WHEN THE DRUGS DON'T WORK - MATTHEW EDWARDS, NICOLA OLIVER AND ROSS HAMILTON (IFOA ANTIBIOTIC RESISTANCE WORKING PARTY) - INSTITUTE AND FACULTY ...
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When the drugs don’t work… Matthew Edwards, Nicola Oliver and Ross Hamilton (IFoA Antibiotic Resistance Working Party)
Agenda
MEDICAL MODEL
INTRODUCTION
OVERVIEW STRUCTURE
‘RESULTS’ AND
PARAMETERISATION
NEXT STEPS
June 14, 2018 2
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
June 14, 2018 3
Working party members
Name Role Firm
Matthew Edwards Chair Willis Towers Watson
Nicola Oliver Medical input & Deputy Medical Intelligence
Chair
Sheridan Fitzgibbon Model structure & Legal & General
parameterisation
Craig Armstrong Parameterisation (2017) Aviva
Ross Hamilton Model development Lloyds Banking Group
Irene Merk General SCOR
Roshane Samarasekera Model development GAD
Soumi Sarkar General Legal & General
Katherine Fossett General Barnett Waddingham
June 14, 2018 4
Medical overview June 14, 2018
What is antibiotic resistance… "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 June 14, 2018 6
How do antibiotics work? (the science!) June 14, 2018 7
What are the sources of resistance?
Sources of resistance How animals can pass on resistant bacteria
Infographics sourced from “Review on Antimicrobial Resistance” 2014
June 14, 2018 8
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
0 18 40 60 80 Age (years)
Urinary Tract Skin and Surgical Site
Respiratory Tract Abdomen
June 14, 2018 9
Mortality
Criteria “The major objective of the
Health-care burden
global priority pathogens list
Community burden
(global PPL) is to guide the
Prevalence of resistance prioritization of incentives
10-year trend of resistance and funding, help align R&D
Transmissibility priorities with public health
Preventability in the community needs and support global
Preventability in health-care setting coordination in the fight
against antibiotic-resistant
Treatability
bacteria”
Pipeline
June 14, 2018 10A. baumannii
Pseudomonas
Enterobacteriaceae
June 14, 2018 11A.A.baumannii
Baumannii
Driven by AB
use and poor Healthcare
infection Setting
control
Resistant to Wound
Pneumonia
colistin in 4% Resilient Infection
of cases
Bloodstream
Urinary Tract
Infection
June 14, 2018 12Pseudomonas
Found widely in the environment
Common cause of mild and serious
infections
Risk profile similar to A. Baumannii
Wound Bloodstream
Pneumonia
Infection Infection
June 14, 2018 13These bacteria are associated with
higher frequency of inappropriate
antimicrobial therapy, poorer clinical
response, and longer length of
hospital stay
16
14
% of samples resistant
12
10
8
6
4
2
0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Upper quartile Median Lower quartile UK
Third-generation cephalosporin resistance rates in E.
coli across Europe, showing the UK, 1999 to 2012
Enterobacteriaceae (Department of Health, 2015)
June 14, 2018 14…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
June 14, 2018 15June 14, 2018 16
June 14, 2018 17
Model structure and parameterisation Ross Hamilton June 14, 2018
Objectives & Research
• Model ABR impact on:
Define • Mortality
Objectives • Morbidity
Literature • KPMG / RAND model
Review • Research papers
• Complex enough to model scenario
Model • Not overly complex
Structure • Capable of being adapted by users
June 14, 2018 19Chosen model structure
Modelling criteria
• Simplicity
σ(H,S) σ(H,R) • Availability of data
• Appropriate outputs
σ(S,H) σ(R,H)
Basic structure decided on:
• Multi-state Markov model
• Calibrate to current observed levels
of mortality and morbidity
σ(S,D) σ(R,D) • Project varying resistance over
time and calculate the change in
mortality and morbidity
June 14, 2018 20Data 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.
June 14, 2018 21Data 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.
June 14, 2018 22Trends in resistance can be observed…
ECDC EARS-Network has data on how
resistance has increased over time
June 14, 2018 23…and extrapolated forwards
This data can be
used to inform
projections of the
future position
June 14, 2018 24…and extrapolated forwards
3000
2500
This
2000
data can be
used to inform
1500
projections of the
future
1000 position
500
0
-500
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
Continued resistace Tapered resistance Decreasing resistance
June 14, 2018 25Future of resistance?
30 years since a
new class of
antibiotics was last
introduced….
Barriers to
R&D
Investment
Cautious
optimism in
2 new
compounds
Infographics sourced from “Review on Antimicrobial Resistance” 2014
June 14, 2018 26‘Results’ and next steps June 14, 2018
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
Results:
Central 1% increase Perhaps ~0.2% /
Allowing for all main
scenario in mortality rate (qx) 0.25% pa reduction
strains of bacteria
from one strain to CMI model LTR?
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)
June 14, 2018 28Working party – next steps
Model development
Sessional meeting • Parameterisation – other
main bacteria (5)
February 2019 • 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
June 14, 2018 29Questions 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.
June 14, 2018 30You can also read
