Inverse probability weighting leads to more accurate incidence estimates for healthcare-associated infections in intensive care units – results from two national surveillance systems

Background: Two main approaches are employed to monitor healthcare-associated infections (HAIs): longitudinal surveillance, which allows the measurement of incidence rates, and point prevalence surveys (PPSs). PPSs are less time-consuming; however, they are affected by length-biased sampling, which can be corrected through inverse probability weighting. We assessed the accuracy of this method by analysing data from two Italian national surveillance systems. Methods: Ventilator-associated pneumonia (VAP) and central-line-associated bloodstream infection (CLABSI) incidence measured through a prospective surveillance system (GiViTI) was compared with incidence estimates obtained through conversion of crude and inverse probability weighted prevalence of the same HAIs in intensive care units (ICUs) measured through a PPS. Weighted prevalence rates were obtained after weighting all patients inversely proportional to their time-at-risk. Prevalence rates were converted into incidence per 100 admissions using an adapted version of the Rhame and Sudderth formula. Findings: Overall, 30,988 patients monitored through GiViTI, and 1435 patients monitored through the PPS were included. A significant difference was found between incidence rates estimated based on crude VAP and CLABSI prevalence and measured through GiViTI (relative risk 2.5 and 3.36; 95% confidence interval 1.42–4.39 and 1.33–8.53, P=0.006 and 0.05, respectively). Conversely, no significant difference was found between incidence rates estimated based on weighted VAP and CLABSI prevalence and measured through GiViTI (P=0.927 and 0.503, respectively). Conclusions: When prospective surveillance is not feasible, our simple method could be useful to obtain more accurate incidence rates from PPS data.