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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-transmitted infections (TTI) are calculated, including the rationale for using these calculations.
We publish estimates of the residual risks of transfusion-transmitted infections (TTI) as a service to clinicians to guide transfusion decision-making and informed consent processes.
We publish this in the Blood Component Information (BCI) booklet, Medilink newsletter and on this web site.
The viral risk estimates have recently been revised based on Blood Service data from 1 January 2009 to 31 December 2010.
Our estimates are based on published methods and represent the median risk estimate derived using 3 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)
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 tests results for a ‘rolling’ 2-year period.
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%).
Point estimates are derived by 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.
The models assess the rate of seroconversion (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 model 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 models also incorporate the average inter-donation interval for all seroconverters (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 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 seroconversion 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).
Whereas one model includes a correction factor for the incidence rate to compensate for the transient nature of HBsAg, the other two do not.
None of the three models accounts for Chronic Occult HBV infection (OBI).
Implementation of 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.
These limitations potentially confound HBV residual risk estimation with the relative impact dependent on the proportion of acute versus chronic HBV infection in the donor population.
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—chance of being killed in a road accident is about 1 in 10,000 (see Calman Chart).
The Calman Chart for Explaining Risk (UK risk per 1 year)
|Negligible||<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).