title: Big Data and Disease Prevention: From Quantified Self to Quantified Communities.
abstract: Big data is often discussed in the context of improving medical care, but it also has a less appreciated but equally important role to play in preventing disease. Big data can facilitate action on the modifiable risk factors that contribute to a large fraction of the chronic disease burden, such as physical activity, diet, tobacco use, and exposure to pollution. It can do so by facilitating the discovery of risk factors for disease at population, subpopulation, and individual levels, and by improving the effectiveness of interventions to help people achieve healthier behaviors in healthier environments. In this article, we describe new sources of big data in population health, explore their applications, and present two case studies illustrating how big data can be leveraged for prevention. We also discuss the many implementation obstacles that must be overcome before this vision can become a reality.
authors.affiliation: 1 Center for Health and Community, University of California-San Francisco , San Francisco, California.|2 School of Public Health, Berkeley, University of California-Berkeley , Berkeley, California.
authors.forename: Meredith A
authors.initials: MA
authors.lastname: Barrett
authors.affiliation: 1 Center for Health and Community, University of California-San Francisco , San Francisco, California.|2 School of Public Health, Berkeley, University of California-Berkeley , Berkeley, California.
authors.forename: Olivier
authors.initials: O
authors.lastname: Humblet
authors.affiliation: 3 Department of Epidemiology and Biostatistics, University of California-San Francisco , San Francisco, California.
authors.forename: Robert A
authors.initials: RA
authors.lastname: Hiatt
authors.affiliation: 1 Center for Health and Community, University of California-San Francisco , San Francisco, California.
authors.forename: Nancy E
authors.initials: NE
authors.lastname: Adler