Healthcare-associated infections

The burden of healthcare-associated infections is heavy worldwide, primarily due to the high and increasing prevalence of antimicrobial resistance (AMR). Patients admitted to hospitals are particularly susceptible to colonization by and infection with antibiotic-resistant bacteria (ARB) due to their frail condition, recent medical procedures, and the high level of antibiotic use in healthcare facilities. While intensified infection prevention and control due to the SARS-CoV-2 pandemic have indirectly contributed to restricting the spread of ARB over the past few years, nosocomial ARB prevalence may also have ended up being exacerbated by hospital disorganization and the increased use of antimicrobials in COVID-19 patients.

Hospital networks

Hospitals are connected to the general population through their patients and staff and to other healthcare institutions through shared patients. Previous studies, several of which were led by our consortium, have underlined the importance of the healthcare networks formed by the combined flows of patients that connect all hospitals within a country and the role they may play in the global AMR transmission pathway.

Objectives

In this context, the objective of this project is to better understand how the dynamic structure of healthcare networks impacts the spread of ARB at the regional, national, and transnational levels and propose efficient strategies to control this spread. In particular, we will go beyond previous research by exploring the role of temporal changes in networks, hospitalization characteristics, and the nature of seeding events. We propose combining data analysis, mathematical modeling, and computer simulations to do this.

Project

We will use France, Germany, and the shared border region as illustrative examples to underline how country-level differences in healthcare networks and adapted protocols impact AMR spread and explore the role cross-border exchanges may play in AMR dynamics. This will allow us to assess, over time, the potential impact of control measures at local, regional, and national scales within and between these two example countries. In future work, our conclusions on which control measures are most efficient depending on network characteristics may prove helpful for public health decision-making in France, Germany, and other countries.