David G. Schlundt, Ph.D. Associate Professor of Psychology CRC Research Skills January 20, 2011.
-
Upload
davin-prestwood -
Category
Documents
-
view
215 -
download
0
Transcript of David G. Schlundt, Ph.D. Associate Professor of Psychology CRC Research Skills January 20, 2011.
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, 2011
OverviewNIH party line on translation researchProblems with the party lineReductionism in modern scienceProblems with reductionismComplex systems science as an alternativeProblems with complex systems scienceExamining the obesity epidemic as a real-life exemplarIntegrating scientific approachesImplications for basic and applied research on obesity
What is Translation Research?
Problem: basic research findings take years or decades to find their way into evidence-based practiceProblem: Landmark clinical trials take years or decades to find their way into evidence-based practiceProblem: The investment in basic research has not resulted in a corresponding improvement in health care deliveryGoal: Translate the discoveries of basic scientific research into population level gains in health
NIH Road MapNew Pathways to Discovery - unravel the complexity of biologic systems and their regulationResearch Teams of the Future – break down the barriers to interdisciplinary and transdisciplinary researchRe-engineering the Clinical Research Enterprise – bring more scientists into clinical researchSolution: Clinical Science Translation Awards (CTSA) – infrastructure to support clinical and translation research at academic institutions
The T’s of translationT1 – 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 healthThese 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)
Problems with the T1-T2 visionThe amount of resources at the NIH continues to be disproportionately allocated for basic researchThe basic scientists in charge have underestimated the difficulty and amount of time required to plan and execute translation research studiesThe clinical relevance of basic research findings is overestimatedTranslation research proposals are too often reviewed by basic scientists who review translation studies using their basic research frameworkMuch greater improvement in population health could be achieved by improving current health care delivery – based standards of care that are not implementedMuch greater improvement in population health could be achieved through health care reform
Meta Scientific ModelsThere are assumptions and frameworks behind the practice of science that drive the questions, the methodologies, and the development of new knowledgePhilosophical 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
everythingMethodological 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 explanations
Reductionism in ActionMuch “basic” research follows a reductionist framework in biological and behavioral sciencesReductionism
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 processesEven 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
Reductionism Impedes Clinical Discoveries
Reductionism is not the most efficient way to improve the physical and mental health of populations of human beingsMost “breakthroughs” in basic health and neuroscience do not lead to new diagnostic or treatment approachesThe overspecialization of disciplines makes it difficult for any one scientist to pull together enough basic knowledge to create meaningful new diagnostics or interventionsFunding of basic science does not encourage interdisciplinary or transdisciplinary cooperation needed to create clinical applications
Unintended consequences of reductionism
In reductionism, causality moves one way from low order phenomenon to higher order phenomenonIgnores the possibility of complex higher order systems exerting a causal influence on more basic lower order systemsBiogenetic 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 problems
Alternatives to Reductionism
Holism – systems cannot be understood by taking them apartEmergent 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 linkages
http://necsi.org/projects/mclemens/cs_char.gif
Problems with Complex systems
Goals of science are the same (understanding, prediction, and control) but the methods are differentRequires 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 explanations
Example: Obesity Epidemic
The United States and other developed countries are experiencing an epidemic of obesityWhy 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 21st century
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1985
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1986
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1987
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1988
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1989
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1990
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1991
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1992
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1993
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1994
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1995
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1996
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1997
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% ≥20%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1998
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% ≥20%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1999
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% ≥20%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 2000
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% ≥20%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 2001
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% ≥25%
Source: Behavioral Risk Factor Surveillance System, CDC.
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
Obesity Trends* Among U.S. AdultsBRFSS, 2002
No Data <10% 10%–14% 15%–19% 20%–24% ≥25%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 2003
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% ≥25%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 2004
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% ≥25%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 2005
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 2006
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 2007
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 2008
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 2009
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 2010
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
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?
BiobehavioralSelf
Causal Nexus
Past Present Future
Antecedents Consequences
Reductionist ScienceCom
plex Systems Science
Cascade of Causal Influences
Cascade of Causal Influences
Self
Environment
ReflectionsThe question is not which approach is the best approach, but which is the best for solving a specific problemReductionism does not automatically lead to translation researchComplex systems science may have much more translation potentialComplex systems science requires interdisciplinary research, different methodological approaches, and the abandonment of simple one-cause explanations
What characterizes “translation” research?
Addresses problems in clinical care and population healthEvidence-based (based on best science available)Involves transfer of knowledge and or methods across disciplinary boundariesRequires consideration of context (target is imbedded in real-world systems)Coalitions and partnershipsEngagement of communitiesMoves away from trying to find a single causal factor and towards
Familiar example of complex systems approach to improve chronic disease management
ChallengesPersonalized 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?