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A multi-state approach to patients affected by chronic heart failure

Author

Listed:
  • Francesco Grossetti

    (Universitá Commerciale L. Bocconi)

  • Francesca Ieva

    (Politenico di Milano)

  • Anna Maria Paganoni

    (Politenico di Milano)

Abstract

Healthcare administrative databases are becoming more and more important and reliable sources of clinical and epidemiological information. They are able to track several interactions between a patient and the public healthcare system. In the present study, we make use of data extracted from the administrative data warehouse of Regione Lombardia, a region located in the northern part of Italy whose capital is Milan. Data are within a project aiming at providing a description of the epidemiology of Heart Failure (HF) patients at regional level, to profile health service utilization over time, and to investigate variations in patient care according to geographic area, socio-demographic characteristic and other clinical variables. We use multi-state models to estimate the probability of transition from (re)admission to discharge and death adjusting for covariates which are state dependent. To the best of our knowledge, this is the first Italian attempt of investigating which are the effects of pharmacological and outpatient cares covariates on patient’s readmissions and death. This allows to better characterise disease progression and possibly identify what are the main determinants of a hospital admission and death in patients with Heart Failure.

Suggested Citation

  • Francesco Grossetti & Francesca Ieva & Anna Maria Paganoni, 2018. "A multi-state approach to patients affected by chronic heart failure," Health Care Management Science, Springer, vol. 21(2), pages 281-291, June.
    • Handle: RePEc:kap:hcarem:v:21:y:2018:i:2:d:10.1007_s10729-017-9400-z
    DOI: 10.1007/s10729-017-9400-z
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    References listed on IDEAS

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    1. de Wreede, Liesbeth C. & Fiocco, Marta & Putter, Hein, 2011. "mstate: An R Package for the Analysis of Competing Risks and Multi-State Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 38(i07).
    2. Jackson, Christopher, 2011. "Multi-State Models for Panel Data: The msm Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 38(i08).
    3. Jackson, Christopher, 2016. "flexsurv: A Platform for Parametric Survival Modeling in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(i08).
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    Cited by:

    1. Saligrama Agnihothri & Leon Cui & Mohammad Delasay & Balaraman Rajan, 2020. "The value of mHealth for managing chronic conditions," Health Care Management Science, Springer, vol. 23(2), pages 185-202, June.

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