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

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, as risk factors and moderators are added to the model interpretation can become difficult. The current study presents finite mixture regression as an alternative approach, where population subgroups are identified based on the pattern of associations between multiple risk 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 factors (early sexual debut, heavy episodic 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. Gender, race, and grade were included as predictors of latent class membership. Results suggest that prevention programs focused on mediating these particular risk factors may be most effective for adolescents who are at lower risk for later engaging in risky sexual behaviour; however, for the subgroup of adolescents who go on to have the most sexual partners, the evidence is less conclusive and warrants further study.