title: Factors associated with readmission to a general hospital in Brazil.
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.
authors: Escola Nacional de Saúde Pública, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil. mmcastro@uai.com.br
authors: Mônica Silva Monteiro de
authors: MS
authors: Castro
authors: Marilia Sá
authors: MS
authors: Carvalho
authors: Cláudia
authors: C
authors: Travassos
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001938:Brazil / Q000453:epidemiology; D002648:Child; D002675:Child, Preschool; D005260:Female; D006769:Hospitals, General / Q000706:statistics & numerical data; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007902:Length of Stay / Q000706:statistics & numerical data; D008297:Male; D008875:Middle Aged; D010359:Patient Readmission / Q000706:statistics & numerical data*; D016012:Poisson Distribution; D016016:Proportional Hazards Models; D016019:Survival Analysis