title: Impact of using different blood donor subpopulations and models on the estimation of transfusion transmission residual risk of human immunodeficiency virus, hepatitis B virus, and hepatitis C virus in Zimbabwe.
abstract: Various models for estimating the residual risk (RR) of transmission of infections by blood transfusion have been published mainly based on data from high-income countries. However, to obtain the data required for such an assessment remains challenging for most developing settings. The National Blood Service Zimbabwe (NBSZ) adapted a published incidence-window period (IWP) model, which has less demanding data requirements. In this study we assess the impact of various definitions of blood donor subpopulations and models on RR estimates. We compared the outcomes of two published models and an adapted NBSZ model.
  
  
  
  The Schreiber IWP model (Model 1), an amended version (Model 2), and an adapted NBSZ model (Model 3) were applied. Variably the three models include prevalence, incidence, preseroconversion intervals, mean lifetime risk, and person-years at risk. Annual mean RR estimates and 95% confidence intervals for each of the three models for human immunodeficiency virus (HIV), hepatitis B virus (HBV), and hepatitis C virus (HCV) were determined using NBSZ blood donor data from 2002 through 2011.
  
  
  
  The annual mean RR estimates for Models 1 through 3 were 1 in 6542, 5805, and 6418, respectively for HIV; 1 in 1978, 2027, and 1628 for HBV; and 1 in 9588, 15,126, and 7750, for HCV.
  
  
  
  The adapted NBSZ model provided comparable results to the published methods and these highlight the high occurrence of HBV in Zimbabwe. The adapted NBSZ model could be used as an alternative to estimate RRs when in settings where two repeat donations are not available.
authors.affiliation: Unit of PharmacoEpidemiology & PharmacoEconomics (PE2), Department of Pharmacy, University of Groningen, Groningen, the Netherlands.|National Blood Service Zimbabwe, Harare, Zimbabwe.
authors.forename: Tonderai
authors.initials: T
authors.lastname: Mapako
authors.affiliation: Julius Center for Health Science and Primary Health Care, University Medical Center Utrecht, the Netherlands.
authors.forename: Mart P
authors.initials: MP
authors.lastname: Janssen
authors.affiliation: National Blood Service Zimbabwe, Harare, Zimbabwe.
authors.forename: David A
authors.initials: DA
authors.lastname: Mvere
authors.affiliation: National Blood Service Zimbabwe, Harare, Zimbabwe.
authors.forename: Jean C
authors.initials: JC
authors.lastname: Emmanuel
authors.affiliation: Department of Community Medicine, College of Health Sciences, University of Zimbabwe, Harare, Zimbabwe.
authors.forename: Simbarashe
authors.initials: S
authors.lastname: Rusakaniko
authors.affiliation: Unit of PharmacoEpidemiology & PharmacoEconomics (PE2), Department of Pharmacy, University of Groningen, Groningen, the Netherlands.|Institute of Science in Healthy Aging & Health caRE (SHARE), University Medical Center Groningen (UMCG).
authors.forename: Maarten J
authors.initials: MJ
authors.lastname: Postma
authors.affiliation: Unit of PharmacoEpidemiology & PharmacoEconomics (PE2), Department of Pharmacy, University of Groningen, Groningen, the Netherlands.|Department of Clinical Pharmacy and Toxicology, Martini Hospital, Groningen, the Netherlands.
authors.forename: Marinus
authors.initials: M
authors.lastname: van Hulst
mesh_terms: D000293:Adolescent; D001782:Blood Donors / Q000600:supply & distribution*; D005260:Female; D015658:HIV Infections / Q000635:transmission / Q000821:virology; D006509:Hepatitis B / Q000635:transmission / Q000821:virology; D006526:Hepatitis C / Q000635:transmission / Q000821:virology; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D018570:Risk Assessment; D065227:Transfusion Reaction; D014777:Virus Diseases / Q000635:transmission* / Q000821:virology; D055815:Young Adult; D015030:Zimbabwe / Q000453:epidemiology