A method for classification of HIV exposure category for women without HIV risk information.

An increasing number of cases of human immunodeficiency virus (HIV) and acquired immunodeficiency syndrome (AIDS) among women is reported to state and territorial health departments without exposure risk information (i.e., no documented exposure to HIV through any of the recognized routes of HIV transmission). Because surveillance data are used to plan prevention and other services for HIV-infected persons, developing methods to accurately estimate exposure risk for HIV and AIDS cases initially reported without risk information and assisting states to analyze and interpret trends in the HIV epidemic by exposure risk category is important. In this report, a classification model using discriminant function analysis is described. The purpose of the classification model is to develop a proportionate distribution of exposure risk category for cases among women reported without risk information. The distribution was estimated based on behavioral and demographic data obtained from interviews with HIV-infected women; the interviews were conducted in 12 states during 1993-1996. Variables used in the analysis were alcohol abuse, noninjection-drug use, and crack use; year of HIV/AIDS diagnosis; age; employment; and region. As a result of the classification procedure, nearly all cases among women with no reported risk were classified into an exposure risk category: 81%, heterosexual contact; and 16%, injection-drug use. These proportions are higher than the current redistribution fractions (calculated from risk reclassification patterns and weighted by demographic characteristics) and reflect the increasing proportion of cases among women attributable to heterosexual contact with an infected partner. This report provides one method that could be applied to HIV surveillance data at the national level to estimate the proportion of cases in exposure risk categories. However, because the study in this report is limited in sample size and geographic representativeness, other models are also needed for adjusting risk exposure data at the national, state, and local levels.