We developed the following frequently asked questions and answers to provide health professionals with a better understanding of how the Blood Service residual risk estimates for viral transfusion-transmissible infections (TTI) are calculated, including the rationale for using these calculations.
We publish estimates of the residual risks of transfusion-transmissible infections (TTI) as a service to clinicians to guide transfusion decision-making and informed consent processes.
We publish theTTI residual risk estimates in the Blood Component Information (BCI) booklet, and on this web site. The viral risk estimates are reviewed and updated annually.
Our estimates are based on published methods and represent the median risk estimate derived using four different models.
Although the method has been refined over time, it is essentially as described in the article on residual risk estimates by Seed, Kiely and Keller.(1) An additional refinement since 2011 is a new model applied to HBV which specifically addresses the risk of occult hepatitis B infection (OBI).(2)
To reflect the uncertainty of the estimates, a ‘plausible range’ defined by the upper and lower estimate for the least and most conservative model is defined.
These estimates are updated annually using blood donation viral screening test results for a ‘rolling’ two year period, or in the case of the OBI model, the most recent 12 months' data.
It should be noted that, as the order of magnitude of these risks is very small, the calculated median risk estimate may fluctuate from year to year.
Furthermore the estimates are conservative since they are based on the ‘worst case’ assumption that an infectious donation is always issued for transfusion and, that if transfused will always lead to infection in the recipient (ie, infectivity is 100%). There are other factors which may mitigate the risk of transmission including the volume of plasma in the component transfused, the number of viral particles per unit volume and the immune status of the recipient.
Three of the four models derive point estimates determining the probability of an undetected ‘window period’ (WP) donation in a given time period. WP is defined as the interval between infection and first positive test marker in the bloodstream.
These WP-based models assess the rate of incident infection (ie, positive donors who have previously tested negative at the Blood Service for the same viral marker) in the repeat donor (RD) population as a measure of viral incidence (ie, the rate of newly acquired infection).
In order to incorporate the incidence in first time donors (FTD) (who have no previous testing at the Blood Service), one of the three WP-based models uses a separate calculation whereas the other two use a correction factor for the RD incidence based on the proportion of NAT positive/antibody negative (ie, NAT 'yield') donors in the FTD and RD populations, respectively.
Two of the WP-based models also incorporate the average inter-donation interval for all incident donors (in days) between the positive result and previous negative result. 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.
The fourth model, applied only to HBV, estimates the risk specifically for 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. The full method is available in reference.(2)
The three WP-based models assume that the risk of collecting blood from an infectious donor predominantly relates to them being in the WP (ie, incident infection) and the best estimate of incidence in the donor population is the rate of incident donors in the RD population.
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 (ie, HBsAg negative/anti-HBc positive).
One WP-based model includes a correction factor for the incidence rate to compensate for the transient nature of HBsAg, the other two do not. While this addresses some of the chronic HBV infection risk associated with seroconversion to anti-HBc in the early stages of chronicity, it does not satisfactorily address the risk of long-term OBI. Therefore the Blood Service 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 the Blood Service.
When considering the significance of specific risks, it is often useful to compare them to the risks associated with everyday living.
The risks of transfusion transmitted infection with virus is very small compared to risks of everyday living. (see Calman Chart).
|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||1:1,000–1:10,000||Death from a road accident|
|Moderate||1:100–1:1,000||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 the viral risk estimates are well below this level, being considered as either ‘minimal’ (HBV) or ‘negligible’ (HIV and HCV).
- 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. Internal Medicine Journal 2005;35(10):592–598.
- Seed CR, Kiely P, Hoad VC, Keller AJ: Refining the risk estimate for transfusion-transmission of occult hepatitis B virus. Vox Sang. 2016.DOI: 10.1111/vox.12446