An identity-based motivational model of the effects of perceived discrimination on health-related behaviors

Perceived discrimination is associated with increased engagement in unhealthy behaviors. We propose an identity-based pathway to explain this link. Drawing on an identity-based motivation model of health behaviors (Oyserman, Fryberg, & Yoder, 2007), we propose that erceptions of discrimination lead individuals to engage in ingroup-prototypical behaviors in the service of validating their identity and creating a sense of ingroup belonging. To the extent that people perceive unhealthy behaviors as ingroup-prototypical, perceived discrimination may thus increase motivation to engage in unhealthy behaviors. We describe our theoretical model and two studies that demonstrate initial support for some paths in this model. In Study 1, African American participants who reflected on racial discrimination were more likely to endorse unhealthy ingroup-prototypical behavior as self-characteristic than those who reflected on a neutral event. In Study 2, among African American participants who perceived unhealthy behaviors to be ingroup-prototypical, discrimination predicted greater endorsement of unhealthy behaviors as self-characteristic as compared to a control condition. These effects held both with and without controlling for body mass index (BMI) and income. Broader implications of this model for how discrimination adversely affects health-related decisions are discussed.

Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci.

Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted.