Risk Matrices

Report
Blood Transfusion Public Health Risk
to Explore Limitations of the Common Risk Matrix
Shabnam Vatanpour
Outline
•
•
•
•
•
Background
Objectives
Methods
Results
Conclusions
Risk Management
ISO International Standard
Monitoring
& review
Communication
& consultation
Risk
treatment
Establishing
the context
Risk
Risk
Assessment
Assessment
Risk Matrix
*U.K.
National Health Service Guidance
Simple to use
Capable of
assessing a broad
range of risks
Consistent
Simple to adapt
to meet specific
needs
Risk Matrix
Cox’s Concerns
Ambiguous
inputs and
outputs
Poor
resolution
Sub-optimal
allocation of
resources
In some
situations,
worse than a
random guess
Cox’s Theoretical Example
Risk = Frequency × Severity
where
Frequency = 0.75 – Severity
(for severity between 0 and 0.75)
Severity
Frequency
1
High
Low
High
0.5
0
1
Medium
High
Low
Medium
0.5
Low
0
Negative Correlation
Frequency
Severity
[0,0.25] → [0.5,0.75] Medium
[0.25,0.5] → [0.25,0.5] Low
1 [0.5,0.75] → [0,0.25]
Medium
0.9
Risk (Frequency × Severity) = F × S
0.8
Risk (0.45, 0.3) =0.13 → Low risk
Risk ( 0.32,0.43) =0.14 → Low risk
Risk (0.1, 0.65)=0.07 → Medium risk
Risk (0.55, 0.2) =0.11 → Medium risk
Risk
0.7
0.6
0.5
0.4
Low
0.3
Medium
Medium
0.2
0.1
0
0
0.2
0.4
0.6
Frequency
0.8
1
Objective
Evaluation of risk matrix
• using a public health risk scenario
tainted blood transfusion risk
• when frequency and severity are
negatively correlated
Methods
Data collection: (Frequency, Severity)
Assess relationship between frequency
and severity
Fit an appropriate risk curve to
frequency and severity & estimate risks
Construct risk matrix and assign risk
levels
Compare risk levels and estimated risks
Severity and Frequency of Blood Infectious Diseases in
Canada, 1987-1996
Infectious
Severity
Diseases
Severity
Frequency
Category
Frequency
Source
Category
HIV
105
Very High
0.000001
Extremely Low
Blood Donors
HTLV
104
High
0.0000008
Extremely Low
Blood Donors
Hepatitis B
103
Medium
0.00001
Very Low
Blood Donors
Hepatitis C
103
Medium
0.000004
Extremely Low
Blood Donors
Hepatitis G
10
Very Low
0.01
High
Blood Donors
Bacterial
102
Low
0.000026
Very Low
Blood Donors
Cytomegalovirus
102
Low
0.4
Very High
Blood Donors
Epstein-Barr virus
102
Low
0.9
Very High
Blood Donors
TT virus
10
Very Low
0.3
Very High
Blood Donors
SEN virus
10
Very Low
0.02
High
Blood Donors
CJD/vCJD
105
Very High
0.000001
Extremely Low
Population
Syphilis
104
High
0.000006
Extremely Low
Blood Donors
Contamination
National Health Service Criteria
National Health Service Criteria
Results
Negative correlation
Spearman correlation: -0.81
Logarithmic transformation
log-Risk = log-Frequency + log-Severity
Relationship between frequency and severity
log-Severity = 0.24 log-Frequency2 + 1.01 log-Frequency +1.99
Risk function estimation
log-Risk = 1.99 + 2.01 log-Frequency + 0.24 log-Frequency2
4
Risk = Frequency x Severity
2
Epstein-Barr virus
0
TT virus
HIV
-2
CJD/vCJDC
SEN virus
Syphilis
Hepatitis G
Hepatitis B
HTLV
Hepatitis C
Bacterial Contamination
Fitted curve
Observations
-4
log-Risk
Cytomegalovirus
-6
-4
-2
log-Frequency
0
Blood Infectious Diseases
Frequency of
Risk Matrix
Infection
Observed risk:
Risk = Frequency × Severity
Estimated risk:
log-Risk = 1.99 + 2.01 logFrequency + 0.24 log-Frequency2
Severity of consequences
Very Low
Low
Very High
TT virus
†Obs 3 ‡Est 10
Cytomegalovirus
†Obs 35 ‡Est 13
Epstein-Barr
virus †Obs 90
‡Est 79
High
SEN virus
†Obs 0.2 ‡Est
0.19 Hepatitis
G
†Obs
0.11 ‡Est 0.10
Medium
High
Very High
Syphilis
†Obs 0.06
‡Est 0.01
HTLV
†Obs 0.01
‡Est 0.05
HIV
†Obs 0.13
‡Est 0.03
CJD/vCJD
†Obs 0.1
‡Est 0.04
Medium
Low
Risk Color Coding
Low
Medium
High
Very
High
Very Low
Extremely
Low
Bacterial
contamination
†Obs 0.003
‡Est 0.007
Hepatitis B
†Obs 0.01
‡Est 0.01
Hepatitis C
†Obs 0.004
‡Est 0.014
†Risk estimation based on the fitted risk function
‡Observed risk based on the risk generic function
Risk Estimation
Risk{(Hep B,10-5, 103)} = 0.01
Risk{(TT, 0.3, 10)} =10
Risk{(Ep. Barr, 0.9, 100)} = 79
Low Risk
Low Risk
Medium Risk
Higher risk diseases
tend to have higher risk ranks in the risk matrix.
4
Generating Data
Frequency
Risk
Severity
Epstein-Barr virus
2
Generated
Data
0.00003
0.0003
10
Data 2
0.00021
0.21
1000
0.00006
0.0006
CJD/vCJDC
10
0.005
0.5
SEN virus
Syphilis
Hepatitis B
HTLV
Hepatitis C
100
Hepatitis G
Bacterial Contamination
Generated data 3
Generated data 1
-4
Data 4
TT virus
Generated data 2
HIV
-2
Data 3
Generated data 4
0
Data 1
log-Risk
Cytomegalovirus
-6
-4
-2
log-Frequency
Fitted curve
Observations
0
Blood Infectious Diseases
Frequency
Risk Matrix
Observed risk:
Risk = Frequency × Severity
Estimated risk:
log-Risk = 1.99 + 2.01 logFrequency + 0.24 log-Frequency2
Severity
Very Low
Low
Very High
TT virus
†Obs 3 ‡Est 10
Cytomegalovirus
†Obs 35 ‡Est 13
Epstein-Barr virus
†Obs 90 ‡Est 79
High
SEN virus
†Obs 0.2 ‡Est 0.19
Hepatitis G
†Obs 0.11 ‡Est
0.10
*Generated
Medium
Low
Medium
High
Very High
Syphilis
†Obs 0.06
‡Est 0.01
HTLV
†Obs 0.01
‡Est 0.05
HIV
†Obs 0.13
‡Est 0.03
CJD/vCJD
†Obs 0.1
‡Est 0.04
data 4
‡Est 0.50
*Generated
*Generated
data 3
‡Est 0.0006
data 2
‡Est 0.21
Risk Color Coding
Low
Medium
High
*Generated
Very
High
Very Low
Extremely Low
data 1
‡Est 0.0003
Bacterial
contamination
†Obs 0.003
‡Est 0.007
Hepatitis B
†Obs 0.01
‡Est 0.01
Hepatitis C
†Obs 0.004
‡Est 0.014
†Risk estimation based on the fitted risk function
‡Observed risk based on the risk generic function
Risk Estimation
Generated Data
Risk{(Hep B,10-5, 103)} = 0.01
Risk{(TT, 0.3, 10)} =10
Risk{(Data 2, 0.00021, 103)} = 0.21
Risk{(Data 4, 0.005, 100)} = 0.5
Low Risk
Low Risk
Medium Risk
Medium Risk
Higher risk diseases
tend to have lower risk ranks in the risk matrix
for some scenarios.
Conclusions
• Careful reconsideration of uses of the risk matrix in
risk management
• Use risk matrix outputs as an operational input to risk
management decision-making,
• Avoid risk matrix outputs to drive or even become the
risk management decision.
Reference
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