STUDY OBJECTIVE: Although national variation in short-term prognosis (that is, 30 day mortality) after a patient's first hospitalisation for heart failure may depend on individual differences between patients, dissimilarities in hospital practices may also influence prognosis. This study, therefore, sought to disentangle patient determinants from institutional factors that might explain such variation. DESIGN: A multilevel logistic regression modelling was performed with patients (1st level) nested in hospitals (2nd level). Institutional effects (that is, 2nd level variance and intra-hospital correlation) were calculated unadjusted and adjusted for specific patient (that is, age and previous diseases) and institutional (that is, size of hospital) characteristics. Patients were followed up until death or 30 days from hospital admission. SETTING: Hospitals in Sweden. PATIENTS: The study identified all the 20420 men and 17923 women (ages 65 to 85) admitted to the 90 acute care hospitals in Sweden during the period 1992-1995 for their first hospitalisation attributable to heart failure. MAIN RESULTS: Patient age and previous diseases (particularly senile dementia) were major determinants of impaired prognosis. Institutional factors explained only 1.6% and 2.3% of the total variation in 30 day mortality in men and women, respectively. These modest institutional effects remained after adjusting for patient age and previous diseases, but were in part explained by hospital size. CONCLUSIONS: National variation in short-term prognosis after an initial hospitalisation for heart failure was mainly explained by differences between patients, with hospital factors playing a minor part. Of the latter, hospital size seemed to emerge as one determinant (that is, the greater the number of patients, the better the individual prognosis).
STUDY OBJECTIVE: Although national variation in short-term prognosis (that is, 30 day mortality) after a patient's first hospitalisation for heart failure may depend on individual differences between patients, dissimilarities in hospital practices may also influence prognosis. This study, therefore, sought to disentangle patient determinants from institutional factors that might explain such variation. DESIGN: A multilevel logistic regression modelling was performed with patients (1st level) nested in hospitals (2nd level). Institutional effects (that is, 2nd level variance and intra-hospital correlation) were calculated unadjusted and adjusted for specific patient (that is, age and previous diseases) and institutional (that is, size of hospital) characteristics. Patients were followed up until death or 30 days from hospital admission. SETTING: Hospitals in Sweden. PATIENTS: The study identified all the 20420 men and 17923 women (ages 65 to 85) admitted to the 90 acute care hospitals in Sweden during the period 1992-1995 for their first hospitalisation attributable to heart failure. MAIN RESULTS:Patient age and previous diseases (particularly senile dementia) were major determinants of impaired prognosis. Institutional factors explained only 1.6% and 2.3% of the total variation in 30 day mortality in men and women, respectively. These modest institutional effects remained after adjusting for patient age and previous diseases, but were in part explained by hospital size. CONCLUSIONS: National variation in short-term prognosis after an initial hospitalisation for heart failure was mainly explained by differences between patients, with hospital factors playing a minor part. Of the latter, hospital size seemed to emerge as one determinant (that is, the greater the number of patients, the better the individual prognosis).
Authors: J Merlo; U Lindblad; H Pessah-Rasmussen; B Hedblad; J Rastam; S O Isacsson; L Janzon; L Råstam Journal: Eur J Epidemiol Date: 2000-03 Impact factor: 8.082
Authors: Wei-Ching Chang; William K Midodzi; Cynthia M Westerhout; Eric Boersma; Judith Cooper; Elliot S Barnathan; Maarten L Simoons; Lars Wallentin; E Magnus Ohman; Paul W Armstrong Journal: J Epidemiol Community Health Date: 2005-05 Impact factor: 3.710
Authors: J Merlo; P O Ostergren; O Hagberg; M Lindström; A Lindgren; A Melander; L Råstam; G Berglund Journal: J Epidemiol Community Health Date: 2001-11 Impact factor: 3.710
Authors: Juana A Flores-Le Roux; Josep Comin; Juan Pedro-Botet; David Benaiges; Jaume Puig-de Dou; Juan J Chillarón; Alberto Goday; Jordi Bruguera; Juan F Cano-Perez Journal: Cardiovasc Diabetol Date: 2011-05-14 Impact factor: 9.951