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The integration of
social and biological mechanisms
for healthcare prediction and intervention
A follow up from:
The integration of social, behavioural and biological
mechanisms
in models of pathogenesis
Mike Kelly, Rachel Kelly, and Federica Russo
Aetiology and prediction:
the difference between
pathogenesis and prevention
Mike Kelly & Federica Russo
Overview
The pathogenic approach for communicable diseases
Causal models of disease and Predictive models of interventions
Non-communicable diseases
Why the pathogenic model does not work
The contribution of ‘the social’
The role of human behaviour in disease aetiology
Predictive models of intervention
Regress analysis and the means-end relation
3
THE PATHOGENIC MODEL
FOR COMMUNICABLE DISEASES
4
Causes and mechanisms
The conceptualisation of disease
The outcome of exposure to a pathogen or other noxious factor
Pathogens
Cause disease
Initiate complex mechanisms that lead to disease
Complications
Multiple pathogens at work
Factors that mediate interactions
Individuals experience multiple morbidities
etc
5
Intervening on the pathogens
T1: enough knowledge about good health state, biopathogenesis of
disease, risk of getting disease, etc
Action A: treatment of disease, alleviation, protection from risk
protecting people from microorganisms through isolation,
providing clean water,
removing sewage,
immunisation and improving nutritional status and housing conditions
T2: predict evolution of disease, prevention, etc.
Underlying conception:
Necessary and sufficient conditions
6
T1  A  T2
NON-COMMUNICABLE DISEASES
7
Why the pathogenic model
does not work
NCDs: non-infectious, non-transmittable among people
T1  A  T2 often fails
Actions:
reduce exposure to some environmental factors;
advice about physical activity, nutrition, smoking habits, â€Ķ
How much control do we have?
On environmental factors – to some extent
On human behaviour – much less
8
Asymmetry between
aetiology and prediction in NCDs
Aetiology
Biopathogenesis of CDs
Biological causes and
mechanisms
Behaviour does contribute to
risk in NCDs
Aetiology: bio-psycho-social
pathogenesis
Prediction
Public health interventions
T1AT2 model has been
largely successful
Intervention models did not
shift to a bio-psycho-social
approach
Or, if if it did, it happened very
late
9
THE CONTRIBUTION OF
‘THE SOCIAL’ TO AETIOLOGY
10
Sociology. And health.
Sociology attempts to explain and predict
human behaviour
Societies manifest observable patterns of change
Humans are thinking acting beings
Their thought and action take place within the constraints imposed by
social structures
What links behaviour and health?
11
Social causes are proximal
The proximal – distal distinction
Biological causes are proximal, social causes are distal
Distal causes do not exert direct influence on health
Hence, social causes are at best ‘classificatory devices’,
but not active causes in disease aetiology
Against the proximal – distal distinction
12
The ‘lifeworld’
Relationships with significant
others, neighbours, friends
Local services, shops
Communities and workplaces
The immediate physical and
microbiological
environments
Mediates exposure to toxins,
hazards, pathogens, etc
Drives health states of
individuals and populations
Is the product of the
interaction between human
agency and social structure
13
An integrated pathogenic approach
The ‘social’ and the ‘biological’ are integrated in the
aetiology
Behavioural factors are active parts of disease
mechanisms
An integrated pathogenic approach leads us
to rethink models of intervention
14
PREDICTIVE MODELS OF
INTERVENTION
15
Communicable diseases
Causal model of disease Predictive Model of intervention
Recovery /
improved health
status
Biological
mechanisms
Treatment /
public health
intervention
Exposure to
pathogens
Biological
mechanisms
Disease
Cause-effect
Means-end
16
Non-Communicable diseases
Causal model of disease Predictive model of intervention
Recovery /
improved health
status
Biological
mechanisms
Public health
intervention
Life world
Bio-psycho-
social
mechanisms
Disease
Multiple
bio-
psycho-
social
paths
17
Multipl
e
means-
end
relation
s
Some remarks
The ‘bio-psycho-social paths’ and ‘means-end relations’
complex networks
not linear causal relations
Models of interventions are conceptualised
in terms of means-end:
Identify the function of a psycho-social factor
Intervening on the function may lead to intervene on something
different than the corresponding cause
The function of psycho-social factor is highly context dependent
18
What is function?
In the context of a causal mechanism:
Functions are role-functions
The theoretical underpinnings of causal factors
They are part of the description of the functioning
of a component part of a mechanism
A strong conceptual link between functions and causes
19
Example:
alcohol consumption,
the lifeworld, and interventions
In the pathogenic approach
Alcohol consumption is a
‘single’, ‘homogeneous’
behaviour
Reduce exposure to the
pathogen ( = ethanol)
To reduce liver diseases,
cancer, obesity, accidents,
injury, violence
Actions: change in prices,
licensing regimes, education
campaign
21
T1  A  T2
Alcohol consumption is
a social structure
It varies across friends,
family, social groups,
populations, age
groups, etc
Alcohol consumption is
part of the lifeworld
of individual and of
groups
Targeted groups
Function of alcohol
consumption in their
lifeworld
Targeted interventions
22
In an integrated pathogenic approach
TO SUM UP
23
For communicable diseases
The pathogenic approach is largely
successful
Causal model of disease
Predictive model of intervention
For non-communicable diseases
The pathogenic approach is wanting
on both sides
Causal model of disease
Integrate bio-social mechanisms
Predictive model of intervention
Recast causal paths in terms of
means-end relation, according to
the functions of social factors in the
lifeworld
24

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Aetiology and prediction: the difference between pathogenesis and prevention

  • 1. The integration of social and biological mechanisms for healthcare prediction and intervention A follow up from: The integration of social, behavioural and biological mechanisms in models of pathogenesis Mike Kelly, Rachel Kelly, and Federica Russo
  • 2. Aetiology and prediction: the difference between pathogenesis and prevention Mike Kelly & Federica Russo
  • 3. Overview The pathogenic approach for communicable diseases Causal models of disease and Predictive models of interventions Non-communicable diseases Why the pathogenic model does not work The contribution of ‘the social’ The role of human behaviour in disease aetiology Predictive models of intervention Regress analysis and the means-end relation 3
  • 4. THE PATHOGENIC MODEL FOR COMMUNICABLE DISEASES 4
  • 5. Causes and mechanisms The conceptualisation of disease The outcome of exposure to a pathogen or other noxious factor Pathogens Cause disease Initiate complex mechanisms that lead to disease Complications Multiple pathogens at work Factors that mediate interactions Individuals experience multiple morbidities etc 5
  • 6. Intervening on the pathogens T1: enough knowledge about good health state, biopathogenesis of disease, risk of getting disease, etc Action A: treatment of disease, alleviation, protection from risk protecting people from microorganisms through isolation, providing clean water, removing sewage, immunisation and improving nutritional status and housing conditions T2: predict evolution of disease, prevention, etc. Underlying conception: Necessary and sufficient conditions 6 T1  A  T2
  • 8. Why the pathogenic model does not work NCDs: non-infectious, non-transmittable among people T1  A  T2 often fails Actions: reduce exposure to some environmental factors; advice about physical activity, nutrition, smoking habits, â€Ķ How much control do we have? On environmental factors – to some extent On human behaviour – much less 8
  • 9. Asymmetry between aetiology and prediction in NCDs Aetiology Biopathogenesis of CDs Biological causes and mechanisms Behaviour does contribute to risk in NCDs Aetiology: bio-psycho-social pathogenesis Prediction Public health interventions T1AT2 model has been largely successful Intervention models did not shift to a bio-psycho-social approach Or, if if it did, it happened very late 9
  • 10. THE CONTRIBUTION OF ‘THE SOCIAL’ TO AETIOLOGY 10
  • 11. Sociology. And health. Sociology attempts to explain and predict human behaviour Societies manifest observable patterns of change Humans are thinking acting beings Their thought and action take place within the constraints imposed by social structures What links behaviour and health? 11
  • 12. Social causes are proximal The proximal – distal distinction Biological causes are proximal, social causes are distal Distal causes do not exert direct influence on health Hence, social causes are at best ‘classificatory devices’, but not active causes in disease aetiology Against the proximal – distal distinction 12
  • 13. The ‘lifeworld’ Relationships with significant others, neighbours, friends Local services, shops Communities and workplaces The immediate physical and microbiological environments Mediates exposure to toxins, hazards, pathogens, etc Drives health states of individuals and populations Is the product of the interaction between human agency and social structure 13
  • 14. An integrated pathogenic approach The ‘social’ and the ‘biological’ are integrated in the aetiology Behavioural factors are active parts of disease mechanisms An integrated pathogenic approach leads us to rethink models of intervention 14
  • 16. Communicable diseases Causal model of disease Predictive Model of intervention Recovery / improved health status Biological mechanisms Treatment / public health intervention Exposure to pathogens Biological mechanisms Disease Cause-effect Means-end 16
  • 17. Non-Communicable diseases Causal model of disease Predictive model of intervention Recovery / improved health status Biological mechanisms Public health intervention Life world Bio-psycho- social mechanisms Disease Multiple bio- psycho- social paths 17 Multipl e means- end relation s
  • 18. Some remarks The ‘bio-psycho-social paths’ and ‘means-end relations’ complex networks not linear causal relations Models of interventions are conceptualised in terms of means-end: Identify the function of a psycho-social factor Intervening on the function may lead to intervene on something different than the corresponding cause The function of psycho-social factor is highly context dependent 18
  • 19. What is function? In the context of a causal mechanism: Functions are role-functions The theoretical underpinnings of causal factors They are part of the description of the functioning of a component part of a mechanism A strong conceptual link between functions and causes 19
  • 21. In the pathogenic approach Alcohol consumption is a ‘single’, ‘homogeneous’ behaviour Reduce exposure to the pathogen ( = ethanol) To reduce liver diseases, cancer, obesity, accidents, injury, violence Actions: change in prices, licensing regimes, education campaign 21 T1  A  T2
  • 22. Alcohol consumption is a social structure It varies across friends, family, social groups, populations, age groups, etc Alcohol consumption is part of the lifeworld of individual and of groups Targeted groups Function of alcohol consumption in their lifeworld Targeted interventions 22 In an integrated pathogenic approach
  • 24. For communicable diseases The pathogenic approach is largely successful Causal model of disease Predictive model of intervention For non-communicable diseases The pathogenic approach is wanting on both sides Causal model of disease Integrate bio-social mechanisms Predictive model of intervention Recast causal paths in terms of means-end relation, according to the functions of social factors in the lifeworld 24

Editor's Notes

  1. Note about change of title – better represent our ideas, which is work in progress Follow up paper with Mike and with Rachel Kelly on integration of biological and social mechs of disease. Will see during the presentation in what ways it builds on that. Work in progress about different models for aetiology and prediction
  2. Increased complexity in aetiology, but not quite in prediction