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13b Data analysis and causal inference – 2
- 1. 4/19/2011 Data analysis and causal inference 1 Data analysis and causal inference – 2 Victor J. Schoenbach, PhD home page Department of Epidemiology Gillings School of Global Public Health University of North Carolina at Chapel Hill www.unc.edu/epid600/ Principles of Epidemiology for Public Health (EPID600)
- 2. 7/1/2009 Data analysis and causal inference 2 Causal relations and public health Many public health questions hinge on causal relations, e.g. • Does dietary fiber prevent colon cancer? • Do abstinence-only sex education programs raise the age of sexual debut? • What level of arsenic in drinking water is harmful? • Does higher patient volume reduce knee replacement complication rates? • Does male circumcision prevent HIV infection?
- 3. 12/30/2001 Data analysis and causal inference 3 Conceptual issues in causal relations • In general we cannot “see” causal relations but must infer their existence. • “Proving” causation means creating a belief – our own and others’. • Causal inference is therefore a social process. • What we regard as “causes” depends on our conceptual framework.
- 4. 12/30/2001 Data analysis and causal inference 4 Pre-20th century causal discoveries • Food poisoning from shellfish, pork • Plumbism from wine kept in lead-glazed pottery (Romans) • Contagion (isolation, quarantine) • Scurvy and citrus fruit (James Lind) • Scrotal cancer in chimney sweeps (Percival Pott)
- 5. 12/30/2001 Data analysis and causal inference 5 Pre-20th century causal discoveries • Smallpox vaccination • Cowpox vaccination (Edwin Jenner) • Waterborne transmission of typhoid fever (William Budd) and cholera (John Snow) • Person-to-person transmission of measles (Peter Panum) • Puerperal fever and handwashing (Ignaz Semmelweis)
- 6. 7/29/2009 Data analysis and causal inference 6 Rise of the germ theory • Invention of the microscope enabled direct observation of microorganisms • Seeing microbes ≠ Seeing microbes cause disease • Henle-Koch postulates for proving that a microorganism causes a disease
- 7. Even seeing involves inference I LQVF FRIDEMIQLQCX 4/19/2011 Data analysis and causal inference 7
- 8. Inference that is not always correct I LQVF FRIDEMIQLQCX 4/19/2011 Data analysis and causal inference 8
- 9. Inference that is not always correct van der Helm’s “Kaleidoscope Motion” (from Michael’s “Visual Phenomena & Optical Illusions”) www.michaelbach.de/ot/ www.michaelbach.de/ot/mot_feet_lin/ www.michaelbach.de/ot/mot_kaleidoscope/ 4/19/2011 Data analysis and causal inference 9
- 10. 4/22/2002 Data analysis and causal inference 10 Henle-Koch postulates 1. The parasite must be present in all who have the disease; 2. The parasite can never occur in healthy persons; 3. The parasite can be isolated, cultured and capable of passing the disease to others
- 11. 12/30/2001 Data analysis and causal inference 11 E.H. Carr – What is history? “History … is ‘a selective system’ … of causal orientations to reality.… from the infinite ocean of facts [and] … the multiplicity of sequences of cause and effect [the historian] extracts those, and only those, which are historically significant; and the standard of historical significance is his ability to fit them into his pattern of rational explanation and interpretation. Other sequences of cause and
- 12. 4/22/2002 Data analysis and causal inference 12 E.H. Carr – What is history? effect have to be rejected as accidental, not because the relation between cause and effect is different, but because the sequence itself is irrelevant. The historian can do nothing with it; it is not amenable to rational interpretation, and has no meaning either for the past or the present.” (E.H. Carr, What is History, p. 138).
- 13. 12/30/2001 Data analysis and causal inference 13 When to act? “All scientific work is incomplete – whether it be observational or experimental. All scientific work is liable to be upset or modified by advancing knowledge. That does not confer upon us a freedom to ignore the knowledge we already have, or to postpone the action that it appears to demand at a given time.” A.B. Hill, The environment and causation, p. 300
- 14. 12/30/2001 Data analysis and causal inference 14 Is cigarette smoking harmful to health? • Surgeon General's Advisory Committee on Smoking and Health, chaired by Dr. Luther Terry.
- 15. 11/16/2004 Data analysis and causal inference 15 Surgeon General’s Advisory Committee on Smoking and Health • Long existing concern about health effects of smoking • Accumulation of scientific studies in 1950’s • Committee of the Royal College of Physicians in Britain issued a report in 1962 indicting cigarette smoking as a cause of lung cancer and bronchitis and probably of CVD • Major health problem, major industry, $$$
- 16. 12/30/2001 Data analysis and causal inference 16 “Criteria for causal inference” 1. Strength of the association 2. Consistency - replication 3. Specificity of the association 4. Temporality 5. Biological gradient 6. Plausibility 7. Coherence 8. Experiment 9. Analogy
- 17. 12/30/2001 Data analysis and causal inference 17 1. Strength of the association • Is there an association? • Is there really an association? (not chance, not bias, not confounding) • Stronger associations less likely to be entirely due to confounding • How strong is strong?
- 18. 4/22/2002 Data analysis and causal inference 18 How strong is strong? Relative risk “Meaning” 1.1-1.3 “Weak” 1.4-1.7 “Modest” 1.8-3.0 “Moderate” 3-8 “Strong” 8-16 “Very strong” 16-40 “Dramatic” 40+ “Overwhelming”
- 19. 12/30/2001 Data analysis and causal inference 19 2. Consistency - replication • Has this association been observed in other studies? • By other investigators? • Working independently? • With different methods? • (Problematic for one-time events)
- 20. 12/30/2001 Data analysis and causal inference 20 3. Specificity of the association • Does what we see conform to what our conceptual model says we should see? • If we expect a specific causal relation, is that what we see? • The more accurately we define the factors, the greater the relative risk.
- 21. 12/30/2001 Data analysis and causal inference 21 4. Temporality • In everyday life, a cause must be present before its effects, at least by an instant. • Subclinical disease states may be present long before the outcome is detected.
- 22. 12/30/2001 Data analysis and causal inference 22 5. Biological gradient • “Dose-response” relation – if we expect one. • Often think that bias would not produce a dose-response relation. • Biological model might predict threshold and/or saturation.
- 23. 12/28/2002 Data analysis and causal inference 23 Possible dose-response curves Incidence Incidence 0 0Dose Dose Threshold Saturation
- 24. 12/30/2001 Data analysis and causal inference 24 6. Plausibility • Can we explain the relation on the basis of existing biological (psychological, social, etc.) knowledge? • Problematic for new types of causes
- 25. 11/16/2004 Data analysis and causal inference 25 7. Coherence Does all of what we know fit into a coherent picture? – Descriptive epidemiology of the exposure and disease by person, place, and time – Related biological, economic, geographical factors
- 26. 11/16/2004 Data analysis and causal inference 26 8. Experiment Epidemiologic experiments can provide unique evidence – exposure precedes outcome; substitute population may be valid. –Randomized trials –Quasi-experimental studies –Natural experiments
- 27. 12/30/2001 Data analysis and causal inference 27 9. Analogy • Like plausibility, but weaker • We are readier to accept something similar to what we’ve seen in other contexts. • This criterion illustrates the point that causal inference involves getting people to change their beliefs
- 28. 11/16/2004 Data analysis and causal inference 28 Causal inference in epidemiology and law • Decision about facts must be reached on the evidence available • Emphasis on integrity of the process of gathering and presenting information • Requirement for adequate representation of contending views
- 29. 11/16/2004 Data analysis and causal inference 29 Epidemiology and the legal process • Use of standards of certainty for various potential consequences. • Reliance on procedural (methodological) safeguards, since facts are established only as findings of an investigatory process. • Justice (i.e., proper procedures / methodology) must be done and also seen to be done
- 30. 11/20/2007 Data analysis and causal inference 30 Epidemiology in the courtroom • Increasingly, epidemiologists and epidemiologic data are entering the courtroom. • E.g.’s, Benedectin, silicon breast implants, environmental tobacco smoke, diesel exhaust. For more on causal inference, see the 2005 AJPH special issue on science and the law
- 32. What is this graph saying? 12/31/2009, B6
- 33. 4/19/2011 Role of epidemiology in public health 33 Why we need epidemiology We can see effects that are regular and immediate. We need epidemiology for outcomes that are rare, delayed, inconsistent, subtle, multifactorial.
- 34. 12/2/2001 Role of epidemiology in public health 34 How to remember what you’ve learned – how to tell others 10 fundamentals of epidemiology
- 35. 12/2/2001 Role of epidemiology in public health 35 1. Epidemiology studies populations Epidemiology is the study of health and disease in populations for the purposes of (i) understanding disease dynamics, (ii) controlling disease, and (iii) promoting health. Comparison across and within populations is the key strategy of epidemiologic inquiry.
- 36. 12/3/2002 Role of epidemiology in public health 36 2. Populations are diverse Populations (meaningful collections of people) are diverse, heterogeneous, dynamic, and interconnected. Epidemiology depends on these qualities in order to make useful comparisons. Comparisons must not be confounded by uncontrolled diversity.
- 37. 7/29/2002 Role of epidemiology in public health 37 3. Measures for studying populations 1) Counts of people – rates, proportions, and ratios, e.g., birth rate, death rate, incidence, prevalence, abortion ratio; 2) Distributions of characteristics of people, e.g., mean age, mean education, mean cholesterol level; 3) Characteristics of groups or environment, e.g., sexual networks
- 38. 12/2/2001 Role of epidemiology in public health 38 4. Incidence Fundamental concept Rate (incidence rate, “incidence density”) or proportion (incidence proportion, cumulative incidence). Incidence rate measures the process of disease occurrence; incidence proportion measures the result of a process.
- 39. 12/2/2001 Role of epidemiology in public health 39 5. Measurement Observation and measurement are fundamental to scientific advances. Choosing a measure – objective, conceptual model, and availability of data (technology, feasibility, and ethics).
- 40. 12/2/2001 Role of epidemiology in public health 40 6. Error All measurement involves error. Science seeks to minimize error and to quantify it as a guide to interpreting data. Sources of error include random error (e.g., variability from sampling) and systematic error (e.g., selection bias, information bias).
- 41. 12/3/2002 Role of epidemiology in public health 41 7. Epidemiology is mass production Collection, processing, management, and analysis of epidemiologic data (medical records, questionnaires, interviews, biological specimens, environmental measurements) involve mass production. Skillful management and quality control are key though often unadvertised components of epidemiology.
- 42. 12/2/2001 Role of epidemiology in public health 42 8. Health and disease are processes Health and disease are complex, dynamic processes affected by multiple, interacting factors acting at multiple levels. Can be challenging to define and to measure. Interpretation must take this complexity into account but not become lost in it.
- 43. 12/2/2001 Role of epidemiology in public health 43 9. Interpretation, inference, and action Interpretation takes account: 1. how data were collected 2. underlying conceptual framework. We are the source of our data and their spokesperson. Conclusions from data require inference and the weighing of evidence. One of the most difficult decisions is deciding when to act. Action should be accompanied by monitoring.
- 44. 12/2/2001 Role of epidemiology in public health 44 10. Awareness and humility Breadth of awareness and humility are important assets. More factual knowledge but major public health problems and failings. Good people can make mistakes, resist new knowledge, take deplorable actions. When confronting the unfamiliar, how can we tell fact from illusion, insight from fantasy?
- 45. 12/2/2001 Role of epidemiology in public health 45 Where have we come from, where do we need to go?
- 46. 12/2/2001 Role of epidemiology in public health 46 Why Men Are Not Secretaries Husband’s note on refrigerator to his wife: “Someone from the Guyna College called: They said Pabst beer is normal”
- 47. 12/5/2006 Role of epidemiology in public health 47 From A Prairie Home Companion Pretty Good Joke Book, 4th Edition The secretary was leaving the office when she saw the CEO standing by a shredder with a piece of paper in his hand. “Listen,’ said the CEO, ‘this is a very important document. Can you make this thing work?”
- 48. 12/5/2006 Role of epidemiology in public health 48 From A Prairie Home Companion Pretty Good Joke Book, 4th Edition The secretary turned the machine on, inserted the paper, and pressed the start button. “Great,” said the CEO as his paper disappeared inside the machine. “I just need one copy.” p177
- 49. 4/19/2011 Data analysis and causal inference 49 Thank you Arigato Kamsa-hamnida Asanti Kob-Khun Camon Merci Danke Ngiyabonga Dhanyavaad Obrigado Efharisto Shokran Gracias Spasibo Grazie Xie xie
Editor's Notes
- Xin chao, Guten tag, wilkommen, karibuni, dumela, merhaba, shalom, huan-ying, bienvenidos, boa tarde This is the second part of the two-part lecture on data analysis and causal inference.
- When we study associations, our motivation is usually a search for causal relations. Many public health questions hinge on causal relations. Several examples are: Does dietary fiber prevent colon cancer? Do abstinence-only sex education programs raise the age of sexual debut? What level of arsenic in drinking water is harmful? Does higher patient volume reduce knee replacement complication rates? Does male circumcision prevent HIV infection?
- Although in everyday life we frequently make causal statements, such as “I couldn’t get up on time this morning because I was up late last night responding to my e-mail”, in general we cannot “see” causal relations but can only infer their existence. In practice, “proving” causation usually means creating a belief – our own and others’ – that a relation is causal. Thus, causal inference is a social process, and what we regard as “causes” in any given instance depends upon our conceptual framework. [Science magazine for 4 April 2003 has a special section on techniques to monitor and interpret processes using dynamic imaging in living cells, i.e., “real life in real time”. Even though these methods provide the ability to “see causation”, there remains a need for interpretation and inference.]
- Causal relations, of course, have been of interest to humanity for a long time. It seems likely that religious prohibitions against eating certain foods, such as the Jewish and Muslim prohibitions against eating pork, derive from causal associations with the special hazards from those foods when they are not stored or prepared safely. The Romans gave the name “plumbism” (from the Latin word for lead) to symptoms of lead poisoning that developed from drinking wine kept in lead-glazed pottery. The phenomenon of contagion motivated practices of isolation and quarantine of sick persons in the Middle Ages. In the 1700’s, James Lind conducted experiments among sailors in the British Navy and showed that citrus fruits could cure and prevent scurvy (though the British Navy did not act on this knowledge until well into the next century – either they did not have the belief or they had other reasons not to act). Percival Pott demonstrated the association between scrotal cancer and contact with chimney soot to a degree that convinced the Danish guild of chimney sweeps to introduce hygienic measures that greatly reduced scrotal cancer. His fellow Britons, however, took another hundred years to act on his findings.
- Edwin Jenner noticed that cowmaids tended to be spared the ravages of smallpox, and discovered that smallpox could be prevented by vaccination with the (unseen) cowpox virus. Although innoculation with smallpox virus had already been practiced for decades (the Turks, actually, may have introduced the practice), the use of cowpox represented a dramatic advance. (Unfortunately the British Royal Society did not accept Dr. Jenner’s paper, so that he had to publish it as a monograph and thus did not receive credit for a peer-reviewed publication!) The ability of water to transmit typhoid fever and cholera was demonstrated by William Budd and John Snow, respectively. In Denmark, Peter Panum demonstrated through meticulous studies on the Faroe Islands that measles was transmitted from person-to-person, with a 14-day latency period between infection and infectiousness. In Hungary, Ignaz Semmelweis earned the enmity of his fellow physicians by demonstrating that handwashing could prevent puerperal fever (childbearing fever). Unfortunately handwashing, like data management, is an unexciting practice, so public health is still struggling to take full advantage of Semmelweis’ discovery. These examples from history illustrate numerous causal relations in health and disease that have been discovered and demonstrated over the centuries. Note that in none of these cases could the agent of disease, such as the bacteria or the virus, be seen. The existence of these relations had to be inferred.
- Antony van Leeuwenhoek (1632-1723)’s descriptions of “animalcules” are the first recorded observations of living microorganisms. Now, for the first time, the actual agents of disease, whose existence had been postulated but never “proved”, could be seen with the eyes. But though improvements in the microscope and experimental science provided increasingly powerful ways to gather evidence, being able to see microbes did not enable medical scientists to see them “cause” disease. Making that connection required inference. The work of, among others, Louis Pasteur, Jacob Henle, and his student, Robert Koch, including the Henle-Koch postulates for proving (i.e., inferring) that a microorganism causes a disease, provided foundations for the science of bacteriology.
- For example, can you guess what this statement says?
- Illusion courtesy of Neuroscience for Kids, http://faculty.washington.edu/chudler/chvision.html. This illusion comes from http://faculty.washington.edu/chudler/hidnfk.html
- Seeing should not always be believing. van der Helm’s “Kaleidoscope Motion” (from Michael’s “Visual Phenomena & Optical Illusions”) http://www.michaelbach.de/ot/mot_kaleidoscope/
- The Henle-Koch postulates said that in order to conclude that a microorganism (they used the term “parasite”) causes a disease, 1. The parasite must be present in all who have the disease; 2. The parasite can never occur in healthy persons, and 3. The parasite can be isolated, cultured, and capable of passing the disease to others (or to animals). Adoption (i.e., acceptance by the scientific community) of these rules of inference provided a framework for major advances in medical science and the birth of the science of bacteriology. Numerous bacteria were identified and associated with specific diseases. As often happens, however, the dominance of one conceptual model – in this case the germ theory and the new science of bacteriology – tended to hold back progress in identifying causal relations that did not fit this model, such as pellagra.
- Although epidemiologists emphasize data and objectivity, we should never forget that data do not make causal inferences. People do. The way people make inferences, though, may be easier to see in other fields. In a series of lectures entitled What is history?, the celebrated British historian Edward Hallett Carr explains the thinking process of the historian in interpreting the record of the past. In Carr’s words, “History … is ‘a selective system’ … of causal orientations to reality.… from the infinite ocean of facts [and] … the multiplicity of sequences of cause and effect [the historian] extracts those, and only those, which are historically significant; and the standard of historical significance is his ability to fit them into his pattern of rational explanation and interpretation. Other sequences of cause and
- “effect have to be rejected as accidental, not because the relation between cause and effect is different, but because the sequence itself is irrelevant. The historian can do nothing with it; it is not amenable to rational interpretation, and has no meaning either for the past or the present.” (E.H. Carr, What is History, p. 138). Carr (What is history, p. 137) illustrates his point with a hypothetical situation, in which Jones, driving from a party where he has drunk too much, in a car with defective brakes, at an intersection with poor visibility runs down and kills Robinson, who was crossing the road to buy cigarettes. In analyzing this incident, we would entertain alcohol, defective brakes, and poor visibility as causes (and potential targets for preventive action), but not cigarette smoking, even though it is probably the case that had Robinson not been a cigarette smoker he would not have been killed that evening.
- Historical research, however, is primarily a scholarly activity. What about in an applied field such as public health? In his classic article on association and causation, Sir Austin Bradford Hill reminds us that questions of causation in epidemiology have implications for action. In Hill’s words, “All scientific work is incomplete – whether it be observational or experimental. All scientific work is liable to be upset or modified by advancing knowledge. That does not confer upon us a freedom to ignore the knowledge we already have, or to postpone the action that it appears to demand at a given time.” A.B. Hill, The environment and causation, p. 300 Do epidemiologists and other public health professionals have a responsibility to consider about implications for changes in clinical or public health practice or policy? Kenneth Rothman, the founding editor of the journal Epidemiology, has frequently argued that epidemiologists should not include policy implications and recommendations in scientific reports, and several of my colleagues have argued that epidemiologists are not qualified to advocate in policy matters. Others have argued that epidemiologists should make policy recommendations and advocate – see the first chapter of my Evolving Text).
- But even if we accept Hill’s assertion that knowledge demands action, there remains the question of how to make judgments about causation. This question was a central issue confronting the U.S. Surgeon General’s Advisory Committee on Smoking and Health, convened by Surgeon General Luther Terry, which produced the historical landmark Smoking and Health: Report of the Advisory Committee to the Surgeon General of the Public Health Service in 1964. The history of the 1964 Surgeon General’s report is a fascinating story. It is available at the National Library of Medicine website: http://sgreports.nlm.nih.gov/NN/Views/Exhibit/narrative/smoking.html The page contains links to the Report itself. Let’s take ourselves back to the early 1960’s – before the Civil Rights victories of the mid-sixties and the environmental, feminist, and anti-war movements of the late sixties. The Surgeon General’s Advisory Committee, of which Bradford Hill was a member, set out to review the over 7,000 studies on smoking and health that had been published by that time.
- During the 20th century there was a longstanding concern about adverse health effects of smoking. Although a number of studies had appeared during the first half of the century, the 1950’s brought an outpouring of research, beginning with the publication of three major studies in 1950 – one by Ernst L. Wynder and Evarts Graham. Tobacco smoking as a possible etiologic factor in bronchiogenic carcinoma: a study of 684 proven cases. JAMA 1950;143:329-36. A study by Sir Richard Doll and Austin Bradford Hill, British Medical Journal, 1950, and a study by Morton L. Levin, Hyman Goldstein, and Paul R. Gerhardt. Cancer and Tobacco Smoking. JAMA, May 27, 1950. [Interestingly, until persuaded by Dr. Levin, the editor of the Journal of the American Medical Association was reluctant to publish these now classic papers, since the hypothesis relating smoking and lung cancer was at the time neither well known nor documented (see Mervyn Susser. Epidemiology in the United States after World War II: the evolution of technique. Epidemiologic Reviews 1985; 7:147-177)]. Pressure on the United States public health authorities grew in 1962, when a Committee of the Royal College of Physicians issued a report indicting cigarette smoking as a cause of lung cancer, bronchitis, and probably of cardiovascular disease. Lung cancer was a burgeoning health problem. Tobacco was a major industry. Thousands of lives and billions of dollars rode on the outcome of the Advisory Committee’s deliberations. So intense was the interest in the deliberations, that the Committee agreed among themselves that none of them would quit smoking before the report was presented, so as not to telegraph their conclusions to the press (and to the lobbyists).
- The deliberations by the Advisory Committee were the largest scale and most prominent effort to date to reach a conclusion about causation largely on the basis of epidemiologic evidence. The Committee “vigorously” discussed various meanings and conceptions of the term “cause”, and specifically noted that its “considered decision to use the words ‘a cause’” did not imply that smoking was the only factor in lung cancer. Because of the centrality of epidemiologic evidence for a conclusion about the effects of cigarette smoking on human health, the Committee arrived at certain criteria which were, in their words, “especially significant for judgments based on the epidemiologic method”. The Committee explained that, “The causal significance of an association is a matter of judgment which goes beyond any statement of statistical probability. To judge or evaluate the causal significance of the association between the attribute or agent and the disease, or effect upon health, a number of criteria must be utilized, no one of which is an all-sufficient basis for judgment.” (pg 20) The Committee then listed criteria 1-4 and 7 from the list on the slide. That list is: 1. Strength of the association, 2. Consistency – replication, 3. Specificity of the association, 4. Temporality, 5. Biological gradient, 6. Plausibility, 7. Coherence, 8. Experiment, and 9. Analogy. Austin Bradford Hill later presented the full set of nine criteria in his classic 1965 article, the “Environment and disease: association or causation?”, and the criteria became known as Hill’s criteria for causal inference.
- The first criterion listed by Hill was strength of the observed association. Although the thrust of his article concerns whether an association is causal or not, the first question is whether indeed there is an association, and is the observed association due to some artifact such as selection bias or confounding. Strong associations are somewhat less likely to be due to confounding, since for a confounder to be entirely responsible for a strong association, the confounder would itself have to be strongly associated with both the exposure and the disease, in which case it would probably not have been overlooked. But what do we mean by “strong”?
- Conventionally the strength of association is stated in terms of the relative risk (for a rare outcome, the odds ratio serves as a measure of relative risk; if the outcome is not rare, then the fact that the odds ratio will generally be farther from 1.0 than the risk ratio needs to be taken into account in using the above scale). The scale shown on the slide is my own, but I constructed it to correspond to the usage one sees in the literature. Note that strength in this context does not mean “importance”, since if an exposure is widespread, even a 10% or 20% relative increase in risk (a relative risk of 1.1 or 1.2) corresponds to a substantial effect on health. But although the impact may be large, if the relative risk is modest then the conclusion that the association is indeed causal will depend more heavily on other types of evidence than if the relative risk is large. Note that a preventive exposure, with a relative risk less than 1.0, can be evaluated by inspecting the reciprocal of the relative risk.
- Hill’s second criterion, and one that is widely used in science, is consistency – the replication by other epidemiologic studies of the finding. Replication is more persuasive when the association has been observed by different investigators, working independently, using the same (to demonstrate that the finding can be replicated) and different (to show “robustness”) methods. The reasons are logical as well as sociological, since it is less likely that the same mistake or bias will occur in multiple, independent situations. Of course, none of the criteria, except perhaps temporality, is essential for inferring causality. In particular it is more difficult to demonstrate consistency of an association based on a one-time occurrence.
- Hill’s third criterion, also employed by the Advisory Committee, was specificity of the association. Rothman and Greenland (in their textbook Modern Epidemiology) dismiss this criterion as not logically important for causal inference, since there is no particular reason that an exposure should cause only a single condition. In fact, cigarette smoking is one of the best examples of an exposure that leads to many diseases. Of course, cigarette smoking is a very complex exposure, with thousands of chemical compounds inhaled, so it could be that there are specific linkages between individual compounds and particular diseases. In any case, this criterion of specificity and several others may perhaps best be formulated in terms of how closely observations conform to the conceptual model of causation. If we expect a specific causal relation, then we feel more confident that it exists when the data conform to that. Another aspect is that the more accurately we define the exposure and disease, the stronger will be the relative risk. If we measure physiological exposure to the most relevant constituents of cigarette smoke, as opposed to simply the number of cigarettes reported, we expect to see a stronger association. If certain types of lung cancer are related to cigarette smoking more so than other types, then we expect to see a stronger relative risk for those types than for all lung cancers. Many causal relations are specific in these ways, so I continue to regard specificity as a useful criterion.
- The fourth criterion, temporality, means that it can be established that the “cause” preceded the “effect”. Although theoretical physics might take issue with the proposition that a cause must be present before its effects, that is our expectation in everyday life. This criterion can be a difficult one to demonstrate, since even in a prospective study it is possible that a subclinical disease state is present long before the outcome is detectable. For example, the atherosclerotic process begins well before middle age, but cohort studies of CHD generally enroll middle-aged persons. Although the exposures are measured well before CHD becomes manifest, it is likely that many of the participants already have subclinical atherosclerosis. In such cases temporality may be established in respect to the progression of atherosclerosis and with respect to clinical CHD, but not in respect to the initiation of atherosclerosis.
- Number five, Hill’s criterion “biological gradient”, is more often referred to as “dose-response”, meaning the greater the level of the exposure, the greater the increase in risk, or for a preventive exposure, the greater the level of exposure, the greater the decrease in risk. This criterion is another of those that comes under the concept of whether our observations fit our conceptual model, since in some cases we might not expect a dose-response relation (see next slide). The phenomena of threshold (in other words, no response at all occurs until exposure reaches a certain level) and saturation (beyond a certain exposure level no additional response occurs) may explain the lack of a dose-response relation, or at least of a pronounced one. One reason for the dose-response criterion is that a dose-response relation may be harder to explain in terms of bias than a relation that does not exhibit a gradient.
- This slide shows how the dose-response curve might look in the presence of a threshold effect (left side) or saturation (right side).
- Hill’s sixth criterion is plausibility. Can we explain the relation on the basis of existing biological (scientific) knowledge? We are most ready to infer causality when knowledge that we already possess can explain the observed association. Obviously this criterion makes it more difficult to infer causality for new types of causes. This criterion reminds us that causal inference is a social process, but it stands to reason that inferring causation by applying existing knowledge to a new instance is a smaller step than is inferring causation by accepting a new mechanism as well.
- Hill’s seventh criterion, coherence, is one that is often confused with the preceding criterion (plausibility) and sometimes with the second criterion, (consistency). As I read Hill’s article, plausibility deals with mechanisms, consistency deals with replication of epidemiologic findings, and coherence deals with everything else. The criterion of coherence considers the extent to which everything known about the exposure and disease fits into a coherent picture. That includes the descriptive epidemiology of the exposure and disease – how they are distributed by person, place, and time – as well as related biological, economic, geographical, and other factors. We ask the question that a detective would ask in trying to solve a murder mystery – do all of the pieces of the puzzle fit into place?
- Hill’s eighth criterion is experiment. As we have discussed, an intervention trial, especially a large, randomized trial, is unique in epidemiologic study designs in its ability to avoid confounding by unknown and unmeasured risk factors. In addition, an intervention trial provides unambiguous evidence of temporality. Evidence from quasi-experimental studies, where randomization is not used, and from “natural experiments” where it appears that circumstances have created a situation approximating a randomized trial, partially satisfy the experiment criterion. Every so often a large randomized trial fails to find an association that has regularly been observed in observational studies and for which a great deal of evidence exists. A recent example is hormone replacement therapy (HRT), which had been found to be inversely associated with CHD risk in numerous observational epidemiologic studies. Indeed, many argued that it would be unethical to conduct a randomized trial of HRT since the control group would be deprived of the treatment. Yet in the spring of 2002, the HRT arm of the Women’s Health Initiative was stopped before its planned termination because of apparent harm to women receiving estrogen plus progestin, with no indication of reduction in CHD risk. Thus, epidemiologic experiments retain a key role in inferring causality. Yet they are not always available, and we are often forced to reach a conclusion without them. Moreover, even the findings from randomized trials require interpretation.
- Hill’s ninth and final criterion is analogy. This criterion resembles plausibility, though it is weaker. This criterion acknowledges that we are readier to accept a relation as causal if we have seen something similar in other contexts. For example, having seen that a virus can cause cancer (e.g., hepatitis B and liver cancer), we are readier to entertain a causal inference for another virus-cancer association (e.g., human papilloma virus and cervical cancer). The existence of a causal association does not provide strong evidence that an analogous association is causal, but as a practical matter analogies do contribute to believability.
- I find it interesting to consider the similarities between causal inference in epidemiology and the legal process. In both cases a decision may need to be made on the basis of existing evidence, even though it is acknowledged to be incomplete. In both cases, the consequences of a decision either way can be quite serious. Both epidemiology and the legal process place considerable emphasis on integrity of the process of gathering and presenting information. They also both require adequate representation of contending arguments.
- Both epidemiology and the legal process make use of standards of certainty for various consequences. In epidemiology we are hesitant to make strong recommendations without strong evidence. In law, a search warrant can be granted on the basis of probable cause, but a criminal conviction requires proof beyond a reasonable doubt. Both fields rely on procedural (which epidemiology calls “methodological”) safeguards, since a process that includes full consideration of alternative explanations provides the best assurance that truth will out. Finally, both fields value the openness of the process. Justice must not only be done but must be seen to be done if it is to inspire confidence in the outcome. Similarly, epidemiologists present their methods and are supposed to make the details available for scrutiny upon request.
- Epidemiology and the legal process intersect directly in the courtroom. Increasingly, epidemiologists and epidemiologic data are being called upon to resolve questions of legal liability. Benedectin, a drug that has been found useful in reducing nausea in pregnant women, was removed from the market following lawsuits by women who had taken Benedectin and later had a child with a birth defect. Epidemiologists were not impressed by the scientific evidence, but the court ruled against the pharmaceutical manufacturer. A major court battle has been fought to establish whether women have been harmed by silicone breast implants following surgery for breast cancer. My colleague Barbara Hulka was appointed as a special master to advise the judge hearing these cases. She received almost an entire bookcase of material to read and review in providing her advice to the court. Environmental tobacco smoke and diesel exhaust are two other exposures whose health effects have been considered in legal and regulatory settings. Courtroom epidemiology is not for the faint of heart, but it is perhaps only to be expected that if epidemiologists claim that our discipline is a powerful source of evidence, then plaintiffs and defendants will want to avail themselves of it.
- We can see effects that are regular and immediate. We need epidemiology for outcomes that are rare, delayed, inconsistent, subtle, multifactorial.
- We’ve covered a lot of material in the course and a lot of concepts. So in this closing lecture I would like to begin with a summary of the 10 fundamentals of epidemiology, to help summarize what you have learned to make it easier to remember and easier to tell others about epidemiology.
- The first fundamental is that epidemiology studies populations for the purposes of (i) understanding disease – its precursors, promoters, and dynamics, (ii) controlling disease – especially through prevention, and (iii) promoting health. Comparison across and within populations is the key strategy of epidemiologic inquiry. Whether we are policymakers, administrators, nutritionists, biostatisticians, health educators, physicians, whatever – when we study health and disease in a population we are in effect practicing epidemiology.
- 2. Populations are diverse – they differ from one another. Populations are heterogenous – the people who make them up differ from one another within the same population. Also, populations are dynamic – they are always changing in size, age structure, ethnic composition, educational levels, etc. Populations are interconnected – what happens in one often affects what happens in others. Interconnectedness has always existed, but it is constantly growing. These differences and changes make epidemiology possible, since epidemiology compares health and disease across populations, across subgroups within populations, and across different time periods in the same population. At the same time, researchers must take care that these comparisons are not confounded by differences other than the ones we think we are examining. Hence we use standardization and other methods of adjustment for potential confounders to control for the influence of differences that we regard as “extraneous”. What we regard as extraneous depends on our objective and our conceptual understanding (our conceptual model).
- 3. Studying populations requires concepts and measures designed for that purpose. Some measures are based on counts of people who experience an event. Examples of rates are birth, death, and incidence rates, which are counts of events divided by people and time. Examples of proportions are cumulative incidence, prevalence, and case fatality ratio. An example of a ratio based on counts of people, but not a rate or proportion, is the abortion ratio (abortions / live births). In all of these, the denominator provides the context for interpreting the numerator. Other measures describe the distribution of characteristics of individuals. Examples are mean age, mean education, mean cholesterol. Still other measures exist at the level of the community and may not have an individual-level equivalent. Examples are physical geography (lattitude, elevation) and sexual networks.
- 4. Perhaps the most fundamental concept in epidemiology is incidence – the occurrence of new cases of disease in a population or population subgroup. We measure incidence as the incidence rate (also known as “incidence density”, ID) and as the incidence proportion (also known as “cumulative incidence”, or CI). The incidence rate measures the rapidity of disease occurrence per unit time, either at a given moment or averaged over an interval. The incidence proportion measures the cumulative result of that rate during a period of time. Cumulative incidence is the proportion of a baseline population that has been affected after a certain interval. Prevalence is the proportion of a population that is affected at a given time, the result of incidence, but also of survival, recovery, and migration.
- The 5th fundamental is measurement. In empirical science, systematic observation and measurement are fundamental to advances in knowledge. But both of these depend on the objective being pursued, the conceptual model of the phenomenon, and the availability of data, which in turn reflects available technology, feasibility, and ethical considerations. Molecular biology, for example, has transformed our understanding of so many aspects of physiology and disease.
- The 6th fundamental is error. Measurement involves error. A major challenge in science is to minimize error and to quantify error that cannot be avoided, to guide us in interpreting results. Sources of error include what is often referred to as “random error”, such as variability due to sampling, and “systematic error”, such as selection bias and information bias. The potential for random error is quantified by measures of precision, such as confidence intervals, and measures of reliability, such as correlation coefficients and the kappa coefficient. Random error is referred to as “unsystematic”, but it can have systematic effects, generally in the direction of obscuring associations. Potential for systematic error is quantified by selection probabilities (alpha, beta, gamma, delta), sensitivity (probability of identifying a “case”), specificity (probability of identifying a “non-case”), and predictive value (probability that a positive test does indeed represent a case).
- The 7th fundamental of epidemiology is that epidemiology involves mass production. Epidemiologic data come from medical records, questionnaires, interviews, biological specimens, environmental measurements, and statistical tabulations. Collection, processing, management, and analysis of them involves mass production, because the number of observations is usually large. Mass production requires skillful management – planning, budgeting, hiring, training, supervising, purchasing, organizing, documenting, and archiving. Although it is often unadvertised, skillful management, including detailed quality control, is a key component of successful epidemiologic research. So is raising money and assembling other resources, such as space.
- The 8th fundamental is that health and disease are processes. Health and disease are complex, dynamic biological processes that are affected by multiple factors at all levels, including the molecular, microbiologic, physiologic, anatomical, emotional, cognitive, behavioral, social, economic, and environmental. A popular model in public health is the Ecological Model, which explicitly recognizes a hierarchy of levels of influence. Cosmic and spiritual influences may also exist. All these factors interact to influence health and disease in individuals and groups. Health and disease, as well as their determinants, can be challenging to define and especially to measure. Moreover, interpretation must take this complexity into account but not become lost in it.
- The 9th fundamental is interpretation, inference, and action. Interpretation of epidemiologic data must take into account how the data were collected and the conceptual framework which underlies the data collection. We need to control for possible confounding. The data do not speak for themselves. We are their source and their spokesperson. Causal conclusions from data always require inference and weighing of evidence from all available sources. One of the most difficult decisions is deciding what actions are warranted at a given time. At least action should be accompanied by monitoring so that we can become aware of the effects of action. Even so, some effects will be too subtle for our monitoring methods or too delayed for our event horizon. Prostate cancer screening is an example of a public health problem that has been very difficult to figure out how to treat on the basis of epidemiologic evidence.
- The 10th fundamental is awareness and humility. Maintaining broad awareness and a degree of humility about our knowledge and abilities are important assets in advancing public health as in other areas of life. We have more factual knowledge than ever before, yet we also have major public health problems and notable failings. Good intelligence and good intentions by no means guarantee good results. History shows us that intelligent, well-meaning people make mistakes, resist new knowledge, and sometimes take regrettable, even deplorable actions. Research is the endeavor of trying to learn what is not known and to understand what is not understood. New knowledge and insight may come in an unfamiliar way, place, or form. When confronting the unfamiliar, how do we tell fact from fiction, insight from fantasy? How can we differentiate between appearance and reality? There may not be any sure way. In his review of the writings of epidemiologists whose research led them to conclusions that have since been abandoned [“Those who were wrong”, American Journal of Epidemiology 1989;130(1):3-5], Jan Vandenbrouke writes, "Maybe, at the cutting edge of research, as new discoveries are being made, we ought to give up all hope of deciding by methodological principles which scientific statements will ultimately prove to be right and which will not. … history cannot teach us a method to discern future right from wrong. Only after several decades will it become clear which scientists took the right side.”
- And so, with these 10 fundamentals of epidemiology in our possession, I would like to invite you to consider with me some fundamental determinants of public health, determinants which will become increasingly obvious in the coming decades. In case it is not obvious, I should emphasize that this is my own exposition of not necessarily original ideas and is not presented ex cathedra.
- And: “Meanwhile in Europe, the enlightenment was a reasonable time. Voltaire invented electricity and also wrote a book called Candy. Gravity was invented by Issac Walton. It is chiefly noticeable in the autumn when the apples are falling off the trees.”
- And: “Meanwhile in Europe, the enlightenment was a reasonable time. Voltaire invented electricity and also wrote a book called Candy. Gravity was invented by Issac Walton. It is chiefly noticeable in the autumn when the apples are falling off the trees.”
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