Is Public Health Ready for Complexity?

I want to thank FUSE  -- the Centre for Translational Research in Public Health -- for the opportunity to present a brief overview of the value of the complexity sciences for public health (and, in turn the value of public health for complexity science!!!) on 14 February 2019 at Newcastle University.

For those in attendance, here is a LINK TO THE PRESENTATION.  Also, more generally for readers, here are the main six points I made, which I hope others find useful in advancing the usage of the complexity sciences in public health.


Public health, presently, is at a difficult crossroads.  Its massive success in making the world a healthier place has led to a global embrace of its incredible insights; but still, the challenges currently faced have not given in so easily, as they are deeply entrenched complex problems -- or, alternatively, what are more generally referred to as wicked problems!  ~The global spread of infectious disease; an exponentially growing (or, alternatively, greying) population throughout many parts of the world; the negative impact ecological upset is having on climate and health; urbanization and the development of mega cities and metropolitan regions; the increasing costs of health and healthcare; air pollution; the opiod epidemic; and so forth.

Still, despite this increasing complexity, public health has been rather resistant to making the shift, falling back on tried-and-true ways of thinking about and modelling public health issues.  This is particularly true when it comes to the harsh realities of getting funded or published!!!!!  This needs to change!  The challenge, however, is how?

Here are, in my mind, six things that public health researchers and practitioners can do to make more efective usage of the complexity sciences and advance the usage of these ideas across the field:

Six ways to advance the study of complexity in public health

There are six key issues that public health needs to address to move forward regarding the issues of complexity:

  • 1. Public health is in a difficult position: it realizes its work is more complex, but it is struggling to embrace the tools and concepts of complexity science and computational modelling, as it means doing things differently. 
    • This is particularly problematic in terms of funding streams and publishing in journals.
    • The only way forward, then, is to get on with it and actually start funding and publishing such work.  High risk can lead to high reward! 
  • 2. Related, the best way forward is for public health to employ a mixed-methods approach, as most public health issues require more than one method, including computational modelling. 
    • This includes embracing the old and the new, particularly in terms of complex networks, machine intelligence, participatory systems mapping, qualitative comparative analysis (QCA), and agent-based modelling.
  • 3. Public health needs to adopt a critical approach to complexity, as not all methods or theories are equally useful.  In other words, the advance of complexity thinking in public health has to be more than the simple application of hard science methods. 
    • For example, while complex network analysis is powerful, it has significant limits.
  • 4. Public health also needs to develop its theoretical and conceptual understanding of public health topics as complex.   This is also true in terms of policy evaluation.
  • 5. Public health needs to recognise the important role it plays – both in terms of theory and practical experience – in the development of the complexity sciences, as most of these scholars are trained in other fields.   Practitioner expertise, combined with the latest advances in computational methods, will go a long way to improving health.  It cannot, however, just be one or the other. 
  • 6. Finally, public health needs to adopt a case-based approach to modelling its various complex topics, as health (be it an individual or population) is about cases. 
    • In turn, it needs to move away from the strict study of variables and variable-based statistics.
    •  Statistics remains very important for complexity modelling; but variables need to be attached to context and cases and their various path-dependent trajectories.
    • Related, the field needs to shift to modelling multiple case-based trajectories, rather than designing a single model. 

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