Beyond Public Health Genomics: Can Big Data and Predictive Analytics Deliver Precision Public Health?

Abstract

The field of public health genomics has matured in the past two decades and is beginning to deliver genomic-based interventions for health and health care. In the past few years, the terms precision medicine and precision public health have been used to include information from multiple fields measuring biomarkers as well as environmental and other variables to provide tailored interventions. In the context of public health, “precision” implies delivering the right intervention to the right population at the right time, with the goal of improving health for all. In addition to genomics, precision public health can be driven by “big data” as identified by volume, variety, and variability in biomedical, sociodemographic, environmental, geographic, and other information. Most current big data applications in health are in elucidating pathobiology and tailored drug discovery. We explore how big data and predictive analytics can contribute to precision public health by improving public health surveillance and assessment, and efforts to promote uptake of evidence-based interventions, by including more extensive information related to place, person, and time. We use selected examples drawn from child health, cardiovascular disease, and cancer to illustrate the promises of precision public health, as well as current methodologic and analytic challenges to big data to fulfill these promises.