Understanding Health: Theoretical challenges and

possible approaches

September 25, 2006

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

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)

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.

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.”

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.

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 2006;35:562

The many-to-many relation, with common pathway

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

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)

Structured randomness (Bagatelle)

Random, but with environmental influences, and different probabilities of high scores

What may a complexity approach look like? (1) Waddington’s 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

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)

Complexity perspective (3)Branch track diagrams

(Personalitydetermines shift to

different set ofresponse options)

Decides to join fitness club

Chooses not to join

Overweight patient

Distressed by perceived implication of being fat;resentment reinforces

sedentary lifestyle.Triumph of idleness

Grudgingly starts walking program

(Further decisionnodes)