A primary care level algorithm for identifying HIV-infected adolescents in populations at high risk through mother-to-child transmission

Objective To present an algorithm for primary‐care health workers for identifying HIV‐infected adolescents in populations at high risk through mother‐to‐child transmission. Methods Five hundred and six adolescent (10–18 years) attendees to two primary care clinics in Harare, Zimbabwe, were recruited. A randomly extracted ‘training’ data set (n = 251) was used to generate an algorithm using variables identified as associated with HIV through multivariable logistic regression. Performance characteristics of the algorithm were evaluated in the remaining (‘test’) records (n = 255) at different HIV prevalence rates. Results HIV prevalence was 17%, and infection was independently associated with client‐reported orphanhood, past hospitalization, skin problems, presenting with sexually transmitted infection and poor functional ability. Classifying adolescents as requiring HIV testing if they reported >1 of these five criteria had 74% sensitivity and 80% specificity for HIV, with the algorithm correctly predicting the HIV status of 79% of participants. In low‐HIV‐prevalence settings (<2%), the algorithm would have a high negative predictive value (≥99.5%) and result in an estimated 60% decrease in the number of people needing to test to identify one HIV‐infected individual, compared with universal testing. Conclusions Our simple algorithm can identify which individuals are likely to be HIV infected with sufficient accuracy to provide a screening tool for use in settings not already implementing universal testing policies among this age‐group, for example immigrants to low‐HIV‐prevalence countries.