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Dose response modelling of staphylococcal enterotoxins using outbreak data: which model,
which precision?
L. Guillier
ANSES FOOD SAFETY LABORATORY
BFR Symposium Zoonosen und Lebensmittelsicherheit
10th and 11th November 2016
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Outline
1. Dose-response modeling
2. Data available and modeling for Staphylococcus aureus
enterotoxins
3. Conclusion and perspectives
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1. Dose-response modeling
- Definition(s)
- Which data ?- Which models ?
1. Dose-response modeling
2. Data available and modeling for Staphylococcus aureus enterotoxins
3. Conclusions and perspectives
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Definition(s)
• Objective of dose-response model:
To establish a link between exposure to a hazard and the probability of occurrence of an effect
• According to the hazard (toxin, infectious micro-organism): different effects (infection, illness, death, ...) can be of interest
Ingested dose Infection Illness
Pill=Pill/inf.Pinf
Ingested dose Illness
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Definition(s)
• Warning: distinction between dose-effect and dose response!
Eff
ect
(in
ten
sity
)
Res
po
nse
(%)
http://www.reptox.csst.qc.ca/documents/plusencore/notions/htm/notions06.htm
Dose Dose
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Which data?
• Self experiment (e.g. Yersinia Redey, 1974)
• Human volunteers (e.g. 1950s studies for Clostridium perfringens and Salmonella)
• Animal model (e.g. gerbil for Listeria monocytogenes)
• Cell cultures… ethical problems, relevance of animal models, health status of
volunteers
• Alternative: outbreaks– Salmonella (Teunis et al., 2010)
– Trichinella (Teunis et al., 2012)
– Norovirus (Thébault et al., 2013)
– C. perfringens (Jaloustre, 2013)
– …
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Which data?
• Data needed to be collected during the investigation– Effect
– Observed attack rate Pill = Nill/Ne
– Ingested dose = Hazard concentration x food intake
• To establish a dose response model: several outbreaks
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Ills (Nill)
Exposed (Ne)
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Models used for infectious organism
• Hypothesis
– Each ingested cell can trigger infection
– Cells act independently
• Simple example
– If homogeneous contamination
– Each cells have the same
probability to cause infection (r)
Pill(d)=1 – exp(-r x dose)
If r=10-6
Lo
g10
(ill
nes
s p
rob
abil
ity)
Dose log10(cells)
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Models used for toxin
• « Benchmark dose (BMD) methodology »
• BMDx = dose that induces effects in x% of the exposed
population
• “Reference” value classically used in toxicology (also for allergen)
= BMD10 or its lower 95%-confidence interval (BMDL10)
• Tools:
– RIVM PROAST
– EPA BMDS
1. Introduction: Context of dose-response modeling for S. aureus enterotoxins
2. M&M: Data available and modeling approach used
3. Results: Characterization of the effects and dose-response model
0
0.2
0.4
0.6
0.8
1
0 20 40 60 80 100 120 140 160 180
Fra
ction A
ffecte
d
dose
Weibull Model with 0.95 Confidence Level
05:01 07/25 2012
BMDL BMD
Weibull
FAO/WHO (2012)
Att
ack
rate
Histamine dose (ppm)
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2. Data available and modeling for Staphylococcus aureus enterotoxins
- General information on outbreaks
- Data collected during investigation- BMDL for SEA- What use of DR
1. Dose-response modeling
2. Data available and modeling for Staphylococcus aureus enterotoxins
3. Conclusion and Perspectives
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Staphylococcal enterotoxins
• Staphylococcal food poisoning (SFP) is one of the most
common food-borne diseases
• SFP is caused by ingestion of staphylococcal enterotoxins (SEs:
SEA, SEB, …)
• In France, quantification of SEs is (often) performed during
outbreak investigation
• Doses of approximately 20 to 100 ng have been reported
effective in causing SFP
Objective: to establish a dose response model for SEs
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General information on outbreaks
• 63 outbreaks (mainly French)
– Period: 2010 to 2014
– The causative food is identified
– At least one SE quantified
• For description of effects: 63 outbreaks can be used
• For dose response:
– Only possible for SEA
– Not systematically known:
number of people exposed
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Data collected during investigation
• Effects: in the epidemiological investigation form
– Time of onset of symptoms in hours
– Observed symptoms (to choose within a list)
• Microbiological information (EURL CPS methods)
– Presence: extraction-dialysis-qualitative detection test
– Quantification for each enterotoxin : double sandwich ELISA
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Description of symptoms
Repartition of the identified symptoms in the 63 SFP outbreaks
(Venn diagram)
Individual reported symptoms
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Description of symptoms
Repartition of the identified symptoms in the 63 SFP outbreaks (Venn diagram)By grouping symptoms
N/V
AP/D
F
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Description of symptoms
• Symptoms:
– importance toxin types? No
– Same symptoms for large outbreaks? No
SEA toxin Other SE toxins
<10 ills >10 ills
APDAPD
APD APD
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Time of onset of symptoms
• Distribution of times of onset of symptoms of the 63 SFP
outbreaks
• Variability not explained by :
– The nature of SE involved
– The amount of toxin
Rel
ati
ve
freq
uen
cy
Time of onset (h)
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A BMD for SEA
• Weibull model
• BMDL10 for SEA ∼ 6 ng
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What use of dose response for SEs
Are SE detection methods able to detect concentration that causes illness?
• BMDL10 for SEA ∼ 6 ng
• For a 100 g serving size, the LOD for qualitative methods should be lower than 0.06 ng/g for SEA
Perspectives
• Bayesian approach for taking into account uncertainty on doses
• Continuous gathering data (interest for other toxins and understanding the effect of cocktail of SEs)
LOD
method y
LOD
method x
0.06 ng/g
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What use of dose response for SEs
Quantative microbial risk assessment
• Contamination in cfu of raw food, or during process : N0
+
• Predictive microbiology models exist for S. aureus (growth and/or inactivation)
+
• Relation (missing) between cfus and SE production
+
• Relation that gives illness for known SE concentration
Perspectives
• To confirm/adapt used thresholds (104, 106.5 cfu/g)
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3. Conclusion and Perspectives
- Conclusion
- Perspectives
1. Dose-response modeling
2. Data available and modeling for Staphylococcus aureus enterotoxins
3. Conclusion and Perspectives
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Conclusion
• Successful construction of dose response for SEA
• Outbreaks are unique data to learn on dose-response
• BMDL10 for SEA used in the context of acceptance of detection
method (LOD of the method should permit to detect BMD
Yet ….
• Bayesian approach for taking into account uncertainty on doses
• Continuous gathering data (interest for other toxins and
understanding the effect of cocktail of SEs)
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Perspectives
• Uncertainty: Did we fully take it into account?
– Yes for attack rate
– For ingested dose ? (concentration x ingested food mass)
Ongoing: Bayesian approach for taking into account uncertainty on doses
• Continuous gathering data :
– interest for other toxins
– understanding the effect of cocktail of SEs (simply additive effect?)
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Thank you
for your
attention!