blood transfusion public health risk to explore limitations of the common risk matrix
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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. Risk Assessment. Risk Matrix * U.K. National Health Service Guidance. - PowerPoint PPT PresentationTRANSCRIPT
Blood Transfusion Public Health Risk to Explore Limitations of the Common Risk Matrix
Shabnam Vatanpour
Outline
• Background• Objectives• Methods• Results• Conclusions
Risk ManagementISO International Standard
Communication & consultation
Establishing the context
Risk Assessment
Risk treatment
Monitoring & review
Risk Assessment
Risk Matrix*U.K. National Health Service Guidance
Simple to use Consistent
Capable of assessing a broad
range of risks
Simple to adapt to meet specific
needs
Risk Matrix
Cox’s Concerns
In some situations,
worse than a random guess
Sub-optimal allocation of
resources
Ambiguous inputs and
outputs
Poor resolution
Cox’s Theoretical Example
Risk = Frequency × Severity where
Frequency = 0.75 – Severity(for severity between 0 and 0.75)
SeverityFrequency Low High
High Medium High
Low Low Medium
0.5
0.5
101
0
Negative Correlation
Risk (Frequency × Severity) = F × S
Risk (0.45, 0.3) =0.13 → Low riskRisk ( 0.32,0.43) =0.14 → Low riskRisk (0.1, 0.65)=0.07 → Medium riskRisk (0.55, 0.2) =0.11 → Medium risk
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
LowMediumMedium
[0,0.25] → [0.5,0.75] Medium [0.25,0.5] → [0.25,0.5] Low [0.5,0.75] → [0,0.25] Medium
Frequency
Ris
k
Frequency Severity
Objective
Evaluation of risk matrix • using a public health risk scenario tainted
blood transfusion risk• when frequency and severity are
negatively correlated
Data collection: (Frequency, Severity)
Assess relationship between frequency and severityFit an appropriate risk curve to frequency and severity & estimate risks
Construct risk matrix and assign risk levels
Compare risk levels and estimated risks
Methods
Severity and Frequency of Blood Infectious Diseases in Canada, 1987-1996
Infectious
Diseases
Severity Severity
Category
Frequency Frequency
Category
Source
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
Contamination
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
National Health Service Criteria
National Health Service Criteria
Negative correlationSpearman correlation: -0.81
Logarithmic transformationlog-Risk = log-Frequency + log-Severity
Relationship between frequency and severitylog-Severity = 0.24 log-Frequency2 + 1.01 log-Frequency +1.99
Risk function estimationlog-Risk = 1.99 + 2.01 log-Frequency + 0.24 log-Frequency2
Results
Risk = Frequency x Severity
-6 -4 -2 0
-4-2
02
4
log-Frequency
log-
Ris
k
Fitted curveObservations
HIVCJD/vCJDC
Cytomegalovirus
Epstein-Barr virus
Hepatitis C
Hepatitis B
Hepatitis G
SEN virus
TT virus
Syphilis
HTLVBacterial Contamination
Blood Infectious DiseasesRisk Matrix
Severity of consequencesFrequency of Infection Very Low Low Medium High Very High
Very HighTT 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
Low
Very Low
Bacterial contamination †Obs 0.003 ‡Est 0.007
Hepatitis B †Obs 0.01 ‡Est 0.01
Extremely Low
Hepatitis C †Obs 0.004 ‡Est 0.014
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
†Risk estimation based on the fitted risk function ‡Observed risk based on the risk generic function
Low Medium High Very High
Risk Color Coding
Observed risk:Risk = Frequency × Severity
Estimated risk:log-Risk = 1.99 + 2.01 log-Frequency + 0.24 log-Frequency2
Risk Estimation
Risk{(Hep B,10-5, 103)} = 0.01 Low RiskRisk{(TT, 0.3, 10)} =10 Low RiskRisk{(Ep. Barr, 0.9, 100)} = 79 Medium Risk
Higher risk diseases tend to have higher risk ranks in the risk matrix.
Generating Data
-6 -4 -2 0
-4-2
02
4
log-Frequency
log-
Ris
k
Fitted curveObservations
HIVCJD/vCJDC
Cytomegalovirus
Epstein-Barr virus
Hepatitis C
Hepatitis B
Hepatitis G
SEN virus
TT virus
Syphilis
HTLVBacterial Contamination
Generated data 4
Generated data 2
Generated data 3
Generated data 1
Generated Data Frequency Risk Severity
Data 1 0.00003 0.0003 10
Data 2 0.00021 0.21 1000
Data 3 0.00006 0.0006 10
Data 4 0.005 0.5 100
Blood Infectious DiseasesRisk Matrix
†Risk estimation based on the fitted risk function ‡Observed risk based on the risk generic function
Low Medium High Very High
Risk Color Coding
Observed risk:Risk = Frequency × Severity
Estimated risk:log-Risk = 1.99 + 2.01 log-Frequency + 0.24 log-Frequency2
Severity
Frequency Very Low Low Medium High Very High
Very HighTT 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*Generated data 4 ‡Est 0.50
Low*Generated data 3 ‡Est 0.0006
*Generated data 2 ‡Est 0.21
Very Low
*Generated data 1 ‡Est 0.0003Bacterial contamination †Obs 0.003 ‡Est 0.007
Hepatitis B †Obs 0.01 ‡Est 0.01
Extremely Low Hepatitis C †Obs 0.004 ‡Est 0.014
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
Risk Estimation Generated Data
Risk{(Hep B,10-5, 103)} = 0.01 Low RiskRisk{(TT, 0.3, 10)} =10 Low RiskRisk{(Data 2, 0.00021, 103)} = 0.21 Medium RiskRisk{(Data 4, 0.005, 100)} = 0.5 Medium Risk
Higher risk diseases tend to have lower risk ranks in the risk matrix
for some scenarios.
Frequency
SeverityConclusions
• 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.
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Thank you.