Announcing ‘The Philosophy of Public Health’ by Benjamin Smart

It is a delight to share the publication of The Philosophy of Public Health by CPEMPH co-Director, Professor Benjamin Smart of the University of Johannesburg. This is an important and timely book which exemplifies the best of applied philosophical thinking: it identifies deep conceptual problems that arise in real-world contexts, and uses rigorous philosophical tools to reach conclusions that can guide public health practice.

At its core, the book develops a powerful account of health as a property of complex systems. Rather than treating health as a feature of isolated organs or discrete individuals, Ben argues that health is an emergent, capacities-dependent property instantiated at multiple biological and social levels: cells, organs, organisms, and—crucially—populations. This move allows him to dissolve familiar puzzles about “population health” and to provide a framework that aligns far more closely with what public health professionals actually confront.

A second major contribution concerns the goal of public health. Ben rejects the simplistic idea that public health should merely raise aggregated individual health scores, noting that such metrics neglect inequality, autonomy, and the broader social determinants of health. Instead, he argues that public health should aim to increase the capacities that matter for individuals’ ability to realise the goods of life—capacities that range from access to clean water and functioning healthcare systems, to education, mobility, and the structural conditions required for dignified living.

The book also provides a philosophically grounded defence of Evidence-Based Public Health that is sensitive to context, values, and the limitations of traditional hierarchies of evidence. Ben engages seriously with recent failures in global pandemic response, arguing for a more nuanced and context-aware understanding of what it means to “follow the science”.

In the final chapters, he turns to ethics and the question of decolonising public health, offering a principled but pragmatic framework for navigating public health decision-making across profoundly unequal societies. Throughout, the book is shaped by his decade of experience living and working in South Africa, but its arguments travel far beyond this context.

The result is a work that will influence both philosophers and practitioners. It is a rare example of philosophy that is simultaneously conceptually rigorous, policy-relevant, and deeply humane. I could not be more pleased to see it in print, and I recommend it warmly to anyone working in public health, philosophy of medicine, or the conceptual foundations of health policy.

Congratulations, Ben. 

Paper: ‘Complexity in Epidemiology and Public Health: Addressing complex health problems through a mix of epidemiologic methods and data’

Delighted to share the online-first publication of this paper in Epidemiology with a number of wonderful co-authors, led by Naja Hulvej Rod. Abstract below.

Complexity in Epidemiology and Public Health: Addressing complex health problems through a mix of epidemiologic methods and data

Public health and the underlying disease processes are complex, often involving the interaction of biologic, social, psychological, economic, and other processes that may be non-linear and adaptive and have other features of complex systems. There is therefore a need to push the boundaries of public health beyond single-factor data analysis and expand the capacity of research methodology to tackle real-world complexities. This paper sets out a way to operationalize complex systems thinking in public health, with particular focus on how epidemiologic methods and data can contribute towards this end. Our proposed framework comprises three core dimensions–patterns, mechanisms, and dynamics–along which complex systems may be conceptualized. These dimensions cover seven key features of complex systems–emergence, interactions, non-linearity, interference, feedback loops, adaptation, and evolution. We relate this framework to examples of methods and data traditionally used in epidemiology. We conclude that systematic production of knowledge on complex health issues may benefit from: formulation of research questions and programs in terms of the core dimensions we identify, as a comprehensive way to capture crucial features of complex systems; integration of traditional epidemiologic methods with systems methodology such as computational simulation modeling; interdisciplinary work; and continued investment in a wide range of data types. We believe that the proposed framework can support systematic production of knowledge on complex health problems, with the use of epidemiology and other disciplines. This will help us understand emergent health phenomena, identify vulnerable population groups, and detect leverage points for promoting public health.

IFK Panel 27 May: Data and Delusion after Covid 19 – Shakir Mohammed (Google Deepmind), Chris Harley (UJ Engineering), Olaf Dammann (Tufts Public Health and Community Medicine) https://universityofjohannesburg.us/4ir/covid-19-webinar-3/ #epitwitter @mediauj

Please join us for a panel discussion on Data and delusion after Covid 19, Wednesday 27 May @ 1pm South Africa, W Europe |  12 noon UK | 7am US East Coast | 7pm Beijing China. Please “arrive” (log in) 15 minutes beforehand to ensure time for you to be admitted prior to the event as we admit participants individually for security reasons. We start sharp on the hour. To join you first need to register.

Panelists:

  • Dr. Shakir Mohammed is a Senior Researcher at Google DeepMind in London, United Kingdom (UK).
  • Professor Charis Harley is an academic based in the Faculty of Engineering and the Built Environment at the University of Johannesburg (UJ), South Africa.
  • Professor Olaf Dammann is Vice-Chair of Public Health at Tufts University in Boston, United States (US), Professor of Perinatal Neuroepidemiology at Hannover Medical School, Germany, and Adjunct Professor in the Department of Neuromedicine and Movement Science at the University of Science and Technology in Trondheim, Norway.

Facilitated by Professor Alex Broadbent, Director of the Institute for the Future of Knowledge at the University of Johannesburg

Please register if you wish to watch this live. A recording will also be posted afterwards.

This is the third in a series of webinars on Reimagining the World After COVID-19, organised by the Institute for the Future of Knowledge in collaboration with the UJ Library and Information Centre on the initiative of the Vice Chancellor’s Office at the University of Johannesburg.

Data and delusion after COVID-19

An epidemic has a single centre from which disease spreads: an epicenter. A pandemic is what happens when the disease no longer spreads from a single centre but circulates and spreads throughout the population. The COVID-19 pandemic has been accompanied by a pandemic of data. Data is offered, analysed, re-packaged and criticized by mighty international organisations and by tiny local outfits. Even private individuals with no prior expertise or interest in data, disease, or statistics spend hours poring over graphs and critiquing case fatality estimates.

Yet this proliferation of data and analysis has not yielded effective predictions. Instead, it has demonstrated how ill-equipped we are to deal with this new, non-hierarchical, distributed information context. Leading scientists have proved dramatically wrong. Or perhaps not – it depends who you ask. The unfolding pattern of spread still surprises us at every turn – except those who predicted it all along. Nothing is more common than the common cold, and coronavirus variants are one of its causes: yet we seem unable make reliable predictions about COVID-19.

This webinar explores a range of issues relating to data and trust in science in the aftermath of COVID-19. What went wrong with the modelling approach to prediction – if, indeed, anything did go wrong? How should policy and scientific research interact, and how should policy makers make use of data? Can people without domain-specific knowledge use data to predict better than the experts in that domain? If not, then can data analysts themselves make predictions merely by studying patterns in data? Turning to the generation of data, how does the individual interest in privacy weight against the public interest in private information, notably location, which can be very useful in the context of a pandemic?

Our improved data processing abilities did not help us as much as we might have imagined in this situation. Machine learning, in particular, thrives on spotting complex patterns in noisy datasets, and doing it fast; yet is has been conspicuously absent from the efforts to predict the course of this pandemic.

Register here

 

 

COVID on the Breadline

The Institute for the Future of Knowledge at the University of Johannesburg has partnered with Picturing Health to make a short documentary depicting the impact of severe lockdown measures on those living in poverty in the developing world.

COVID on the Breadline from PICTURING HEALTH on Vimeo.

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