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From association to causation

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From association to causation

  1. 1. From Association to Causation: Deriving Inferences from Epidemiologic Studies Najibullah Safi, MD, MSc. HPM NPO/PHC – WHO Country Office Afghanistan
  2. 2. Presentation outline• Approaches for studying diseases etiology• Ecologic studies• Type of association• Type of causal relationships3/11/2013 2
  3. 3. Approaches for studying diseases etiology• Expose animals to risk factors such as carcinogens in control lab – Control the exposure dose – Control other environmental conditions and genetic factors – Keep lost to follow-up to minimum• Can we extrapolate data across species and from animal to human population?3/11/2013 3
  4. 4. Approaches to etiology in human population• Epidemiology build on unplanned or natural experiments – People exposed to risk for non-study purposes • e.g. exposure to atomic bomb radiation in Hiroshima and Nagasaki 19453/11/2013 4
  5. 5. Approaches to etiology in human population cont.• Sequences of studies in human population Clinical observation Available data Case control studies Cohort studies Randomized trials3/11/2013 5
  6. 6. Approaches to etiology in human population cont.• Conceptually, a two step process is followed in carrying out studies and evaluating evidence – Determining association between an exposure or characteristics and the risk of a disease • Studies of group characteristics: ecological studies • Studies of individual characteristics: case control and cohort – If association exist – determine whether the observed association is likely to be a causal one3/11/2013 6
  7. 7. Ecologic studies• Studies of group characteristics3/11/2013 7
  8. 8. Ecologic studies cont.• Higher the average dietary fat consumption for a country, the higher breast cancer incidence• No information on individuals (outcome - breast cancer vs. exposure - high dietary fat intake)• Ascribing to members of a group, characteristics that they in fact do not possess as individuals (ecologic fallacy)3/11/2013 8
  9. 9. Ecologic studies cont. What is the problem? The authors wrote: “the observed association is between pregnancy during an influenza epidemic and subsequent leukemia in the offspring of that pregnancy. It is not known if the mothers of any of these children actually had influenza during their pregnancy”.3/11/2013 9
  10. 10. Type of association (Non-causal)3/11/2013 10
  11. 11. Type of association cont.• McMahon’s study: observed association of coffee consumption with risk of pancreatic cancer Coffee Drinking Coffee Drinking Smoking Pancreatic Cancer Pancreatic Cancer Causal association Non-causal association (due to confounding3/11/2013 11
  12. 12. Types of causal relationships3/11/2013 12
  13. 13. Types of causal relationships cont.• Necessary and sufficient – Without that factor the diseases never develops, and in its presence the disease always develops – This situation rarely occurred Factor A Disease3/11/2013 13
  14. 14. Types of causal relationships cont.• Necessary, but not sufficient – Factor is necessary but not sufficient to produce the disease e.g. Tubercle bacillus – Multiple factors are required, often in a specific temporal sequence Factor A + Factor B Disease + Factor C3/11/2013 14
  15. 15. Types of causal relationships cont.• Sufficient, but not necessary – The factor alone can produce the disease, but so can other factors • Radiation, benzene – either can produce leukemia • Cancer does not develop in everyone who has experienced radiation or benzene exposure Factor A or Factor B Disease or Factor C3/11/2013 15
  16. 16. Types of causal relationships cont.• Neither sufficient nor necessary – More complex model – Probably most accurately represents the causal relationships that operate in most chronic diseases Factor A + Factor B or Factor C + Factor D Disease or Factor E + Factor F3/11/2013 16
  17. 17. Evidence for a causal relationship• Infectious diseases: Henle assumptions 1840 – which was expanded by Koch in 1880s: – The organism is always found with the disease – The organism is not found with any other disease – The organism, isolated from one who has the disease, and cultured through several generations, produces the disease (in experimental animals)• NCDs, no organism to detect and culture --- causal relationship more complex3/11/2013 17
  18. 18. Guidelines for judging whether an association is causal1. Temporal relationship2. Strength of association3. Dose response relationship4. Replication of findings5. Biologic plausibility6. Consideration of alternate explanations7. Cessation of exposure8. Consistency with other knowledge9. Specificity of the association3/11/2013 18
  19. 19. Temporal relationship• Exposure to the factor must occurred before the disease developed• It is easy to establish a temporal relationship in a prospective cohort study than case control and retrospective cohort• Length of the interval between the exposure and disease (asbestos in lung cancer)3/11/2013 19
  20. 20. Temporal relationship cont.3/11/2013 20
  21. 21. Guidelines for judging whether an association is causal1. Temporal relationship2. Strength of association3. Dose response relationship4. Replication of findings5. Biologic plausibility6. Consideration of alternate explanations7. Cessation of exposure8. Consistency with other knowledge9. Specificity of the association3/11/2013 21
  22. 22. Strength of association• Strength of association is measured by Relative Risk or Odds Ratio• The stronger the association, the more likely the relation is causal3/11/2013 22
  23. 23. Guidelines for judging whether an association is causal1. Temporal relationship2. Strength of association3. Dose response relationship4. Replication of findings5. Biologic plausibility6. Consideration of alternate explanations7. Cessation of exposure8. Consistency with other knowledge9. Specificity of the association3/11/2013 23
  24. 24. Dose response relationship• As the dose of exposure increase, the risk of disease also increases• If a dose response relationship is present, it is strong evidence for a causal relationship• Absence of dose response relationship does not necessarily rule out a causal relationship• In some cases a threshold may exist3/11/2013 24
  25. 25. Dose response relationship cont.3/11/2013 25
  26. 26. Guidelines for judging whether an association is causal1. Temporal relationship2. Strength of association3. Dose response relationship4. Replication of findings5. Biologic plausibility6. Consideration of alternate explanations7. Cessation of exposure8. Consistency with other knowledge9. Specificity of the association3/11/2013 26
  27. 27. Replication of findings• If the relationship is causal, we would expect to find it consistently in different studies and in different population• It is expected to be present in subgroups of the population3/11/2013 27
  28. 28. Guidelines for judging whether an association is causal1. Temporal relationship2. Strength of association3. Dose response relationship4. Replication of findings5. Biologic plausibility6. Consideration of alternate explanations7. Cessation of exposure8. Consistency with other knowledge9. Specificity of the association3/11/2013 28
  29. 29. Biologic plausibility• Coherence with the current body of biologic knowledge• Sometimes, epidemiological observation preceded biologic knowledge – E.g. Gregg’s observation on Rubella and congenital cataracts preceded any knowledge of teratogenic viruses• If epidemiological findings are not consistent with the existing knowledge – interpreting the meaning of observed association might be difficult3/11/2013 29
  30. 30. Guidelines for judging whether an association is causal1. Temporal relationship2. Strength of association3. Dose response relationship4. Replication of findings5. Biologic plausibility6. Consideration of alternate explanations7. Cessation of exposure8. Consistency with other knowledge9. Specificity of the association3/11/2013 30
  31. 31. Consideration of alternate explanations• Explanation of a relationship as causal or due to confounding• The extent to which the investigators have taken other possible explanations into account and the extent to which they have ruled out such explanations are important considerations3/11/2013 31
  32. 32. Guidelines for judging whether an association is causal1. Temporal relationship2. Strength of association3. Dose response relationship4. Replication of findings5. Biologic plausibility6. Consideration of alternate explanations7. Cessation of exposure8. Consistency with other knowledge9. Specificity of the association3/11/2013 32
  33. 33. Cessation of exposure• If a factor is a cause of a diseases, the risk of the disease to decline when exposure to the factor is reduced or eliminated3/11/2013 33
  34. 34. Cessation of exposure cont. Eosinophilia myalgia syndrome caused by L-tryptophan3/11/2013 34
  35. 35. Guidelines for judging whether an association is causal1. Temporal relationship2. Strength of association3. Dose response relationship4. Replication of findings5. Biologic plausibility6. Consideration of alternate explanations7. Cessation of exposure8. Consistency with other knowledge9. Specificity of the association3/11/2013 35
  36. 36. Consistency with other knowledge• If a relationship is causal, we would expect the findings to be consistent with other data3/11/2013 36
  37. 37. Guidelines for judging whether an association is causal1. Temporal relationship2. Strength of association3. Dose response relationship4. Replication of findings5. Biologic plausibility6. Consideration of alternate explanations7. Cessation of exposure8. Consistency with other knowledge9. Specificity of the association3/11/2013 37
  38. 38. Specificity of the association• An association is specific when a certain exposure is associated with only one disease – The weakest point of the guideline – should be removed – Smoking is linked with lung, pancreatic & bladder cancers; hearth disease, emphysema … – When specificity of an association is found, it provides additional support for a causal inference – With a dose response relationship, absence of specificity in no way negates a causal relationship3/11/2013 38
  39. 39. More on causal inferences• Bias• Confounding• Interaction3/11/2013 39
  40. 40. 3/11/2013 40

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