Towards a rational risk analysis of antimicrobial resistance

1
Towards a rational risk analysis of antimicrobial
resistance

• David Vose Consultancy
• 24400 Les Lèches
• Dordogne
• France
• www.risk-modelling.com

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What is Risk Analysis?

• Should be a decision-focused exercise. Determine
• How big the risk issue is
• What it makes sense to do to manage the risk
• What knowledge we have to produce a reasoned
  argument between plausible risk management
  arguments
• What data would allow us to consider and compare
  more options
• Not a model focused exercise
• Not building a risk assessment model of the
  process, then seeing if it answers any decision
  questions

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Codex Alimentarius CommissionFAO/WHO (1995)

• Microbial risk assessment is a scientifically-base
  d process consisting of the four steps of this
  symposium
• Hazard Identification The identification of known
  or potential health affects associated with a
  particular agent
• Exposure Assessment The qualitative and/or
  quantitative evaluation of the degree of intake
  likely to occur
• Hazard Characterization The quantitative and/or
  qualitative evaluation of the nature of the
  adverse effects associated with biological,
  chemical and physical agents that may be present
  in food For biological agents a dose-response
  assessment should be performed if the data are
  available
• Risk Characterization Integration of Hazard
  Identification, Hazard Characterization and
  Exposure Assessment into an estimation of the
  adverse effects likely to occur in a given
  population, including attendant uncertainties.

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What have we done with these CODEX guidelines?
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Current microbial risk assessment

• Microbial QRA is a developing science
• Were making a lot of progress, but it is still
  in infancy
• Mostly producing farm-to-fork
• Model the whole system but very poorly
• Not designed to model any decision question well
• Often rely on poor data, surrogates, and guesses
• Almost never is a decision question posed
  beforehand
• Assessors have probably over-sold QRAs
  usefulness
• Managers have expected too much
• Focused on there being exposure and D-R models
  (eg see WHO guidelines and developed models)

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Salmonella dose-responseEpi and feeding trial
comparison
Review by Amir Fazil in FAO/WHO (2001) D-R
mathematical models review by Haas (2002)
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Dutch observations on past QRAHavelaar, Jansen
(2002)

• The lessons learnt from risk analysis
  experiences
• Risk management has not always been an integral
  part of risk analysis so far
• Risk managers should be trained to understand
  risk assessment, and risk assessors should be
  trained to explain their work
• Available data are often of limited use for risk
  assessment and communication of data needs
  between risk assessors, food scientists and risk
  managers is a critical issue
• The risk manager questions usually require rapid
  results, whereas (farm-to-fork) risk assessment
  projects require several years to complete.
  Solving this conflict requires open
  communication
• Uncertainty is often large.

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Our surveyInternet based, voluntary
participation, 39 valid responses
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(No Transcript)
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Completion times of some farm-to-fork QRAs
Final report
Final report
Draft report
Being revised
Draft report
Final report
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USDA-FSIS-FDA Salmonella Enteritidis

• Although the goal was to make the model
  comprehensive, it has some important limitations.
  It is a static model and does not incorporate
  possible changes in SE over time as either host,
  environment or agent factor change. For many
  variables, data were limited or nonexistent. Some
  obvious sources of contamination, such as food
  handlers, restaurant environment, or other
  possible sites of contamination on or in the egg
  (such as the yolk), were not included. And, as
  complex as the model is, it still represents a
  simplistic view of the entire farm-to-table
  continuum. Finally, the model does not yet
  separate our uncertainty from the inherent
  variability of the system. Much more work is
  needed to address this, and all other,
  limitations.

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FDA Listeria risk assessment

• No specific decision questions attached
• Attempted to look at relative importance of a
  large list of Listeria-carrying foods
• Given the data available, perhaps the only method
  possible to estimate which food types contribute
  the greatest risk
• So a good QRA application

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Complete modelling of antimicrobial system?

• Very little data available, system being modelled
  is hugely complex!
• Exposure pathways generally unknown
• Hazard resistance determinant
• Exposure pathways generally unknown
• Difficult because microbial population is mix of
  resistant and susceptible
• Risk analysis needs to incorporate entire
  picture. RM action and probable total change in
  human health outcome
• World issue travel and food imports can
  contribute enormously
• Multi-resistance
• F2F considers only pathogen on the food source
• E.g. not E.coli produced during life of animal,
  appearing in water, vegetables, farmers exposure

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Complete modelling of antimicrobial system? (2)

• Predictive microbiology still unreliable
• Lab data doesnt translate well to actual food
• Models crude
• Attenuation may not be death
• D-R models inadequate
• Feeding trial data dont match epidemiological
  data can hugely underestimate the risk
• Epidemiological data not available for sporadic
  cases (eg Campylobacter)
• Little cost-benefit analysis effort made
• Including actions affecting several risk issues
• Requires enormous resources impractical for
  many countries

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Conflict between microbiology and risk assessment
focus(Adapted from presentation by Maarten
Nauta, 2002)

• Microbiology
• About the detectable
• Micro-world
• Selected strains
• Against variability
• Qualitative
• Science

• Risk Assessment
• What is there (prevalence, load)
• Macro-world
• All strains
• Pro variability
• Quantitative
• Decision tool

• Risk assessors and microbiologists need to work
  more closely together. Microbiologists need to
  learn new methods of procedure and reporting.

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The complicated nature of resistance

• Co-selection (genes encoding resistance often
  linked together so use of one drug can select for
  resistance to unrelated drug)
• Resistance can enhance spread of infection or
  duration of fecal shedding in animal populations
  therefore increasing availability to humans
• Nosocomial infections
• Resistant commensals
• Human and animal use of the same antimicrobials
• Conclusion Far more complicated than usual
  bacterial food safety type risk issues

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Human use of antimicrobialsOne of the indicator
organisms in EARSS is Streptococcus pneumoniae.
It is of major clinical importance for pneumonia,
bacterial meningitis and S. pneumoniae is
representative of organisms that are transmitted
in the community.
The logodds of resistance to penicillin among
invasive isolates of Streptoccus pneumoniae
against outpatient sales of beta-lactam
antibiotics antimicrobial resistance data are
from 1998 to 1999 and antibiotic sales data are
from 1997. DDD defined daily dose.
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Attribution of resistance

• Is it necessary to prove a causal relationship
  between antimicrobial use and resistance?
• We cant prove with statistics, despite some
  claims
• Because of complex pathways, we do need to use
  more sophisticated methods for risk attribution
• Eg DAGs, Bayesian model averaging, classification
  trees
• But we need to do so fairly
• Very easy to misdirect arguments
• Eg Cox (2002) attribute campylobacteriosis to
  gender, and surrogate measures of wealth (have
  health insurance), visiting a restaurant, etc
  instead of sources of Campylobacter

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Regulators v industry

• Opponents of restriction of use of antimicrobials
  more focused than for food safety
• Few, very large pharmaceutical companies
• Large intensive farmers for whom antimicrobials
  essential
• Financial agenda in potential conflict with
  reduction of resistance
• Tend to be conservative, lag behind in risk
  analysis expertise
• Limits debate and cooperation
• Tend to use tactics in fighting off restrictions,
  not reasoned debate
• Major potential source of information, but
  withhold it
• Have produced some nonsense analyses and
  arguments
• Have not accepted that animal drug use is a risk
  factor for antimicrobial resistance in human
  disease

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APHA press release June 2001

• Visby, Sweden, 13 June 2001 The use of
  antibiotics in farm animals has little connection
  with the emergence of so-called super-bugs,
  according to the Animal and Plant Health
  Association (APHA). This statement is based on a
  new study by the European Federation of Animal
  Health (FEDESA).
• The study estimates that farm animals consumed
  only 35 of all the antibiotics administered in
  the EU during 1999 while humans consumed 65.
• By showing that farm animals account for only
  one-third of the antibiotics used in the European
  Union even though their combined liveweight is
  three times greater than the human patient
  population this study suggests that treating
  sick animals with modern medicine can only be a
  very small contributing factor to the problem of
  antimicrobial resistance, if its a factor at
  all, The study should make government
  officials across the EU pause before jumping to
  the erroneous conclusion that they can stop the
  emergence of new bacteria by over-regulating the
  use of antibiotics and restricting the proper
  treatment of farm animals.
• Although there is no evidence that the use of
  antibiotics in farm animals represents a risk to
  human health, some government officials have
  suggested that the EU limit the medications that
  can be administered to treat sick cows, pigs,
  horses, sheep, goats, chickens and rabbits as a
  way to stop the spread of these new bacteria.
  The new study undermines those arguments because
  it illustrates that farm animals consume far less
  antibiotics than people.

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Remedies focusing on decisions

• Consider what is known about the risk problem,
  and data available immediately or within
  acceptable time frame
• Use epi data as much as possible
• Collect more epi data (e.g. Japan, Denmark)
• Consider what analysis could be done with this
  knowledge
• i.e. a risk-based reasoned argument for
  evaluating particular actions
• Estimate the possible magnitude of benefit for a
  risk action
• Note that it may not be possible to evaluate all
  actions
• Perform a cost-benefit analysis on these actions
• Get into proportion eg FQ-Res Salmonella v total
  salmonella
• Legacy issue needs addressing in valuing risk
• Once present, resistant bacterial population has
  effect until antimicrobial use over
• Perform a Value of Information analysis
• Determines whether it is worth collecting more
  data before making a decision
• Consider strategy to validate whether predicted
  improvement occurs
• Train data producers to supply maximally useful
  data
• E.g. microbiologists taken more than one cfu from
  a plate

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Danish Vet Service Salmonella QRAA Bayesian
Approach to Quantify the Contribution of
Animal-food Sources to Human Salmonellosis -
Hald, Vose, Koupeev (2002)
Estimated number of cases of human salmonellosis
in Denmark in 1999 according to source Model
ranks food sources by risk. Easily updateable
with each years data. Bayesian update improves
estimate and checks validity of assumptions.
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Fluoroquinolone-resistant Campylobacter risk
assessment
Model Contaminated carcasses after slaughter
plant probability affected people
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Broiler
house



Transport

Slaughterhouse
model


Slaughter house

Hanging

Example of Farm-to-Fork model Campylobacter
in poultry Draft report 2001 Institute of Food
Safety and Toxicology Division of Microbiological
Safety Danish Veterinay and Food Administration
Scalding

Defeathering

Evisceration

Washing

Chilling

Export

Chicken parts
Whole
chickens



Chilled
Frozen


Further

Import

Processing

Retail

Catering

Cross

contamination

Consumer
model


Heat

treatment

Consumer



Cross
contamination
Behaves the same way as CVM model if prevalence
is reduced


Heat
treatment
Dose response

Risk estimation

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Risk Assessment of Campylobacter infection
transmission from pigs to man using erythromycin
resistance as a marker by David BurchPaper at
Intl Conf on Antimicrobial Agents in Vet Med,
Helsinki, 2002

• Erythromicin resistance high in Campylobacter
  coli (57) in pigs, but low in humans and
  chickens (both 15)
• Erythromicin resistance high in Campylobacter
  jejuni (35) in pigs, but low in humans (2) and
  chickens (4)
• C.jejuni most common in humans (92), chickens
  (90) and cattle (99), but C.coli most common
  in pigs (96)
• Conclusion patterns between humans and pigs
  dont match, so pig production not a major human
  threat.
• Noted different picture observed for retail liver
  (Kramer et al, 2000), but considers
  cross-contamination the reason

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Thank you Presentation available
atwww.risk-modelling.com
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References

• Burch DGS (2000). www.pigjournal.co.uk/news/campyl
  obacter.htm
• Cox LA (2002). Re-examining the causes of
  campylobacteriosis. Intl J Infectious Diseases 6
  3S26-3S36 (an industry sponsored supplement)
• Haas CN, (2002), Conditional Dose-Response
  Relationships for Micro-organisms Development
  and Applications. Risk Analysis 22 (3) 455-464.
• Havelaar H and Jansen J, (2002), Practical
  Experience in the Netherlands with quantitative
  microbiological risk assessment and its use in
  food safety policy. Draft paper, RIVM, Bilthoven,
  The Netherlands.
• Joint FAO/WHO Expert Consultation on the
  Application of Risk Analysis to Food Standards
  Issues (Joint FAO/WHO, 1995).
• Joint FAO/WHO Expert Consultation on Risk
  Assessment of Microbiological Hazards in Foods
  Risk characterization of Salmonella spp. in eggs
  and broiler chickens and Listeria monocytogenes
  in ready-to-eat foods. (2001), FAO headquarters,
  Rome.
• Kramer JM, Frost JA Bolton FJ and Wareing DRA
  (2000). J Food Protection, 63 12 1654-1659.