You can improve public health! by modeling it as complex Brian - - PDF document

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Brian Castellani, Ph.D. Professor of Medical Sociology, Kent State University Adjunct Professor of Psychiatry, Northeast Ohio Medical University

You can improve public health! by modeling it as complex

Brian Castellani, Ph.D. Professor of Medical Sociology, Kent State University Adjunct Professor of Psychiatry, Northeast Ohio Medical University

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I would like to thank AcademyHealth and the Robert Wood Johnson Foundation for the opportunity to be one of their 2016 systems science scholarship recipients, which afforded me the

• pportunity to present my work at the 2016 ARM.

Brian Castellani, Ph.D. Professor of Medical Sociology, Kent State University Adjunct Professor of Psychiatry, Northeast Ohio Medical University

Public Health in the 20th Century

SOURCE: http://www.cdc.gov/about/history/tengpha.htm

It is a story of tremendous success

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• To continue this story of success into the 21st

century, however, we need to recognize that things have changed.

• The world in which we live has grown

significantly more complex.

Public Health in the 21st Century – The 21st century is different in both quality and quantity. – Reasons:

• globalization
• cyberinfrastructure and big data
• global population overload
• global warming and climate change
• Increasing ecological challenges – access to water, food, etc.
• cultural conflict and terrorism
• global disease transmission – fast moving epidemics, pandemics
• Importance of health behaviors and access to health care
• And, significant shifts in the human microbiota and microbiome

– As a note, the human microbiota consists of the trillions of symbiotic microbial cells harbored by each person, primarily bacteria in the gut and colon; in turn, the human microbiome consists of the genes these cells harbor.

Public Health in the 21st Century

http://www‐personal.umich.edu/~mejn/cartograms/

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In response, we need to embrace complexity. We need to model the world in complex systems terms

Public Health in the 21st Century Public Health in the 21st Century

SOURCE: http://blogs.scientificamerican.com/the‐curious‐wavefunction/stephen‐hawkings‐advice‐for‐twenty‐first‐century‐grads‐embrace‐complexity/

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What Is Complexity?

• By complex I mean that

– a disease or public health issue is . . . » emergent, self‐organizing, nonlinear, chaotic, etc. – But, more important, I mean it is . . . 1. situated within different systems and factors, as such, it is causally complex. 2. evolves differently across time‐space. 3. resulting in different major and minor trends, different sub‐ types and sub‐trajectories. 4. requires different clinical and community level approaches to treatment 5. demands different methods, specifically those grounded in the computational and complexity sciences.

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Educate Your Immune System

how our bodies are confused by this 21st‐century world

• EXAMPLES OF COMPLEXITY ALL AROUND US!
• New York Times (3 June 2016) article by Velasquez‐Manoff,
• Explores research into autoimmune disease, specifically the prevalence of

type 1 diabetes and celiac disease. – Over the past three decades, the prevalence of type 1 diabetes and celiac disease have increased much faster than expected. – Why? – It seems, this increased in many ways, to be linked to the human microbiota/microbiome and society‐wide shifts in human microbial communities.

Educate Your Immune System

how our bodies are confused by this 21st‐century world

– Dr. Ramnik Xavier and colleagues found that, in comparison to a cohort

• f Russian Karelia kids who were genetically at risk for diabetes, an
• therwise similar cohort of Finnish kids were significantly more likely to

develop an autoimmune disease. – Again, the question was “Why?” – The hygiene hypothesis – As MOISES VELASQUEZ‐MANOFF explains

• “In order to develop properly, the hypothesis holds — to avoid the hyper‐

reactive tendencies that underlie autoimmune and allergic disease — the immune system needs a certain type of stimulation early in life. It needs an education.”

• “The Russian kids evidently received this education courtesy of their distinct
• microbiomes. The Finnish seemingly did not.”

http://www.nytimes.com/2016/06/05/opinion/sunday/educate‐your‐immune‐system.html?_r=0

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Educate Your Immune System

how our bodies are confused by this 21st‐century world

– And, what factors account for these differences in education? – First, different complex systems – Second, these systems are situated in different temporal‐spatial situations evolving across time.

• Overall, it appears that Russian Karelia resembles Finland before

WWII, with such things as:

– Higher rates of fecal‐oral infections – i.e., hepatitis A – Untreated well water – A richer and more diverse community of microbes – kids encountering key microbes earlier in life

Educate Your Immune System

how our bodies are confused by this 21st‐century world

– Third, these differences lead to different trends.

• For example, as is more generally know, certain infections (such as

enteroviruses) bring about autoimmune diseases like Type 1 diabetes.

• As such, in a study such Xavier and colleagues one would expect the

Russian kids to have a higher rate of Type 1 diabetes.

• But, they didn’t.
• As VELASQUEZ‐MANOFF explains,

– “It seems that toughening the immune system early in life alters how we respond to hits later, making those viral infections less likely to provoke autoimmunity. – “Another is that the kind of microbiome you have when the virus arrives determines how you respond. – And yet another is that when you first encounter viral infections determines how dangerous they are. If they arrive when infants are protected by their mothers’ antibodies, as they probably do in Russian Karelia, no problem. But if they arrive after that protection has waned, they can push you toward autoimmunity.”

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Educate Your Immune System

how our bodies are confused by this 21st‐century world

–Fourth, these differences lead to different clinical interventions.

• For Xavier and colleagues different environments lead

to different forms of treatment. For example, if there was a way to ‘bottle’ parts of the environment in which the Russian children are raised, it could be shared with the Finnish kids.

• In turn, aspects of the Finnish environment are

needed for the Russian kids, as the Finnish kids (autoimmune disease aside) tend to live, overall, a much healthier and longer life.

–Finally, it demonstrates different methods.

• When you look at the biographies of Xavier and

colleagues you see they have wholeheartedly embraced the idea of public health as complex and therefore the need for new methods grounded in the computational and complexity sciences.

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So, what makes these methods so different?

• First, we can talk about what they are not

– Computational and complexity science methods are . . . 1. Not focused on variables. 2. Not focused on simple aggregate averages. 3. Not reductionist 4. Not static 5. Not focused on one‐size‐fits‐all clinical and community level interventions or services.

On 13 of the 19 measures – roughly 68% ‐‐ things were worse or the same.

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So, what makes these methods so different?

• Second, we can talk about what they are:

– From genetic algorithms and agent‐based modeling to network analysis and geospatial modeling . . . – These methods all contain a combination of the old and new

• The new is the usage of computation, machine intelligence

and high‐powered computing.

• The old is a focus on cases! The core of medicine, public

health and health services research – Be it movie preferences on NetFlix, item interests on Amazon, or credit card purchasing patterns, the focus in all of these methods is on mapping cases and their differences.

BACKGROUND: Building on the case‐comparative methods of Charles Ragin and, more recently, the case‐based complexity theory of David Byrne a new set of methodological techniques and arguments have emerged for the study of complex systems, called case‐based complexity science and case‐based modeling‐‐‐for more information, click here to see Byrne and Ragin’s 2009, Sage Handbook of Case‐Based Method.

“According to case‐based complexity, cases are complex profiles comprised of a set of inter‐dependent variables, which are contextually dependent, nonlinear, dynamic, evolving, self‐organizing, emergent, etc. in short, cases have the same characteristics as a complex

• system. Theoretically speaking, then, cases can be treated and modeled as complex system...”

CASE‐BASED COMPLEXITY SCIENCE: Scholarly activity that seeks to actively integrate case‐based methods with complexity science for the purpose of modeling complex systems as cases. PREMISE OF CASE‐BASED COMPLEXITY SCIENCE: Cases are the methodological equivalent of complex systems; or, alternatively, complex systems are cases and therefore can be studied as such. SACS TOOLKIT: A new, case‐based computationally‐grounded mixed‐methods platform for modeling complex systems.

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Case‐Based Complexity Modeling

First, we can model a disease in complex systems terms

We can identify multiple major and minor trends and their corresponding sets of trajectories; particularly when high dynamic!

Understanding Comorbid Depression/ Physical Health Trajectories: A Longitudinal Study of Primary Care

Brian Castellani,a Rajeev Rajaram,a Jane Gunn,c Frances Griffithsf

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What is also key is that we never lose the complexity of our data, as this topographical neural net map

• shows. Furthermore, our results

are intuitively understandable We can also identify key macroscopic dynamics and patterns across all of the trends, such as periodic

• rbits, saddle points, etc.

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We can also data‐mine these differing trends to identify key clinical differences – which leads to better collaborative, personalized medicine Finally, we can simulate the movement

• f the cases and trends in our study

using agent‐based modeling, case‐based density modeling, etc.

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https://video.kent.edu/media/Summit_Sim_NETLOGO/1_xzkri5ju https://video.kent.edu/media/Summit_Sim_NETLOGO/1_xzkri5ju

• But, that is not the end of it. There is another reason

why you need to be the catalyst for this change. – As Duncan Watts (the famous network physicist) pointed out in his Annual Review of Sociology (2004) article on network science, while most public health and social scientists are not well trained in the computational and complexity sciences; in turn, most natural and computational scientists are poor social scientists, with little to no knowledge of social theory.

Public Health in the 21st Century

6/16/2016 17 The Big Data Failure of Google Flue Trends