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<p>Multimorbidity, a major driver of healthcare demand and clinical complexity, is often addressed in a disease-centric manner and remains insufficiently understood in its population-level dynamics. Using data from a 10-year population-based cohort of 5.5 million adults in Catalonia, Spain, we quantified multimorbidity-associated clinical complexity using the Adjusted Morbidity Groups (AMG) index to predict progression from low/moderate ( &lt; P<sub>80</sub>) to high/very high ( ≥ P<sub>80</sub>) complexity. Machine learning models identified predictive factors, while network analyses explored co-occurrence patterns among chronic conditions. During follow-up, 39.2% of the individuals who remained alive throughout the analysis period transitioned to high/very high complexity. Baseline AMG score was the strongest predictor of progression, surpassing models relying solely on individual diagnoses. The most prevalent conditions were nutritional and endocrine disorders, anxiety, and hypertension, with notable sequential links between mental and physical disorders. Findings emphasize the need for integrated, patient-centred care strategies and population-based prevention approaches to mitigate multimorbidity progression.</p>

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Ten-year population-based assessment of multimorbidity burden progression in a regional cohort of 5.5 million adults

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月31日

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  • Damià Valero-Bover,
  • David Monterde,
  • Gerard Carot-Sans,
  • Emili Vela,
  • Rubèn González-Colom,
  • Josep Roca,
  • Caridad Pontes,
  • Xabier Michelena,
  • Maria Mercedes Nogueras,
  • Pilar Aparicio,
  • Inmaculada Corrales,
  • Teresa Biec,
  • Isaac Cano,
  • Jordi Piera-Jiménez

摘要

Multimorbidity, a major driver of healthcare demand and clinical complexity, is often addressed in a disease-centric manner and remains insufficiently understood in its population-level dynamics. Using data from a 10-year population-based cohort of 5.5 million adults in Catalonia, Spain, we quantified multimorbidity-associated clinical complexity using the Adjusted Morbidity Groups (AMG) index to predict progression from low/moderate ( < P80) to high/very high ( ≥ P80) complexity. Machine learning models identified predictive factors, while network analyses explored co-occurrence patterns among chronic conditions. During follow-up, 39.2% of the individuals who remained alive throughout the analysis period transitioned to high/very high complexity. Baseline AMG score was the strongest predictor of progression, surpassing models relying solely on individual diagnoses. The most prevalent conditions were nutritional and endocrine disorders, anxiety, and hypertension, with notable sequential links between mental and physical disorders. Findings emphasize the need for integrated, patient-centred care strategies and population-based prevention approaches to mitigate multimorbidity progression.