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%




No Data <10% 10%–14%




No Data <10% 10%–14%




No Data <10% 10%–14%




No Data <10% 10%–14%




No Data <10% 10%–14%




No Data <10% 10%–14% 15%–19%




No Data <10% 10%–14% 15%–19%




No Data <10% 10%–14% 15%–19%




No Data <10% 10%–14% 15%–19%




No Data <10% 10%–14% 15%–19%




No Data <10% 10%–14% 15%–19%




No Data <10% 10%–14% 15%–19% ≥20%




No Data <10% 10%–14% 15%–19% ≥20%




No Data <10% 10%–14% 15%–19% ≥20%




No Data <10% 10%–14% 15%–19% ≥20%




No Data <10% 10%–14% 15%–19% 20%–24% ≥25%




No Data <10% 10%–14% 15%–19% 20%–24% ≥25%




No Data <10% 10%–14% 15%–19% 20%–24% ≥25%




No Data <10% 10%–14% 15%–19% 20%–24% ≥25%




No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%




No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%




No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%




No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%




No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%




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?

David G. Schlundt, Ph.D. Associate Professor of Psychology CRC Research Skills January 20, 2011.

Documents

basic health

basic research framework

translation research

applied research

basic knowledge

basic scientists

clinical research findings

clinical research enterprise