Objectives: Nosocomial infections constitute a major medical problem leading to increased morbidity and mortality of patients. Besides prolongation of length of hospital stay, hospital mortality attributable to those infections is often the quantity of interest when describing their impact and consequences. Since occurrence of nosocomial infections is a time-dynamic process, estimation of this quantity might be hampered by that fact. A general framework shall be developed for defining and estimating attributable mortality that in addition is taking discharge of patients as competing risk and potential censoring of observation time into account.
Methods: Since the term "attributable mortality" is used in a variety of meanings we first review basic definitions; the quantities of interest are then derived in terms of transition probabilities arising in a suitably defined multistate model that allows straightforward estimation and interpretation. Bootstrap resampling is used to calculate corresponding standard errors and confidence intervals.
Results: The methodology is applied to the data of the SIR-3 study, a prospective cohort study on the incidence of nosocomial infections in intensive care unit patients. Occurrence of nosocomial pneumonia is shown to be associated with increased mortality; the population-attributable fraction is estimated as 7.7% (95% confidence interval: 2.6-12.8%) for an observation period of 120 days.
Conclusion: Attributable mortality is an important risk measure in epidemiology. If risk exposure is time dependent, multistate models provide an easily understandable framework to define and estimate attributable mortality. The approach is capable of handling competing events, which are omnipresent in clinical research and censoring.