Abstract PO-074: Understanding men’s concerns about linking health data in the context of precision medicine

Abstract

Abstract Background and Importance: Linking data is a critical feature of precision medicine initiatives that involves integrating information from multiple sources to improve researchers’ and clinicians’ ability to deliver care. To date, we have limited understanding of how racial and ethnic minorities perceive linking data as it relates to precision medicine. Objective: The aim of this study was to identify how demographics, co-morbidities, and beliefs about precision medicine influence two outcomes related to linking data: beliefs about linking data and concerns about linking data among minority men. Methods: Adult individuals were recruited from primary care practices at a large clinical research university. 375 individuals were invited to participate and 124 completed a cross-sectional survey that included questions about demographic characteristics, comorbidities, beliefs, benefits, and limitations of precision medicine, and two outcomes of interest: beliefs about the value of linking data and concerns about linking data. Descriptives, bivariate, and multivariable regression were conducted in SAS version 9.4. Results: Participants had positive perceptions about beliefs about linking data for precision medicine (M=4.05/5) and average concern about linking data (M=2.1/5). Final multivariable models revealed that higher levels of loneliness are associated with more positive beliefs about linking data (B=0.41, p=0.027). Races other than Black or African American (B=-0.64, p=0.009) and those with lower perceived limitations of precision medicine were less likely to be concerned about linking data (B=-0.75, p=-.0006). Conclusion and Relevance: Our results contribute to the lack of literature about perceptions of linking data for use in clinical and research studies among men. Better understanding of factors associated with more positive perceptions of data linkages could help improve how researchers recruit and engage participants. Citation Format: Melanie Jefferson, Gayenell Magwood, Cathy Melvin, Oluwole Babatunde, Chanita Hughes Halbert, Caitlin G. Allen. Understanding men’s concerns about linking health data in the context of precision medicine [abstract]. In: Proceedings of the AACR Virtual Conference: Thirteenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2020 Oct 2-4. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(12 Suppl):Abstract nr PO-074.