Understanding Health: Theoretical challenges and possible approaches

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Understanding Health Theoretical challenges and
possible approaches

• September 25, 2006

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Evolving perspectives on poverty-health link

• C19. Miasma-style multiple interacting factors
  but no clear mode of action
• c. 1920-30. Agent-host-environment triad poor
  environments constrain host resistance limit
  behaviours (nutrition, hygiene, etc.)
• c.1950-1960. Patterns of causes interacting
  chains of events (Morris, 1964)
• c.1960-1985. Risk factor approach (e.g., MRFIT)
  focused interventions for specific diseases
  reverse engineering etiology

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Critiques (1)

• Epidemiology has produced a Hotch-potch of
  multivariate associations between diseases and
  lifestyle risk factors (Tannahill, 1992)
• There are almost no necessary (or sufficient)
  causes.
• Chains of events a simplification multiple,
  interacting sequences occur together. Field or
  systems theory may be helpful (Morris, 1964).
• Susser (1973) agent and host are in continuing
  interaction with an enveloping environment
• The multiple cause black box paradigm of the
  current risk factor era in epidemiology is
  growing less serviceable (Susser, 1973)

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Critiques (2)

• Pearce (1996) Epidemiology has become a set of
  generic methods for measuring associations of
  exposure and disease, rather than functioning as
  part of a multidisciplinary approach to
  understanding the causation of disease in
  populations. We seem to be using more and more
  advanced technology to study more and more
  trivial issues, while the major population causes
  of disease are ignored.
• Inherent vagueness of the risk factor concept.

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Critiques (3)

• Hennekens Buring (1987) the use of
  multivariate analysis can appear like a black
  box strategy in which all of the variables are
  entered () and the net result is a single value
  representing the magnitude of the association
  between the exposure and the disease after the
  effects of all confounders have been taken into
  account.

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Evolving perspectives (2)

• 1990s. Bringing the context back in Chinese box
  epidemiology (Susser Susser, 1996). Concentric
  circle models. Multilevel, but interacting
  processes analytical approach not clear.
• 1995 onwards lifestyles lifecourse.
  Brings time dimension back in.
• 2000 onwards. Multilevel analyses hierarchical
  modeling. Confounding factors studied in their
  own right. Critique of reductionism.
• Opening up the black box molecular genetic epi.

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Critiques Weiss Buchanan

• Statistical methods unsuited to detecting
  many-to-many relationships, each with small
  effects
• Individual cases often multifactorial (or
  multiple paths from single cause to disease)
• Diseases given same name may be distinct
• Many alleles can cause single disease selection
  acts on phenotypes, not genotypes.
• Scientific method can be fallible false
  falsifications can reject acceptable hypotheses.
  For example, when a disease comes to be defined
  by its cause, the causal hypothesis is no longer
  falsifiable
• True probabilistic causation vitiates
  replicability falsifiability

e.g., Dissecting complex disease. Int J
Epidemiol 200635562
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The many-to-many relation, with common pathway
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Critiques (4)

• Multilevel analyses retain the basic linear
  regression models and mechanistic notions of
  causation
• It moves beyond focus on adding up figures on
  individual risks, but has not re-thought
  explanation has not accommodated complexity
• Relationships between variables are not
  necessarily static but evolve through experience
  and over time
• Non-linear interactions not covered well
• Not clear whether equivalent analyses should be
  applied at individual and collective levels

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Possible directions

• Reconsider the meaning of chance random error
  in regressions
• Structured chance (Bagatelle metaphor)
• Bring the individual back in formally include
  susceptibility. Models include
• Epigenetic landscapes (Beattie, 2005, from
  Waddington, 1940). Models concurrent interacting
  influences of genes environment
• Or probabilistic neural networks (PNNs)

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Structured randomness (Bagatelle)
Random, but with environmental influences, and
different probabilities of high scores
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What may a complexity approach look like? (1)
Waddingtons Epigenetic Landscape (1957)
The ball rolls downward, but may take many
different routes, each of which then sub-divides
again. While features of the landscape will
influence which route it takes, the landscape
itself changes over time, with erosion and as a
result of the balls rolling down. Waddington
also drew the undersideof the diagram,
representing the surface of the hill as
evolving, pulled by numerous strings, each
attached toa gene, so the landscape in which
weinteract is influenced by nature andby
nurture.
http//www.usc.edu/hsc/dental/odg/jaskoll01.htm
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Complexity perspective (2) Probabilistic Neural
Network
Inputs are processed throughmultiple, hidden
(cf. black box)nodes that have multiple
links. The prediction of the outcomederives
mainly from the patternof interconnections
betweennodes, not from the complexityof each.
The effect of each variable can change
accordingto the status of others in the system
(which was what we sawwith smoking and
occupation inthe Whitehall study)
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Complexity perspective (3)Branch track diagrams
(Further decisionnodes)
Decides to join fitness club
Grudgingly starts walking program
Overweight patient
Chooses not to join
(Personalitydetermines shift to different set
ofresponse options)
Distressed by perceived implication of being
fatresentment reinforcessedentary
lifestyle.Triumph of idleness