Colliding crises: antimicrobial resistance and ageing
Lead Research Organisation:
London School of Hygiene & Tropical Medicine
Department Name: Epidemiology and Population Health
Abstract
Antimicrobial resistance (AMR) is increasing and is making infections harder to treat and prevent. This public health crisis collides with an ageing human population. More than 1 in 6 people in the world in 2050 are predicted to be over 60 years old and they have a 20-fold increase in bacterial infection incidence compared to younger adults.
However, the likelihood that infections are caused by resistant bacteria varies by the age of the person, the bacterial species and the antibiotic in a currently unexplained way. A simple analysis of the proportion of isolates resistant to different antibiotics by age in European infection data has shown that resistance to methicillin in Staphylococcus aureus (MRSA) is ~18% in those age 5-18 years old, but over 40% in the elderly (65 years and older). Whereas resistance to vancomycin in Enterococci is roughly equal by age at ~3%. My research on multidrug-resistant Mycobacterium tuberculosis infection suggests that globally there is a peak in infection in those aged 15-25 years old.
Understanding these trends by age matters as this would provide an insight into the underlying mechanisms of AMR selection and transmission which could then inform improved patient care and prescribing regimens (e.g., more subtle antibiotic use recommendations by age).
Addressing this knowledge gap requires the quantitative analysis I will use here to systematically map, for the first time, AMR patterns in infections by age using global datasets. I will ask whether the patterns could be due to changes in clinical care or to recent or past differences in antibiotic usage, to then improve antibiotic prescribing and interventions against AMR.
Specifically, I will pair data analysis of trends in AMR prevalence by age from global open access datasets with a mathematical model to simulate the underlying processes. This two-pronged approach will map the patterns and use modelling to determine the contribution of underlying processes such as transmission in hospitals and duration of carriage of resistant bacteria. It will require careful consideration of data collection processes and both period and cohort effects.
To understand these trends in AMR, one must also consider antibiotic usage variation by age. However, global antibiotic usage data is hard to find and is usually not available segregated by age nor at the individual patient level. In this project I will use a unique dataset from England that links antibiotic usage with microbiology information at the individual patient level to determine the relative importance of recent (in the past year) versus past antibiotic usage to risk of infection with an antibiotic resistant bacterium. This is important as healthcare usage and hence antibiotic exposure varies with age, so any association of age and AMR could be masking underlying antibiotic use trends.
Using this information, I will then assess interventions for AMR control by investigating the impact of age-targeted interventions in the mathematical model developed to simulate the underlying processes. I will also adapt an existing tool for informing empiric antibiotic prescribing to account for age-based or antibiotic usage associations with AMR. Empiric prescribing, when antibiotics are given in the absence of microbiological information, is the most common use of antibiotics globally and hence increased subtlety in use has enormous potential to reduce AMR selection and spread.
By dissecting AMR complexity by age, this research will provide novel insights into the risks of infection with AMR bacteria and the origins of AMR to support evidence-based policy making. The broader impact will be a fundamental shift in our understanding of the heterogeneity and trends in AMR by host age which could enable long-term improvements in patient care.
However, the likelihood that infections are caused by resistant bacteria varies by the age of the person, the bacterial species and the antibiotic in a currently unexplained way. A simple analysis of the proportion of isolates resistant to different antibiotics by age in European infection data has shown that resistance to methicillin in Staphylococcus aureus (MRSA) is ~18% in those age 5-18 years old, but over 40% in the elderly (65 years and older). Whereas resistance to vancomycin in Enterococci is roughly equal by age at ~3%. My research on multidrug-resistant Mycobacterium tuberculosis infection suggests that globally there is a peak in infection in those aged 15-25 years old.
Understanding these trends by age matters as this would provide an insight into the underlying mechanisms of AMR selection and transmission which could then inform improved patient care and prescribing regimens (e.g., more subtle antibiotic use recommendations by age).
Addressing this knowledge gap requires the quantitative analysis I will use here to systematically map, for the first time, AMR patterns in infections by age using global datasets. I will ask whether the patterns could be due to changes in clinical care or to recent or past differences in antibiotic usage, to then improve antibiotic prescribing and interventions against AMR.
Specifically, I will pair data analysis of trends in AMR prevalence by age from global open access datasets with a mathematical model to simulate the underlying processes. This two-pronged approach will map the patterns and use modelling to determine the contribution of underlying processes such as transmission in hospitals and duration of carriage of resistant bacteria. It will require careful consideration of data collection processes and both period and cohort effects.
To understand these trends in AMR, one must also consider antibiotic usage variation by age. However, global antibiotic usage data is hard to find and is usually not available segregated by age nor at the individual patient level. In this project I will use a unique dataset from England that links antibiotic usage with microbiology information at the individual patient level to determine the relative importance of recent (in the past year) versus past antibiotic usage to risk of infection with an antibiotic resistant bacterium. This is important as healthcare usage and hence antibiotic exposure varies with age, so any association of age and AMR could be masking underlying antibiotic use trends.
Using this information, I will then assess interventions for AMR control by investigating the impact of age-targeted interventions in the mathematical model developed to simulate the underlying processes. I will also adapt an existing tool for informing empiric antibiotic prescribing to account for age-based or antibiotic usage associations with AMR. Empiric prescribing, when antibiotics are given in the absence of microbiological information, is the most common use of antibiotics globally and hence increased subtlety in use has enormous potential to reduce AMR selection and spread.
By dissecting AMR complexity by age, this research will provide novel insights into the risks of infection with AMR bacteria and the origins of AMR to support evidence-based policy making. The broader impact will be a fundamental shift in our understanding of the heterogeneity and trends in AMR by host age which could enable long-term improvements in patient care.
Technical Summary
Antimicrobial resistance (AMR) is on the rise, making infections harder to treat and prevent. This public health crisis collides with an increasing population of individuals at substantially higher risk of bacterial infection (the elderly). However, the prevalence of AMR in infection by host age varies by bacteria and antibiotic in a currently unexplained dynamic.
In this project, I will fill this knowledge gap by providing the first systematic analysis and mapping, across countries, of trends in AMR prevalence in infection by host age. I will then determine what drives these patterns: "recent" or "distant" AMR acquisition events such as antibiotic exposure or transmission. To do this I will statistically analyse large, detailed individual-patient level data. This approach will be complemented by cohort simulation modelling to determine the relative contribution of different AMR drivers. I will use the conclusions from these two strands of work to inform improved empiric prescribing design and tailored interventions against AMR.
Key goals will be the mapping and dissection of the complexity of AMR, by exploiting the host age dimension. With this detail, I can further quantify the dynamics of AMR drivers, leading to an increased understanding of risks of infection with, and the origins of, AMR.
In this project, I will fill this knowledge gap by providing the first systematic analysis and mapping, across countries, of trends in AMR prevalence in infection by host age. I will then determine what drives these patterns: "recent" or "distant" AMR acquisition events such as antibiotic exposure or transmission. To do this I will statistically analyse large, detailed individual-patient level data. This approach will be complemented by cohort simulation modelling to determine the relative contribution of different AMR drivers. I will use the conclusions from these two strands of work to inform improved empiric prescribing design and tailored interventions against AMR.
Key goals will be the mapping and dissection of the complexity of AMR, by exploiting the host age dimension. With this detail, I can further quantify the dynamics of AMR drivers, leading to an increased understanding of risks of infection with, and the origins of, AMR.
