Abstract IA51: Federally supported tools and resources to support data integration efforts for health disparities research

Abstract

Abstract The etiology of many cancer-related health disparities that continue to persist is oftentimes complex, interactive, and multifactorial. Because of this complexity and to understand these causes, it behooves cancer researchers to utilize data that represent different types and sources of these determinants measured at multiple levels of abstraction, from biology to policy. By incorporating these different data sources into their studies, researchers can answer novel (and better) cancer control research questions that cannot be answered with any one data set. To do these types of integrative analyses, however, researchers will most likely need to merge or link independent data, and this may be difficult or impossible if the data are too heterogeneous in regard to the instruments or measures used to represent variables in the data. To increase the ability to do data integration, it typically requires harmonized data that contain common data elements of the important constructs of interest. In this presentation, Dr. Moser will highlight tools and resources, especially those supported by the federal government, that can be used for data integration to conduct health disparities research. These tools include ontologies, standardized measures, collaborative sites, and data repositories that promote the use of common data elements to create or analyze harmonized data. This presentation will also mention National Institutes of Health (NIH) funding announcements that could be used to support data integration efforts and health disparities research. Citation Format: Richard P. Moser. Federally supported tools and resources to support data integration efforts for health disparities research [abstract]. In: Proceedings of the Eleventh AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2018 Nov 2-5; New Orleans, LA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl):Abstract nr IA51.