blood transfusion public health risk to explore limitations of the common risk matrix

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.