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
Dose response modelling of staphylococcal enterotoxins using - - PowerPoint PPT Presentation
Dose response modelling of staphylococcal enterotoxins using outbreak data: which model, which precision? L. Guillier ANSES F OOD SAFETY LABORATORY BFR Symposium Zoonosen und Lebensmittelsicherheit 10 th and 11 th November 2016 Outline 1.
ANSES FOOD SAFETY LABORATORY BFR Symposium Zoonosen und Lebensmittelsicherheit 10th and 11th November 2016
1. Dose-response modeling 2. Data available and modeling for Staphylococcus aureus enterotoxins 3. Conclusion and perspectives
1. Dose-response modeling 2. Data available and modeling for Staphylococcus aureus enterotoxins 3. Conclusions and perspectives
To establish a link between exposure to a hazard and the probability of occurrence of an effect
be of interest
Ingested dose Infection Illness Pill=Pill/inf.Pinf Ingested dose Illness
response!
Effect (intensity) Response (%)
http://www.reptox.csst.qc.ca/documents/plusencore/notions/htm/notions06.htm
Dose Dose
Salmonella)
… ethical problems, relevance of animal models, health status of volunteers
– Salmonella (Teunis et al., 2010) – Trichinella (Teunis et al., 2012) – Norovirus (Thébault et al., 2013) –
– …
– Effect – Observed attack rate Pill = Nill/Ne
– Ingested dose = Hazard concentration x food intake
Exposed (Ne)
– Each ingested cell can trigger infection – Cells act independently
– If homogeneous contamination – Each cells have the same probability to cause infection (r)
Pill(d)=1 – exp(-r x dose) If r=10-6
Log10(illness probability) Dose log10(cells)
population
= BMD10 or its lower 95%-confidence interval (BMDL10)
– RIVM PROAST – EPA BMDS
1. Introduction: Context of dose-response modeling for
2. M&M: Data available and modeling approach used 3. Results: Characterization of the effects and dose- response model
0.2 0.4 0.6 0.8 1 20 40 60 80 100 120 140 160 180 Fraction Affected dose Weibull Model with 0.95 Confidence Level 05:01 07/25 2012 BMDL BMD Weibull
FAO/WHO (2012)
Attack rate Histamine dose (ppm)
1. Dose-response modeling 2. Data available and modeling for Staphylococcus aureus enterotoxins 3. Conclusion and Perspectives
common food-borne diseases
SEA, SEB, …)
effective in causing SFP Objective: to establish a dose response model for SEs
– Period: 2010 to 2014 – The causative food is identified – At least one SE quantified
– Only possible for SEA – Not systematically known: number of people exposed
– Time of onset of symptoms in hours – Observed symptoms (to choose within a list)
– Presence: extraction-dialysis-qualitative detection test – Quantification for each enterotoxin : double sandwich ELISA
Repartition of the identified symptoms in the 63 SFP outbreaks (Venn diagram)
Individual reported symptoms
Repartition of the identified symptoms in the 63 SFP outbreaks (Venn diagram)
By grouping symptoms
N/V AP/D F
– importance toxin types? No – Same symptoms for large outbreaks? No
SEA toxin Other SE toxins <10 ills >10 ills
APD APD APD APD
– The nature of SE involved – The amount of toxin Relative frequency Time of onset (h)
A BMD for SEA
What use of dose response for SEs
Are SE detection methods able to detect concentration that causes illness?
than 0.06 ng/g for SEA
Perspectives
effect of cocktail of SEs)
LOD method y LOD method x 0.06 ng/g
What use of dose response for SEs
Quantative microbial risk assessment
+
inactivation) +
+
Perspectives
1. Dose-response modeling 2. Data available and modeling for Staphylococcus aureus enterotoxins 3. Conclusion and Perspectives
method (LOD of the method should permit to detect BMD
Yet ….
understanding the effect of cocktail of SEs)
– Yes for attack rate – For ingested dose ? (concentration x ingested food mass) Ongoing: Bayesian approach for taking into account uncertainty on doses
– interest for other toxins – understanding the effect of cocktail of SEs (simply additive effect?)