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Studying factors related to pressure ulcers prevention: a marginal scale model for modelling heterogeneity among hospitals
Author(s) -
Baldi Ileana,
Ferrando Alberto,
Foltran Francesca,
Ciccone Giovannino,
Gregori Dario
Publication year - 2010
Publication title -
journal of evaluation in clinical practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.737
H-Index - 73
eISSN - 1365-2753
pISSN - 1356-1294
DOI - 10.1111/j.1365-2753.2009.01257.x
Subject(s) - scale (ratio) , medicine , estimator , inference , generalized estimating equation , observational study , econometrics , statistics , computer science , mathematics , artificial intelligence , physics , pathology , quantum mechanics
Rationale, aims and objectives The purpose of this study was to analyse risk factors associated with the presence of pressure ulcer development in patients referred to several Italian hospitals, taking properly into account the within‐hospital outcome correlation. Methods We analysed data from a prevalence survey coordinated by the European Pressure Ulcer Advisory Panel on 12 000 hospitalized patients in Italy, collecting information on patient's risk, presence of ulcers and prevention measures. The article describes the bases which generalized estimating equations rely on as well as their statistical properties. The article compares different model specifications in the light of background knowledge of the survey data and model assumptions, and discusses the potential for this modelling approach to apply in similar statistical situations. Results In accordance with existing literature, factors associated with pressure ulcers in hospitalized patients were identified as Braden scale, age and assistance‐connected aspects. Between‐hospital variability seemed to be explained by the adopted degree of prevention (use of preventive equipment combined with a repositioning strategy). Conclusions Modelling the covariance matrix or the scale argument of the correlated binary responses (presence/absence of pressure ulcers) by using moment estimators based on generalized estimating equations prevents optimistic inference and provides an important insight into the role of structural differences among hospitals.
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