1. When the drugs don’t work…
Matthew Edwards, Nicola Oliver and Ross Hamilton
(IFoA Antibiotic Resistance Working Party)

2. ‘RESULTS’ AND NEXT STEPS
Agenda
INTRODUCTION
MEDICAL OVERVIEW
MODEL STRUCTURE
PARAMETERISATION
‘RESULTS’ AND NEXT STEPS
December 6, 2019

3. Working party background
ABR Event
Staple Inn
May 2016
Develop a simple modelling framework with plausible parameterisation to allow actuaries to develop their own views on likely and stress mortality impacts
This framework would be developed in a UK context but would be expected to be readily transferable to other countries
Working party started in January 2017
December 6, 2019

4. Working party members Name Role Firm Matthew Edwards Chair
Willis Towers Watson
Nicola Oliver
Medical input & Deputy Chair
Medical Intelligence
Sheridan Fitzgibbon
Model structure & parameterisation
Legal & General
Craig Armstrong
Parameterisation (2017)
Aviva
Ross Hamilton
Model development
Lloyds Banking Group
Irene Merk
General
SCOR
Roshane Samarasekera
GAD
Soumi Sarkar
Katherine Fossett
Barnett Waddingham
December 6, 2019

5. Medical overview
December 6, 2019

6. What is antibiotic resistance…
Resistance to all antimicrobials, including antivirals and antifungals, is increasing, but of greatest concern is the rapid development of bacterial resistance to antibiotics. If the number of hard-to-treat infections continues to grow, then it will become increasingly difficult to control infection in a range of routine medical care settings and it will be more difficult to maintain animal health and protect animal welfare..
"The thoughtless person playing with penicillin treatment is morally responsible for the death of the man who succumbs to infection with the penicillin-resistant organism.“
Sir Alexander Fleming, 1928
December 6, 2019

7. How do antibiotics work? (the science!)
Bactericidal drugs are those that kill target organisms. Examples of bactericidal drugs include aminoglycosides, cephalosporins, penicillins, and quinolones. Bactericidal antibiotics kill the bacteria causing the infection through direct action, usually by causing the cells to split open, or lyse. They do this by altering the biochemical pathway through which bacteria make the cell wall. As the antibiotic is taken into the cell, it stops the biochemical machinery of the cell producing or attaching one the major components of cell wall structure.
The cell wall produced is thinner than usual. As the cell divides, the two daughter cells then also have weaker cell walls and they cannot strengthen them because they are also prevented from making all of the necessary components. As they try to divide subsequently, the cell walls of these daughter bacteria fail. Lysis of the cell follows and the bacterium dies. Penicillin antibiotics work in this way, as do the cephalosporins.
Bacteriostatic drugs inhibit or delay bacterial growth and replication. Examples of such include tetracyclines, sulfonamides, and macrolides. Bacteriostatic antibiotics act on the internal workings of the bacterial cell to stop it dividing and so slow down the advance of the infection. A bacterial population that divides more slowly, or that cannot divide at all is much more easily dealt with by the body’s immune system. These antibiotics all tend to work in a similar way, by binding to part of the ribosome structure that controls the synthesis of proteins in the bacterial cell.
Some antibiotics can be both bacteriostatic and bactericidal, depending on the dose, duration of exposure and the state of the invading bacteria. For example, aminoglycosides, fluoroquinolones, and metronidazole exert concentration-dependent killing characteristics; their rate of killing increases as the drug concentration increases.
Cell wall: structure and protection.
Cell membrane: selectively permeable controlling what enters and leave the cell.
Flagellum: movement.
Chromosome: DNA and protein carrying genes.
Ribosome: protein synthesis.
Cytoplasm: liquid portion of the cell in which all metabolic reactions occur.
Plasmid: circular piece of DNA that gives the bacterium special traits such as antibiotic resistance
December 6, 2019

8. What are the sources of resistance?
How animals can pass on resistant bacteria
Discussion on sources of resistance
AB use
Inappropriate AB use
ABs entering the food chain – key issue – arising through AB use as a growth promoter thereby constant flow of ABs to humans, and also passage of AB resistant bacteria in poorly cooked meat. Regulation is tight in the UK, but no regulation on importing meat
Infographics sourced from “Review on Antimicrobial Resistance” 2014
December 6, 2019

9. How does ABR affect people and our work?
Morbidity
Septicaemia
Mortality
Trauma
Pneumonia
Routine cuts and grazes
Chemotherapy
Heart surgery
Meningitis
STIs
Childbirth
Bowel surgery
Joint replacement
Key points:
ABR affects
Life saving surgery – leading to higher mortality rates
Life improving surgery – leading to less surgery / higher risks of surgery – either way leads to increase costs of healthcare through not having surgery and a poorer quality of life or surgery becoming riskier leading to increased hospitalisation after surgery
Routine healthcare issues – preventative anti-biotics eg. in child birth, trauma / easily treated infections eg. STIs now become greater public health issue and greater risk to the individual – could lead to further more complex treatment 18 40 60 80
Age (years)
Urinary Tract
Skin and Surgical Site
Respiratory Tract
Abdomen
December 6, 2019

10. Criteria
Mortality
Health-care burden
Community burden
Prevalence of resistance
10-year trend of resistance
Transmissibility
Preventability in the community
Preventability in health-care setting
Treatability
Pipeline
“The major objective of the global priority pathogens list (global PPL) is to guide the prioritization of incentives and funding, help align R&D priorities with public health
needs and support global coordination in the fight against antibiotic-resistant bacteria”
Which pathogens are the greatest threat?
In 2016, WHO was requested by member states to create a priority list of other antibiotic-resistant bacteria to support research and development of effective drugs. They used a multicriteria decision analysis method to prioritise antibiotic-resistant bacteria; this method involved the identification of relevant criteria to assess priority against which each antibiotic-resistant bacterium was rated. The final priority ranking of the antibiotic-resistant bacteria was established after a preference-based survey was
used to obtain expert weighting of criteria. .
December 6, 2019

11. A. baumannii Pseudomonas
Enterobacteriaceae
This is the final list of 12 from a potential 20 bacterial species. They stratified the priority list into three tiers (critical, high, and medium priority), using the 33rd percentile of the bacterium’s total scores as the cut-off.
lets take a look at the top 3 from this list.
December 6, 2019

12. A. baumannii Wound Infection Pneumonia Bloodstream Infection
Healthcare Setting
Resilient
Resistant to colistin in 4% of cases
Driven by AB use and poor infection control
A. baumannii
Pneumonia
Wound Infection
The clinical significance of A. baumannii is mostly due to its ability to easily acquire resistance to different groups of antimicrobials and to survive for long periods of time on dry surfaces
A baumannii is intrinsically resistant to many antimicrobial agents due to its selective ability to prevent various molecules from penetrating the bacterial outer membrane
A.baumannii infections frequently occur in patients with severe underlying diseases, mainly in the ICU setting, and are often related to invasive procedures or indwelling devices.
Frequent healthcare-associated infections caused by A. baumannii include pneumonia, bloodstream infections, wound infections, urinary tract infections and meningitis after neurosurgical procedures. rarely been reported as a cause of serious community-acquired pneumonia
Bloodstream Infection
Urinary Tract
December 6, 2019

13. Bloodstream Infection
Pseudomonas
Found widely in the environment
Common cause of mild and serious infections
Risk profile similar to A. Baumannii
Pseudomonas infection is caused by strains of bacteria found widely in the environment; the most common type causing infections in humans is called Pseudomonas aeruginosa. Serious Pseudomonas infections usually occur in people in the hospital and/or with weakened immune systems. Infections of the blood, pneumonia, and infections following surgery can lead to severe illness and death in these people.
Pneumonia
Wound Infection
Bloodstream Infection
December 6, 2019

14. These bacteria are associated with higher frequency of inappropriate antimicrobial therapy, poorer clinical response, and longer length of hospital stay
Enterobacteriaceae are large family of bacteria that includes many familiar pathogens such as salmonella and E.coli.
As a working party, we identified E.coli, part of this family, to use in our initial modelling, due to recent trends in resistance rates, and availability of data on rates and associated mortality.
Enterobacteriaceae
Third-generation cephalosporin resistance rates in E. coli across Europe, showing the UK, 1999 to 2012 (Department of Health, 2015)
December 6, 2019

15. …and why it is important?
“We have reached a critical point and must act now on a global scale to slow down antimicrobial resistance” – Professor Dame Sally Davies, UK Chief Medical Officer
Tackling resistance takes a long time…
Changing behaviours
Developing new antibiotics
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18. Model structure and parameterisation
Ross Hamilton
Thanks Nicola
Intro to modelling and parametrisation workstream
December 6, 2019

19. Objectives & Research Define Objectives Literature Review
Model ABR impact on:
Mortality
Morbidity
Define Objectives
KPMG / RAND model
Research papers
Literature Review
Complex enough to model scenario
Not overly complex
Capable of being adapted by users
Model Structure
Define Objective
Impact of ABR on mortality & morbidity
Model structure needs to be simple but complex enough to capture the characteristics of the issue
Needs to be flexible so the users can develop / amend as required to their specific questions
Literature review
Searched through various academic papers and most recently the RAND / KPMG models commission by the O’Neill review – these were the models that informed the headline result of +10m deaths by 2050 due to ABR
These models whilst informative, had a heavier focus on the economic drivers that could impact population growth whereas we wished to study resistance at a more granular level
Academic papers were an excellent guide here and the majority utilised a multi-state Markov model to simulate the process
December 6, 2019

20. Chosen model structure
Modelling criteria
Simplicity
Availability of data
Appropriate outputs
Basic structure decided on:
Multi-state Markov model
Calibrate to current observed levels of mortality and morbidity
Project varying resistance over time and calculate the change in mortality and morbidity
Summary of chosen model
As a result, the working group agreed to use a similar approach and an illustrative diagram of the model is on the slide
The model consists of 4 states – H/S/R/D
We have decided to use a discrete time version
The working group agreed this was an appropriate way forward and it satisfied the objectives we had set out at the start
Once model structure agreed, parametrisation workstream was set up to inform the transition intensities between each state
December 6, 2019

21. Data sources – incidence
Incidence rates for bacteraemia
Sick (S)
Healthy
Sick (R)
Limitations
Limited data. E. coli monitoring in England goes back to 2013.
Limited evidence for how resistance interacts with incidence.
Bias? Monitoring is of HCAIs.
Incidence rate for a bacteraemia – which is the presence of a bacteria in the blood.
Public health England produces data on such incidence rates
We used this information, combined with the resistance data from EARS (European Anti-Biotic Resistance Surveillance Network) to determine transition intensities from H to S
We have highlighted some of limitations with the available data here
Key sources have been set out on the slide:
Public health England
ECDC or specifically the EARS (European antibiotic resistance surveillance) network
ONS
PLOS (public library of science) used for supplementary research papers
There are a few key themes across the data:
Limited availability / history
Potential bias
Data recording issues
So where did we start?
EARS network collects European data on resistance rates from a variety of bacteria to different anti-biotics
We were able to obtain data specifically for UK and Ireland going back some 15 years
This informs the historical rate of resistance to determine which state someone would move into from healthy ie. Sick with resistant strain / sick with non-resistant strain
However it is worth highlighting at this stage that resistance is not absolute and in fact the most dangerous form of resistance is multi-drug where even the last line of defence anti-biotics do not work effectively
December 6, 2019

22. Data sources – mortality
Death rates for bacteraemia
Sick (S)
Dead
Sick (R)
Limitations
Granularity of data:
Confounding causes of death?
Academic literature is helpful here.
Large error bounds around estimates of the relative virulence of resistant and susceptible strains.
Bias? The most ill are more likely to be sampled.
Similarl to incidence rates, death rates for bacteraemia were also informed by Public Health England data
However, we did not have sufficient information to differentiate death rates for resistant and non-resistant strains
We supplemented the data using research available on PLOS to link resistant and non-resistant death rates
Again we have highlighted some of the issues with data on this slide, notably confounding causes of death – was it the bacterial infection / respiratory failure that caused an individual to die?
Completed defining the transition intensities for a single year based on current data, can think about how to isolate the impact of an increasing trend in resistance.
December 6, 2019

23. Trends in resistance can be observed…
EARS network collects European data on resistance rates from a variety of bacteria to different anti-biotics
We were able to obtain data specifically for UK and Ireland going back some 15 years
This informs the historical rate of resistance to determine which state someone would move into from healthy ie. Sick with resistant strain / sick with non-resistant strain
However it is worth highlighting at this stage that resistance is not absolute and in fact the most dangerous form of resistance is multi-drug where even the last line of defence anti-biotics do not work effectively
ECDC EARS-Network has data on how resistance has increased over time
December 6, 2019

24. …and extrapolated forwards
This data can be used to inform projections of the future position
Here we have illustrated for an initial run, resistance has been extrapolated using the straight line method from the observed data up to 2037
December 6, 2019

25. …and extrapolated forwards
This data can be used to inform projections of the future position
Although we note that a variety of extrapolation approaches could be used and we will look to complete some scenario analysis on this.
Some examples are shown on the chart here, where resistance could
continue to grow unbounded
taper off or even
revert back to current levels after a point
December 6, 2019

26. Barriers to R&D Investment Cautious optimism in 2 new compounds
Future of resistance?
30 years since a new class of antibiotics was last introduced….
Barriers to R&D Investment
Cautious optimism in 2 new compounds
There are many considerations that could influence the shape of resistance in the future and we have highlighted a few on this slide:
As an example, it has been 30 years since a new class of anti-biotic has been discovered which could lead to unbounded resistance
Alternatively an improved global public awareness campaign may curtail use and even lead to inflexion point
The intention is to have some commentary / functionality to incorporate these effects into the model which can be developed over time.
Infographics sourced from “Review on Antimicrobial Resistance” 2014
December 6, 2019

27. ‘Results’ and next steps
We have generated a single run of the model to illustrate the effects of ABR to mortality.
December 6, 2019

28. Initial Results: E. coli resistance
Parametrisation based on:
Growth in E. coli bacteria resistant to 3rd generation cephalosporin antibiotics
Ages 19-64, i.e. working age population
Projected position in 2037, i.e. 20 years’ time
1% increase in mortality rate (qx) from one strain
Perhaps ~0.2% / 0.25% pa reduction to CMI model LTR?
Allowing for all main strains of bacteria
Results:
Central scenario
So what did we look at?
Single strain of E.coli bacteria resistance to 3rd gen cephalosporin antibiotics
UK population of working age
Projected resistance impact in 20 years time
Results
Central scenario: 1% uplift to the 1 year probability of death due to increasing resistance rates
Extreme scenario: 2%-3% increase
This is just for one strain of bacteria
Intention is the full model results will be intended to supplement current actuarial thinking and scenario testing
As an example, the illustrative results could be thought of as a reduction to the CMI model long term trend of around 0.25% pa
In a bad scenario (95% confidence level not 1-in-200), there could be a 10-20% increase in overall mortality (with all main strains)
December 6, 2019

29. Working party – next steps
Sessional meeting February 2019
Model development
Parameterisation – other main bacteria (5)
Interactions between pathogens
Validation / Documentation
Full model release
Suggested parameterisation based on UK data
Associated paper – main issues relating to sources of ABR, mitigation actions, recent trends, other projection results / methodologies, and background to our model and results from the model
December 6, 2019

30. Questions
Comments
Expressions of individual views by members of the Institute and Faculty of Actuaries and its staff are encouraged. The views expressed in this presentation are those of the presenter.
December 6, 2019