Disease 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.
We 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.
Five 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.
Although 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.
A model predicting undiagnosed human immunodeficiency virus infection in the sexual partners of human immunodeficiency virus-infected persons in North Carolina allows small reductions in number of in partners pursued during partner notification while maintaining high sensitivity.
From the *Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC; †University of North Carolina Center for Public Health Preparedness, North Carolina Institute for Public Health, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC; ‡Division of Infectious Diseases, Department of Medicine, University of North Carolina, Chapel Hill, NC; and §HIV/STD Prevention and Care Branch, North Carolina Division of Public Health, Raleigh, NC
The authors would like to thank the North Carolina Communicable Disease Branch (Evelyn Foust, Michael Hilton, and Del Williams) and the North Carolina HIV/STD Field Services Unit (Rhonda Ashby, Maxi Mackalo, Kristen Patterson, Todd Vanhoy, and the disease intervention specialists of Regions 3 and 4) for collecting and giving us access to the study data.
B.E.H. was supported by National Institute of Allergy and Infectious Diseases (NIAID) grant 5T32AI070114-05.
Correspondence: William C. Miller, MD, PhD, Division of Infectious Diseases, CB 7030, Bioinformatics Bldg, 130 Mason Farm Rd, 2nd Floor, Chapel Hill, NC 27599-7030. E-mail: firstname.lastname@example.org.
Received for publication March 14, 2011, and accepted August 30, 2011.