David G Schlundt PhD Associate Professor of Psychology CRC Research Skills January 20 2011 Overview NIH party line on translation research Problems with the party line Reductionism in modern science ID: 383593
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Slide1
Reductionism and Complex Systems Science: Implications for Translation Research in the Health and Behavioral Sciences
David G.
Schlundt
, Ph.D.
Associate Professor of Psychology
CRC Research Skills
January 20, 2011Slide2
Overview
NIH party line on translation research
Problems with the party line
Reductionism in modern science
Problems with reductionism
Complex systems science as an alternative
Problems with complex systems science
Examining the obesity epidemic as a real-life exemplar
Integrating scientific approaches
Implications for basic and applied research on obesitySlide3
What is Translation Research?
Problem:
basic research findings take years or decades to find their way into evidence-based practice
Problem:
Landmark clinical trials take years or decades to find their way into evidence-based practice
Problem:
The investment in basic research has not resulted in a corresponding improvement in health care delivery
Goal:
Translate the discoveries of basic scientific research into population level gains in healthSlide4
NIH Road Map
New Pathways to Discovery
- unravel the complexity of biologic systems and their regulation
Research Teams of the Future
– break down the barriers to interdisciplinary and
transdisciplinary
research
Re-engineering the Clinical Research Enterprise
– bring more scientists into clinical research
Solution:
Clinical Science Translation Awards (CTSA) – infrastructure to support clinical and translation research at academic institutionsSlide5
The T’s of translation
T1 – from bench to bedside
Taking basic biological sciences and using them to create useful diagnostic tests, drugs, and therapies
T2 – from bedside to community
Moving clinical research findings into evidence-based practice and looking at the impact on the public’s health
These definitions:
Were created by the basic scientists who run the NIH research enterprise
Imagine a one-way flow of knowledge from basic research to improved health care
Over simplify what is a complicated problem (how to improve human health)Slide6
Problems with the T1-T2 vision
The amount of resources at the NIH continues to be disproportionately allocated for basic research
The basic scientists in charge have underestimated the difficulty and amount of time required to plan and execute translation research studies
The clinical relevance of basic research findings is overestimated
Translation research proposals are too often reviewed by basic scientists who review translation studies using their basic research framework
Much greater improvement in population health could be achieved by improving current health care delivery – based standards of care that are not implemented
Much greater improvement in population health could be achieved through health care reformSlide7
Meta Scientific Models
There are assumptions and frameworks behind the practice of science that drive the questions, the methodologies, and the development of new knowledge
Philosophical Reductionism
Offshoot of materialist philosophy
Idea that one science (biology) can be reduced to the principals of another science (chemistry)
Drive to find the most basic explanation
There is potentially a single, underlying physical science that explains everything
Methodological Reductionism
The best scientific explanations come from breaking problems into their most fundamental elements
Goal of science is to identify, isolate, and study basic causal mechanisms
Approach is to create experiments in which only one parameter is allowed to vary so that its causal effect can be isolated
Goal is to develop mechanistic explanationsSlide8
Reductionism in Action
Much “basic” research follows a reductionist framework in biological and behavioral sciences
Reductionism
Leads to increasing specialization
Leads to problems being broken down into ever smaller problems
Leads to a rapidly expanding base of knowledge in which the pieces are largely disconnected from each other
Leads to new technologies and methodologies for achieving tighter and tighter control
of ever smaller processes
Even when the rationale for the research is an important clinical problem (e.g., diabetes, depression, schizophrenia), the research itself ends up isolating only a small piece of the problem and studying it out of context Slide9
Reductionism Impedes Clinical Discoveries
Reductionism is not the most efficient way to improve the physical and mental health of populations of human beings
Most “breakthroughs” in basic health and neuroscience do not lead to new diagnostic or treatment approaches
The overspecialization of disciplines makes it difficult for any one scientist to pull together enough basic knowledge to create meaningful new diagnostics or interventions
Funding of basic science does not encourage interdisciplinary or
transdisciplinary
cooperation needed to create clinical applicationsSlide10
Unintended consequences of reductionism
In reductionism, causality moves one way from low order phenomenon to higher order phenomenon
Ignores the possibility of complex higher order systems exerting a causal influence on more basic lower order systems
Biogenetic determinism moves explanation of social and behavioral problems to the genes
Individual rather than social conditions or economic inequities is responsible for problems
However, the individual is not responsible, the genes are responsible
Many modern individuals have a sense of helplessness due to a naive
reductionism (obesity and depression good examples)
Much effort is put towards finding new drugs that will solve
social/interpersonal/emotional/economic/political
problemsSlide11
Alternatives to Reductionism
Holism –
systems cannot be understood by taking them apart
Emergent Properties
– as components associate into systems, new properties of the systems emerge which cannot be predicted from the properties of the components (e.g.,
hydrogen + oxygen
water)
Complex systems science
– systems form hierarchies of increasing complexity and exhibit adaptive behavior at each level of
analysis
Homeostasis
Feedback loops
Cross-level linkagesSlide12
http://necsi.org/projects/mclemens/cs_char.gifSlide13
Problems with Complex systems
Goals of science are the same (understanding, prediction, and control) but the methods are different
Requires different frameworks and methodologies which are not as well developed as experimental reductionism
Mathematical simulations
Complex statistical modeling
Nonlinear models
Multilevel
models
Evaluation of real-world interventions
It becomes difficult to make reassuring cause and effect
statements;
Scientists are
forced
to live with
uncertainty.
It becomes difficult to create unambiguous mechanistic explanationsSlide14
Example: Obesity Epidemic
The United States and other developed countries are experiencing an epidemic of obesity
Why is this happening?
What can be done to reverse the trends?
Problem is so serious that life expectancies may begin to decline by the middle of the 21
st
centurySlide15
Obesity Trends* Among U.S. Adults
BRFSS, 1985
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14%Slide16
Obesity Trends* Among U.S. Adults
BRFSS, 1986
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14%Slide17
Obesity Trends* Among U.S. Adults
BRFSS, 1987
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14%Slide18
Obesity Trends* Among U.S. Adults
BRFSS, 1988
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14%Slide19
Obesity Trends* Among U.S. Adults
BRFSS, 1989
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14%Slide20
Obesity Trends* Among U.S. Adults
BRFSS, 1990
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14%Slide21
Obesity Trends* Among U.S. Adults
BRFSS, 1991
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% Slide22
Obesity Trends* Among U.S. Adults
BRFSS, 1992
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% Slide23
Obesity Trends* Among U.S. Adults
BRFSS, 1993
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% Slide24
Obesity Trends* Among U.S. Adults
BRFSS, 1994
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% Slide25
Obesity Trends* Among U.S. Adults
BRFSS, 1995
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% Slide26
Obesity Trends* Among U.S. Adults
BRFSS, 1996
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% Slide27
Obesity Trends* Among U.S. Adults
BRFSS, 1997
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19%
≥20%Slide28
Obesity Trends* Among U.S. Adults
BRFSS, 1998
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19%
≥20%Slide29
Obesity Trends* Among U.S. Adults
BRFSS, 1999
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19%
≥20%Slide30
Obesity Trends* Among U.S. Adults
BRFSS, 2000
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19%
≥20%Slide31
Obesity Trends* Among U.S. Adults
BRFSS, 2001
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24%
≥25%Slide32
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
Obesity Trends* Among U.S. Adults
BRFSS, 2002
No Data <10% 10%–14% 15%–19% 20%–24%
≥25%Slide33
Obesity Trends* Among U.S. Adults
BRFSS, 2003
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24%
≥25%Slide34
Obesity Trends* Among U.S. Adults
BRFSS, 2004
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24%
≥25%Slide35
Obesity Trends* Among U.S. Adults
BRFSS, 2005
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
Slide36
Obesity Trends* Among U.S. Adults
BRFSS, 2006
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
Slide37
Obesity Trends* Among U.S. Adults
BRFSS, 2007
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
Slide38
Obesity Trends* Among U.S. Adults
BRFSS, 2008
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
Slide39
Obesity Trends* Among U.S. Adults
BRFSS, 2009
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
Slide40
Obesity Trends* Among U.S. Adults
BRFSS, 2010
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
Slide41
How can we explain this?
What are some possible explanations?
Is there a single cause we need to be looking for?
If there are multiple causes, how do we study them?
Are the causes additive or synergistic?
Do the causes cascade across levels of analysis (e.g., macroeconomic factors influencing individual behaviors)?
Does our framework (reductionism versus complex systems science) make a difference in how we approach these problems?Slide42Slide43
Reflections
The question is not which approach is the best approach, but which is the best for solving a specific problem
Reductionism does not automatically lead to translation research
Complex systems science may have much more translation potential
Complex systems science requires interdisciplinary research, different methodological approaches, and the abandonment of simple one-cause explanationsSlide44
What characterizes “translation” research?
Addresses problems in clinical care and population health
Evidence-based (based on best science available)
Involves transfer of knowledge and or methods across disciplinary boundaries
Requires consideration of context (target is imbedded in real-world
systems)
Coalitions
and partnerships
E
ngagement
of
communities
Moves away from trying to find a single causal factor and towardsSlide45Slide46
Familiar example of complex systems approach to improve chronic disease managementSlide47
Challenges
Personalized medicine?
Matching drugs to genes
How about matching treatment to other systems that are influencing health
Family
Neighborhood
Work setting
Psychology (cognition and emotion)
Health services research?
Are there gains to be had from adopting complex systems framework?
Need viable alternatives to the clinical trial
Implementation science?
Can methods such as continuous quality improvement become scientific tools for answering questions about improving clinical care and population health
What other methods can be adapted?