Big Data and the Study of Social Inequalities in Health: Expectations and Issues

  • Delpierre C
  • Kelly-Irving M
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Understanding the construction of the social gradient in health is a major challenge in the field of social epidemiology, a branch of epidemiology that seeks to understand how society and its different forms of organization influence health at a population level. Attempting to answer these questions involves large datasets of varied heterogeneous data suggesting that Big Data approaches could be then particularly relevant to the study of social inequalities in health. Nevertheless, real challenges have to be addressed in order to make the best use of the development of Big Data in health for the benefit of all. The main purpose of this perspective is to discuss some of these challenges, in particular: i) the perimeter of Big Data in health, which must be broader than a vision centred solely on care, the individual and his or her biological characteristics; ii) a the lack of training of the various actors, health professionals and the civilian population, in order to better understand the notion of data and algorithms; iii) a the need for regulation and control of data and their uses by public authorities for the common good and the fight against social inequalities in health. To face these issues, it seems essential to integrate different approaches into a close dialogue, integrating methodological, societal and ethical issues. This question cannot escape an interdisciplinary approach, including users or patients.




Delpierre, C., & Kelly-Irving, M. (2018). Big Data and the Study of Social Inequalities in Health: Expectations and Issues. Frontiers in Public Health, 6.

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