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Integrating Evidence

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Book cover Causation in Population Health Informatics and Data Science

Abstract

In this concluding chapter we describe our view how different kinds of information are integrated in order to arrive at causal explanation in population health science. In particular, such information comes from individuals and populations (target), from epidemiology and the bench sciences (method), and from observation and experiment (manipulation). We discuss recent “systems” approaches in biology, medicine, and epidemiology in the section on “method” and the question what it is about “manipulation” that convinces many of its capability to support causal inference. In the final section, we look at complexity versus simplicity and suggest that the simplification inherent in epidemiological evidence generation may be an asset, not a liability.

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Notes

  1. 1.

    We prefer using the term evidence integration to the more common knowledge integration and information integration. Knowledge integration has been defined as “the process of synthesizing multiple knowledge models (or representations) into a common model (representation). Compared to information integration, […], knowledge integration focuses more on synthesizing the understanding of a given subject from different perspectives.” (Wikipedia, accessed 6/19/2012). In keeping with our DIEK-model (see Sect. 6.3), this chapter is about evidence, not so much about information or knowledge.

  2. 2.

    https://gradepro.org/.

  3. 3.

    available at http://gdt.guidelinedevelopment.org/app/handbook/handbook.html.

  4. 4.

    http://www.who.int/hiv/pub/mtct/antiretroviral2010/en/.

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Authors and Affiliations

  1. Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA, USA

    Olaf Dammann

  2. The African Centre for Epistemology and Philosophy of Science, University of Johannesburg, Johannesburg, South Africa

    Benjamin Smart

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Dammann, O., Smart, B. (2019). Integrating Evidence. In: Causation in Population Health Informatics and Data Science. Springer, Cham. https://doi.org/10.1007/978-3-319-96307-5_7

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