Estimation of extra hospital stay attributable to nosocomial infections: heterogeneity and timing of events

Infections acquired in hospital are likely to affect the duration of hospit alization. Suitable statistical methods for estimating the extra days spent in hospital due to nosocomial infections should allow modeling of the hete rogeneity of the patient population and the timing of events, as failure to account for important covariates and failure to model adequately the timin g of events may lead to biased results. Three approaches have been used in the past to estimate the extra stay: a comparison of duration of stay of in fected and uninfected patients, matching of infected and uninfected patient s with respect to potentially important determinants of the length of hospi tal stay, and matching for time-to-infection in addition to the other facto rs. While these approaches can allow for the heterogeneity of the patient p opulation, none takes sufficient account of the real timing of events and m ay overestimate the effect of nosocomial infections. We explored the statis tical methods available for analyzing time-to-event data and derived altern ative methods to estimate the extra stay that appropriately account for het erogeneity and timing. Data from two prospective cohort studies on postoper ative wound infection and on nosocomial pneumonia showed that the two-group comparison yields the highest estimates of extra stay (21 and 14 extra day s), while matching for confounders and time reduced the estimates to 11 and 8 extra days; our methods yield even lower results (10-12 and 3-4 extra da ys). (C) 2000 Elsevier Science Inc. All rights reserved.