title: Assessing the accuracy of three viral risk models in predicting the outcome of implementing HIV and HCV NAT donor screening in Australia and the implications for future HBV NAT.
abstract: BACKGROUND
  Risk modeling is now the most practical method of estimating the residual risk of viral transmission in developed countries. One method of assessing the accuracy of a risk model is to measure the observed against the predicted outcome after implementing a new screening method. The primary objective of this paper is to assess the accuracy of three published models in predicting the impact of implementing HIV and HCV NAT in Australia.
  
  
  METHODS
  Viral screening data on Australian donors for 2000 and 2001 were retrospectively analyzed. The data were applied to the three models to estimate the risk of transmission and predicted NAT yield for HIV, HCV, and HBV.
  
  
  RESULTS
  The median risk estimates for the three models were 1 in 3,415,000 for HIV NAT, 1 in 911,000 for HCV NAT, and 1 in 483,000 for HBsAg. The predicted NAT yield for the three models ranged from 0.17 to 0.30 per million donations for HIV, 1.20 to 5.55 for HCV, and 0.47 to 1.01 for HBV. The observed NAT yield was not significantly different from the expected yield with any of the three models for either HIV or HCV.
  
  
  CONCLUSIONS
  First, the residual risk in Australian donors is small in comparison with other transfusion complications and comparable to or lower than the risk in US and European nonremunerated donors. Second, mathematical risk modeling has sufficient precision to be used as a predictive tool for risk-benefit assessments of novel screening procedures. Finally, in relation to the case for implementing HBV NAT and/or anti-HBc in Australia, we conclude that at present, there is inadequate information about our donor population to perform an evidence-based risk-benefit analysis.
authors.affiliation: Australian Red Cross Blood Service, 290 Wellington Street, Perth, Western Australia 6000. cseed@arcbs.redcross.org.au
authors.forename: Clive R
authors.initials: CR
authors.lastname: Seed
authors.forename: Anthea
authors.initials: A
authors.lastname: Cheng
authors.forename: Susan L
authors.initials: SL
authors.lastname: Ismay
authors.forename: Wayne V
authors.initials: WV
authors.lastname: Bolton
authors.forename: Philip
authors.initials: P
authors.lastname: Kiely
authors.forename: Trevor J
authors.initials: TJ
authors.lastname: Cobain
authors.forename: Anthony J
authors.initials: AJ
authors.lastname: Keller
mesh_terms: D000328:Adult; D001315:Australia / Q000453:epidemiology; D001782:Blood Donors; D006678:HIV / Q000302:isolation & purification; D015658:HIV Infections / Q000097:blood / Q000453:epidemiology / Q000517:prevention & control* / Q000635:transmission / Q000821:virology; D006679:HIV Seropositivity / Q000453:epidemiology; D016174:Hepacivirus / Q000302:isolation & purification; D006509:Hepatitis B / Q000097:blood / Q000453:epidemiology / Q000517:prevention & control* / Q000635:transmission / Q000821:virology; D006515:Hepatitis B virus / Q000302:isolation & purification; D006526:Hepatitis C / Q000097:blood / Q000453:epidemiology / Q000517:prevention & control* / Q000635:transmission / Q000821:virology; D006801:Humans; D007118:Immunoassay; D008403:Mass Screening; D008962:Models, Theoretical; D010043:Outcome and Process Assessment, Health Care; D015995:Prevalence; D011336:Probability; D012367:RNA, Viral / Q000097:blood*; D012189:Retrospective Studies; D018570:Risk Assessment; D065227:Transfusion Reaction; D014766:Viremia / Q000821:virology
chemical_list: D012367:RNA, Viral