Metapopulation ecology links antibiotic resistance, consumption, and patient transfers in a network of hospital wards

Modelling antimicrobial resistance transmission to guide personalized antimicrobial stewardship interventions and infection control policies in healthcare setting: a pilot study

Infection control programs and antimicrobial stewardship have been proven effective in reducing the burden of diseases due to multidrug-resistant organisms, but quantifying the effect of each intervention is an open issue. For this aim, we propose a model to characterize the effect of interventions at single ward level. We adapted the Ross-Macdonald model to describe hospital cross-transmission dynamics of carbapenem resistant Klebsiella pneumoniae (CRKP), considering healthcare workers as the vectors transmitting susceptible and resistant pathogens among admitted patients. The model parameters were estimated from a literature review, further adjusted to reproduce observed clinical outcomes, and validated using real life data from a 2-year study in a university hospital. The model has been further explored through extensive sensitivity analysis, in order to assess the relevance of single interventions as well as their synergistic effects. Our model has been shown to be an effective tool to describe and predict the impact of interventions in reducing the prevalence of CRKP colonisation and infection, and can be extended to other specific hospital and pathological scenarios to produce tailored estimates of the most effective strategies.

Mathematical Model of Antibiotic Resistance Determinants' Stability Under Space Flight Conditions

Increasing antibiotic resistance (AR) poses dangers of treatment complications and even treatment failure to astronauts. An AR determinant is a gene of resistance carried by bacteria. This article considers the issue of the stability of AR determinants and the influence of manned spaceflight conditions on this characteristic. A phenomenological model has been developed that makes it possible to evaluate the integral value of the stability of determinants of AR in bacteria as a function of time. Based on experimental results obtained during implementation of the SALYUT 7 space program, the stability of determinants of AR in Escherichia coli strains isolated before and after a spaceflight in 16 astronauts was evaluated. In addition, an assessment was made of the integral value of the stability of determinants of AR in bacteria during in vitro experiments, both in spaceflight and terrestrial conditions, after preincubation in space. The calculation using the developed phenomenological model showed that the stability of AR determinants in E. coli bacteria isolated from astronauts before the spaceflight is 33% higher than after the flight. The in vitro experiment carried out on board the International Space Station showed the opposite situation-an increase in the stability of AR determinants by 33% in cultures that have been in space compared with terrestrial control. This indicates an additional influence on the stability of determinants and of the astronaut's immune system, as well as space conditions. The common result in these two types of studies is the experimental fact that the largest number of bacteria, in space conditions, had two determinants of AR. The importance of fighting bacteria with two determinants is that at least three different antibiotics are required to have an effect. This circumstance makes it possible to predict a possible strategy for the use of antibiotics in autonomous spaceflights.

Exploration de l’association possible entre la consommation d’antibiotiques et l’émergence de résistance dans un centre hospitalier universitaire mère-enfant

Background

The emergence of antibiotic resistance has contributed to the development of multidrug-resistant bacteria, which is a major concern.

Objectives

The primary objective was to explore the possible association between antibiotic use and the emergence of resistance in a mother-child university hospital.

Method

This retrospective study was conducted in a university hospital centre. Antibiotic-bacteria pairs were established, taking into account the number of isolates, actual antibiotic use, and clinical relevance. For each pair, a comparison of 2 variables (antibiotic utilization and rate of resistance) was quantified with the Pearson coefficient. Three analyses were conducted: no lag between utilization and resistance, 1-year lag, and 2-year lag.

Results

Thirty antibiotic-bacteria pairs were selected from hematology-oncology and 18 from neonatology. In hematology-oncology, 6 pairs had a positive correlation (Pearson coefficient > 0.7): 2 pairs involving meropenem, 2 involving ceftazidime, and 2 involving piperacillin-tazobactam. In 3 of these cases, there was no lag between consumption of antibiotics and presence of resistance. In neonatology, 3 antibiotic-bacteria pairs had a positive correlation, 1 each involving vancomycin, cloxacillin, and meropenem.

Conclusions

It is possible to explore the potential association between consumption of antibiotics and emergence of resistance in a particular centre. Our exploratory approach was based on manual data processing. It would be interesting to consider a continuous systematic approach, allowing automatic generation of correlations.

Microbiome-pathogen interactions drive epidemiological dynamics of antibiotic resistance: A modeling study applied to nosocomial pathogen control

The human microbiome can protect against colonization with pathogenic antibiotic-resistant bacteria (ARB), but its impacts on the spread of antibiotic resistance are poorly understood. We propose a mathematical modeling framework for ARB epidemiology formalizing within-host ARB-microbiome competition, and impacts of antibiotic consumption on microbiome function. Applied to the healthcare setting, we demonstrate a trade-off whereby antibiotics simultaneously clear bacterial pathogens and increase host susceptibility to their colonization, and compare this framework with a traditional strain-based approach. At the population level, microbiome interactions drive ARB incidence, but not resistance rates, reflecting distinct epidemiological relevance of different forces of competition. Simulating a range of public health interventions (contact precautions, antibiotic stewardship, microbiome recovery therapy) and pathogens (Clostridioides difficile, methicillin-resistant Staphylococcus aureus, multidrug-resistant Enterobacteriaceae) highlights how species-specific within-host ecological interactions drive intervention efficacy. We find limited impact of contact precautions for Enterobacteriaceae prevention, and a promising role for microbiome-targeted interventions to limit ARB spread.

Modelling how antimicrobial resistance spreads between wards

Moving patients between wards and prescribing high levels of antibiotics increases the spread of bacterial infections that are resistant to treatment in hospitals.