Differential Effects for Sexual Risk Behavior: An Application of Finite Mixture Regression

Stephanie T. Lanza*, 1, 2, Kari C. Kugler1, Charu Mathur1
1 The Methodology Center, The Pennsylvania State University, University Park, PA 16802, USA
2 The College of Health and Human Development, The Pennsylvania State University, University Park, PA 16802, USA

© 2011 Lanza et al;

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Methodology Center, The Pennsylvania State University, 204 E. Calder Way, Suite 400, State College, PA 16801, USA; Tel: 814-865-7095; Fax: 814-863-0000; E-mail:


Understanding the multiple factors that place individuals at risk for sexual risk behavior is critical for developing effective intervention programs. Regression-based methods are commonly used to estimate the average effects of risk factors; however, such results can be difficult to translate to prevention implications at the individual level. Although differential effects can be examined to some extent by including interaction terms, interpretation can become difficult as risk factors and moderators are added to the model. The current study presents finite mixture regression as an alternative approach, in which population subgroups are identified based on the pattern of associations between multiple risk and protective factors and sexual risk behavior. Data from participants in the National Longitudinal Study on Adolescent Health were used to explore the effects of five adolescent risk and protective factors (early sexual debut, pastyear binge drinking, school connectedness, positive consequences of having sex, and negative consequences of having sex) on the total number of sexual partners in adulthood. Four latent classes were identified on the basis of the Poisson regression parameter estimates. Males and Black adolescents were significantly more likely to be in subgroups characterized by higher-risk behavior. Results suggest that prevention programs focused on mediating these particular risk factors may be most effective for adolescents at lower risk for later engaging in risky sexual behavior; however, for the subgroup of adolescents who go on to have the most sexual partners, the regression weights are significantly weaker, warranting further investigation into the most salient risk factors, and therefore targets for intervention programs, for highrisk adolescents.

Keywords: Adolescent risk factors, Lifetime sexual partners, HIV prevention, Mixture model.