Risks of transfusion-transmissible infection calculated on Lifeblood data
|Agent and testing standard||Window period||Estimate of residual risk ‘per unit' (a)|
|HIV (antibody/p24 Ag + NAT)||5 days||Less than 1 in 1 million [1, 2]|
|HCV (antibody + NAT)||3 days||Less than 1 in 1 million [1, 2]|
|HBV (HBsAg + NAT)||17 days||Less than 1 in 1 million [1-3]|
|HTLV 1 & 2 (antibody)||51 days||Less than 1 in 1 million (b)|
|vCJD [No testing]||Less than 1 in 1 million |
|Malaria (antibody)||7–14 days||Less than 1 in 1 million |
|T. pallidum (antibody)||30 days||Less than 1 in 1 million |
vCJD = variant Creutzfeldt-Jakob Disease
(a) HIV and HCV WP risk estimates are based on Lifeblood data from 1 January 2021 to 31 December 2022. HBV WP risk based on Lifeblood data from 1 January 2020 to 31 December 2022.
(b) No HTLV incident donors recorded for the period – residual risk estimate derived from single model using first-time and repeat donor calculation and based on Lifeblood data from 1 January 2021 to 31 December 2022.
The relative risks of transfusion transmitted viral infections are very small compared to the health risks of everyday living (refer Relative risk of transfusion chart).
There have been no reported cases of transmission by transfusion of sporadic Creutzfeldt-Jakob Disease (sCJD), and retrospective studies suggest that the possibility of such transmission of sCJD is remote .
To date, there have been no reported cases of vCJD in Australia. In the UK, there have been a small number of reported cases of putative transfusion transmission since 2004. The risk of transfusion transmission resulting in a clinical case of vCJD in Australia in 2020 was estimated to be less than 1 in 1.4 billion per unit transfused .
- Seed CR, Kiely P, Keller AJ. Residual risk of transfusion transmitted human immunodeficiency virus, hepatitis B virus, hepatitis C virus and human T lymphotrophic virus. Intern Med J 2005;35(10):592–598.
- Weusten J, Vermeulen M, van Drimmelen H, et al. Refinement of a viral transmission risk model for blood donations in seroconversion window phase screened by nucleic acid testing in different pool sizes and repeat test algorithms. Transfusion 2011;51:203-215.
- Seed CR, Kiely P, Hoad VC, Keller AJ: Refining the risk estimate for transfusion-transmission of occult hepatitis B virus. Vox Sang 2017;112(1):3-8.
- McManus, H, Seed, CR, Hoad, VC, et al. Risk of variant Creutzfeldt–Jakob disease transmission by blood transfusion in Australia. Vox Sang 2022;May 24 doi: 10.1111/vox.13290
- Seed CR, Kee G, Wong T, Law M, Ismay S. Assessing the safety and efficacy of a test based, targeted screening strategy to minimise transfusion transmitted malaria. Vox Sang 2010;98:e182–192.
- Jayawardena T, Hoad V, Styles C, et al. Modelling the risk of transfusion-transmitted syphillis: a reconsideration of blood donation testing strategies. Vox Sang 2019 114(2): 107-116.
- Crowder LA, Schonberger LB, Dodd RY, Steele WR. Creutzfeldt-Jakob disease lookback study: 21 years of surveillance for transfusion transmission risk. Transfusion. 2017 Aug;57(8):1875-8.
- What TTI residual risk estimates do we publish and where are they published?
We publish the TTI residual risk estimates in the Blood Component Information (BCI) booklet, and on this website. The viral risk estimates are reviewed and updated periodically. Our estimates are based on published methods.
- In very simple terms, what is the basis of the model calculations?
The 'window period' (WP) is defined as the interval between infection and first positive test marker in the bloodstream.
WP-based models assess the rate of incident infection (i.e. positive donors who have previously tested negative at Lifeblood for the same viral marker) in the repeat donor (RD) population as a measure of viral incidence (i.e. the rate of newly acquired infection).
The average inter-donation interval for all incident donors between the positive result and previous negative result is also incorporated. The longer this interval for an individual donor, the lower the probability that the donor was in the WP at the time of donation. In other words, the inter-donation interval is inversely proportional to the risk.
For infections subject to NAT testing (HIV, HBV and HCV), the Weusten model uses incidence to estimate the risk of infection in a recipient of a tested blood component based on the lower limit of detection of the applied NAT test and the probability of transmission based on a number of factors, including the volume of transfused plasma and the presumed ‘infectious dose’ of the infectious agent.
The WP model used for HTLV estimates only the probability of failing to detect a WP donation in a given time period based on either the rate of incident infection and inter-donation interval, or the prevalence in first-time donors.
The final model, applied only to HBV, estimates the risk specifically for occult HBV infection (OBI). The method is based on assessing the probability of 'non-detection' by HBV NAT and the average probability of HBV transmission from NAT non-reactive donations. NAT non detection is derived by examining HBV NAT data and assessing the frequency of prior NAT non-detectable donations from donors identified as OBI by NAT. The transmission function is based on investigation of the outcome of transfusions from blood components (termed lookback) sourced from donors with OBI.
- What is the rationale for using the above calculations?
The WP-based models assume that the risk of collecting blood from an infectious donor predominantly relates to them being in the WP (i.e. incident infection) and the best estimate of incidence in the donor population is the rate of incident donors in the repeat donor population.
The Weusten model assumes a concentration-dependent probability that the virus is not detected in the log-linear ramp-up phase of plasma viraemia in acute infection, and a dose-dependent probability that an infection develops in the recipient of the contaminated blood product. Both these probabilities contribute to the overall residual risk.
While the assumption that WP donors account for the majority of risk seems to hold true for HIV, HCV and HTLV, HBV is problematic because of 'chronic' infection (i.e. HBsAg negative/anti-HBc positive with low levels of HBV DNA). WP-based models do not satisfactorily address the risk of long-term OBI. Therefore, Lifeblood developed a specific model to estimate the OBI risk which is summed with the WP risk to derive the overall HBV residual risk estimate. Importantly, HBV NAT will incrementally identify OBI donors since the vast majority can be detected using the highly sensitive ID NAT employed by Lifeblood.
- How do these residual risk estimates compare to the risks associated with everyday living?
When considering the significance of specific risks, it is useful to compare them to the risks associated with everyday living. Levels of risk are compared in this Calman Chart.
The Calman Chart for Explaining Risk (UK risk per 1 year)
Classification Risk range Example Negligible <1:1 000 000 Death from a lightning strike Minimal 1:100 000–1:1 000 000 Death from a train accident Very low 1:10 000–1:100 000 Death from an accident at work Low 1:1000–1:10 000 Death from a road accident Moderate 1:100–1:1000 Death from smoking 10 cigarettes per day High >1:100 Transmission of chickenpox to susceptible household contacts
Source: Calman K. Cancer: science and society and the communication of risk. BMJ 1996;313:801.
The chance of dying in a road accident, for example, is about 1 in 10 000 per year which is considered a ‘low’ risk. Comparatively, all our viral risk estimates are well below this level, and would be classified as a ‘negligible risk’.
The relative risks of transfusion-transmitted infections are also very small compared to the health risks of everyday living (refer Relative risk of transfusion chart).