Comparison of two methods for attributing length of hospital stay to healthcare-acquired infection

The development of models of the cost-effectiveness of infection control programs must be informed by valid estimates of the extra length of hospital stay due to healthcare-acquired infection (HAI). The aim of this study was to compare two competing methods for attributing extra hospital stay to healthcare-acquired infection. Data were collected prospectively from two Australian hospitals. The two competing methods used to attribute extra hospital days to HAI were: (i) a matched cohort design; and (ii) a procedure for selecting predictors associated with length of stay and their subsequent inclusion in a statistical model of length of stay. There were 40 patients exposed to multiple HAIs among 4488 patients recruited over 95 days of data collection (incidence rate 0.89%). The estimates from five separate matching procedures were between 23.38 and 31.78 extra days in hospital, and the results of the statistical model suggested an exposed patient independently increased length of hospital stay by a mean of 21.0 days (95% confidence interval 17.3, 25.1). Matching studies may suffer from two potential sources of bias: the selection of exposed patients from over-matching may underestimate the true cost of HAI, and the omission of important predictors may overstate the cost of HAI. Statistical regression techniques can be used to mitigate both potential problems.