Your SlideShare is downloading. ×
Russo Variation Epidemiology
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Russo Variation Epidemiology

721

Published on

Published in: Education, Health & Medicine
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
721
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
24
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. Variational Causal Claims in Epidemiology Federica Russo Philosophy, Louvain & Kent
  • 2. Overview
    • Causality
      • Definition; Evidence; Rationale
    • Context and motivation
      • Regularity claims in epidemiology?
    • Variational claims in epidemiology
      • Goals
      • Definitions
      • Methodology
  • 3. Causality: definition
    • What is causality/cause/causal factor?
      • By means of its properties
      • By means of its function
    • Russo&Williamson 2007
      • in the health sciences, interpret causality
      • as the agent’s ultimate belief
      • (epistemic theory)
  • 4. Causality: evidence
    • What supports a causal claim?
      • Probabilities
      • Mechanisms
      • Regular behaviour
      • Lawlikeness
    • Russo&Williamson 2007
      • in the health sciences, support causal claims
      • by probabilistic and mechanistic evidence
  • 5. Causality: rationale
    • What notion guides causal reasoning?
      • Rationale:
      • principle or notion underlying some opinion,
      • action, hypothesis, model, and the like.
      • Rationale of causality:
      • the notion that guides causal reasoning
      • (e.g., model building and model testing)
    • Russo 2008
      • in the social sciences, causal models are regimented by a rationale of variation
  • 6. Variational claims in epidemiology goals definitions methodology
  • 7. The context
    • Epidemiology:
      • studies the distributions of disease
      • in and across populations
      • seeks to identify the causes
      • that determine those distributions
    • Philosophical and causal issues:
      • Explicit causal stance; cognitive/action-
      • oriented goal; relation to public health;
      • levels of causation; ‘ecological’ views;
      • web of causation; …
  • 8. Motivation
    • Does epidemiology aim to establish
    • regularity claims?
      • Regularity views (rings a bell?)
      • “ a cause is an object followed by another,
      • and where all objects similar to the first are
      • followed by objects similar to the second”
    • A double reading
      • Metaphysical: causality is regularity
      • Epistemological: we infer causality because
      • we observe regularities
  • 9. Goals of epidemiology
    • To study the variability of disease
    • due to variability of exposure
    • Reference to regular behaviour is absent
    • Epidemiologists hunt for variations ,
    • not for regularities.
    • Variation guides causal reasoning
  • 10.
    • Jewell 2004, Statistics for epidemiology
      • In this book we describe the collection of data that speak to relationships between the occurrence of disease and various descriptive characteristics in individuals in a population. Specifically, we want to understand whether and how differences in individuals might explain patterns of disease distribution across a population.
  • 11.
    • Susser 1973
    • Causal thinking in the health sciences
      • Epidemiologists in search for causes want to make asymmetrical statements that have direction. They seek to establish that an independent variable X causes changes in the dependent variable Y and not the reverse.
      • The central problem of cohort studies is to cope with the change that occurs with the passage of time. The study of cause involves the detection of change in a dependent variable by change in an independent variable .
  • 12.
    • Lilienfeld and Stolley 1994
    • Foundations of epidemiology
      • A relationship is considered causal whenever evidence indicates that the factors form part of the complex circumstances which increase the probability of occurrence of disease and that a diminution of one or more of these factors decreases the frequency of disease.
  • 13.
    • Bohopol (1999)
    • Am.J. Public Health 89(9)
      • Certain beliefs—that epidemiology is about the study of health and disease in populations, that there is a population group variation in disease that is worth of scientific study, and that such variation is important to public health policy and practice—were common to virtually all textbooks.
  • 14. Definitions of cause in epidemiology
    • Varieties of accounts, no consensus.
    • Yet, slight preference for probabilistic definition
    • Often, definitions of causal factor
    • rather than causality
    • Invariably, causal factors
    • ‘ make things change’
    • rather than ‘occur regularly’
  • 15.
    • Lagiou et al (2005)
    • Eu.J. Epidemiology 20
      • A factor is a cause of a certain disease when alterations in the frequency or intensity of this factor , without concomitant alterations in any other factors, are followed by changes in the frequency of occurrence of the disease , after the passage of a certain time period.
  • 16.
    • Susser (1973)
    • Causal thinking in the health sciences
      • A determinant [of health] can be any factor, whether an event, characteristic, or other definable entity so long as it brings about change for better or for worse in a health condition.
  • 17.
    • Karhausen (2000)
    • Medicine, Health Care and Philosophy 3
      • Being a cause is a special characterisation of some special state of affairs characterised by change , i.e. an event, fact, a state, or a deed: in medicine and epidemiology a cause makes a disease happen or not happen.
  • 18. Definitions of cause in epidemiology
    • Bad definitions ?
    • Better taking them as characterisations ?
    • Is there something
    • beside/ prior to regularity?
      • Yes – variation . It conceptually comes first and grasps a necessary aspect of causation. Regularity doesn’t.
    • Is there any role left to regularity?
      • Yes – as a constraint
  • 19. Methodology
    • The claim:
      • Methods in epidemiology measure and test
      • variations to establish causal claims
        • Observational studies
        • (cohort, case-control,
        • prospective, retrospective)
        • Odds and risks
        • Regression models
  • 20. Why is variation really relevant for methodology?
    • The argument
    • Methodology should mirror
    • one’s epistemology and metaphysics
      • Why? Take both into account and you’ll have
      • a better grip on causation
    • Methodology ‘operationalise’
    • one’s epistemology and metaphysics
      • What do we have to observe?
        • (meaningful) variations
      • What do we try to establish?
        • Which variations are causal, by imposing constraints
        • (invariance/stability, regularity, …)
  • 21. To sum up and conclude
    • In the philosophy of causality
      • Distinguish:
      • (1) definition (2) evidence (3) rationale
    • In epidemiology
      • Variation sheds light on
      • epistemological and metaphysical issues:
        • Variation guides causal reasoning
        • Variation grasps a necessary feature of causality
  • 22. Selected references
    • Bohopol R. (1999), “Paradigms in epidemiology textbooks: in the footsteps of Thomas Khun”, Am. J. Public Health, 89(8), 1162-1165.
    • Jewell N.P. (2004), Statistics for epidemiology , Chapman&Hall/CRC.
    • Karhausen L.R. (2000), “Causation: the elusive grail of epidemiology”, Medicine, Health Care and Philosophy , 3, 59-67.
    • Lagiou P., Adami H-O., Trichopoulos D. (2005), “Causality in cancer epidemiology”, Eu. J. Epidemiology , 20, 565-574.
    • Lilienfeld D.E. & Stolley P.D. (1994), Foundations of epidemiology , Oxford University Press, 3 rd edition.
    • Russo F. (2008). Causality and causal modelling in the social sciences. Measuring variations . Springer. In press.
    • Russo F. & Williamson J. (2007), “Interpreting causality in the health sciences”, ISPS , 21(2), 157-170.
    • Susser M. (1973), Causal thinking in the health sciences. Concepts and strategies of epidemiology , Oxford University Press.

×