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Surveillance programs for human immunodeficiency virus (HIV) infection are based on the reporting of newly diagnosed cases. In order to guarantee a more accurate estimate of the trends and behaviours of infected people, simple and reliable methods aimed at identifying recent (< 6 months) HIV infections are needed. We evaluated the accuracy of the avidity index (AI) of anti-HIV antibodies on 357 serum samples obtained from 127 subjects for whom an estimated date of seroconversion was calculated on the basis of the interval between the last negative and first positive anti-HIV test result.The ROC curve analysis performed at different thresholds of the AI showed that a cutoff of 0.80 (93.0% sensitivity and 98.5% specificity) yields the best overall accuracy (95.8%) and should be employed for surveillance purposes, whereas the application of the anti-HIV AI on individual cases is not recommended.
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