OBJECTIVE: To study the relationships between prevalence and incidence in the case of nosocomial infections of the urinary tract, and to evaluate if repeated prevalence measures may be useful to obtain an estimate of incidence. METHODS: Methodology is based on a simple and reasonable assumption on the infection dynamics: starting from a difference equation modeling the evolution of hospital population, it is obtained a set of equations allowing to calculate the incidence by means of the knowledge of prevalence. RESULTS: The numerical validation of the model done by computer simulations, shows that the model obtains a better estimate of incidence than the approach given by the classical rule prevalence = incidence x duration. CONCLUSIONS: The proposed strategy permits to forecast the incidence of the urinary tract nosocomial infections by using repeated measures of prevalence. It is hence possible to estimate the incidence from cross-sectional prevalence data with sufficient accuracy to monitor and estimate the time dynamics of these infections.
OBJECTIVE: To study the relationships between prevalence and incidence in the case of nosocomial infections of the urinary tract, and to evaluate if repeated prevalence measures may be useful to obtain an estimate of incidence. METHODS: Methodology is based on a simple and reasonable assumption on the infection dynamics: starting from a difference equation modeling the evolution of hospital population, it is obtained a set of equations allowing to calculate the incidence by means of the knowledge of prevalence. RESULTS: The numerical validation of the model done by computer simulations, shows that the model obtains a better estimate of incidence than the approach given by the classical rule prevalence = incidence x duration. CONCLUSIONS: The proposed strategy permits to forecast the incidence of the urinary tract nosocomial infections by using repeated measures of prevalence. It is hence possible to estimate the incidence from cross-sectional prevalence data with sufficient accuracy to monitor and estimate the time dynamics of these infections.
Authors: G Kampf; P Gastmeier; N Wischnewski; J Schlingmann; M Schumacher; F Daschner; H Rüden Journal: J Hosp Infect Date: 1997-10 Impact factor: 3.926