Literature DB >> 16021256

Factors associated with readmission to a general hospital in Brazil.

Mônica Silva Monteiro de Castro1, Marilia Sá Carvalho, Cláudia Travassos.   

Abstract

The objective of this study was to compare different modeling strategies to identify individual and admissions characteristics associated with readmission to a general hospital. Routine data recorded in the Hospital Information System on all admissions to the Regional Public Hospital of Betim, Minas Gerais State, Brazil, from July 1996 to June 2000 were analyzed. Cox proportional hazards model and variants designed to deal with multiple-events data, like Andersen-Gill (AG), Prentice, Williams and Peterson (PWP), and random effects models were fitted to time between hospital admissions or censoring. For comparison purposes, a Poisson model was fitted to the total number of readmissions, using the same covariates. We analyzed 31,648 admissions of 26,198 patients, including 17,096 adults and 9,102 children. Estimates for the PWP and frailty models were very similar, and both approaches should be fitted and compared. If clinical characteristics are available, the PWP model should be used. Otherwise the random effects model can account for unmeasured differences, particularly some related to severity of the disease. These methodologies can help focus on various related readmission aspects such as diagnostic groups or medical specialties.

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Year:  2005        PMID: 16021256     DOI: 10.1590/s0102-311x2005000400021

Source DB:  PubMed          Journal:  Cad Saude Publica        ISSN: 0102-311X            Impact factor:   1.632


  3 in total

1.  Readmissions due to traffic accidents at a general hospital.

Authors:  Luciana Paiva; Damiana Aparecida Trindade Monteiro; Daniele Alcalá Pompeo; Márcia Aparecida Ciol; Rosana Aparecida Spadotti Dantas; Lídia Aparecida Rossi
Journal:  Rev Lat Am Enfermagem       Date:  2015 Jul-Aug

2.  Prevalence and factors associated with frailty in an older population from the city of Rio de Janeiro, Brazil: the FIBRA-RJ Study.

Authors:  Virgílio Garcia Moreira; Roberto Alves Lourenço
Journal:  Clinics (Sao Paulo)       Date:  2013-07       Impact factor: 2.365

3.  Identification of risk factors for hospital admission using multiple-failure survival models: a toolkit for researchers.

Authors:  Leo D Westbury; Holly E Syddall; Shirley J Simmonds; Cyrus Cooper; Avan Aihie Sayer
Journal:  BMC Med Res Methodol       Date:  2016-04-26       Impact factor: 4.615

  3 in total

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