Is big data reworking public health’s “Prison of the Proximate”?

• In Event: Big Health Data and Algorithmic Authority

Abstract

Public health researchers’, practitioners’ and policy-makers’ interest in big data has grown rapidly In the decade since Wired magazine proclaimed “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete” (Anderson, 2008). Whether explicitly or implicitly, public health mobilises specific theories or concepts of the population (ranging from approaches that forge the “prison of the proximate” via their focus on individual risk factors, through to concepts of “the population” as situated in social, political and environmental contexts). These differences matter, as they inform more or less just and more or less effective responses to health inequities at the population level.

Some public health actors anticipate a future in which “precision public health” will dispatch with the fraught problem of what constitutes a population, embracing promises about “the end of theory”. Others contest it, e.g. they point to the methodological and theoretical expertise informing the development of functional algorithms, and often cast big data as a new frontier into which public health expertise can expand. This paper interrogates experimentation with big data for disease prevention to ask whether the turn to big data is dismantling or fortifying public health’s prison of the proximate, or introducing new ways of thinking collectivity and the population for public health.

Author

• Niamh Stephenson, UNSW Sydney