Bayesian Analysis of Nosocomial Infection Risk and Length of Stay in a Department of General and Digestive Surgery
Article first published online: 8 JAN 2010
© 2009, International Society for Pharmacoeconomics and Outcomes Research (ISPOR)
Value in Health
Volume 13, Issue 4, pages 431–439, June/July 2010
How to Cite
Sáez-Castillo, A. J., Olmo-Jiménez, M. J., Pérez Sánchez, J. M., Negrín Hernández, M. Á., Arcos-Navarro, Á. and Díaz-Oller, J. (2010), Bayesian Analysis of Nosocomial Infection Risk and Length of Stay in a Department of General and Digestive Surgery. Value in Health, 13: 431–439. doi: 10.1111/j.1524-4733.2009.00680.x
- Issue published online: 30 JUN 2010
- Article first published online: 8 JAN 2010
- asymmetric logit;
- Bayesian analysis;
- length of stay in hospital;
- logistic regression;
- nosocomial infection risk;
- Poisson-Gamma model
Objective: Nosocomial infection is one of the main causes of morbidity and mortality in patients admitted to hospital. One aim of this study is to determine its intrinsic and extrinsic risk factors. Nosocomial infection also increases the duration of hospital stay. We quantify, in relative terms, the increased duration of the hospital stay when a patient has the infection.
Methods: We propose the use of logistic regression models with an asymmetric link to estimate the probability of a patient suffering a nosocomial infection. We use Poisson-Gamma regression models as a multivariate technique to detect the factors that really influence the average hospital stay of infected and noninfected patients. For both models, frequentist and Bayesian estimations were carried out and compared.
Results: The models are applied to data from 1039 patients operated on in a Spanish hospital. Length of stay, the existance of a preoperative stay and obesity were found the main risk factors for a nosomial infection. The existence of a nosocomial infection multiplies the length of stay in the hospital by a factor of 2.87.
Conclusion: The results show that the asymmetric logit improves the predictive capacity of conventional logistic regressions