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


Published on

Published in: Education, Health & Medicine
  • Be the first to comment

  • Be the first to like this

No Downloads
Total Views
On Slideshare
From Embeds
Number of Embeds
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

No notes for slide


  • 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.