The dynamics of drug resistance within hospital populations of Gram-negative bacteria
Lead Research Organisation:
London School of Hygiene & Tropical Medicine
Department Name: Epidemiology and Population Health
Abstract
When bacteria become resistant to antibiotics the infections they cause are harder to treat. In the UK, and globally, we are seeing an increase in the number of infections being caused by antibiotic resistant bacteria. This could lead us back into the pre-antibiotic era before the early 1900s when infections of simple cuts may become life threatening and cancer treatments which suppress the immune system, and rely on antibiotics to prevent infections, will be unusable. Antibiotic resistance arises through the use and misuse of antibiotics and so, even if we develop new antibiotics, it is likely that we will always be faced with the problem of resistant strains.
We need to consider how best to preserve our existing antibiotics without compromising patient care. One of the ways to do this is to look at why some places have less resistance than others and to try to work out what they are doing right. For example, some hospitals have fewer infections with resistant bacteria than others. In this project I will explore what the reasons for these differences might be and translate these findings into ways to better prevent resistance from spreading.
To do this I will build mathematical models of antibiotic resistance spread. Mathematical models are frameworks in which different subpopulations are separated out and the rates at which these sub-groups increase or decrease are calculated. For example, a model would split a hospital population into those with and without infections with resistant or susceptible bacteria. It would then consider at what rate and by what mechanism people become infected, and then explore what the difference is between those with and without resistance. By writing this framework down mathematically, we get a better understanding of the processes underlying the spread of resistance and can identify the key targets - for example overuse of a certain type of antibiotic - that need to be tackled. From this understanding of the resistance spread, the model can be used to predict what will happen in the future without interventions and then compare this to what would happen if certain interventions were introduced.
I will build mathematical models that capture what is happening in different hospitals and determine why some have lower rates of resistance than others. In particular, I will look at the development of resistance within a group of bacteria called the Gram-negatives. These bacteria are often found living in the gut, but they can travel to other parts of the body and, in the UK, are the most common cause of serious hospital-associated infections such as bacteraemia (infection of the blood). Increasingly we are seeing strains of these bacteria becoming resistant to common, powerful antibiotics and so they are a key contributor to antibiotic resistance.
Groups of bacteria can have very different characteristics and can grow extremely rapidly. Their genetic make-up is very flexible which means that new genetic changes can occur or new pieces of genetic material can jump between bacteria creating and spreading resistance. In a bacterial population there will then be many different strains that may have many different resistances. This diversity has rarely been considered in mathematical models before, and so we may be missing a key part of resistance evolution. In this project I will develop mathematical models to incorporate this diversity and to determine how much of an impact it is having on resistance spread.
Mathematical models must be grounded in data in order to be relevant to clinicians and public health. In this project I will use the newly collected hospital level data on antibiotic usage and resistance to both gain parameters for my models and to determine what patterns the models should capture. My results will then be immediately useful for clinicians and the NHS, and will directly influence the interventions used to control the appearance and spread of antibiotic resistance.
We need to consider how best to preserve our existing antibiotics without compromising patient care. One of the ways to do this is to look at why some places have less resistance than others and to try to work out what they are doing right. For example, some hospitals have fewer infections with resistant bacteria than others. In this project I will explore what the reasons for these differences might be and translate these findings into ways to better prevent resistance from spreading.
To do this I will build mathematical models of antibiotic resistance spread. Mathematical models are frameworks in which different subpopulations are separated out and the rates at which these sub-groups increase or decrease are calculated. For example, a model would split a hospital population into those with and without infections with resistant or susceptible bacteria. It would then consider at what rate and by what mechanism people become infected, and then explore what the difference is between those with and without resistance. By writing this framework down mathematically, we get a better understanding of the processes underlying the spread of resistance and can identify the key targets - for example overuse of a certain type of antibiotic - that need to be tackled. From this understanding of the resistance spread, the model can be used to predict what will happen in the future without interventions and then compare this to what would happen if certain interventions were introduced.
I will build mathematical models that capture what is happening in different hospitals and determine why some have lower rates of resistance than others. In particular, I will look at the development of resistance within a group of bacteria called the Gram-negatives. These bacteria are often found living in the gut, but they can travel to other parts of the body and, in the UK, are the most common cause of serious hospital-associated infections such as bacteraemia (infection of the blood). Increasingly we are seeing strains of these bacteria becoming resistant to common, powerful antibiotics and so they are a key contributor to antibiotic resistance.
Groups of bacteria can have very different characteristics and can grow extremely rapidly. Their genetic make-up is very flexible which means that new genetic changes can occur or new pieces of genetic material can jump between bacteria creating and spreading resistance. In a bacterial population there will then be many different strains that may have many different resistances. This diversity has rarely been considered in mathematical models before, and so we may be missing a key part of resistance evolution. In this project I will develop mathematical models to incorporate this diversity and to determine how much of an impact it is having on resistance spread.
Mathematical models must be grounded in data in order to be relevant to clinicians and public health. In this project I will use the newly collected hospital level data on antibiotic usage and resistance to both gain parameters for my models and to determine what patterns the models should capture. My results will then be immediately useful for clinicians and the NHS, and will directly influence the interventions used to control the appearance and spread of antibiotic resistance.
Technical Summary
Mathematical models of the spread of antibiotic resistant bacteria (ARB) in a hospital usually do not take into account individual hospital level data on antibiotic usage or resistance. Nor do they consider the full complexity of resistance mechanisms, usually considering only a binary state. In this project I will construct new frameworks for ARB spread in hospitals.
Firstly, I will extend an existing deterministic mathematical model to take into account not only diversity in fitness (or transmissibility) between bacteria, but also diversity in ability to survive at different antibiotic concentrations (i.e. resistance level). Diversity will be incorporated through a weighted mean function and the model will be analysed using a new diversity metric function. This will provide a theoretical framework to determine how resistance within a bacterial population changes under constant and then changing selective pressure. This framework will be adapted to account for mobile genetic element movement within Gram-negative bacteria and to include a stochastic rate at which resistances appear.
The second part of the project will utilise hierarchical statistical methods that I wish to gain knowledge of and will have training in during this fellowship. These will allow me to determine trends in antibiotic usage and resistance. I will use time series analysis and multivariate regression to link these two data. Multi-level modelling will then test the links between hospital and patient level factors in driving differences in resistance and antibiotic usage.
The final part will bring together the information on the bacterial population and the input the data on clinical trends. An initial deterministic homogeneously mixing compartmental model will be extended to include further population stratification and stochastic transmission, to compare intervention impact. This will be fitted to clinical data using Bayesian and Markov Chain Monte Carlo methods.
Firstly, I will extend an existing deterministic mathematical model to take into account not only diversity in fitness (or transmissibility) between bacteria, but also diversity in ability to survive at different antibiotic concentrations (i.e. resistance level). Diversity will be incorporated through a weighted mean function and the model will be analysed using a new diversity metric function. This will provide a theoretical framework to determine how resistance within a bacterial population changes under constant and then changing selective pressure. This framework will be adapted to account for mobile genetic element movement within Gram-negative bacteria and to include a stochastic rate at which resistances appear.
The second part of the project will utilise hierarchical statistical methods that I wish to gain knowledge of and will have training in during this fellowship. These will allow me to determine trends in antibiotic usage and resistance. I will use time series analysis and multivariate regression to link these two data. Multi-level modelling will then test the links between hospital and patient level factors in driving differences in resistance and antibiotic usage.
The final part will bring together the information on the bacterial population and the input the data on clinical trends. An initial deterministic homogeneously mixing compartmental model will be extended to include further population stratification and stochastic transmission, to compare intervention impact. This will be fitted to clinical data using Bayesian and Markov Chain Monte Carlo methods.
Planned Impact
My choice of Professor Alison Holmes, Director of the Health Protection Research Unit (HPRU) on Hospital Associated Infections and Antimicrobial Resistance and Director of Infection Prevention and Control (DIPC) for Imperial College Healthcare NHS Trust, as my primary sponsor, means that I am basing my mathematical modelling in an interdisciplinary environment of clinicians, epidemiologists, statisticians and public health implementers with direct links to the NHS. This decision was made in order to maximise the clinical impact and relevance of the modelling tools developed during this project. Moreover by using data from Imperial College Healthcare NHS Trust and other Trusts, I will increase the credibility, applicability and impact of my modelling results.
This HPRU builds on a long-standing relationship between Imperial College London and Public Health England (PHE), which I have formalised here by including Dr Susan Hopkins and PHE as my second sponsor and Project Partner. I will therefore be able to not only have a direct impact on policy within the Imperial College NHS Trust, but also on national level decision making through PHE. Similarly, by engaging and directly modelling specific hospitals across England I aim to have an impact both on their individual methods of antibiotic resistance control, but also on the ways in which national guidelines are designed, implemented and their impact assessed. An extension to impact at the international stage will be made through Professor Holmes' international network, through international workshops and through continuing relationships with researchers at the London School of Hygiene and Tropical Medicine.
The other aspect of the impact will be on other modellers working in the field of antibiotic resistance by developing new tools and frameworks for the inclusion of antibiotic usage. Moreover, by analysing and publishing analysis of hospital level data in a format useful to modellers, I hope to improve the accessibility of data from a range of diverse sources, aiding in the development of a larger community of modellers engaging directly with antibiotic resistance and other problems within clinical settings. By developing such tools, I hope to make such modelling more accessible to those in developing countries where we have very little handle on resistance development.
By focusing this fellowship on a clear public health issue (antibiotic resistance spread), one aspect of the impact will be with clinicians and policy makers. This will be by providing a greater understanding of the drivers and differences in antibiotic resistance prevalence across England, which will inform better methods to control and prevent resistance spread. Moreover, I hope to improve the reporting and quantitative analysis of clinical data by demonstrating the power of such investigation. Ultimately, the impact will then be on improved patient care and a reduction in the number suffering from difficult to treat infections with resistant bacteria.
This HPRU builds on a long-standing relationship between Imperial College London and Public Health England (PHE), which I have formalised here by including Dr Susan Hopkins and PHE as my second sponsor and Project Partner. I will therefore be able to not only have a direct impact on policy within the Imperial College NHS Trust, but also on national level decision making through PHE. Similarly, by engaging and directly modelling specific hospitals across England I aim to have an impact both on their individual methods of antibiotic resistance control, but also on the ways in which national guidelines are designed, implemented and their impact assessed. An extension to impact at the international stage will be made through Professor Holmes' international network, through international workshops and through continuing relationships with researchers at the London School of Hygiene and Tropical Medicine.
The other aspect of the impact will be on other modellers working in the field of antibiotic resistance by developing new tools and frameworks for the inclusion of antibiotic usage. Moreover, by analysing and publishing analysis of hospital level data in a format useful to modellers, I hope to improve the accessibility of data from a range of diverse sources, aiding in the development of a larger community of modellers engaging directly with antibiotic resistance and other problems within clinical settings. By developing such tools, I hope to make such modelling more accessible to those in developing countries where we have very little handle on resistance development.
By focusing this fellowship on a clear public health issue (antibiotic resistance spread), one aspect of the impact will be with clinicians and policy makers. This will be by providing a greater understanding of the drivers and differences in antibiotic resistance prevalence across England, which will inform better methods to control and prevent resistance spread. Moreover, I hope to improve the reporting and quantitative analysis of clinical data by demonstrating the power of such investigation. Ultimately, the impact will then be on improved patient care and a reduction in the number suffering from difficult to treat infections with resistant bacteria.
Organisations
- London School of Hygiene & Tropical Medicine (Fellow, Lead Research Organisation)
- UNIVERSITY OF OXFORD (Collaboration)
- Lancaster University (Collaboration)
- PUBLIC HEALTH ENGLAND (Collaboration)
- Great Ormond Street Hospital (GOSH) (Collaboration)
- Erasmus MC (Collaboration)
- ST GEORGE'S UNIVERSITY OF LONDON (Collaboration)
- CAMBRIDGE UNIVERSITY HOSPITALS NHS FOUNDATION TRUST (Collaboration)
- Public Health England (Project Partner)
Publications
Baede VO
(2022)
Dehydration Tolerance in Epidemic versus Nonepidemic MRSA Demonstrated by Isothermal Microcalorimetry.
in Microbiology spectrum
Clifford S
(2020)
Effectiveness of interventions targeting air travellers for delaying local outbreaks of SARS-CoV-2.
in Journal of travel medicine
Endo A
(2020)
Implication of backward contact tracing in the presence of overdispersed transmission in COVID-19 outbreaks
in Wellcome Open Research
Endo A
(2021)
Implication of backward contact tracing in the presence of overdispersed transmission in COVID-19 outbreaks
in Wellcome Open Research
Endo A
(2020)
Implication of backward contact tracing in the presence of overdispersed transmission in COVID-19 outbreaks.
in Wellcome open research
Description | Member of SPI-M: reporting back analysis on |
Geographic Reach | National |
Policy Influence Type | Membership of a guideline committee |
Description | MRC DTP LSHTM-SGUL, Studentship awarded to Jacob Wildfire |
Amount | £2,090,490 (GBP) |
Funding ID | MR/N013638/1 |
Organisation | Medical Research Council (MRC) |
Sector | Public |
Country | United Kingdom |
Start | 08/2020 |
End | 03/2024 |
Description | MRC DTP LSHTM-SGUL, Studentship awarded to Quentin Leclerc |
Amount | £2,090,490 (GBP) |
Funding ID | MR/N013638/1 |
Organisation | Medical Research Council (MRC) |
Sector | Public |
Country | United Kingdom |
Start | 08/2018 |
End | 03/2022 |
Description | MRC LID |
Amount | £2,090,490 (GBP) |
Funding ID | MR/N013638/1 - MRC DTP LSHTM-SGUL, Studentship awarded to Alastair Clements |
Organisation | Medical Research Council (MRC) |
Sector | Public |
Country | United Kingdom |
Start | 09/2021 |
End | 03/2025 |
Description | Nosocomial transmission of SARS-CoV-2 |
Amount | £128,055 (GBP) |
Funding ID | MR/V028456/1 |
Organisation | Medical Research Council (MRC) |
Sector | Public |
Country | United Kingdom |
Start | 08/2020 |
End | 08/2021 |
Title | Effectiveness of interventions targeting air travellers for delaying local outbreaks of SARS-CoV-2 (Supporting Information) |
Description | This supplementary material serves as supporting information alongside the article "Effectiveness of interventions targeting air travellers for delaying local outbreaks of SARS-CoV-2". The supplementary material contains the following content:(1) Table S1 shows the percentage of the delays in Figures 1, S1-S3. (2) Table S2 presents the full summary statistics of the sensitivity analysis.(3) Figures S1-S4: All scenarios for Figure 2 - complementary cumulative density functions (CCDF) of the estimated number of days an outbreak is delayed given an intervention consisting of a combination of traveller screening and sensitization and contact tracing. |
Type Of Material | Database/Collection of data |
Year Produced | 2020 |
Provided To Others? | Yes |
URL | https://scholardata.sun.ac.za/articles/dataset/Effectiveness_of_interventions_targeting_air_travelle... |
Title | Effectiveness of interventions targeting air travellers for delaying local outbreaks of SARS-CoV-2 (Supporting Information) |
Description | This supplementary material serves as supporting information alongside the article "Effectiveness of interventions targeting air travellers for delaying local outbreaks of SARS-CoV-2". The supplementary material contains the following content:(1) Table S1 shows the percentage of the delays in Figures 1, S1-S3. (2) Table S2 presents the full summary statistics of the sensitivity analysis.(3) Figures S1-S4: All scenarios for Figure 2 - complementary cumulative density functions (CCDF) of the estimated number of days an outbreak is delayed given an intervention consisting of a combination of traveller screening and sensitization and contact tracing. |
Type Of Material | Database/Collection of data |
Year Produced | 2020 |
Provided To Others? | Yes |
URL | https://scholardata.sun.ac.za/articles/dataset/Effectiveness_of_interventions_targeting_air_travelle... |
Title | SARS-CoV-2 antibodies protect against reinfection for at least 6 months in a multicentre seroepidemiological workplace cohort |
Description | To identify the potential for SARS-CoV-2 reinfection |
Type Of Material | Database/Collection of data |
Year Produced | 2022 |
Provided To Others? | Yes |
URL | https://www.immport.org/shared/study/SDY2035 |
Description | Collaboration on nosocomial transmission of SARS-CoV-2 |
Organisation | Lancaster University |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Worked together on English data to explore levels of nosocomial transmission of SARS-CoV-2 - I developed models to determine the missed nosocomial cases |
Collaborator Contribution | Worked together on English data to explore levels of nosocomial transmission of SARS-CoV-2 - data sharing from PHE as well as analysis input |
Impact | Nosocomial COVID grant (MR/V028456/1) with co-supervision of a reseach assistant for a year and 4 publications. |
Start Year | 2020 |
Description | Collaboration on nosocomial transmission of SARS-CoV-2 |
Organisation | Public Health England |
Department | Antimicrobial Resistance and Healthcare-Associated Infections Reference Unit |
Country | United Kingdom |
Sector | Public |
PI Contribution | Worked together on English data to explore levels of nosocomial transmission of SARS-CoV-2 - I developed models to determine the missed nosocomial cases |
Collaborator Contribution | Worked together on English data to explore levels of nosocomial transmission of SARS-CoV-2 - data sharing from PHE as well as analysis input |
Impact | Nosocomial COVID grant (MR/V028456/1) with co-supervision of a reseach assistant for a year and 4 publications. |
Start Year | 2020 |
Description | Collaboration on nosocomial transmission of SARS-CoV-2 |
Organisation | University of Oxford |
Department | Nuffield Department of Medicine |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Worked together on English data to explore levels of nosocomial transmission of SARS-CoV-2 - I developed models to determine the missed nosocomial cases |
Collaborator Contribution | Worked together on English data to explore levels of nosocomial transmission of SARS-CoV-2 - data sharing from PHE as well as analysis input |
Impact | Nosocomial COVID grant (MR/V028456/1) with co-supervision of a reseach assistant for a year and 4 publications. |
Start Year | 2020 |
Description | Estee Torok & Addenbrooke's |
Organisation | Cambridge University Hospitals NHS Foundation Trust |
Country | United Kingdom |
Sector | Public |
PI Contribution | New collaboration with Estee Torok and the clinical team at Addenbrooke's Hospital to explore usage data at Cambridge University NHS Hospital Trust. I have led the collaboration which has currently resulted in a protocol and NHS Ethics Approval (IRAS). We are waiting on LSHTM ethics approval. |
Collaborator Contribution | The partners have aided so far in the protocol development and then will be collecting the antibiotic usage and resistance data from the hospital. |
Impact | - IRAS ethics approval |
Start Year | 2018 |
Description | GOSH data analysis |
Organisation | Great Ormond Street Hospital (GOSH) |
Country | United Kingdom |
Sector | Hospitals |
PI Contribution | We are using statistical analysis to explore resistance pattern changes in patients carrying MRSA. |
Collaborator Contribution | GOSH have provided the data (under appropriate confidentiality agreements) for us to analyse, as well as clinical input on interpretation and MRSA interventions. |
Impact | Multi-disciplinary: statistics and clinical data analysis, with microbiology. Not outputs yet - stlll in early stages of data analysis. |
Start Year | 2022 |
Description | MRSA resistance dynamics |
Organisation | St George's University of London |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Co-supervision of multidisciplinary PhD students |
Collaborator Contribution | Co-supervision of multidisciplinary PhD students |
Impact | MACOTRA grant 3 MRC-LID studentships thru LSHTM/St George's grant Multi-discplinary: mathematical modelling and microbiology |
Start Year | 2018 |
Description | Utrecht data analysis |
Organisation | Erasmus MC |
Country | Netherlands |
Sector | Hospitals |
PI Contribution | I work to extract parameters from their data to infer the underlying bacterial growth. Together we work to understand the impact of dehydration on MRSA strain growth, variation between strains and the link to epidemic success. |
Collaborator Contribution | They provide bacterial strain growth and survival data for me to analyse. Together we work to understand the impact of dehydration on MRSA strain growth, variation between strains and the link to epidemic success. |
Impact | Multi-discplinary - microbiology and mathematical modelling One paper under review, another in progress. |
Start Year | 2020 |
Description | Presentation at Parliament |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | Presentation of empirical prescribing modelling work at a Parliamentary Office for Science and Technology (POST) event at Portcullis House. Good networking with other academics including the global sewage surveillance project which may lead to a future collaboration. |
Year(s) Of Engagement Activity | 2020 |
URL | https://twitter.com/i/events/1232714747763724288 |