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Title: Risk Assessment and Risk Management of Emerging and Re-emerging Infectious Diseases: Public Health / Risk Assessors Perspective


1
Risk Assessment and Risk Management of Emerging
and Re-emerging Infectious Diseases Public
Health / Risk Assessors Perspective
Susie ElSaadany, Chief, Statistics and Risk
Assessment Adjunct Professor, Faculty of
Medicine, University of Ottawa
  • Blood Safety Surveillance and Health Care
    Acquired Infections Division
  • Centre for Communicable Diseases and Infection
    Control
  • Infectious Diseases and Emergency Preparedness
    Branch
  • Public Health Agency of Canada

Mathematics in Emerging Infectious Disease
Management, Cuernavaca, Mexico, January 10-14,
2011
2
Outline
  • Background
  • Blood Safety Programme at The Public Health
    Agency of Canada
  • Challenges and Responsibility
  • Risk Assessment Tools
  • Expert Elicitation (as an Informative Process)
  • Examples

3
The Blood Safety Program
4
Healthcare Associated Infections (HAI)
Parenteral Transmission (Bloodborne Pathogens)
Nosocomial Transmission
SEPSIS
Nosocomial jurisdiction, if microbes cultured
and/or identified. If no microbes can be cultured
or identified, then the case becomes Bloodborne
Pathogens jurisdiction, usually related to an
unknown virus. The syringe issue related to a
nosocomial transmission of bloodborne pathogens
(BBP) by reuse of a single syringe. This requires
a BBP risk assessment along with nosocomial and
BBP risk mitigation strategies.
5
Sources of Emerging PathogensTransmitted via
Blood
Zoonotic, parasitic, viral, bacterial and prion
infections
6
Sources of Emerging Pathogens Transmitted
Parenterally via Healthcare
Blood, Components Products
Cells, Tissues, Organs Semen
Surgical Implants of Animal Tissues
Medical Devices
Healthcare Associated Infections (HAI)
(TPD)
Biologics
(includes vaccines)
(BGTD)
Grandfathered-in Biologics
Surveillance initiated and/or under development
high risk
Surveillance not yet initiated high risk
significant gaps
Considered low risk except for CJD/vCJD and other
prion diseases
eg., heparins
TPD Therapeutic Products Directorate, Health
Canada BGTD Biologics and Genetic Therapies
Directorate, Health Canada
7
SRA Section Roles and Responsibilities
  • Statistics and Risk Assessment Section (SRA)
    within Blood Safety Surveillance and Health Care
    Acquired Infections Division
  • Special focus on risk assessments for rare and
    emerging diseases to meet modern health care
    needs for better knowledge in the face of little
    to no scientific information
  • Promoting the development of statistical
    techniques surrounding issues of modeling
    uncertainty.
  • Facilitating iterative communication between
    regulators (Health Canada, Biologics and Genetic
    Therapies Directorate) and policy makers

8
Data Limitations for Rare and Emerging Diseases
  • Scientific information required to model the
    public health risks for rare and emerging
    diseases and events may be limited or unavailable
    due to
  • poor understanding of the disease / event
  • limited case numbers
  • lack of scientific study
  • lack of valid information in the bio-medical
    literature
  • obstacles in communicating what information is
    known
  • These limitations need to be managed against
    urgent needs for risk assessment and regulatory
    policy development to mitigate public health
    risks.

9
SRA Experience
  • Have encountered 3 types of insufficient data
  • Data do not exist
  • Data cannot be published (proprietary)
  • Data exist, but are incomplete
  • Dealing with two kinds of uncertainty in
    modelling public health threats
  • heterogeneity or stochasticity
  • incomplete knowledge and/or systematic
    measurement errors

10
SRA Experience Tools
Viral Infections Due to Improper Re-use of
Syringes
  • International and National Expert Advisory Group
    providing inputs
  • Expert Elicitation as a tool to gather
    quantitative information, but also excellent
    qualitative tool for identification of knowledge
    gaps in science
  • Fuzzy Logic and Analysis for modeling of
    infectious diseases (uncertainty)
  • Linear deterministic models (variability)
  • Logical Data Analysis (data mining, artificial
    intelligence, pattern recognition)

11
Decision-Making Framework for Identifying,
Processing and Managing Health Risks
  • Collect Assumptions to Input in Model
  • Published literature
  • Expert consensus

Identify the Issue and Its Context
Monitor and Evaluate Results
Assess Risks and Benefits
Run Model Calculations
INVOLVE INTERESTED AND AFFECTED PARTIES
Implement the Strategy
Identify and Analyze Options
Consideration of other Parameters outside of
Model, by decision-makers
Select a Strategy
12
Risk Analysis Framework
A brief description of the situation Product or
commodity involved The values expected to be
placed at risk (e.g. human health, economic
concerns) Potential consequences Consumer
perception of the risks The distribution of risks
and benefits
Risk Management
  • Assessment of effectiveness of measures taken
  • Review risk management and/or
  • assessment as necessary

Value judgements and policy choices for the risk
assessment process
Risk Communication
  • Hazard identification
  • Hazard characterisation
  • Exposure assessment
  • Risk characterisation

Risk Assessment
  • Identification of available management options
  • Selection of preferred management option,
    including consideration of an appropriate safety
    standard
  • Final management decision

Regulatory or other control measures
13
Risk Assessment to Risk ManagementThe Process
Perspective
  • At the highest level, the logic underlying a risk
    assessment is always the same

Seriousness of Consequences Seriousness of Consequences Seriousness of Consequences
Low Medium High
Probability of Event High Top concern
Probability of Event Medium
Probability of Event Low Low concern
Risk Matrix as a tool for priority setting,
Health Canada 2010
14
Risk Assessment Structure - Data Requirements
Template developed by TSE Science and Policy
Teams under the TSE Secretariat, HPFB
15
The concept of De minimis risk
16
Zooming in
  • Bloodborne pathogens and health
  • care acquired infection

17
Emerging Infectious DiseaseCurrent Activities
  • Emerging infections are a continuing threat to
    human health
  • Some transmissible by blood
  • Others show evidence of possible transmissibility
  • HIV
  • HBV/HCV, Burden of illness studies
  • West Nile virus, SARS
  • Babesia species, Dengue virus, and vCJD
  • Xenotropic murine leukemia related virus (XMRV)
    and blood
  • H5N1 and blood safety
  • H1N1 and pandemic influenzas in Relation to PPE
  • Anti-microbial resistance (AMR)
  • Chronic Wasting Diseases (CWD)
  • Lyme disease

18
Precautionary Principle and Expert Judgment in
Public Health
  • Expert consultation is needed where information
    is scarce, knowledge is limited, uncertainties
    are great and risks are theoretically very low
  • Showing due diligence in mitigating infinitesimal
    or theoretical risk in public health practice by
    applying the Precautionary Principle

19
Expert Elicitation for Risk Disease Models
  • N95 masks and infection of healthcare workers by
    SARS
  • Dose-response of the Anthrax
  • vCJD carried out in March 2009 (funded by PrioNet
    Canada and PHAC)
  • XMRV carried out in September 2010 (PHAC)
  • CWD to be carried out in 2011 (funded by PrioNet
    Canada)

20
Structured Expert Elicitation Approach
21
(No Transcript)
22
Method Overview (1)
  • 1. A group of problem domain experts (e) is
    selected. E is the number of experts
    participating.
  • 2. Experts meet in person to assess a set of
    calibration items, whose true values are known.
    Each expert expressing his or her views as
    elemental uncertainty distributions.
  • 3. Expert responses are treated as statistical
    hypotheses and scored for statistical likelihood
    that they are correct and display an appropriate
    ability to gauge uncertainty in their own
    responses.
  • 4. Calibration and Information scores are
    computed for each expert e.

23
Method Overview (2)
  • 5. The two measures (calibration and information)
    are combined to form a weight for each expert.
  • This is done using strictly proper scoring
    rules experts receive their maximal expected
    weight only by stating their true degrees of
    belief over all the items
  • 6. Experts are then elicited individually
    regarding their uncertainty judgments in relation
    to questions of interest, and the
    performance-based calibration scores obtained in
    Step 4 are applied to the individual responses.
  • The result is a combined output, which displays
    the best answer but more importantly, weighted
    poolings of the group of experts uncertainty
    distributions around the best answer

24
Method
Expert Elicitation and the Quantification of risk
estimation and uncertainty in relation to
infection disease risk
  • Dr. Roger Cooke
  • (TU Delft, Resources for the Future)
  • Classical model Methodology for combining
    expert opinion
  • Rational Consensus
  • Dr. Willy Aspinall
  • (Aspinall Associates)
  • Facilitator and consultant

25
The Software
  • EXCALIBUR? TU Delft
  • http//dutiosc.twi.tudelft.nl/risk/

26
  • The five steps
  • 1. Experts Selection
  • A group of problem domain experts (E) is selected
  • 2. Calibration
  • These experts assess a set of (n) variables
    within their field (seed items), the true
    values of which are known
  • Each expert expressing his or her views as
    elemental uncertainty distributions with
    quantitative support across selected
    inter-quantile ranges

Expert Elicitation (Cookes method)
27
  • E Number of experts responding
  • e One representative expert
  • The set of seed/calibration variables to be
    assessed, where n is the number of seed
    variables
  • i One representative calibration question
  • The set of true values for X (recall
    calibration requires a set of
    carefully chosen assessment questions
    with known values)
  • Ex.
  • The set of an experts
    best estimate (median)
  • and 5-95 confidence bounds for each
    calibration
  • question i




28
  • Given , the 5, 50, and 95 percentiles split
    up the variable xs range into 4 intervals

29
  • Experts are scored via two measures

Calibration score likelihood that expert
distributions over the set of seed items
correspond to the observed or measured results
based on a chi-squared test
Information score measure of informativeness
compared to a given background distribution,
usually a uniform or log-uniform distribution
30
  • The calibration score of e depends on the
    proportion of times that the true values
    (x1,,xn) lie in the intervals created by their
    estimates
  • Sj(e) the probability that value x lies in
    interval Ij,i,e
  • j1, 2, 3, 4 and e1, , E
  • where and 0 otherwise.
  • Then, the probability distribution for
    individual expert is

31
  • To complete the calibration score, a comparison
    is needed using a measure of relative
    information, I(SP)
  • Where the quantity,
    is chi-squared distributed with 3 degrees of
    freedom (P is a background distribution)
  • The final element of the calibration score (
    ) is the p-value of the statistical test (He)
    that an expert is well calibrated
  • Where

and p (.05,.45,.45,.05)
32
  • The expression information is taken in a very
    specific context in scoring an expert
    elicitation
  • the information score evaluates the extent of
    concentration of the elicited distribution
  • Like the calibration score, the relative
    information relationship is defined, however in
    continuous terms
  • Where f and g are any two probability density
    functions.
  • For each variable i, let gi be the uniform
    distribution on Li, Ui as background
    distribution
  • L, U are chosen by the analyst using an k
    overshoot rule

33
  • Using the continuous relative information
    relationship, the actual information score
    becomes the average relative information from one
    expert over all of the variables
  • Where fi,e is the minimal informative density
    function with
  • respect to the prior gi,e, such that
  • and where Fi,e is the cumulative distribution
    fn. of fi,e

34
  • The relationship for comes from the
    following
  • in the case of a uniform prior gi,e, fi,e is a
    step fn
  • for over the intervals defined by
    (x5,i,e,x50,i,e,x95,i,e)
  • thus it follows that
  • And the information score is created.

35
  • Calibration and information scores are combined
    to form a weight for each expert.
  • Using an optimal and representative sample of
    experts to represent the group, the performance
    weight score for expert e is
  • Where 1a gives zero weights to the opinions of
    those who have a p-value less than a (which is
    found such that the weight of the decision maker,
    wa(DM) is a maximum).
  • In this scoring scheme, statistical accuracy
    (calibration) strongly
  • dominate informativeness an expert cannot
    compensate for poor statistical performance.

36
  • After weighting and after experts are elicited
    individually regarding their uncertainty
    judgments in relation to questions of interest
    (Target Items) the Cooke Classical Model
    decision maker is computed
  • Where hi,e is the weighted pooling of the group
    of expert es uncertainty distribution for
    element i
  • Recall also, that a is chosen such that wa(DM)
    is at a maximum

37
Expert Elicitation (Cookes method)
2. Calibration (Contd) Each expert gives his/her
best guess within the intrinsic range m, M the
median (X50)
, the 5 (X5) and 95 (X95) confidence bounds
45
45
5
5
X50
X95
X5
38
Expert Elicitation (Cookes method)
True value for variable
2. Calibration (Contd)
1
n
Ti is the number of times the true values lie in
the ith interval out of n
39
Expert Elicitation (Cookes method)
2. Calibration (Contd)
H0 Expert is well Calibrated
H0 (S1,S2,S3,S4) (.05,.45,.45,.05)
Density of Chi-square distribution with 3
degrees of freedom
40
Expert Elicitation (Cookes method)
3. Scoring
X
X
a is of value that maximizes the scores of the
decision maker

Score of the Expert
41
Expert Elicitation (Cookes method)
4. Experts Elicitation Experts are then elicited
individually regarding their uncertainty
judgments in relation to questions of interest
(target items), again within their domain of
expertise. 5. Aggregation The weighted pooling
of the group of experts uncertainty
distributions (hi,e) gives the so-called Global
decision maker (DM). There are several forms
that the DM can take, the weighted average is one
of them
42
Risk Management of TSEs Structured Expert
Elicitation
Following calibration, the experts were asked to
answer 22 target questions
43
Seven Questions out of the Twenty Two (1/2)
  • 1. What is the dose in grams that would result
    in 50 of the exposed population becoming
    infected from human consumption of BSE cervical
    spinal cord or brain stem from near clinical or
    clinical cases? (grams)
  • What is the current prevalence of vCJD infection
    in the Canadian population? (1 in xxx)
  • 3. What is the mean incubation period for a
    primary vCJD infected
  • human by oral route? (years)
  • What is the median length of time in months
    between the oral
  • infection of a human with BSE agent and the
    capability of one
  • unit of his/her blood to transmit vCJD?
    (months)

44
Seven Questions out of the Twenty Two (2/2)
5. What is the mean incubation period for a
secondary vCJD infected human by
transfusion? (years) 6. What is the
probability of transmission of vCJD by
contaminated neurosurgery instruments if reused
in neurosurgery after one cycle of standard
sterilization procedures? () 7. How long is
the incubation period for secondary transmission
of vCJD by vCJD contaminated neurosurgical
instruments brain to brain usage?
(months)
45
What did the experts say?
46
Target Question 1
  • What is the dose in grams that would result
    in 50 of the exposed population becoming
    infected from human consumption of BSE cervical
    spinal cord or brain stem from near clinical or
    clinical cases? (grams)

Best expert judgment about 1 gram.
47
Target Question 2
  • What is the current prevalence of vCJD
    infection in the Canadian population? (1 in xxx)

Best expert judgement the current prevalence of
vCJD in Canada is less than 1 in 500,000.
48
Target Question 3
  • What is the mean incubation period for a
    primary vCJD infected human by oral route?
    (years)

Best expert judgment the mean incubation period
for vCJD following oral infection with the BSE
agent is 20 years.
49
Target Question 4
  • What is the median length of time in months
    between the oral infection of a human with BSE
    agent and the capability of one unit of his/her
    blood to transmit vCJD? (months)

Best expert judgment it would take over 3
years after oral infection with the BSE agent for
human blood to become infectious.
50
Target Question 5
  • What is the mean incubation period for a
    secondary vCJD infected human by transfusion?
    (years)

Best expert judgment the incubation period for
vCJD contacted via blood transfusion nearly 5
years (as compared to 20 years for oral
infection).
51
Target Question 6
  • What is the probability of transmission of
    vCJD by contaminated neurosurgery instruments if
    reused in neurosurgery after one cycle of
    standard sterilization procedures? ()

Best expert judgment approximately 7
probability of transmission.
52
Target Question 7
  • How long is the incubation period for
    secondary transmission of vCJD by vCJD
    contaminated neurosurgical instruments brain to
    brain usage? (months)
  • Best expert judgment incubation period of less
    than a year
  • (less than the 5 year incubation period for vCJD
  • acquired by blood transfusion).

53
Twelve Risks and Pairwise Comparisons
54
Twelve Risks and Pairwise Comparisons
6
3
Platelet transfusion FFP plasma transfusion Whole blood transfusion Dura Mater transplant Packed red blood cells Dental tissue graft Corneal transplant Hematopoietic stem cell transplant Human derived urine fertility products Bone marrow transplant pdFVIII pdFXI
10
4
11
2
1
7
5
8
9
12
55
Twelve Risks and Pairwise Comparisons
Platelet transfusion FFP plasma transfusion Whole blood transfusion Dura Mater transplant Packed red blood cells Dental tissue graft
Platelet transfusion          
FFP plasma transfusion        
Whole blood transfusion      
Dura Mater transplant    
Packed red blood cells  
Dental tissue graft
56
Twelve Risks and Pairwise Comparisons
Platelet transfusion gt FFP plasma
transfusion FFP plasma transfusion gt Whole
blood transfusion Platelet transfusion
Whole blood transfusion Circular triads
Platelet transfusion FFP plasma transfusion Whole blood transfusion Dura Mater transplant Packed red blood cells Dental tissue graft
Platelet transfusion gt   lt    gt gt 
FFP plasma transfusion  gt  gt  lt lt 
Whole blood transfusion gt  gt  lt 
Dura Mater transplant  gt  gt
Packed red blood cells  gt
Dental tissue graft
gt
lt
57
Preliminary Analysis
  • Test of Inconsistency
  • Measure of consistence

To test whether each expert gave his/her
responses randomly
Chi-square test with 21 degrees of freedom
11 tests are significant with p-valuelt.01
0.9714
0.9429
0.8429
0.7429
2 experts
2 experts
1
0.9679
0.9143
0.8143
0.6571
58
Preliminary Analysis
  • Statistical Test of Agreement
  • Coefficient of Agreement

To test whether the agreement between the group
of experts is due to chance
Chi-square test with 90 degrees of freedom
Test is significant with p-valuelt.01
CoA.3218
59
Probabilistic Inversion
  • Assign a random utility function (U1 , , U12) to
    each item
  • Scale the utilities to the interval 0,1
  • From the aggregated preference matrices

60
Probabilistic Inversion
  • Given the joint density function f, we can find
    Pij
  • Given Pij ,we want to find the joint density
    function f

61
Probabilistic Inversion
  • Numerical algorithms
  • Iterative Proportional Fitting (IPF)
  • Parameter Fitting for Uncertain Models (PARFUM)
  • UNIBALANCE ? TU Delft
  • http//dutiosc.twi.tudelft.nl/risk/

62
Probabilistic Inversion
  • Marginal distributions
  • Items scores (the mean of the random utility)
  • Correlation

63
Results
Transmission Route Score St. dev.
1 Platelet transfusion 0.5599 0.2318
2 FFP plasma transfusion 0.6266 0.2501
3 Whole blood transfusion 0.7384 0.2075
4 Dura Mater transplant 0.9520 0.0370
5 Packed red blood cells 0.6002 0.2742
6 Dental tissue graft 0.2756 0.2484
7 Corneal transplant 0.6953 0.2263
8 Hematopoietic stem cell transplant 0.3197 0.1752
9 Human derived urine fertility products 0.3160 0.2034
10 Bone marrow transplant 0.4966 0.2195
11 pdFVIII 0.4353 0.2524
12 pdFXI 0.3992 0.2340
64
Results
Relative Ranking Transmission Route Score (normalized)
1. Dura Mater transplant 1.0000
2. Whole blood transfusion 0.6842
3. Corneal transplant 0.6206
4. FFP plasma transfusion 0.5190
5. Packed red blood cells 0.4799
6. Platelet transfusion 0.4203
7. Bone marrow transplant 0.3268
8. pdFVIII 0.2360
9. pdFXI 0.1827
10. Hematopoietic stem cell transplant 0.0652
11. Human derived urine fertility products 0.0597
12. Dental tissue graft 0.0000
65
Results
66
Results
67
BSE and vCJD EE Exercises in OttawaMarch 2008,
2009
  • What is the size of the bovine to human species
    barrier in the MM genotype for oral exposure to
    the classical BSE agent?

2008
2009
68
More research is needed
  • Opinion aggregation for different inter-dependent
    schools
  • Testing inconsistency of experts opinions
  • Finding numerical algorithms for probabilistic
    inversion method
  • Fuzzy logic in EE

69
Submitted
  • Comparative Expert Judgment Elicitation using the
    Classical Model and EXCALIBUR under Conditions of
    Uncertainty for Prion Disease Risks
  • Expert Elicitation for the Judgment of Prion
    Disease Risk Uncertainties using the Classical
    Model and , EXCALIBUR and UNIBALANCE
  • By Michael G. Tyshenko, Susie ElSaadany, , Tamer
    Oraby, Shalu Darshan, Willy Aspinall, Roger
    Cooke, and Daniel Krewski

70
Conclusion
  • Best communication of complex methods to
    stakeholders?
  • Validity of complex methods when high uncertainty
    involved?
  • Best contribution in the name of precautionary
    principle?
  • National international collaboration in
    development of specific tools
  • Involvement of all partners
  • Face to face meetings, scientific and policy
    networks combined.

71
If you will begin with certainties, you shall end
in doubts, but if you will content to begin with
doubts, you shall end in almost certainties.
- Francis Bacon
72
Funded by High Impact GrantProject teams from
  • Public Health Agency of Canada, Statistics and
    Risk Assessment Section
  • Dr. Susie ElSaadany
  • Angela Catford
  • Caroline Desjardins
  • McLaughlin Centre for Population Health Risk
    Assessment, University of Ottawa
  • Dr. Daniel Krewski
  • Dr. Michael Tyshenko
  • Dr. Shalu Darshan
  • Dr. Mustafa Al-Zoughool
  • Dr. Tamer Oraby
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