Dr. Yoga Nathan Senior Lecturer in Public Health GEMS UL

1. Dr. Yoga Nathan Senior Lecturer in Public Health GEMS UL

3. If exposure X is associated with outcome Y…..then how do we decide if X is a cause of Y Applying guidelines for causal inference If exposure X is associated with outcome Y…..then how do we decide if X is a cause of Y

4. Is this association causal? • Two-stage process: • Stage I: • Consider alternative “non-causal explanations” for the association • In Stage I, we ask ourselves could the association be due to: • Bias? • Confounding? • Chance? • Stage II: If the association is unlikely to be due to bias, confounding or chance… • ….we apply ‘guidelines’ for causal inference

5. Assessing a reported association between an exposure and an outcome in an epidemiological study Could the observed association be due to: Selection or measurement bias No Confounding Stage I No Apply Guidelines for Causal Inference Chance Probably Not Could it be causal? Stage II

6. “In what circumstances can we pass from an observed association to a verdict of causation? Upon what basis should we proceed to do so?” Nine ‘aspects of an association’ should be considered before deciding that the most likely interpretation is causation

7. Aspects of an association that should be considered when inferring causality • Plausibility • Coherence • Experimental evidence • Analogy • Strength • Consistency • Specificity • Temporality • Dose-response

8. Chapter 5 pp 83 - 96

9. Consistency • Repeated observation of an association in studies conducted on different populations under different circumstances • If studies conducted by…. • different researchers • at different times • in different settings • on different populations • using different study designs ……all produce consistent results, this strengthens the argument for causation e.g. The association between cigarette smoking and lung cancer has been consistently demonstrated in a number of different types of epidemiological study (ecological, case-control, cohort)

10. Consistency • Repeated observation of an association in studies conducted on different populations under different circumstances • If studies conducted by…. • different researchers • at different times • in different settings • on different populations • using different study designs ……all produce consistent results, this strengthens the argument for causation • e.g. The association between cigarette smoking and lung cancer has been consistently demonstrated in a number of different types of epidemiological study (ecological, case-control, cohort)

11. Is there a causal relationship between fluoride in water and bone fractures? • 18 studies have investigated the association between hip fractures (outcome) and water fluoride level (exposure) • 30 separate statistical analyses • 14 analyses produced a ‘positive association’ • 13 analyses produced a ‘negative association’ • 3 ‘no association’ The inconsistency of these results casts doubt on the hypothesis that there is a causal relationship between fluoride in water and bone fractures

12. Is there a causal relationship between fluoride in water and bone fractures? • 18 studies have investigated the association between hip fractures (outcome) and water fluoride level (exposure) • 30 separate statistical analyses • 14 analyses produced a ‘positive association’ • 13 analyses produced a ‘negative association’ • 3 ‘no association’ • The inconsistency of these results casts doubt on the hypothesised causal relationship between fluoride in water and bone fractures

13. Oral Contraceptive Use and Ovarian Cancer + ve Association -ve Association Hildreth et al, 1981 Rosenberg et al, 1982 La Vecchia et al, 1984 Tzonou et al, 1984 Booth et al, 1989 Hartge et al, 1989 WHO, 1989 Wu et al, 1988 Prazzini et al, 1991 Newhouse et al, 1977 Casagrande et al, 1979 Cramer et al, 1982 Willet et al, 1981 Weiss, 1981 Risch et al, 1983 CASH, 1987 Harlow et al, 1988 Shu et al, 1989 Walnut Creek, 1981 Vessey et al, 1987 Beral et al, 1988 Hospital-Based Case-Control Community-Based Case-Control Cohort 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 Relative Risk or Odds Ratio Hankinson SE et al. Obstet Gynecol. 1991;80:708-714. www.contraceptiononline.org

14. Oral Contraceptive Use and Ovarian Cancer + ve Association -ve Association Hildreth et al, 1981 Rosenberg et al, 1982 La Vecchia et al, 1984 Tzonou et al, 1984 Booth et al, 1989 Hartge et al, 1989 WHO, 1989 Wu et al, 1988 Prazzini et al, 1991 Newhouse et al, 1977 Casagrande et al, 1979 Cramer et al, 1982 Willet et al, 1981 Weiss, 1981 Risch et al, 1983 CASH, 1987 Harlow et al, 1988 Shu et al, 1989 Walnut Creek, 1981 Vessey et al, 1987 Beral et al, 1988 Hospital-Based Case-Control Community-Based Case-Control Cohort 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 Relative Risk or Odds Ratio Hankinson SE et al. Obstet Gynecol. 1991;80:708-714. www.contraceptiononline.org

15. “….to our knowledge no other data on the association between preschool diet and breast cancer are available” (Michels et al., 2006: 751)

16. Strength of the association • “Measures of association” • used to quantify the strength of the association between an exposure and outcome • e.g. Relative risk, odds ratio • Strong associations are more likely to be causal than weak associations • The larger the relative risk (RR) or odds ratio (OR), the greater the likelihood that the relationship is causal • Weak associations are more likely to be explained by undetected biases or confounders

17. Strength of the association • How large must a relative risk or odds ratio be to be considered ‘strong’: • 2 ? 4 ? 20 ? …..? • No universal agreement regarding what constitutes a ‘strong’ or ‘weak’ association • An OR or RR > 2.0 is ‘moderately strong’ • An OR or RR > 5.0 is ‘strong’ • The relationship between smoking and lung cancer is an excellent example of a ‘strong association’ • odds ratios and relative risks in different studies are in the 4 to 20 range

18. “For one additional serving of French Fries per week, the odds ratio for breast cancer was 1.27” (Michels et al., 2006) i.e. a “weak association”

19. Temporality • This refers to the necessity for the exposure to precede the outcome (effect) in time • Any claim of causation must involve the cause preceding in time the presumed effect • Easier to establish in certain study designs • Prospective cohort study Easiest to establish in a cohort study Lack of temporality rules out causality Exposure Outcome TIME

20. Temporality – British Doctors Cohort Study • This refers to the necessity for the exposure to precede the outcome (effect) in time • Any claim of causation must involve the cause preceding in time the presumed effect • Easier to establish in certain study designs • Prospective cohort study Lack of temporality rules out causality Exposure Outcome TIME

21. Temporality • This refers to the necessity for the exposure to precede the outcome (effect) in time • Any claim of causation must involve the cause preceding in time the presumed effect • Easier to establish in certain study designs • Prospective cohort study • Lack of temporality rules out causality Exposure Outcome TIME

22. Dose-response relationship • Dose-response (‘biological gradient’) • the relationship between the amount of exposure (dose) to a substance and the resulting changes in outcome (response) • If an increase in the level of exposure increases the risk of the outcome • this strengthens the argument for causality R I S K R I S K R I S K R I S K > 20 cigs/day 0 cigs/day < 5 cigs/day 5 - 20 cigs/day

24. Dose-response relationship Dose-Response Percentage of people with hearing loss relative to workplace noise exposure

25. (Biological) Plausibility • Plausibility refers to the biological plausibility of the hypothesised causal relationship between the exposure and the outcome • Is there a logical and plausible biological mechanism to explain the relationship?

26. “A high dose of caffeine could constrict a mother’s blood vessels reducing the blood flow to the placenta” (Biological Plausibility) < 200 mg caffeine/day

27. “There is no accepted biological mechanism to explain the epidemiological results; indeed the relation may be due to chance or confounding” (Draper et al., 2005)

28. Biological plausibility But other researchers have argued that there is a biologically plausible explanation…….. • EMF can induce currents that might alter the voltages across cell membranes • Magnetic fields might cause the movement of ferromagnetic particles within cells • EMF fields might also influence free radicals Power lines might deflect and concentrate cosmic rays on people living within their vicinity

29. Biological plausibility • It is generally easy to ‘manufacture’ biologically plausible explanations for the findings from epidemiological research • Biological plausibility is not a particularly useful viewpoint for assessing a causal relationship

30. STUDY DESIGNRelative ability of different types of study to ‘prove’ causation NB: Assuming study well-designed & conducted & bias etc. minimised

31. Is this association causal?

32. Is this association causal? Is this association causal?

33. Is this association causal? NO Is this association causal?

34. Is this association causal? Is this association causal?

35. Is this association causal? Yes Is this association causal?

36. If exposure X is associated with outcome Y…..then how do we decide if X is a cause of Y Applying guidelines for causal inference If exposure X is associated with outcome Y…..then how do we decide if X is a cause of Y

37. Sir Bradford Hill established the following nine criteria for causation (does factor A cause disorder B). Although developed for use in the field of occupational medicine, these criteria can be used in most situations. • Strength of the association. How large is the effect? • The consistency of the association. Has the same association been observed by others, in different populations, using a different method? • Specificity. Does altering only the cause alter the effect? • Temporal relationship. Does the cause precede the effect?

38. Biological gradient. Is there a dose response? • Biological plausibility. Does it make sense? • Coherence. Does the evidence fit with what is known regarding the natural history and biology of the outcome? • Experimental evidence. Are there any clinical studies supporting the association? • Reasoning by analogy. Is the observed association supported by similar associations?

39. In the following example, we apply Hill’s criteria to the classic case of smoking and lung cancer. • : Strength of Association. “The lung cancer rate for smokers was quite a bit higher than for non-smokers (e.g., one study estimated that smokers are about 35% more likely than non-smokers to get lung cancer)”. • 2: Temporality. Smoking in the vast majority of cases preceded the onset of lung cancer

40. Consistency. Different methods (e.g., prospective and retrospective studies) produced the same result. The relationship also appeared for different kinds of people (e.g., males and females) • Theoretical Plausibility. Biological theory of smoking causing tissue damage which over time results in cancer in the cells was a highly plausible explanation

41. Coherence. The conclusion (that smoking causes lung cancer) “made sense” given the current knowledge about the biology and history of the disease • Specificity in the causes. Lung cancer is best predicted from the incidence of smoking

42. Dose Response Relationship. Data showed a positive, linear relationship between the amount smoked and the incidence of lung cancer. • Experimental Evidence. Tar painted on laboratory rabbits’ ears was shown to produce cancer in the ear tissue over time. Hence, it was clear that carcinogens were present in tobacco tar.

43. Analogy.Induced smoking with laboratory rats showed a causal relationship. It, therefore, was not a great jump for scientists to apply this to humans • References • Doll, R. (1991). Sir Austin Bradford Hill and the progress of medical science. British Medical Journal, 305, 1521-1526. • Hill, B.A. (1965). The environment and disease: Association or causation? Proceedings of the Royal Society of Medicine, 58, 295-300. • Susser, M. (1977). Judgement and causal inference: Criteria in epidemiologic studies. American Journal of Epidemiology, 105, 1-15 • Bradford-Hill A. The environment and disease: Assocation or causation? Proc R Soc Med 1965;58:295-300. • Grimes DA. Cause and effect - or coincidence? Contemporary OB/GYN Jan 1984;109-15. • Peterson HB, Kleinbaum DG. Interpreting the literature in Obstetrics and Gynecology: I. Key concepts in epidemiology and biostatistics. Obstet Gynecol 1991;78(4):710-17.

44. When using them, don’t forget Hill’s advice: • “None of these nine viewpoints can bring indisputable evidence for or against a cause and effect hypothesis …. What they can do, with greater or less strength, is to help answer the fundamental question—is there any other way of explaining the set of facts before us, is there any other answer equally, or more, likely than cause and effect?” (Cited in Doll, 1991).