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I ntroduction: C ausal theories and interrelationships between measures of disease occurrence

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  1. Introduction: Causal theories and interrelationships between measures of disease occurrence Lydia B. Zablotska, MD, PhD Associate Professor Department of Epidemiology and Biostatistics

  2. Learning Objectives • Discuss how causal inference is central to the role of epidemiology • Brief history of causal thinking through the years • Theories of causal inference • Causal models • Sufficient-component cause model • Describe (and critique) Rothman’s causal heuristic • Counterfactual model • Counterfactual effect measures: rate ratios, risk ratios and odds ratios • Effect measures vs. measures of association • Measures of attributable risk • Causal diagrams (eg., directed acyclic graphs) • Discuss how epidemiologic thinking leads to causal inference • Discuss and critique Bradford Hill’s causal criteria

  3. Practice of Epidemiology Example: Study of the association between fiber intake and risk of colorectal cancer SEER 2008 Incidenceratesof colorectal cancer per year in the U.S.: Males – 60 per 100,000 Females – 43 per 100,000

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  7. 0 … Saga continues … Cancer Causes Control. 2005 Apr;16(3):225-33. Dietary intakes of fruit, vegetables, and fiber, and risk of colorectal cancer in a prospective cohort of women (United States). Lin J et al. CONCLUSIONS:“Our data offer little supportfor associations between intakes of fruit, vegetables, and fiber, and colorectal cancer risk. However, our data suggest that legume fiber and/or other related sources may reduce risk of colorectal cancer. “ Int J Cancer. 2006 Oct;119(12):2938-2942 Dietary intake of calcium, fiber and other micronutrients in relation to colorectal cancer risk: Results from the Shanghai Women's Health Study. Shin A et al. CONCLUSIONS:“No apparent associations were found for fiber, total vitamin A, carotene, vitamins B1, B2, B3, C and E with colorectal cancer risk. Our results suggest that calcium may be protective against colorectal cancer development …”

  8. … and continues… Am J Clin Nutr 2007;85:1353– 60.Dietary fiber and whole-grain consumption in relation to colorectal cancer in the NIH-AARP Diet and Health Study1–5. A. Schatzkin et al. CONCLUSIONS:“Total dietary fiber intake was not associated with colorectal cancer. In analyses of fiber from different food sources, only fiber from grains was associated with a lower risk of colorectal cancer... Whole-grain intake was inversely associated with colorectal cancer risk...

  9. … and then continues some more Scand J Gastroenterol. 2010 Oct;45(10):1223-31. Dietary fiber, source foods and colorectal cancer risk: the Fukuoka Colorectal Cancer Study. K. Uchuda et al. Results: Total, soluble and insoluble dietary fibers were not measurably associated with overall risk or subsite-specific risk of colorectal cancer. By contrast, rice consumption was associated with a decreased risk of colorectal cancer (trend p = 0.03), particularly of distal colon and rectal cancer (trend p = 0.02), and high intake of non-rice cereals tended to be related to an increased risk of colon cancer (trend p = 0.07). There was no association between vegetable consumption and colorectal cancer, whereas individuals with the lowest intake of fruits tended to have an increased risk of colorectal cancer. CONCLUSIONS:The present study did not corroborate a protective association between dietary fiber and colorectal cancer, but suggested a decreased risk of distal colorectal cancer associated with rice consumption.

  10. … and just does not stop BMJ. 2011 Nov 10;343:d6617. doi: 10.1136/bmj.d6617. Dietary fibre, whole grains, and risk of colorectal cancer: systematic review and dose-response meta-analysis of prospective studies. D. Aune et al. Results:25 prospective studies were included in the analysis. The summary relative risk of developing colorectal cancer for 10 g daily of total dietary fibre (16 studies) was 0.90 (95% confidence interval 0.86 to 0.94, I(2) = 0%), for fruit fibre (n = 9) was 0.93 (0.82 to 1.05, I(2) = 23%), for vegetable fibre (n = 9) was 0.98 (0.91 to 1.06, I(2) = 0%), for legume fibre (n = 4) was 0.62 (0.27 to 1.42, I(2) = 58%), and for cereal fibre (n = 8) was 0.90 (0.83 to 0.97, I(2) = 0%). CONCLUSIONS:A high intake of dietary fibre, in particular cereal fibre and whole grains, was associated with a reduced risk of colorectal cancer. Further studies should report more detailed results, including those for subtypes of fibre and be stratified by other risk factors to rule out residual confounding. Further assessment of the impact ofmeasurement errorson the risk estimates is also warranted.

  11. … genetic studies are on the horizon…

  12. Epidemiology in the news… Jennifer Kelsey on diet and nutrition articles in The New York Times, “week after week of cause after cause.”

  13. Why worry about causes? So that we can intervene So that we can reduce or prevent disease

  14. What is a cause? “A cause is something that makes a difference. Insofar as epidemiology is a science...[that]aims to discover the cause of health states, the search includes all determinants of health outcomes. These may be both active agents... and static conditions such as the attributes of persons and places.” Mervyn Susser

  15. What is a cause? “A cause is something that makes a difference. Insofar as epidemiology is a science...[that]aims to discover the cause of health states, the search includes all determinants of health outcomes. These may be both active agents... and static conditions such as the attributes of persons and places.” Mervyn Susser

  16. 0 Back to basics: Epidemiology is … “science that focuses on the occurrence of disease rather than on the natural history or some other aspect of the disease” K. Rothman

  17. 0 “… the study of the distribution and determinants of disease frequency” in human populations MacMahon and Pugh (1970)

  18. 0 “… the study of the distribution and determinants of disease frequency” in human populations MacMahon and Pugh (1970) We also add: • … AND the application of this study to • control health problems • improve publichealth

  19. Epidemiology defined: • Aims to find causes of diseases and to explain varying patterns of disease occurrence across populations and groups • The basic science or one of the pillars of public health • Way of thinking and logically structuring scientific inquiry in public health • Scientific discipline with roots in biology, medicine, logic, and the philosophy of science

  20. 0 Societal origins of epidemiology • Epidemiology affects the daily lives of most people • Comes from the Greek words epi and demos, meaning ‘the study of people’ • Originated in the Sanitary Era (XIX century) out of necessity to improve the economic productivity by decreasing squalor of the industrial slums • Epidemiology is the result of the evolution of progressive thinking and our understanding of the basic human rights

  21. And since it is the purpose of epidemiology to… • Identify factors that cause the distribution of disease

  22. And since it is the purpose of epidemiology to… • Identify factors that cause the distribution of disease • This must be the most important lecture of the course…

  23. Historical developments in the understanding of causes of diseases 1. Sanitary era (paradigm: miasma) Miasma theory of Sydenham: • foul emanations from soil, water and air cause all diseases • poverty is at the core of all ills, it is a cause rather then a consequence of disease The Public Health Act of 1848 • Decaying organic matter insanitation  foul emanations diseases poverty  high birth rates among poor Edwin Chadwick

  24. Historical developments in the understanding of causes of diseases 2. Infectious disease era (paradigm: germ theory) Discovery of causal agents of anthrax, tuberculosis and cholera by R. Koch • Bacillus anthracis (1877) • Mycobacterium tuberculosis (1882) • Vibrio cholerae (1883) Robert Koch

  25. Causal Inference: Henle-Koch postulates for causation • The organism is always found with the disease • The organism is notfound with any other disease • The organism, isolated from one who has the disease, and cultured through several generates, produces the disease (in experimental animals)

  26. Historical developments in the understanding of causes of diseases 3. Risk factor epidemiology or chronic disease era(paradigm: black box) Web of causation (MacMahon 1960) • All factors are at the same level • Diseases can be prevented by cutting a few strands of the web • Does not elucidate societal forces or their relation to health “… too much statistics takes away all the pleasure and the message of epidemiology.” Brian MacMahon

  27. Historical developments in the understanding of causes of diseases 4. Ecoepidemiology (paradigm: Chinese (nesting) boxes) Eras in Epidemiology: The Evolution of Ideas (Susser 2009) • Conceptual approach combining molecular, societal, and population-based aspects to study a health-related problem. • People are not only individuals but also members of communities (social context) • Helps to recognize broad dynamic patterns and disease in its social context • Places exposure, outcome and risk in societal context. Mervyn Susser

  28. Causal inference Goal of epidemiology: learn causes of diseases and factors that could prevent or delay disease development Causal inference: a process of determining causal and preventive factors

  29. Theories of causal inference • Deductive reasoning • Inductivism • Bayesianism

  30. 0 • George Simenon’s Inspector Maigret • Arthur Conan Doyle’s Sherlock Holmes • Agatha Christie's Hercule Poirot Pull the clues together, arrive at generalization, i.e. deduct the answer

  31. 0 In Epidemiology we use inductive reasoning: Francis Bacon (XVI century) suggested the conditional inductive tree: formulate laws based on limited observations of recurring phenomenal patterns: Specification of alternative hypotheses Design of crucial experiments to test these hypotheses Exclusion of some alternatives Adoption of what is left (for the time being)

  32. Deductive vs. inductive reasoning Deductive reasoning applies general principles to reach specific conclusions Wikipedia

  33. Deductive vs. inductive reasoning Deductive reasoning applies general principles to reach specific conclusions, whereas inductive reasoning examines specific information, perhaps many pieces of specific information, to derive a general principle. Wikipedia

  34. Causal models

  35. What is a cause? (Rothman) • A cause of a specific disease event [is] an antecedent event, condition or characteristic that was necessary for the disease at the moment it occurred, given that other conditions are fixed. • A cause of a disease is an event, condition, or characteristic that preceded the disease event and without which the disease event would not have occurred at all or would not have occurred until some later time.

  36. What is a cause? (Rothman) • A cause of a specific disease event [is] an antecedent event, condition or characteristic that was necessary for the disease at the moment it occurred, given that other conditions are fixed. • A cause of a disease is an event, condition, or characteristic that preceded the disease event and without which the disease event would not have occurred at all or would not have occurred until some later time.

  37. What is a cause? (Rothman’s sufficient-component cause model) • A cause of a specific disease event [is] an antecedent event, condition or characteristic that was necessary for the disease at the moment it occurred, given that other conditions are fixed. • A cause of a disease is an event, condition, or characteristic that preceded the disease event and without which the disease event would not have occurred at all or would not have occurred until some later time.

  38. Types of causal relationships(Rothman’s sufficient-component cause model) • If a relationships is indeed causal, then… • Necessary and sufficient • E.g., rabies, HIV exposure in AIDS • Necessary but not sufficient • Multiple factors acting in a specific temporal sequence • E.g., multistage carcinogenesis • Sufficient but not necessary • E.g., both ionizing radiation and benzene exposure cause leukemia independently • Neither sufficient nor necessary • Many different pathways of getting the same disease

  39. T U X A B B Sufficient Cause 1 Sufficient Cause 2 Sufficient and component causes A sufficient cause is a set of minimal conditions or events that inevitably produce disease

  40. T U X A B B Sufficient and component causes Component causes SufficientCause 1 SufficientCause 2 A sufficient cause is a set of minimal conditions or events that inevitably produce disease

  41. T U X A B B Sufficient and component causes A component cause is any one of a set of conditions which are necessary for the completion of a sufficient cause Component causes Sufficient Cause 1 Sufficient Cause 2 A sufficient cause is a set of minimal conditions or events that inevitably produce disease

  42. T U X A B B Sufficient and component causes A necessarycomponent cause is a component cause that is a member of every sufficient cause Sufficient Cause 1 Sufficient Cause 2

  43. Necessary but not sufficient Neither necessary nor sufficient For example:Tuberculosis M. tuberculosis M. tuberculosis Poornutrition Immuno-suppression Sufficient Cause 1 Sufficient Cause 2

  44. “Causing” a myocardial infarction Potato chips Y W No exercise

  45. “Causing” a myocardial infarction Potato chips Y W Obesity No exercise A

  46. “Causing” a myocardial infarction Potato chips Y W Obesity No exercise A NO EFFECT

  47. “Causing” a myocardial infarction Potato chips Y W Obesity No exercise A C Genes

  48. T “Causing” a myocardial infarction Potato chips Y W Obesity No exercise High cholesterol A C Genes

  49. T “Causing” a myocardial infarction Potato chips Y W Obesity No exercise High cholesterol A C Genes NO EFFECT

  50. T X B “Causing” a myocardial infarction Potato chips Y W Obesity No exercise High cholesterol A C Genes Smoking Stress