Commentary

Abstract

Big Data has increasingly been promoted as a revolutionary development in the future of science, including epidemiology. However, the definition and implications of Big Data for epidemiology remain unclear. We here provide a working definition of Big Data predicated on the so-called ‘3 Vs’: variety, volume, and velocity. From this definition, we argue that Big Data has evolutionary and revolutionary implications for identifying and intervening on the determinants of population health. We suggest that as more sources of diverse data become publicly available, the ability to combine and refine these data to yield valid answers to epidemiologic questions will be invaluable. We conclude that, while epidemiology as practiced today will continue to be practiced in the Big Data future, a component of our field’s future value lies in integrating subject matter knowledge with increased technical savvy. Our training programs and our visions for future public health interventions should reflect this future.

Pharmacological therapy and blood pressure control in primary health care sites in China: data from 254,848 hypertensive patients

“… 25 Second, this study involved all qualified patients of the four areas; this big-data-based method avoided selective bias and guaranteed authenticity in representative areas and may predict the future of epidemiology. 26 – 28 Third, this study is of follow-up and can truly reflect compliance with treatment and BP control within 1 year. Lastly, the information of drugs in this study – usually difficult to obtain accurately in classical epidemiological fields – was more reliable due to extracting from a real-time record by general practitioners and monitoring by the Center for Disease Control and Prevention.…”

Does case misclassification threaten the validity of studies investigating the relationship between neck manipulation and vertebral artery dissection stroke? Yes

“…Yet larger study populations are most practically assembled through the use of secondary data, or data collected primarily for purposes other than research (ie claims data). As a general principle, with larger quantities of data come challenges in assuring their validity and integrity [9, 10], while biases due to measurement error are independent of the volume of data [11]. Routinely collected electronic data offer an efficient, low-cost approach for identifying large numbers of cases but there is a greater risk of error as compared to the more taxing strategy of verifying disease status for each individual.…”

Toward Greater Implementation of the Exposome Research Paradigm within Environmental Epidemiology

“…This unique feature of the exposome needs to be considered in all phases of research and will stimulate the development of analytic methods that are sensitive to these features. To develop and implement these novel approaches, current and upcoming epidemiologists will require training in complementary disciplines such as computer science, bioinformatics, and advanced biostatistics (27). …”

“…However, these methods must continue to account for confounding factors and provide interpretable estimates of disease risk that can be used within risk assessment and health communication. Similarly, we will require better data management tools and increased available storage for the products of exposome-related research (27). …”