data and spatial heterogeneity. Methodologically, it extends the Lee–Carter model by incorporating spatially varying age effects and spatiotemporal interaction terms, and implements Bayesian inference via the *inlabru* package (built on INLA), employing smooth priors to enhance estimation robustness for small areas. Results reveal persistent mortality disadvantages concentrated in southern, central, and northwestern provinces; regional disparities widened markedly after 2010, particularly among young adult males (25–44 years) and elderly females (≥75 years). The proposed framework advances methodological approaches for high-resolution spatial analysis of population health inequalities.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Italy reports some of the lowest levels of mortality in the developed world. Recent evidence, however, suggests that even in low mortality countries improvements may be slowing and regional inequalities widening. This study contributes new empirical evidence to the debate by analysing mortality data by single year of age for males and females across 107 provinces in Italy from 2002 to 2019. We extend the widely used Lee Carter model to include spatially varying age specific effects, and further specify it to capture space age time interactions. The model is estimated in a Bayesian framework using the inlabru package, which builds on INLA (Integrated Nested Laplace Approximation) for non linear models and facilitates the use of smoothing priors. This approach borrows strength across provinces and years, mitigating random fluctuations in small area death counts. Results demonstrate the value of such a granular approach, highlighting the existence of an uneven geography of mortality despite overall national improvements. Mortality disadvantage is concentrated in parts of the Centre South and North West, while the Centre North and North East fare relatively better. These geographical differences have widened since 2010, with clear age and gender specific patterns, being more pronounced at younger adult ages for men and at older adult ages for women. Future work may involve refining the analysis to mortality by cause of death or socioeconomic status, informing more targeted public health policies to address mortality disparities across Italy's provinces.

Problem

• Analyzing geographical mortality inequalities across Italian provinces by age and gender
• Extending Lee-Carter model to capture spatial age-time mortality interactions
• Identifying widening regional mortality disparities despite national improvements

Innovation

• Extended Lee-Carter model with spatial effects
• Used Bayesian framework with INLA approximation
• Incorporated space-age-time interactions for mortality