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

机译：大数据与健康中的社会不平等问题研究：期望与问题

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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 and the particularity of Big Data in health, which must be broader than a vision centerd solely on care, the individual and his or her biological characteristics; (ii) the need for clarification regarding the notion of data, the validity of data and the question of causal inference for various actors involved in health, such data as researchers, health professionals and the civilian population; (iii) 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 dialog, integrating methodological, societal, and ethical issues. This question cannot escape an interdisciplinary approach, including users or patients.
机译：理解健康中的社会梯度构建是社会流行病学领域的一项重大挑战，这是流行病学的一个分支，旨在了解社会及其不同组织形式如何在人口水平上影响健康。试图回答这些问题涉及到各种异构数据的大数据集，这表明大数据方法可能与健康社会不平等的研究特别相关。尽管如此，为了充分利用健康方面的大数据，造福所有人，必须应对真正的挑战。该观点的主要目的是讨论其中一些挑战，尤其是：（i）大数据在健康方面的范围和特殊性，其范围应比仅以护理，个人及其生物学为中心的愿景更为广阔特性（ii）需要澄清数据的概念，数据的有效性以及涉及健康的各种行为者（例如研究人员，卫生专业人员和平民人口）的因果推理问题; （iii）为了公共利益和打击健康方面的社会不平等，公共当局需要对数据及其使用进行监管和控制。面对这些问题，将不同的方法整合到一个紧密的对话中，整合方法论，社会和道德问题似乎至关重要。这个问题无法避免跨学科的方法，包括使用者或患者。

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