Campylobacter Risk Assessment in Poultry

Helle Sommer,

Bjarke Christensen,

Hanne Rosenquist,

Niels Nielsen and

Birgit Nørrung

P r e v a l e n s

C o n c e n t r a t i o n

SLAUGHTERHOUSE RETAIL CONSUMER RISK

Pfarmh.

Ca.bleeding Probability of Infection

Probability of Exposure

• Data examinations – distributions

• Process model building – explicit equations

• Explicit equations/ simulations

• Cross contamination

• What-if-simulations

Slaughter house modules

Data examinations

• Data for 3 different purposes

- prevalence distribution -> slaughterhouse program

- concentration distribution

- model building, before and after a process

• From mean values to a distribution

• Lognormal/ normal –> illustrations

• Same or different distributions –> variance analysis

From mean values to a distribution

Histogram of the 'after bleeding' data

2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8 4 4.2 4.4

Concentration of Campylobacter [log10 cfu/g skin]

17 log mean values from different flocks and from 2 different studies

From mean values to a distribution

-1 0 1 2 3 4 5 6 7 8

Concentration of Campylobacter (log10 cfu/g skin)

Oosterom et al.

Mead et al.

Sum (Oo+Me)

17 distributions -> one common distribution

Log-normal or normal distribution ?”True” data structure = simulated data (sim.=)

Assumed distribution (dist.=)Published data = means of 4 samples,6 means from one study

sim.= lognormal(6.9,2.3) dist.= normal or lognormalSamples 1 2 3 4 5 6

1 1.293 2.454 2.742 2.751 2.278 1.4822 3.603 5.548 4.238 3.074 2.485 2.1973 3.283 2.866 2.546 2.351 2.793 2.4244 4.505 4.694 2.311 3.311 3.039 2.745

Mean 3.171 3.890 2.959 2.872 2.649 2.212SD 1.355 1.473 0.871 0.416 0.335 0.536

sim.= lognormal, dist.= lognorm

0 1 2 3 4 5 6 7 8 9 10

Concentration, log scale

sum distribution

6 mean values

sim.= lognormal, dist.= normal

0 2000 4000 6000 8000 10000 12000 14000

Concentration, normal scale

sum distribution

0 20000 40000 60000 80000 100000 120000

3 data points 6 mean values

1 2 3 4 5

sim.= normal, dist.= normal

0 2000 4000 6000 8000 10000 12000

Concentration, normal scale

sum distribution

6 mean values

sim.= normal, dist.= lognormal

2.5 2.7 2.9 3.1 3.3 3.5 3.7 3.9

Concentration, log scale

sum distribution

6 mean values

2000 2500 3000 3500 4000

3.3 3.35 3.4 3.45 3.5 3.55 3.6

sim.= lognormal, dist.= normal

0 2000 4000 6000 8000 10000 12000 14000

Concentration, normal scale

sum distribution

0 20000 40000 60000 80000 100000 120000

3 data points 6 mean values

Mean values calculated back from mean of log values

Samples Log obs.1 Obs.1 Log obs.2 Obs.2 Second best 1 Second best 21 1.29 19.63 2.454 284.282 3.60 4009.82 5.548 352988.873 3.28 1919.22 2.866 734.274 4.50 31974.93 4.694 49441.95

Mean 3.17 9480.90 3.890 100862.34 1479.11 7762.47SD 1.35 15084.31 1.473 169659.87

Reference # samples mean log obs. "mean" obs.Mead et al . (1995) 10 3.7 5011.87

Mead et al . (1995) 10 4 10000.00Mead et al . (1995) 15 3.9 7943.28Mead et al . (1995) 15 3.8 6309.57Mead et al . (1995) 15 3.4 2511.89Mead et al . (1995) 15 3.9 7943.28Mead et al . (1995) 15 3.6 3981.07Mead et al . (1995) 15 3.5 3162.28Mead et al . (1995) 15 4.3 19952.62Mead et al . (1995) 15 3.9 7943.28Mead et al . (1995) 15 3.7 5011.87

Real data set

9 "mean" values

Normal scale

New Danish data

Data after wash

0 50 100 150 200 250 300 350 400 450

Concentration, normal scale

Histogram

0 50 100 150 200 250 300 350 400 450 500

Concntration, normal scale

Data after wash

0 0.5 1 1.5 2 2.5 3

Concentration, log scale

Histogram

02468

10121416

0 0.5 1 1.5 2 2.5 3

Concentration, log scale

Slaughterhouse process

Concentration log10 cfu/g skin Concentration log10 cfu/g skin

Building mathematical models

Concentration level through the slaughterhouse processes

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

after bleeding after scalding afterdefeathering

afterevisceration

afterwach+chill

Co

nce

ntr

atio

n [l

og

cfu

/g]

Modelled

Observed

Old methode

95% confidence limit

95% confidence limit

95% confidence limit

Why new mathematical process models ?

A given proces, neutral

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5

Before a process [log cfu/g]

Aft

er

a p

roc

es

s [

log

cfu

/g]

1 : 1

Explicit mathematical process model

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

5

5,5

0 0,5 1 1,5 2 2,5 3 3,5 4 4,5 5 5,5

Before a process [log cfu/g]

Aft

er

a p

roc

es

s [

log

cfu

/g]

1 : 1

A given proces, multiplicativ

Explicit mathematical process model

In normal scale

μy = μx / Δμ

100 = 10000/100

In log scale

μlogy = μlogx – Δμ

2 = 4 - 2

A given proces, multiplicativ

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

5

5,5

0 0,5 1 1,5 2 2,5 3 3,5 4 4,5 5 5,5

Before a process [log cfu/g]

Aft

er

a p

roc

es

s [

log

cfu

/g]

1 : 1

Explicit mathematical process model

In normal scale

μy = μx / Δμ

100 = 10000/100

In log scale

μy = μx – Δμ

2 = 4 - 2

σy2 = β2 · σx

2 Transformation line

y = + β·x

A given proces, multiplicativ

0

12

34

5

67

89

10

0 1 2 3 4 5 6 7 8 9 10

Before a process [log cfu/g]

Aft

er a

pro

cess

[lo

g c

fu/g

]

1 : 1

Δμ

Explicit mathematical process model

Overall model

μy = μx - Δμ

σy2 = β2· σx

2

Local model

Y = + β·xCalculation of

= (1-β)· μx- Δμ

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5

Before scalding [log cfu/g]

Aft

er s

cald

ing

[lo

g c

fu/g

]

1: 1

Explicit mathematical process model

In normal scale

μy / μx = 158

In log scale

μy = μx - 2.2

A given proces, additive process

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

5

5,5

0 0,5 1 1,5 2 2,5 3 3,5 4 4,5 5 5,5

Before a process [cfu/g]

Aft

er a

pro

cess

[cfu

/g]

1 : 1

Explicit mathematical process model

In normal scale

y = x + z

z Є N (μ, σ)

Summing up

• Explicit equations for modelling slaughterhouse processes + Monte Carlo simulations, modelling each chicken with a given status of infection, concentration level, order in slaughtering, etc.

• New data of concentration (input distribution) -> different or same distribution ? (mean and shape)

• Data + knowledge/logical assumptions of the process -> multiplicativ or additive process

Advantage with explicit equations

• Accounts for homogenization within flocks

• More information along the slaughter line does not give rise to more uncertainty on the output distribution.

• Faster than simulations/Bootstrap/Jackknifing