Developing a predictive model to prioritize human immunodeficiency virus partner notification in North Carolina.

backgroundDisease intervention specialists (DIS) in North Carolina have less time to conduct partner notification because of competing responsibilities while simultaneously facing increased case loads because of increased human immunodeficiency virus (HIV) testing. We developed a model to predict undiagnosed HIV infection in sexual partners to prioritize DIS interviews.

methodsWe abstracted demographic, behavioral, and partnership data from DIS records of HIV-infected persons reported in 2 North Carolina surveillance regions between January 1, 2003 and December 31, 2007. Multiple logistic regression with generalized estimating equations was used to develop a predictive model and risk scores among newly diagnosed persons and their partners. Sensitivities and specificities of the risk scores at different cutoffs were used to examine algorithm performance.

resultsFive factors predicted a partnership between a person with newly diagnosed HIV infection and an undiagnosed partner--a period of 4 weeks or fewer between HIV diagnosis and DIS interview, no history of crack use, no anonymous sex, fewer total sexual partners reported to DIS, and sexual partnerships between an older index case and younger partner. Using this model, DIS could choose an appropriate cutoff for locating a particular partner by determining the weight of false negatives relative to false positives.

conclusionsAlthough the overall predictive power of the model is low, it is possible to reduce the number of partners that needs to be located and interviewed while maintaining high sensitivity. If DIS continue to pursue all partners, the model would be useful in identifying partners in whom to invest more resources for locating.