Russo Variation Epidemiology


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Russo Variation Epidemiology

  1. 1. Variational Causal Claims in Epidemiology Federica Russo Philosophy, Louvain & Kent
  2. 2. Overview <ul><li>Causality </li></ul><ul><ul><li>Definition; Evidence; Rationale </li></ul></ul><ul><li>Context and motivation </li></ul><ul><ul><li>Regularity claims in epidemiology? </li></ul></ul><ul><li>Variational claims in epidemiology </li></ul><ul><ul><li>Goals </li></ul></ul><ul><ul><li>Definitions </li></ul></ul><ul><ul><li>Methodology </li></ul></ul>
  3. 3. Causality: definition <ul><li>What is causality/cause/causal factor? </li></ul><ul><ul><li>By means of its properties </li></ul></ul><ul><ul><li>By means of its function </li></ul></ul><ul><li>Russo&Williamson 2007 </li></ul><ul><ul><li>in the health sciences, interpret causality </li></ul></ul><ul><ul><li>as the agent’s ultimate belief </li></ul></ul><ul><ul><li>(epistemic theory) </li></ul></ul>
  4. 4. Causality: evidence <ul><li>What supports a causal claim? </li></ul><ul><ul><li>Probabilities </li></ul></ul><ul><ul><li>Mechanisms </li></ul></ul><ul><ul><li>Regular behaviour </li></ul></ul><ul><ul><li>Lawlikeness </li></ul></ul><ul><ul><li>… </li></ul></ul><ul><li>Russo&Williamson 2007 </li></ul><ul><ul><li>in the health sciences, support causal claims </li></ul></ul><ul><ul><li>by probabilistic and mechanistic evidence </li></ul></ul>
  5. 5. Causality: rationale <ul><li>What notion guides causal reasoning? </li></ul><ul><ul><li>Rationale: </li></ul></ul><ul><ul><li>principle or notion underlying some opinion, </li></ul></ul><ul><ul><li>action, hypothesis, model, and the like. </li></ul></ul><ul><ul><li>Rationale of causality: </li></ul></ul><ul><ul><li>the notion that guides causal reasoning </li></ul></ul><ul><ul><li>(e.g., model building and model testing) </li></ul></ul><ul><li>Russo 2008 </li></ul><ul><ul><li>in the social sciences, causal models are regimented by a rationale of variation </li></ul></ul>
  6. 6. Variational claims in epidemiology goals definitions methodology
  7. 7. The context <ul><li>Epidemiology: </li></ul><ul><ul><li>studies the distributions of disease </li></ul></ul><ul><ul><li>in and across populations </li></ul></ul><ul><ul><li>seeks to identify the causes </li></ul></ul><ul><ul><li>that determine those distributions </li></ul></ul><ul><li>Philosophical and causal issues: </li></ul><ul><ul><li>Explicit causal stance; cognitive/action- </li></ul></ul><ul><ul><li>oriented goal; relation to public health; </li></ul></ul><ul><ul><li>levels of causation; ‘ecological’ views; </li></ul></ul><ul><ul><li>web of causation; … </li></ul></ul>
  8. 8. Motivation <ul><li>Does epidemiology aim to establish </li></ul><ul><li>regularity claims? </li></ul><ul><ul><li>Regularity views (rings a bell?) </li></ul></ul><ul><ul><li>“ a cause is an object followed by another, </li></ul></ul><ul><ul><li>and where all objects similar to the first are </li></ul></ul><ul><ul><li>followed by objects similar to the second” </li></ul></ul><ul><li>A double reading </li></ul><ul><ul><li>Metaphysical: causality is regularity </li></ul></ul><ul><ul><li>Epistemological: we infer causality because </li></ul></ul><ul><ul><li>we observe regularities </li></ul></ul>
  9. 9. Goals of epidemiology <ul><li>To study the variability of disease </li></ul><ul><li>due to variability of exposure </li></ul><ul><li>Reference to regular behaviour is absent </li></ul><ul><li>Epidemiologists hunt for variations , </li></ul><ul><li>not for regularities. </li></ul><ul><li>Variation guides causal reasoning </li></ul>
  10. 10. <ul><li>Jewell 2004, Statistics for epidemiology </li></ul><ul><ul><li>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. </li></ul></ul>
  11. 11. <ul><li>Susser 1973 </li></ul><ul><li>Causal thinking in the health sciences </li></ul><ul><ul><li>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. </li></ul></ul><ul><ul><li>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 . </li></ul></ul>
  12. 12. <ul><li>Lilienfeld and Stolley 1994 </li></ul><ul><li>Foundations of epidemiology </li></ul><ul><ul><li>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. </li></ul></ul>
  13. 13. <ul><li>Bohopol (1999) </li></ul><ul><li>Am.J. Public Health 89(9) </li></ul><ul><ul><li>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. </li></ul></ul>
  14. 14. Definitions of cause in epidemiology <ul><li>Varieties of accounts, no consensus. </li></ul><ul><li>Yet, slight preference for probabilistic definition </li></ul><ul><li>Often, definitions of causal factor </li></ul><ul><li>rather than causality </li></ul><ul><li>Invariably, causal factors </li></ul><ul><li>‘ make things change’ </li></ul><ul><li>rather than ‘occur regularly’ </li></ul>
  15. 15. <ul><li>Lagiou et al (2005) </li></ul><ul><li>Eu.J. Epidemiology 20 </li></ul><ul><ul><li>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. </li></ul></ul>
  16. 16. <ul><li>Susser (1973) </li></ul><ul><li>Causal thinking in the health sciences </li></ul><ul><ul><li>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. </li></ul></ul>
  17. 17. <ul><li>Karhausen (2000) </li></ul><ul><li>Medicine, Health Care and Philosophy 3 </li></ul><ul><ul><li>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. </li></ul></ul>
  18. 18. Definitions of cause in epidemiology <ul><li>Bad definitions ? </li></ul><ul><li>Better taking them as characterisations ? </li></ul><ul><li>Is there something </li></ul><ul><li>beside/ prior to regularity? </li></ul><ul><ul><li>Yes – variation . It conceptually comes first and grasps a necessary aspect of causation. Regularity doesn’t. </li></ul></ul><ul><li>Is there any role left to regularity? </li></ul><ul><ul><li>Yes – as a constraint </li></ul></ul>
  19. 19. Methodology <ul><li>The claim: </li></ul><ul><ul><li>Methods in epidemiology measure and test </li></ul></ul><ul><ul><li>variations to establish causal claims </li></ul></ul><ul><ul><ul><li>Observational studies </li></ul></ul></ul><ul><ul><ul><li>(cohort, case-control, </li></ul></ul></ul><ul><ul><ul><li>prospective, retrospective) </li></ul></ul></ul><ul><ul><ul><li>Odds and risks </li></ul></ul></ul><ul><ul><ul><li>Regression models </li></ul></ul></ul>
  20. 20. Why is variation really relevant for methodology? <ul><li>The argument </li></ul><ul><li>Methodology should mirror </li></ul><ul><li>one’s epistemology and metaphysics </li></ul><ul><ul><li>Why? Take both into account and you’ll have </li></ul></ul><ul><ul><li>a better grip on causation </li></ul></ul><ul><li>Methodology ‘operationalise’ </li></ul><ul><li>one’s epistemology and metaphysics </li></ul><ul><ul><li>What do we have to observe? </li></ul></ul><ul><ul><ul><li>(meaningful) variations </li></ul></ul></ul><ul><ul><li>What do we try to establish? </li></ul></ul><ul><ul><ul><li>Which variations are causal, by imposing constraints </li></ul></ul></ul><ul><ul><ul><li>(invariance/stability, regularity, …) </li></ul></ul></ul>
  21. 21. To sum up and conclude <ul><li>In the philosophy of causality </li></ul><ul><ul><li>Distinguish: </li></ul></ul><ul><ul><li>(1) definition (2) evidence (3) rationale </li></ul></ul><ul><li>In epidemiology </li></ul><ul><ul><li>Variation sheds light on </li></ul></ul><ul><ul><li>epistemological and metaphysical issues: </li></ul></ul><ul><ul><ul><li>Variation guides causal reasoning </li></ul></ul></ul><ul><ul><ul><li>Variation grasps a necessary feature of causality </li></ul></ul></ul>
  22. 22. Selected references <ul><li>Bohopol R. (1999), “Paradigms in epidemiology textbooks: in the footsteps of Thomas Khun”, Am. J. Public Health, 89(8), 1162-1165. </li></ul><ul><li>Jewell N.P. (2004), Statistics for epidemiology , Chapman&Hall/CRC. </li></ul><ul><li>Karhausen L.R. (2000), “Causation: the elusive grail of epidemiology”, Medicine, Health Care and Philosophy , 3, 59-67. </li></ul><ul><li>Lagiou P., Adami H-O., Trichopoulos D. (2005), “Causality in cancer epidemiology”, Eu. J. Epidemiology , 20, 565-574. </li></ul><ul><li>Lilienfeld D.E. & Stolley P.D. (1994), Foundations of epidemiology , Oxford University Press, 3 rd edition. </li></ul><ul><li>Russo F. (2008). Causality and causal modelling in the social sciences. Measuring variations . Springer. In press. </li></ul><ul><li>Russo F. & Williamson J. (2007), “Interpreting causality in the health sciences”, ISPS , 21(2), 157-170. </li></ul><ul><li>Susser M. (1973), Causal thinking in the health sciences. Concepts and strategies of epidemiology , Oxford University Press. </li></ul>