OBJECTIVE: To identify factors associated with hospital readmissions in the elderly. DESIGN: Observational descriptive study. SETTING: Health area 3 of Madrid. PARTICIPANTS: Patients 74 years-old and over with a hospital admission to Hospital Universitario Príncipe de Asturias in 2006. Those with a hospital admission in the previous 6 months were excluded. A total of 1051 patients were identified. MAIN MEASURES: Hospital Discharge Minimum Basic Data Set and primary care information system were used to develop logistical regression models. The dependent variable was the hospital readmission in a 6 month period. Independent variables were socio-demographics, health status and health care activity. RESULTS: There were 22.6% readmissions in the first 6 months. Variables associated with higher risk of readmission were, hospital stay (hospital stay greater than 15 days had an OR: 1.73 (95% CI:1.17-2.54), the total number of medicines prescribed to the patient (OR: 1.05; 95% CI:1.01-1.09), having hypertension (OR:1.56; 95% CI:1.11-2.18), heart failure (OR: 1.56; 95% CI:1.00-2.44) or ischemic heart disease (OR: 1.51; 95% CI:1.00-2.26), and the primary care nursing mean attendance pressure (OR: 0.93; 95% CI:0.87-0.98). The model that integrates information from both systems explains a higher number of factors associated with readmission. CONCLUSIONS: Hospital readmissions have been associated to patient medical condition and to factors related to the health care received. Integrating information from hospital and primary care administrative data bases improves the capacity to identify factors associated with a higher readmission risk.
OBJECTIVE: To identify factors associated with hospital readmissions in the elderly. DESIGN: Observational descriptive study. SETTING: Health area 3 of Madrid. PARTICIPANTS: Patients 74 years-old and over with a hospital admission to Hospital Universitario Príncipe de Asturias in 2006. Those with a hospital admission in the previous 6 months were excluded. A total of 1051 patients were identified. MAIN MEASURES: Hospital Discharge Minimum Basic Data Set and primary care information system were used to develop logistical regression models. The dependent variable was the hospital readmission in a 6 month period. Independent variables were socio-demographics, health status and health care activity. RESULTS: There were 22.6% readmissions in the first 6 months. Variables associated with higher risk of readmission were, hospital stay (hospital stay greater than 15 days had an OR: 1.73 (95% CI:1.17-2.54), the total number of medicines prescribed to the patient (OR: 1.05; 95% CI:1.01-1.09), having hypertension (OR:1.56; 95% CI:1.11-2.18), heart failure (OR: 1.56; 95% CI:1.00-2.44) or ischemic heart disease (OR: 1.51; 95% CI:1.00-2.26), and the primary care nursing mean attendance pressure (OR: 0.93; 95% CI:0.87-0.98). The model that integrates information from both systems explains a higher number of factors associated with readmission. CONCLUSIONS: Hospital readmissions have been associated to patient medical condition and to factors related to the health care received. Integrating information from hospital and primary care administrative data bases improves the capacity to identify factors associated with a higher readmission risk.
Authors: P Narain; L Z Rubenstein; G D Wieland; B Rosbrook; L S Strome; F Pietruszka; J E Morley Journal: J Am Geriatr Soc Date: 1988-09 Impact factor: 5.562
Authors: Pascale Comette; William D'Hoore; Brigitte Malhomme; Dominique Van Pee; Philippe Meert; Christian Swine Journal: Aging Clin Exp Res Date: 2005-08 Impact factor: 3.636
Authors: Mary Courtney; Helen Edwards; Anne Chang; Anthony Parker; Kathleen Finlayson; Kyra Hamilton Journal: J Am Geriatr Soc Date: 2009-02-23 Impact factor: 5.562
Authors: A Jiménez Puente; J Fernández Guerra; L Hidalgo Rojas; S Domingo González; A Lara Blanquer; J García Alegría Journal: An Med Interna Date: 2003-07