Modelling spread of antibiotic resistance helps develop effective hospital controls
11 Jul 2017
This case study forms part of our Investing for Impact report, looking at how MRC- funded research delivers impact. More can be found in the Investing for Impact section of our website.
In 2015, MRC-funded scientists designed powerful mathematical modelling techniques to analyse the spread of antibiotic resistant bacteria in a hospital setting. This work has allowed scientists to evaluate the effectiveness of infection control measures within the hospital, helping to implement safer protocols.
Infectious diseases are not static; they can spread very quickly, and more importantly, they change and adapt in response to control measures such as quarantines and antibiotic use. Understanding these disease dynamics is essential to evaluate how effective potential control measures are. This ensures that only procedures proven to work will be used in the event of an outbreak.
In 2015, Professor Ben Cooper and a team of international collaborators published results from analysing data from all the highest quality hands hygiene intervention studies and found strong evidence that this intervention can significantly improve hands hygiene. They also found that hands hygiene interventions, such as washing with soap or alcohol rub, are associated with reductions in infections with certain drug-resistant bacteria. The team used mathematical models to show how increases in hands hygiene can be associated with substantial reductions in certain resistant infections, and how this can be a very cost-effective intervention. This type of work can have a significant impact on policy decisions by Government, leading to increased take-up of intervention.
Professor Cooper’s research at the University of Oxford uses mathematical modelling and statistical techniques to help understand these infectious disease dynamics. This is particularly relevant in South East Asia, particularly in lower and middle income countries, where antibiotic-resistant bacterial outbreaks can often result in infections that cannot be treated with the antibiotics available in those countries.
Having a mathematical model for studying infection outbreaks allow healthcare workers and clinicians to ask ‘what if’ questions; for example, what if hands hygiene was increased by 20%? What impact would that have on how the bacterial infections spread? How many infections will the patients get? What about the effectiveness of other measures such as glove and gown use by healthcare workers? In 2016, Professor Cooper and a team of collaborators published a model that evaluated hospital infection control measures for a type of antibiotic resistant bacteria known as vancomycin-resistant Enterococci. The study used data from a 17-month longitudinal study in a hospital in Boston, USA involving more than 8,000 patients who were admitted to intensive care units. Intriguingly, they discovered that barrier precautions such as glove and gown use by healthcare workers, along with precautionary measures such as isolation rooms, were not effective at reducing colonisation by these antibiotic-resistant bacteria. They also discovered that half of the vancomycin-resistant Enterococci prevalence was unobserved; in other words, the testing methods used were not sensitive enough to pick up all the infected patients.
This case study demonstrates how mathematical modelling can help us evaluate the effectiveness of control measures. It helps healthcare professionals implement control measures that are proven to work, overturning false assumptions about other measures that actually do not work as well as we expect them to. Mathematical modelling could help us stay one step ahead of AMR by being able to predict how resistant bacteria will behave.
Award details: MR/K006924/1