Biostatistics Seminar Series - J. Richard Landis, PhD, Qi Long, PhD and Ian Barnett, PhD

Title: The RURAL (Risk Underlying Rural Areas Longitudinal) Cohort Study: Opportunities for Advancing Analysis of Big Health DataAbstract: Although a large proportion of individuals living in rural Appalachia and the Mississippi Delta (AMD) experience strikingly high rates of heart and lung disease (hot spots), some other communities in the same region seem to be relatively spared (cold spots). To investigate this regional variation in disease risk and the potential basis of resilience of `cold spots', the RURAL (Risk Underlying Rural Areas Longitudinal) Cohort Study, recently funded by NHLBI, will recruit and study 4000 young-to-middle aged men and women from different race/ethnicity groups living in six high risk and four lower risk poor rural counties in four Southern states. This study will reach these communities using a mobile van, and measure levels of both protective and harmful influences in the people and their neighborhoods, hoping to understand what causes the high burden of heart and lung disease in these rural Southern regions and how to improve and prevent it. In this talk, we will provide an overview of the RURAL study including its goals and long-term vision and its study design, and discuss the key leadership roles of the Statistical and Data Coordinating Center for the RURAL study which is located at Penn. We will describe the multi-modal big health data that will be collected in this study including routine clinical data, neurocognitive tests and social determinant questionnaires, -omics data from collected biospecimens, imaging data, and mobile health (mHealth) data, and discuss analytical challenges and exciting opportunities for advancing methods for integrative analysis of big multi-modal health data. Finally, we will take a deeper dive into the design for the mHealth component including data collection and analysis, and discuss opportunities for developing novel methods for addressing challenges associated with analysis of mHealth data and potential insights that may be gained from such analysis.