Using decision tree analysis to understand the influence of social networks on disclosure of HIV infection status

Disclosure of human immunodeficiency virus (HIV) infection status improves treatment adherence and HIV prevention. Social networks influence such disclosure by people living with HIV/AIDS (PLWH). This study aimed to investigate the disclosure status of Korean PLWH and determine the social network characteristics associated with disclosure. A cross-sectional study design was used, and 148 Korean PLWH answered self-report questionnaires that included items on the characteristics of social networks and disclosure. Logistic regression and decision tree analysis were performed. In total, 81 participants (54.7%) reported disclosing HIV status to the most important supporter. Five factors were found to influence disclosure: age, self-help group participation, living arrangement, social network relationship, and tie strength; three groups had higher percentages of nondisclosure. The findings suggest that healthcare practitioners should provide adequate counseling by considering the characteristics of social networks and disclosure status of PLWH. Researchers should identify high-risk populations using decision tree analysis.restrictio