Estimating incidence rates using exact or interval-censored data with an application to hospital-acquired infections

Abstract

Health-care providers in the UK and elsewhere are required to maintain records of incidents relating to patient safety, including the date and time of each incident. However, for reporting and analysis, the resulting data are typically grouped into discrete time intervals, for example, weekly or monthly counts. The grouping represents a potential loss of information for estimating variations in incidence over time. We use a Poisson point process model to quantify this loss of information. We also suggest some diagnostic procedures for checking the goodness of fit of the Poisson model. Finally, we apply the model to the data on hospital-acquired methicillin-resistant Staphylococcus aureus infections in two hospitals in the north of England. We find that, in one of the hospitals, the estimated incidence decreased by a factor of approximately 2.3 over a 7-year period from 0.323 to 0.097 cases per day per 1000 beds, whereas in the other, the estimated incidence showed only a small and nonsignificant decrease over the same period from 0.137 to 0.131.