Models used to predict the impact of having less stringent deferral policies for men who had sex with men: can we validate these predictions?

Many jurisdictions still do not accept blood donations from men who had sex with men (MSM). Those who defend the status quo argue that a less stringent policy would unduly increase the risk to recipients. In an effort to address this dilemma, investigators have tried to project the impact of having...

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Veröffentlicht in:ISBT science series 2015-04, Vol.10 (S1), p.263-267
Hauptverfasser: Germain, M., Delage, G.
Format: Artikel
Sprache:eng
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Zusammenfassung:Many jurisdictions still do not accept blood donations from men who had sex with men (MSM). Those who defend the status quo argue that a less stringent policy would unduly increase the risk to recipients. In an effort to address this dilemma, investigators have tried to project the impact of having less stringent deferral policies on HIV transmission risk, using mathematical models that rely on empirical data. Under certain assumptions, these models predicted very small but definite increases in risk if MSM were allowed to donate after a temporary deferral period. However, the predicted increase in the number of transfusion‐transmitted infections would be so small as to remain imperceptible in reality. Models also predict a sizeable increase in the number of HIV‐positive donors who would present to donate, an outcome that should be more readily observable. When applied to the Australian experience, where a one‐year deferral policy for MSM was implemented, most models would have predicted significant increases in the prevalence of HIV in male donors. The actual rate of HIV among Australian male donors remained very low and unchanged, suggesting that these models were overly pessimistic. It will be interesting to validate this finding in other countries that implemented a time‐based deferral. The Australian experience, if confirmed in other countries, would suggest that a time‐based deferral for MSM poses an even lower risk than what the models predict.
ISSN:1751-2816
1751-2824
DOI:10.1111/voxs.12111