Abstract:
Background: Two main approaches are employed to monitor healthcare associated infections (HAIs): longitudinal surveillance, which allows to measure incidence rates, and point prevalence surveys (PPS). PPS 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 to 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.
Results: Overall, 30988 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, RR 2.5 and 3.36; 95% confidence interval, CI 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).
Conclusion: When prospective surveillance is not feasible, our simple method could be useful to obtain more accurate incidence rates from PPS data.
Reference:Vicentini C, Bussolino R, Perego M, Silengo D, D’Ancona F, Finazzi S, Zotti CM. Inverse probability weighting leads to more accurate incidence estimates for healthcare associated infections in intensive care units, results from two national surveillance systems. J Hosp Infect. 2024 Oct 29:S0195-6701(24)00357-8. doi: 10.1016/j.jhin.2024.10.009. Epub ahead of print. PMID: 39481646.