Participatory risk assessment: Risk modelling: II

Participatory risk assessment III - Risk modelling II -

‘Learning Event’ on risk analysis and participatory methods

CSRS, November 28, 2014

Kohei Makita Associate Professor of Veterinary Epidemiology at

Rakuno Gakuen University (OIE Joint Collaborating Centre for Food Safety) Joint Appointment Veterinary Epidemiologist at International Livestock Research Institute (ILRI)

Outline

• Risk characterization – Modeling infection/ illness

– Separation of variability from uncertainty

– Sensitivity analysis

– Scenario analysis

• Importation risk assessment

• Beyond

2

Modeling infection/ illness

• Let’s have a look at examples of

– Campylobacter risk model for ready-to-eat beef

– Staphylococcal enterotoxin in milk

3

Encountering ‘a lack of bridge’

4

Encountering ‘a lack of bridge’

• When ‘dose-response relationship’ is not available

• When you would like to connect farm hygiene status and prevalence at the entrance of abattoir

P(A) = k P(B)

Connect probabilities by solving this coefficient

5

Example Human health impact of fluoroquinolone resistant Campylobacter

attributed to the consumption of chicken（FDA）

Coefficient here

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KRes

𝐾𝑟𝑒𝑠 =Nominal mean number of campylobacteriosis cases from chicken

Estimated amount of fluoroquinolone resistant 𝐶𝑎𝑚𝑝𝑦𝑙𝑜𝑏𝑎𝑐𝑡𝑒𝑟 contaminated chicken meat consumed

7

Example Annual incidence rate of brucellosis due to consumption of milk in

Kampala, Uganda (Makita et al., 2010)

Coefficient here

Risk of purchasing contaminated milk

Annual incidence rate based on medical records

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Outline






• Beyond

9

Motivation

• The difference of variability and uncertainty needs to be recognized

– Variability • A function of the system

• Inter-individual variability

– Uncertainty • Lack of knowledge

• How much we don’t know because of the lack of survey

Separation of variability from uncertainty

• Method 1

– Model the total uncertainty first, and change all the variability parameters into single point estimates- the means – show the difference as distributions

– For a complex risk model

• Method 2

– Estimate uncertainty distributions for a series of point estimate variability, and show as a variety of uncertainty distributions

– For a model with a key variability

Staphylococcal poisoning example

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• Each of them are uncertainty distributions

• The variety of uncertainty distributions shows variability

• Variability in this case is the growth speed of S. aureus

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Risk of campylobacteriosis due to consumption of roast beef in Arusha, Tanzania

Outline






• Beyond

14

Motivation

• We need to know:

– How much the result is reliable

– Which factor is the most sensitive because; • Data collection of the sensitive factor may reduce uncertainty the

most

• Control of the sensitive factor can reduce incidence the most

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Sensitivity Tornado

-0.5 0

0.5 1

1.5 2

2.5

p / 1 to 2days G13

Cont rate B24

Boiling C24

p / Day 0 F13

1960 / Cont rate B11

1960 / Cont rate B16

p / 3 to 4 days H13

1960 / Boiling C16

1960 / Boiling C11

109/291 (Arcuri 2010

Temperature D10

N0 D4

Mean of Incidence rate

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Sensitivity analysis

Prob. SA has SE genes

Prob. farmers boil

Prob. consumers boil

Contamination, farm

Store milk 3,4 days

Contamination, centre

Consume on day 0

Prob. centres boil

Contamination, farm

Store milk 1,2 days

Temperature

Initial bacteria population

*It provides efficient control options

How to perform the sensitivity analysis

• In @Risk, click the tab ‘@Risk’

• Click ‘Advanced analysis’

• Choose ‘Advanced sensitivity analysis’

• Choose the cell to monitor

• Choose the cell to analyze sensitivity- among uncertainty parameters

• Set 1000 iterations- larger value gives @Risk high work load

• See Tornado Chart or other charts

• Record 5th, 50th and 95th percentiles of the parameters in the report table

* Of course you can work out manually in R

Expressing in a table (Campylobacterosis example)

Rank Parameters Values with 50th, 1st and

99th percentiles

Mean annual incidence

per 1000 people

1 PCont 0.04 (0.004 - 0.15) 6.65 (0.59 – 23.02)

2 MPN 0.37 (0.1 - 1.27) 6.38 (1.72 - 20.55)

3 PIll|Infected 0.22 (0.11 - 0.38) 6.37 (2.95 - 10.21)

4 QCons (g) 604 (306 - 806) 6.59 (3.47 – 8.95)

5 NCustomer 4,327 (3,707 – 5,042) 6.15 (6.15 - 6.15)

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Outline






• Beyond

19

Motivation

• So far,

– Risk was assessed

– Uncertainty was quantified and presented

– Sensitive factor was identified

• The next interest for policy makers would be to know

– How much effective the possible control options are

– How much they cost and

– How much they benefit

Planning control options

• Risk communication between risk assessor and risk manager on;

– Planning control options

– Some detailed plan of each option

– Agreement of the options to monitor change

• Risk assessor may collect necessary information on cost-benefit analysis

– Estimation of costs and benefits

• Feasibility

• Negative impacts on livelihoods of consumers and value chain actors

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Estimate reduction of risk

• Create duplicate branches of value chain in order to monitor the change

• Change inactivation parameters or quantities passing the duplicated value chains following the control option to monitor

• Model the division of risk after control measure is taken by the original risk: reduction rate of the risk

Sources of the risk by production areas Nakasongola 2.6%

Kayunga 4.1%

Peri-urban Kampala

Urban Kampala 15.9%

Mbarara 70.3%

7.0%

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Sources of the risk by milk sellers

Without refrigerator 1.0%

Roadside vendor 1.1%

Bicycle 7.3%

Small refrigerator

12.8%

Farm gate 15.1%

Bulk cooler 62.7%

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Control options (90% of enforcement)

Control options Reduction Inputs Feasibility Negative impact Assessment

Not to take any option 0.0 None High Risk remains Not recommendable

Construct a boiling centre

in Mbarara 62.3

A boiling centre,

legislation, fuel

Middle-

high Price up Recommendable

Construct boiling centres

in peri-urban Kampala 75.4

Boiling centres,

legislation, fuel Middle Price up Recommendable

Enforce milk shops to boil

milk or to buy boiled milk 68.9

Legislation, fuel,

facilities, enforce Very low

Price up, many shops

cannot afford Not recommendable

Ban of farm gate milk

sales 12.3

Legislation,

enforcement Low

Alternative sales may

not boil

Single measure does

not change the risk

Ban of urban dairy

farming 14.8

Legislation,

enforcement Middle

Livelihood of urban

farmers, milk supply Not recommendable

Ban of milk sales by

traders with a bicycle in

urban areas 6.6

Legislation,

enforcement High

Livelihood of traders,

alternative transport

may not boil

Single measure does

not change the risk

Ban of roadside milk sales 0.8 Legislation,

enforcement High



may not boil

Single measure does

not change the risk

Ban of milk sales at shops

without a refrigerator 0.8

Legislation,

enforcement High



may not boil

Single measure does

not change the risk

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Example of cost-benefit analysis Tick eradication at Kuro-Shima Island, Japan

Contents of cost Calculation JPY

Acaricides Quantity x cost per unit 15,033,640

Labor cost Total working hours x labor cost/h 6,900,962

Construction of fence Number of fence used x cost per unit 1,479,177

Other Communication, transportation, accommodation

409,348

Total cost 23,823,127

Contents of benefit Calculation JPY

Increased growth Shortened grazing period x daily cost 25,742,103

Eradication of babesiosis

Treatment cost x incidence avoided + price of dead animal x death avoided

1,687,089

Other Costs which had been spent before erad. 3,953,407

Total benefit 31,382,599

Benefit-cost ratio＝31,382,599/23,823,127=1.317 Yamane I (1996) Selection of economically the most ideal animal health disease control option 2. Rinsho-Juui14(5): 41-47. 26

Risk management (CAC/GL 63-2007)

• Principle 1: Protection of human health is the primary objective

• Principle 2: Taking into account the whole food chain

• Principle 3: Follow a structured approach

• Principle 4: Process should be transparent, consistent and fully documented

• Principle 5: Ensure effective consultations with relevant interested parties

• Principle 6: Ensure effective interaction with risk assessors

• Principle 7: Take into account of risks resulting from regional differences in hazards in the food chain and regional differences in available risk management options

• Principle 8: Decisions should be subject to monitoring and review and, if necessary, revision

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ALOP

• ALOP: Appropriate level of protection (ALOP)

• Should be decided based on a particular country’s expressed public health goals

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Outline






• Beyond

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30

OIE importation/exportation risk assessment (plus antimicrobial resistance)

Release assessment

Exposure assessment

Consequence assessment

Risk management

Risk assessment

Hazard identification

Risk communication

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Is the disease

/agent exotic?

National

control

program?

Notably

lower

prevalence?

Could diseases be present in the animal of origin?

Can the agent/disease persist in the import?

No

Are there ways the agent could come in contact with

susceptible animals or people?

No

No No No

No

Yes

Yes Yes Yes

Yes

Yes

Hazard

iden

tification

Release assessment

• Assessing probability of agent introduction considering the every possible route coming into the specific environment

Country A Country B

Importation

Example: false negative in quarantine test

Probability of releasing the agent into country B with either animals or commodities

Exposure assessment

• Assessing probability that animals/humans are exposed to the agent released, through every possible route

Country A Country B

Importation

Probability that animals/humans originally in country B are exposed to the agents released

Consequence assessment • Assessing probability of outbreak/ epidemic and the economic loss,

as a result of exposure of animals/humans to the agent released

Country A Country B

Importation

Economic loss due to outbreak/epidemic

Outline






• Beyond

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Beyond of this course

• How to deal with multiple pathogens?

• Intervention using participatory methods

• Contribution of environment to food chain

• Another approach of disease control

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Advantage of participatory risk assessment

I wish you prove it more in intervention programs!

• -Speed

• -Affordability

• -Flexibility in application

• -Understanding of culture

• -Best control option

• -Potential to change behavior

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Infectious disease epidemiology

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• Understanding disease transmission

• Control the disease based on its nature

• Starting with drawing epidemic curve

Example from Ebola (2014 Oct 16, Nature)

Infectious disease modelling

• Basic reproduction number (R0)

– Total number of individuals directly infected by a single infected individual, when introduced to totally susceptible population

– R0<1 Infection dies out

– R0=1 Infection is maintained

– R0>1 Infection takes over

R0 as a communication tool

• Example of Ebola epidemiology

– Nature (2014 Oct 16)

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SIR model and calculation of R0

S I

S: Susceptible

I: Infectious

R

R: Recovered

SIR model


S I

dS/dt = -βSI

R βSI αI

dI/dt = βSI - αI

dR/dt = αI

Modelling deaths

S I

dS/dt = -βSI + μN - μS

R βSI αI

dI/dt = βSI – αI - μI

dR/dt = αI - μR

μS μI μR

μN


S I

βSI = (α + μ)I

R βSI αI

R (Effective reproductive ratio) = {β/(α + μ)}*S

μS μI μR

μN

β/(α + μ)*S = 1


S I

*In case all individuals are susceptible（S0 = N)

R βSI αI

R0 = {β/(α + μ)}*N

μS μI μR

μN

Force of infection

Infection dynamics in SIR model

Herd immunity

S I R

Vaccination

Herd immunity threshold =

0

11

R

*Epidemic does not occur above this level of immunity

Acknowledgements

• CSRS – Professor Bassirou Bonfoh

– Sylvain Traore

– Sylvain Koffi

– All other staff

• Swiss Tropical Public Health Institute

• And the participants!!

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Science

Participatory risk assessment: Risk modelling: II