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Social Justice, Equality and Primary Care: (How) Can ‘Big Data’ Help?

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Abstract

A growing body of research emphasises the role of ‘social determinants of health’ in generating inequalities in health outcomes. How, if at all, should primary care providers respond? In this paper, I want to shed light on this issue by focusing on the role that ‘big data’ might play in allowing primary care providers to respond to the social determinants that affect individual patients’ health. The general idea has been proposed and endorsed by the Institute of Medicine, and the idea has been developed in more detail by Bazemore et al. (2016). In Bazemore et al.’s proposal, patients’ addresses are used to generate information about the patients’ neighbourhood; this information is then included in patients’ health care records and made available to providers. This allows primary care providers to take this information into account when interacting with, and providing care to, patients. I explore three issues arising from this proposal. First, while questions of privacy have been central to discussions about big data, Bazemore et al.’s proposal also allows us to see that there might be costs to not making certain information available. Second, I consider some of the questions arising for primary care from the influence of social factors on health outcomes: given that we know these factors to be significant contributors to social inequalities in health, what precisely should be done about this in the primary care context? Finally, I address problems arising from the use of population level data when dealing with individuals.

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Notes

  1. See, for example, Murdoch and Detsky (2013).

  2. For some discussion of these issues, see Comer et al. (2011) and Stevens (2014).

  3. I treat the two papers cited here as developing the same proposal because there is significant overlap between the groups of authors of the two papers: all the authors of Hughes et al. (2016) are also authors on Bazemore et al. (2016); an additional five authors contributed to Bazemore et al. (2016). Bazemore et al. (2016), which is more detailed than Hughes et al. (2016), is the primary source of information for this paper.

  4. For example, because in certain ways of generating data (such as social media), disadvantaged populations are likely to be underrepresented (Hargittai 2015).

  5. http://www.who.int/social_determinants/thecommission/finalreport/key_concepts/en/

  6. See Braveman et al. (2011) for a review of the available evidence.

  7. While some definitions of the social determinants of health include the health care system as one such determinant (e.g. World Health Organization 2008: 8), other accounts regard the social determinants as factors that fall outside the health care system (e.g. Sreenivasan 2014).

  8. See also Starfield et al. (2012) for more general discussion. While my focus in this paper is on industrialised countries, concerns about possible increases in health inequality arising from the introduction of universal health care have also been raised in the context of low-income countries (e.g. Gwatkin and Ergo 2011).

  9. These issues are discussed further by Hurst (2009) and Bærøe and Bingedal (2011).

  10. See Nuffield Council on Bioethics (2015) for discussion.

  11. The Institute of Medicine also notes that information about social factors may be sensitive to patients. They suggest that providers may therefore want to treat this information differently from other kinds of information but also caution that this approach might perpetuate stigma surrounding particular social factors (Institute of Medicine 2015b: 332).

  12. An interesting example of these kinds of mechanisms is provided by Carla Shedd’s (2015) study of students from different Chicago schools, whose assessment of racial injustice varied depending on how much they moved between neighbourhoods with different levels of affluence and diversity: for Black students who did not leave their neighbourhoods to attend school in a more affluent area, frequent negative encounters with police were ‘normal’ whereas those students who attended schools in more affluent neighbourhoods became aware of differences in police presence and behaviour between different areas.

  13. In fact, ensuring usefulness of the recommendations providers give to their patients is part of what the Institute of Medicine envisages (Institute of Medicine 2015b).

  14. See also Sniderman (2015) for discussion of this concern.

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Acknowledgements

This paper was written while I was a Senior Researcher at the Ethox Centre, University of Oxford. An earlier version of this paper was presented at the Brocher Foundation workshop, ‘Ethical Risk Assessment in Biomedical Big Data’, March 2016; I am grateful to the audience for their comments and suggestions. I would also like to thank two anonymous reviewers of this journal for their input.

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  1. Institute for Health and Social Policy & Department of Philosophy, McGill University, 1130 Pine Ave W, Montreal, QC, H3A 1A3, Canada

    Kristin Voigt

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Voigt, K. Social Justice, Equality and Primary Care: (How) Can ‘Big Data’ Help?. Philos. Technol. 32, 57–68 (2019). https://doi.org/10.1007/s13347-017-0270-6

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