The age of big data is here: The world has created more data in the past two years than in the entire previous history of the human race. USC Leonard Davis School of Gerontology researchers are dissecting treasure troves of information—from sources as diverse as brain scans and the human genome—to fuel groundbreaking research on improving how we age, and to reshape gerontology education to enable future scientists to make an impact in a changing field.
Gerontology is ready to take on the newest tools—in part because the field has always involved big data sets, says Mireille Jacobson, a microeconomist and associate professor of gerontology at the USC Leonard Davis School. For example, her work has relied on large population data sets—and in a way, that hasn’t changed, she says. “It’s mostly that more and more data is available.”
Jacobson works with data from Medicare and other publicly available databases to understand how health insurance affects the well-being of older people. For example, an analysis of Medicare data found that receiving Medicare benefits can help reduce financial stress in people over 65. She also researches health care providers and how they make care decisions in response to various outside factors, including new screening recommendations and drug shortages.
“The effort to digitize and make everything available electronically is a new thing,” she adds.
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Jacobson is part of a group of gerontology researchers at the USC Leonard Davis School who are diving into vast sets of data in order to better understand aging and the lifespan. Their work has important implications for training students and for creating better datasets, which can help researchers better understand individual risk factors, identify the role of genes in disease and develop more precise interventions.
Moving Across Disciplines
Em Arpawong, research assistant professor of gerontology and director of the Gerontology Bioinformatics Core, looks to bring together diverse information to better understand how genetic and environmental components interact to result in different health outcomes in older adults. Her current work integrates the use of both genomewide and twin and family modeling approaches from large datasets representing hundreds of thousands of individuals over many decades, such as the U.S. Health and Retirement Study and the Project Talent Aging Study, both of which span decades of follow-up with tens of thousands of participants.
Source: Tech Xplore