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Corrao G, Rea F, Carle F, et al. Measuring multimorbidity inequality across Italy through the multisource comorbidity score: a nationwide study. Eur J Public Health. 2020;30(5):916-921doi: 10.1093/eurpub/ckaa063.
Corrao, G., Rea, F., Carle, F., Di Martino, M., De Palma, R., Francesconi, P., Lepore, V., Merlino, L., Scondotto, S., Garau, D., Spazzafumo, L., Montagano, G., Clagnan, E., Martini, N. (2020). Measuring multimorbidity inequality across Italy through the multisource comorbidity score: a nationwide study. European journal of public health, 30(5), 916-921. https://doi.org/10.1093/eurpub/ckaa063
Corrao, Giovanni, et al. "Measuring multimorbidity inequality across Italy through the multisource comorbidity score: a nationwide study." European journal of public health vol. 30,5 (2020): 916-921. doi: https://doi.org/10.1093/eurpub/ckaa063
Corrao G, Rea F, Carle F, Di Martino M, De Palma R, Francesconi P, Lepore V, Merlino L, Scondotto S, Garau D, Spazzafumo L, Montagano G, Clagnan E, Martini N. Measuring multimorbidity inequality across Italy through the multisource comorbidity score: a nationwide study. Eur J Public Health. 2020 Oct 01;30(5):916-921. doi: 10.1093/eurpub/ckaa063. PMID: 32433750.
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Eur J Public Health. 2020 Oct 01;30(5):916-921. doi: 10.1093/eurpub/ckaa063.

Measuring multimorbidity inequality across Italy through the multisource comorbidity score: a nationwide study.

European journal of public health

Giovanni Corrao, Federico Rea, Flavia Carle, Mirko Di Martino, Rossana De Palma, Paolo Francesconi, Vito Lepore, Luca Merlino, Salvatore Scondotto, Donatella Garau, Liana Spazzafumo, Giuseppe Montagano, Elena Clagnan, Nello Martini,

Affiliations

  1. Department of Statistics and Quantitative Methods, National Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy.
  2. Unit of Biostatistics, Epidemiology and Public Health, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy.
  3. Center of Epidemiology and Biostatistics, Polytechnic University of Marche, Ancona, Italy.
  4. Department of Epidemiology, Lazio Regional Health Service, Rome, Italy.
  5. Authority for Healthcare and Welfare, Emilia Romagna Regional Health Service, Bologna, Italy.
  6. Regional Health Agency of Tuscany (Agenzia regionale di sanità), Florence, Italy.
  7. Regional Health Agency of Puglia (Agenzia regionale socio-sanitaria), Bari, Italy.
  8. Epidemiologic Observatory, Lombardy Regional Health Service, Milan, Italy.
  9. Epidemiologic Observatory, Sicily Regional Health Service, Palermo, Italy.
  10. Regional Councillorship of Health 'Regione Autonoma della Sardegna', Cagliari, Italy.
  11. Biostatistics Centre, INRCA-IRCCS National Institute, Ancona, Italy.
  12. Basilicata Regional Health Service, Potenza, Italy.
  13. Regional Health Agency of Friuli-Venezia-Giulia (Azienda Regionale di Coordinamento per la Salute), Udine, Italy.
  14. Research and Health Foundation (Fondazione ReS-Ricerca e Salute), Bologna, Italy.

PMID: 32433750 DOI: 10.1093/eurpub/ckaa063

Abstract

BACKGROUND: Multimorbidity is a growing concern for healthcare systems, with many countries experiencing demographic transition to older population profiles. A simple multisource comorbidity score (MCS) has been recently developed and validated. A very large real-world investigation was conducted with the aim of measuring inequalities in the MCS distribution across Italy.

METHODS: Beneficiaries of the Italian National Health Service aged 50-85 years who in 2018 were resident in one of the 10 participant regions formed the study population (15.7 million of the 24.9 million overall resident in Italy). MCS was assigned to each beneficiary by categorizing the individual sum of the comorbid values (i.e. the weights corresponding to the comorbid conditions of which the individual suffered) into one of the six categories denoting a progressive worsening comorbidity status. MCS distributions in women and men across geographic partitions were compared.

RESULTS: Compared with beneficiaries from northern Italy, those from centre and south showed worse comorbidity profile for both women and men. MCS median age (i.e. the age above which half of the beneficiaries suffered at least one comorbidity) ranged from 60 (centre and south) to 68 years (north) in women and from 63 (centre and south) to 68 years (north) in men. The percentage of comorbid population was lower than 50% for northern population, whereas it was around 60% for central and southern ones.

CONCLUSION: MCS allowed of capturing geographic variability of multimorbidity prevalence, thus showing up its value for addressing health policy in order to guide national health planning.

© The Author(s) 2020. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.

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