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<p>This study investigates territorial disparities in healthcare outcomes and service provision across Italian regions through a multidimensional analysis based on the BES (Equitable and Sustainable Well-being) framework. Two distinct but complementary sets of indicators are considered: one focusing on health outcomes (life expectancy, healthy life expectancy, and avoidable mortality), and the other on the structural availability and accessibility of healthcare services (residential beds, home care, access difficulties, and unmet needs). Using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, the study identifies spatial clusters of regions with similar profiles. Results reveal persistent North—South divides in both health and service indicators, with southern regions consistently exhibiting lower performance. While the Health dataset shows relatively homogeneous clusters, the Services dataset highlights more marked disparities. The use of DBSCAN proves effective in detecting regional groupings even in a relatively small sample, offering a valuable tool for territorial policy planning and sustainability-oriented healthcare strategies.</p>

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Models for analyzing territorial inequalities in hospitals for health sustainability: evidence from Italian regions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月31日

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  • Leonardo Salvatore Alaimo,
  • Samuela L’Abbate,
  • Paola Perchinunno,
  • Anna Argese

摘要

This study investigates territorial disparities in healthcare outcomes and service provision across Italian regions through a multidimensional analysis based on the BES (Equitable and Sustainable Well-being) framework. Two distinct but complementary sets of indicators are considered: one focusing on health outcomes (life expectancy, healthy life expectancy, and avoidable mortality), and the other on the structural availability and accessibility of healthcare services (residential beds, home care, access difficulties, and unmet needs). Using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, the study identifies spatial clusters of regions with similar profiles. Results reveal persistent North—South divides in both health and service indicators, with southern regions consistently exhibiting lower performance. While the Health dataset shows relatively homogeneous clusters, the Services dataset highlights more marked disparities. The use of DBSCAN proves effective in detecting regional groupings even in a relatively small sample, offering a valuable tool for territorial policy planning and sustainability-oriented healthcare strategies.