Understanding the Drivers of Surgical Site Infection: Investigating and Modeling the Swissnoso Surveillance Data
Understanding the Drivers of Surgical Site Infection: Investigating and Modeling the Swissnoso Surveillance Data
Surgical site infection (SSI) is the most common healthcare-associated infection, multifactorial in nature, and a typical preventable harm. Many healthcare systems require hospitals to determine the corresponding infection rates as a quality indicator and often stipulate public reporting of these data. Several agencies, among them the World Health Organization (WHO) and the U.S. Centers for Disease Control and Prevention (CDC), have issued evidence-based prevention guidelines. Despite efforts in implementing best practice, SSI continue to be a relevant complication of modern surgical procedures and generate enormous costs for the healthcare system. Moreover, prevention guidelines acknowledge that the evidence backing their recommendations is low to moderate in most cases, which is partly due to the complexity of SSI pathogenesis.
Swissnoso, the Swiss expert group for infection prevention and hospital epidemiology, oversees the nationwide collection of data on select procedures and the associated SSI. Since the inception of this dedicated surveillance in 2009, more than 300'000 procedures have been included and the corresponding patients were followed to ascertain SSIs. Although primarily conceived as a national surveillance system and then used for public reporting starting in 2014, Swissnoso is a prime data source for better understanding the epidemiology of SSI.
Here, the investigators seek to raise the quality of evidence behind future prevention guidelines. For this purpose, the investigators will move from a risk factor analysis for SSI (of which a substantial part occurs after patient discharge from the hospital, rendering surveillance difficult) to the collection of additional data (in order to better characterize certain determinants of SSI and their recognition) and, finally, to a mathematical model (which will simulate the probability of developing SSI so the investigators can test what may modulate this risk).
Aim 1: Descriptive epidemiology and risk factors for (post-discharge) SSI: using the Swissnoso SSI Surveillance data, the investigators will determine patient and institution level risk factors for SSI in Switzerland (with a focus on those occurring post-discharge), explore protective factors (such as antimicrobial prophylaxis and its timing), and describe the epidemiology of SSI in terms of time of occurrence, microbiology, severity, patient outcome, and variation by procedure type, case-mix, and hospital size.
Aim 2: Determinants of SSI: The investigators will investigate determinants of SSI in the following three areas:
A) The surveillance system itself and how the thoroughness of the surveillance process correlates with reported SSI rates; B) The operating room ventilation system and how its parameters correlate with SSI rates; and C) A healthcare institution's perceived culture of safety and how it correlates with infection rates.
To do so, the investigators will enhance and complement the Swissnoso data with new information at the institution level.
Aim 3: A mathematical model of surgical site infection: the investigators will construct a mathematical model that simulates SSI pathogenesis based on data from Swissnoso and other sources, and assesses the impact of different preventive measures. Interventions will be ranked according to the simulated reduction of SSI rates in Switzerland.
Inclusion Criteria:
Exclusion Criteria: