Scenario-based simulation training for the WHO hand hygiene self-assessment framework.

The WHO SAVE LIVES: Clean Your Hands global hand hygiene campaign, launched in 2009 and celebrated annually on the 5th of May, features specific calls to action seeking to increase engagement from stakeholders' collaborations in hand hygiene improvement. WHO calls on everyone to be inspired by the global movement towards universal health coverage (UHC). Infection prevention and control (IPC), including hand hygiene, is critical to achieve UHC as it has a direct impact on quality of care and patient safety across all levels of the health services. In the framework of UHC, the theme for 5 May 2019 is "Clean care for all - it's in your hands". In this context, the WHO has launched a global survey to assess the current level of progress of IPC programmes and hand hygiene activities in healthcare facilities (HCFs) worldwide. This involved the creation of two tools for healthcare facilities: the WHO Infection Prevention and Control Assessment Framework (IPCAF) and the WHO Hand Hygiene Self-Assessment Framework (HHSAF). The objective of this paper is to provide case scenario-based simulation for IPC specialists to simulate and fully assimilate the correct completion of the HHSAF framework in a standardized format. The three case scenarios have been tested and are proposed for the reader to assess the HHSAF of different HCFs in a variety of contexts, even in low-resouce settings. They were designed for simulation training purposes to achieve standardization and interactive learning. These scenarios are meant to be used by professionals in charge of implementing a hand hygiene improvement strategy within their HCF, as well as for simulation and standardized training purposes prior to completing and submitting data for the 2019 WHO Global Survey. Additionally, information provided by the use of the HHSAF can easily be translated into action plans to support the implementation and improvement related to specific indicators of hand hygiene promotion. We invite all HCFs to participate in the 2019 WHO global survey and monitor the level of progress of their IPC programme and hand hygiene activities.

Burden of bloodstream infection caused by extended-spectrum ß-lactamase-producing enterobacteriaceae determined using multistate modeling at a Swiss University Hospital and a nationwide predictive model.

OBJECTIVE: To obtain an unbiased estimate of the excess hospital length of stay (LOS) and cost attributable to extended-spectrum ß-lactamase (ESBL) positivity in bloodstream infections (BSIs) due to Enterobacteriaceae. DESIGN: Retrospective cohort study. SETTING: A 2,200-bed academic medical center in Geneva, Switzerland. PATIENTS: Patients admitted during 2009. METHODS: We used multistate modeling and Cox proportional hazards models to determine the excess LOS and adjusted end-of-LOS hazard ratio (HR) for ESBL-positive and ESBL-negative BSI. We estimated economic burden as the product of excess LOS and average bed-day cost. Patient-level accounting data provided a complementary analysis of economic burden. A predictive model was fitted to national surveillance data. RESULTS: Thirty ESBL-positive and 96 ESBL-negative BSI cases were included. The excess LOS attributable to ESBL-positive and ESBL-negative BSI was 9.4 (95% confidence interval [CI], 0.4-18.4) and 2.6 (95% CI, 0.7-5.9) days, respectively. ESBL positivity was therefore associated with 6.8 excess days and CHF 9,473 per BSI. The adjusted end-of-LOS HRs for ESBL-positive and ESBL-negative BSI were 0.62 (95% CI, 0.43-0.89) and 0.90 (95% CI, 0.74-1.10), respectively. After reimbursement, the average financial loss per acute care episode in ESBL-positive BSI, ESBL-negative BSI, and control cohorts was CHF 48,674, 48,131, and 13,532, respectively. Our predictive model estimated that the nationwide cost of third-generation cephalosporin resistance would increase from CHF 2,084,000 in 2010 to CHF 3,526,000 in 2015. CONCLUSIONS: This is the first hospital-wide analysis of excess LOS attributable to ESBL positivity determined using multistate modeling to avoid time-dependent bias. These results may inform health-economic evaluations of interventions targeting ESBL control.