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- 1. Cause and effect: the epidemiological approach Chapter 14 2015
- 2. On completion of your studies you should understand: The purpose of studying cause and effect in epidemiology is to generate knowledge to prevent and control disease. That cause and effect understanding is difficult to achieve in epidemiology because of the long natural history of diseases and because of ethical restraints on human experimentation. How causal thinking in epidemiology fits in with other domains of knowledge, both scientific and non-scientific. The potential contributions of various study designs for making contributions to causal knowledge.
- 3. Cause and effect Cause and effect understanding is the highest form of achievement of scientific knowledge. Causal knowledge permits rational plans and actions to break the links between the factors causing disease, and disease itself. Causal knowledge can help predict the outcome of an intervention and help treat disease. Quote Hippocrates "To know the causes of a disease and to understand the use of the various methods by which the disease may be prevented amounts to the same thing as being able to cure the disease".
- 4. Epidemiological contributions to cause and effect A philosophy of health and disease. Models which illustrate that philosophy. Frameworks for interpreting and applying the evidence. Study designs to produce evidence. Evidence for cause and effect in the relationships of numerous factors and diseases. Development of the reasoning of other disciplines including philosophy and microbiology, in reaching judgement.
- 5. A cause? The first and difficult question is, what is a cause? A cause is something which has an effect. In epidemiology a cause can be considered to be something that alters the frequency of disease, health status or associated factors in a population. Pragmatic definition. Philosophers have grappled with the nature of causality for thousands of years.
- 6. Epidemiological strategy and reasoning: the example of Semelweis Diseases form patterns, which are ever changing. Clues to the causes of disease are inherent within these pattern. Semelweis (1818-1865) observed that the mortality from childbed fever (now known as puerperal fever) was lower in women attending clinic 2 run by midwives than it was in those attending clinic 1 run by doctors. Do these observations spark off any ideas of causation in your mind?
- 7. Births, deaths, and mortality rates (%) for all patients at the two clinics 1841-1846 First clinic Second clinic Births Deaths Rate Births Deaths Rate 20042 1989 9.92 17791 691 3.38
- 8. Semmelweis’ inspiration In 1847, his colleague and friend Professor Kolletschka died following a fingerprick with a knife used to conduct an autopsy. Kolletschka’s autopsy showed inflammation to be widespread, with peritonitis, and meningitis. “Day and night I was haunted by the image of Kolletschka’s disease and was forced to recognise, ever more decisively that the disease from which Kolletschka died was identical to that from which so many maternity patients died.” Semelweis' inspired idea was that particles had been transferred from the scalpel to the vascular system of his friend and that the same particles were killing maternity patients.
- 9. Semmelweis’ action If so, something stronger than ordinary soap was needed for handwashing He introduced chlorina liquida, and then for reasons of economy, chlorinated lime. The maternal mortality rate plummeted. Semelweis’s discovery was resented in Vienna. .
- 10. Deep knowledge derives from the explanation of disease patterns, rather than in their description. Inspiration is needed, and may come from unexpected sources, as here from Kolletschka’s autopsy. Action cannot always await understanding the mechanism. Epidemiological data to show that laying an infant on its front (prone position) to sleep raises the risk of 'cot death' or sudden infant death syndrome. A campaign to persuade parents to lay their infants on their backs has halved the incidence of cot death. Epidemiologists are reliant on other sciences, laboratory or social, to be equal partners, in pursuit of the mechanisms.
- 11. Epidemiological principles and models of cause and effect Most important of the cause and effect ideas underpinned by epidemiology is that disease is virtually always a result of the interplay of the environment, the genetic and physical makeup of the individual, and the agent of disease. Diseases attributed to single causes are invariably so by definition. The fact that “tuberculosis” is “caused” by the tubercle bacillus is a matter of definition. The causes of tuberculosis, from an epidemiological or public-health perspective, are many, including malnutrition and overcrowding. This idea is captured by several well known disease causation models, such as the line, triangle, the wheel, and the web.
- 12. Clues Stable in incidence Clusters in families Clues Incidence varies rapidly over time or between genetically similar populations Is the disease predominantly genetic or environmental? Genetic Environmental
- 13. Down’s syndrome Phenylketonuria Sickle cell disease Diabetes Asthma Coronary heart disease Stroke Lung cancer Road traffic accidents Genetic Environmental
- 14. Host: Inhalation of infective organism, age, smoking, male sex, cardio-respiratory disease Environment: Presence of cooling towers and complex hot water systems; aerosols created but not contained, meteorological conditions take aerosol to humans Agent: Virulent Legionella organisms, e.g. pneumophila serotype
- 15. Control smoking and causes of immunodeficiency Avoid wet type cooling towers, look for a better design and location, separate towers from population and enhance tower hygiene Minimise growth of organisms and factors which enhance pathogenicity, e.g. algae
- 16. Physical environment Social environment Chemical & biological environment Gene / host The model emphasises the unity of the gene and host within an interactive environmental envelope The overlap between environmental components emphasises the arbitrary distinctions
- 17. Physical environment: availability of health care facilities for diagnosis Social environment: social support to sustain dietary change Chemical & biological environment: diet content Gene defect/ enzyme deficiency/ brain damage
- 18. Models of cause and effect Agent factors, arguably, receive less attention than they deserve. Characterising the virulence of organisms is difficult. In other diseases conceptualising the cause as an agent is not easy. The concept of the disease agent has been applied to infections but it works well with many non-infectious agents, for example, cigarettes, motor cars, and alcohol. The interaction of the host, agent and environment is rarely understood. The effect of cigarette smoking is substantially greater in poor people than in rich people.
- 19. Models: the wheel The wheel of causation. Emphasises the unity of the interacting factors. Emphasises the fact that the division of the environment into components is somewhat arbitrary. Model is applied to phenylketonuria, the archetypal genetic disorder. Phenylketonuria is an autosomal single gene disease . An enzyme required to metabolise the dietary amino-acid phenylalanine and turn it into tyrosine, is deficient.
- 20. The wheel: phenylketonuria Brain damage is the outcome. The cause of this disease could be said to be a gene. The cause of the disease could be considered as a combination of a gene. Exposure to a chemical and biological environment which provides a diet containing a high amount of phenylalanine. A social environment unable to protect the child from the consequences, of a gene disorder.
- 21. Models: the spider’s web For many disorders our understanding of the causes is highly complex. Either the causes are truly complex, or equally likely, our understanding is too rudimentary to permit clarity. These disorders are referred to as multifactorial or polyfactorial disorders. Mechanisms of causation are not apparent. Portrayed by the metaphor of the spider’s web. This modelindicates the potential for the disease to influence the causes and not just the other way around, so-called, reverse causality. It also poses a fundamental question: Where is the spider that spun the web?
- 24. Rothman’s component causes model Rothman's interacting component causes model has emphasised that the causes of disease comprise a constellation of factors. It has broadened the sufficient cause concept to be a minimal set of conditions which together inevitably produce the disease. The concept is shown in figure 11 Three combinations of factors (ABC, BED, ACE) are shown here as sufficient causes of the disease. Each of the constituents of the causal "pie" are necessary. Control of the disease could be achieved by removing one of the components in each "pie" and if there were a factor common to all "pies" the disease would be eliminated by removing that alone.
- 25. Figure 11 A B C A E D A E C Each of the three components of the interacting constellations of causes (ABC, ADE, ACE) are in themselves sufficient and each is necessary
- 26. Application of guidelines/criteria to associations An association rarely reflects a causal relationship but it may. These six criteria are a distillation of, or at least, echo the ten Alfred Evans' postulates in Last's Dictionary of Epidemiology (4th edition) and the nine Bradford Hill criteria.
- 27. Strength and dose response Does exposure to the cause change disease incidence? If not there is no epidemiological basis for a conclusion on cause and effect. Failure to demonstrate this does not, however, disprove a causal role. The usual measure of the increase in incidence is the relative risk and the technical name for this criterion is the strength of the association. Dose-response Does the disease incidence vary with the level of exposure? If yes, the case for causality is advanced. The dose-response relation is also measured using the relative risk.
- 28. Specificity Is the effect of the supposed cause specific to relevant diseases, and, are diseases caused by a limited number of supposed causes? Imagine a factor which was linked to all health effects Why would that be so? Non-specificity is characteristic of spurious associations eg underestimating the size of the denominator. While specificity is not a critically important criterion epidemiologists should take advantage of the reasoning power it offers.
- 29. Consistency Is the evidence within and between studies consistent? Consistency is linked to generalisability of findings. Spurious associations are often local.
- 30. Experiment Does changing exposure to the supposed cause change disease incidence? Often there have been natural experiments. Deliberate experimentation will be necessary. Human experiments or trials are sometimes impossible on ethical grounds. Causal understanding can be greatly advanced by laboratory and experimental observations.
- 31. Biological plausibility Is there a biological mechanism by which the supposed cause can induce the effect? For truly novel advances, however, the biological plausibility may not be apparent. Biologically plausible that laying an infant on its back to sleep may lead to its inhaling vomitus. Overturned by the biologically implausible observation that laying a child on its back halves the risk of cot death. Nonetheless, biological plausibility remains relevant to establishing causality.
- 32. The pyramid of associations 1 Causal and mechanisms understood 2 Causal 3 Non-causal 4 Confounded 5 Spurious / artefact 6 Chance
- 33. Interpretation of data, study design and causal criteria Causal knowledge is born in the imagination and understanding of the disease process of the investigator. Same data can be interpreted in quite different ways. The paradigm within which epidemiologists work will determine the nature of the causal links they see and emphasise. Researchers to make explicit in their writings their guiding research philosophy. No epidemiological design confirms causality and no design is incapable of adding important evidence.
- 34. Summary Cause and effect understanding is the highest form of scientific knowledge. Epidemiological and other forms of causal thinking shows similarity. An association between disease and the postulated causal factors lies at the core of epidemiology. Demonstrating causality is difficult because of the complexity and long natural history of many human diseases and because of ethical restraints on human experimentation.
- 35. Summary All judgements of cause and effect are tentative. Be alert for error, the play of chance and bias. Causal models broaden causal perspectives. Apply criteria for causality as an aid to thinking. Look for corroboration of causality from other scientific frameworks.
- 36. BIAS AND CONFOUNDING Chapter 15 2015
- 37. BIAS Systematic, non-random deviation of results and inferences from the truth, or processes leading to such deviation. Any trend in the collection, analysis, interpretation, publication or review of data that can lead to conclusions which are systematically different from the truth. (Dictionary of Epidemiology, 3rd ed.)
- 38. MORE ON BIAS Note that in bias, the focus is on an artifact of some part of the research process (assembling subjects, collecting data, analyzing data) that produces a spurious result. Bias can produce either a type 1 or a type 2 error, but we usually focus on type 1 errors due to bias.
- 39. MORE ON BIAS Bias can be either conscious or unconscious. In epidemiology, the word bias does not imply, as in common usage, prejudice or deliberate deviation from the truth.
- 40. CONFOUNDING A problem resulting from the fact that one feature of study subjects has not been separated from a second feature, and has thus been confounded with it, producing a spurious result. The spuriousness arises from the effect of the first feature being mistakenly attributed to the second feature. Confounding can produce either a type 1 or a type 2 error, but we usually focus on type 1 errors.
- 41. THE DIFFERENCE BETWEEN BIAS AND CONFOUNDING Bias creates an association that is not true, but confounding describes an association that is true, but potentially misleading.
- 42. EXAMPLES OF RANDOM ERROR, BIAS, MISCLASSIFICATION AND CONFOUNDING IN THE SAME STUDY: STUDY: In a cohort study, babies of women who bottle feed and women who breast feed are compared, and it is found that the incidence of gastroenteritis, as recorded in medical records, is lower in the babies who are breast-fed.
- 43. EXAMPLE OF RANDOM ERROR By chance, there are more episodes of gastroenteritis in the bottle-fed group in the study sample, producing a type 1 error. (When in truth breast feeding is not protective against gastroenteritis). Or, also by chance, no difference in risk was found, producing a type 2 error (When in truth breast feeding is protective against gastroenteritis).
- 44. EXAMPLE OF BIAS The medical records of bottle-fed babies only are less complete (perhaps bottle fed babies go to the doctor less) than those of breast fed babies, and thus record fewer episodes of gastro-enteritis in them only. This is called ias because the observation itself is in error.
- 45. EXAMPLE OF CONFOUNDING The mothers of breast-fed babies are of higher social class, and the babies thus have better hygiene, less crowding and perhaps other factors that protect against gastroenteritis. Crowding and hygiene are truly protective against gastroenteritis, but we mistakenly attribute their effects to breast feeding. This is called confounding. because the observation is correct, but its explanation is wrong.
- 46. PROTECTION AGAINST RANDOM ERROR AND RANDOM MISCLASSIFICATION Random error can work to falsely produce an association (type 1 error) or falsely not produce an association (type 2 error). We protect ourselves against random misclassification producing a type 2 error by choosing the most precise and accurate measures of exposure and outcome.
- 47. PROTECTION AGAINST TYPE 1 ERRORS We protect our study against random type 1 errors by establishing that the result must be unlikely to have occurred by chance (e.g. p < .05). P-values are established entirely to protect against type 1 errors due to chance, and do not guarantee protection against type 1 errors due to bias or confounding. This is the reason we say statistics demonstrate association but not causation.
- 48. PROTECTION AGAINST TYPE 2 ERRORS We protect our study against random type 2 errors by providing adequate sample size, and hypothesizing large differences. The larger the sample size, the easier it will be to detect a true difference, and the largest differences will be the easiest to detect. (Imagine how hard it would be to detect a 1% increase in the risk of gastroenteritis with bottle-feeding).
- 49. KEY PRINCIPLE IN BIAS AND CONFOUNDING The factor that creates the bias, or the confounding variable, must be associated with both the independent and dependent variables (i.e. with the exposure and the disease). Association of the bias or confounder with just one of the two variables is not enough to produce a spurious result.
- 50. In the example just given: The BIAS, namely incomplete chart recording, has to be associated with feeding type (the independent variable) and also with recording of gastroenteritis (the dependent variable) to produce the false result. The CONFOUNDING VARIABLE (or CONFOUNDER) better hygiene, has to be associated with feeding type and also with gastroenteritis to produce the spurious result.
- 51. Were the bias or the confounder associated with just the independent variable or just the dependent variable, they would not produce bias or confounding. This gives a useful rule: If you can show that a potential confounder is NOT associated with either one of the two variables under study (exposure or outcome), confounding can be ruled out.
- 53. Genetic vs. Environmental: Detecting causes of disease Chapter 16 2015
- 54. Learning objectives Critically review the concept of causality in relation to disease. Know Hill’s Criteria for Causality and why such criteria are needed Offer coherent arguments of nature versus nurture on health and disease.
- 55. Different levels of causality
- 56. Thinking about causality What does the term “cause” imply? “That which produces an effect” (Chambers 20th C) Causality is the relation of cause and effect. In health care, we usually talk of cause and effect as etiological factor (cause) and disease or pathological process (effect), e.g: Strep. Pneumoniea causes pneumonia and meningitis. Arterial occlusion causes tissue necrosis
- 57. Is that what we mean? However, what we really mean is: infection with Strep. Pneumoniea, under a limited range of conditions, can lead to the development of pneumonia and meningitis. Arterial occlusion leads to tissue necrosis. Is this splitting hairs? No, because it reflects our thinking about disease: what it is, its causes, and, most importantly, what strategies are used to tackle it.
- 58. Different levels of causality Very few things have single, isolated causes. Instead they reflect chains or nets, temporal sequences of events. Proximal causes: close factors Distal causes: distant factors Predisposing factors Genetic Environmental Lifestyle
- 59. Distinguishing cause and determinants from chance associations Many factors influence the development of disease in addition to the direct cause. Investigation of cause is complex; nature of affected (and unaffected individuals) nature of their exposure
- 60. Koch's Postulates • 1. The specific organism should be shown to be present in all cases of animals suffering from a specific disease but should not be found in healthy animals. • 2. The specific microorganism should be isolated from the diseased animal and grown in pure culture on artificial laboratory media. • 3. This freshly isolated microorganism, when inoculated into a healthy laboratory animal, should cause the same disease seen in the original animal. • 4. The microorganism should be reisolated in pure culture from the experimental infection.
- 61. Hill’s criteria Strength of association Temporal relationship Distribution of the disease Gradient Consistency Specificity Biological plausability Experimental models Preventive trials
- 62. Risk Risk is the likelihood of an event occurring. In health care events, we usually consider a negative consequence arising from exposure to a hazard. Types of risk Absolute: incidence of disease in any population Relative: ratio of the incidence rate in the group exposed to the hazard to the incidence rate in the non-exposed group Attributable: Difference in incidence rates between exposed and non-exposed groups.
- 63. Errors in thinking about causality The following reflect common mistakes in thinking about causes of disease Genes cause disease Disease is due to "Lifestyle” Environment accounts for most variation in disease rates Why are they problematic? What do you think?
- 64. Problematic thinking: “disease-gene” All disease is a product of gene-environment interaction. Genes specify protein structures -ONLY Only when genes come into contact with an environment is their advantage or disadvantage apparent: environment could be cellular or geographic. Lifestyle, (includes ageing, nutrition, infection, toxin exposure)
- 65. Do genes cause disease? It all depends…on who…you ask Differentiate gene a. genetic material instructing proteins that confer relative advantage or disadvantage (inherited polymorphisms): “normal”. b. germ-line mutations: instructing proteins that confer relative advantage or disadvantage (sporadic/random polymorphisms) in germ cells - inheritable c. somatic mutation: instructing proteins that confer relative advantage or disadvantage (sporadic or random) limited to one cell.
- 66. What proportion of cancer is due to “cancer- causing genes”? Can you see what is wrong with this question? Only ~10% of cancers are believed to be related to specific “cancer causing” genes, e.g. BRCA1; Of these, most are “interactive”, accounted for by e.g. Ca prostate (~40% of risk due to heritable factors; Ca Br. 27%; colorectal, 35%). Very few, rare cancers, e.g. retinoblastoma
- 67. Epidemiological model for disease evaluation
- 68. Comparison of US Federal expenditure to allocation of mortality according to epidemiological modelEpidemiological model Federal health expenditure 1974-1976 (%) Allocation of mortality (%) • System of medical care organization 90.2 11 • Lifestyle 1.3 43 • Environment 1.6 19 • Human biology 6.9 27
- 69. Interactions Genes do not “cause” diseases. It is wrong to claim they do. Genes instruct the manufacture of proteins, which may or may not advantage or disadvantage the organism under certain conditions. Similarly, no single disease can be attributed to environment. Even poisoning is influenced by phenotypical detoxification, which is genetically modulated. Lifestyle is even more complex that either genes or environment.
- 70. Barker’s Hypothesis Barker’s Hypothesis Take a look at the above link for further information.
Editor's Notes
- Slides supporting chapter 14 of the book:
- How to use a systematic approach which checks for error, chance and bias before reaching judgements on cause and effect. That epidemiological approaches to and criteria for causality are not a checklist and therefore conclusions must be carefully judged and tentative. The need to synthesise data from epidemiological studies and other disciplines before reaching conclusions
- Epidemiology is best conducted as a multidisciplinary endeavour.
- The model broadens the causal thinking within the gene/host envelope & emphasis the broader cause & provides a basis for prevention and control
- There is no single cause Causes of disease are interacting Disentangling is high possible Causality may be two way
- This lecture is to prompt you to think about some of the issues in understanding how diseases might emerge. There is much publicity, and even wild speculation about the benefits that will be derived from the “cracking” of the genetic code. However, the history of medicine is the history of claims of future benefit (see Porter’s article in the web-link). This lecture looks at some issues, tries to prompt you to think and hopefully, it may open up a creative view that might just lead to identifying a new way forward for some aspect of health care. As retrieved from: R. Fielding Dept. Community Medicine, HKU http://www.commed.hku.hk/
- In this lecture we will critically review the concept of causality in relation to disease. This involves an understanding of how we think about disease, as well as a background in the development of tools we now use to explore the relationship between disease causes and their effects. Finally, we need to consider the relative contributions of nature, as genetic processes, and nurture, as environmental influence, to develop a realistic model of how disease develops.
- We use the word “cause” far too loosely. Few things in medicine have clear causes, though disease determinants, risk factors and precursors can and have been readily identified. Loose language both reflects and leads to loose thinking. When we say a virus “causes” a disease, is that really the case? For example, do the viruses associated with common Colds cause the colds? For causality to be confirmed, certain criteria must be met. The effect should not occur in the absence of the putative cause; The greater the “dose” of the cause, the greater should be the magnitude of the effect; In the case of disease, the mechanisms should be biologically plausible; Does the virus model of colds fit these criteria? The cold viruses are almost always present, but cold symptoms are not; Increasing the viral load increases symptoms. No symptoms in the absence of viruses. Viruses are plausible biological agents. So, viruses seem to be necessary, but they are only part of the picture. And that is the problem because it is all too often assumed that they are the whole picture. And that is wrong.
- It is very rare that only one thing causes one effect so consistently that it is clearly known. It has long been understood that certain compounds or plants have physiological effects if ingested. This is where almost all of our active medicines come from. However, let’s consider bugs some more: Strep. Pneumoniea can lead to pneumonia, but not always. Many other factors will influence this. If the immune system is active and sharp, the body can protect itself from many microbes. But knock out t-lymphocytes, as in AIDS for example, and all kinds of things that were previously not “causes” become highly effective at producing disease. So, is the cause of these AIDS-related diseases, like fungal pneumonia, the fungal agent (proximal cause) or the human immune deficiency virus (pre-disposing factor)? Or is it the damage to the skin that enabled the virus to gain entry to the body? Or is it the emotional resistance to using condoms which enables the virus to cross from one person to another? Or the opportunity for unprotected sexual intercourse with more than one person (distal cause)? When we look at things this way, we see chains or “nets” of cause and effect, with nodes where several factors converge and an effect emerges, or is suppressed, much like constructive and destructive interference between waveforms. (As an illustration of how our thinking influences how we see the world, in the causal network, diseases can be due to the suppression of “good” effects as well as the occurrence of “bad” effects. This implication isn’t apparent from the simple cause-effect model.) How we think about causes will determine what we do to try to fix or prevent a disease. Indeed, we can go further and ask what exactly is a disease?
- Generally, diseases are physiological abnormalities, determined on the basis of a change in form, function or feeling, or on the basis of a physiological measure falling outside of some pre-determined range of values (normal range). Three main classes or dimensions of cause are popularly grouped under the following headings: Genetic: all and any heritable factors associated with a disease Environment: all and any non-heritable factors, particularly those related to physical and/or internal physiological environment. Lifestyle: the exposure history of a person or activities, habits, and locations that constitute their sequence of lived environments and acts. This is dependent upon a person’s location, acts, and interactions. These three groups constitute the majority of popular “explanations” for why disease occurs where and when it does. This disease is due to “faulty genes”, that disease is due to “environmental influences”, another disease is due to “lifestyle factors”, and so on. However, “explanations are not causes, but everywhere they are treated as if they were”. (quote, source unknown). However, there are some clearer and more specific criteria for determining if a agent is causally connected to an effect we label a disease.
- Carefully controlled experiments that test equally rigourously derived hypotheses should be used to examine the effects of each postulated cause. In a laboratory situation it is possible to eliminate, or adjust for the effects of factors that are either not of interest, such as diet or family interaction, or which are considered to confound the impact of some putative cause. The effect of the causal agent can then be assessed by exposing half of the group of subjects to it and preventing exposure in the other half - the control group while both groups are kept in standard conditions. Such a design allows us to be confident that the consequent development of the effects of exposure only in those exposed, are due to the causal agent and not something else. However, in most situations, these kinds of rigourously controlled experiments are impractical on large numbers of people who live their day-to-day lives in the real world. More important, they are often unethical. So we must adopt other methods. Different types of evidence (which ultimately underpin the EBP standards of evidence) that can be used to determine the extent that a cause and effect are linked are found in two main sets of guidelines developed to address these questions: Koch’s Postulates and Hill’s Criteria. Both are very similar, with Hill’s Criteria being derived largely from the earlier work of Koch.
- The principles were developed by Koch, the microbiologist who identified the tuberculosis bacillus. At the time they were proposed (late 19th Century), most disease was believed to arise from infectious agents or other, almost completely unknown processes. Koch’s Postulates are still used today to demonstrate causal association between an infectious agent and the development of a disease. Find a person with the disease, identify the microorganism, culture it, inoculate another animal with the cultured microorganism, and if it develops the same disease as the original person, you have isolated the infectious agent. These principles are in use today over 100 years after Koch proposed them. They work. For infections. For simple uni-causal conditions. However, they do not work for example, to help us identify the cause of lung cancer. So how do we identify causes of diseases that are not a result of infectious, living organisms that can be cultured in a Petri dish, such as diabetes or lung cancer? For these kinds of diseases a different, but related set of decision tools are needed.
- To distinguish cause from simple association several factors need to be considered. Hill’s Criteria outline these. Strength of association: the stronger the association between a putative cause and effect, the more likely there is to be a connection between the two. Timing: The cause should precede the effect. Never the other way around. Distribution: The spatial distribution of the disease should approximate to the spatial distribution of the causal agent. Gradient: The response should correlate with the dose; more dose=more effect. Consistency: The cause-effect relationship needs to be duplicated in different studies in different places. Specificity: People not exposed to the cause should not develop the effect. Biological plausibility: the biological activity of the suspected cause should be consistent with its effects. Experimental models: animal or other experimental models should demonstrate the effect under experimental conditions. Preventive trials: removal of the suspected cause should lead to removal of the effect.
- The above concepts of risk are very important in health care because they represents the strength of the cause-effect relationship for a disease (or associated protective factor, e.g. exercise in Type II diabetes). Absolute Risk is not very useful because there are always variations in exposure to hazards within a population or group. Absolute Risk is an average that cannot show the differences in disease incidence due to differences in exposure. Relative Risk is sometimes expressed as a percentage measure of proportionate increase or decrease (in the case of a protective agent) in disease rates in an exposed group. It is a more useful measure than Absolute Risk. Attributable Risk represents the risk arising from exposure to a particular hazard (or protective factor), I.e. is the risk attributable to the given hazard.
- Why are the above erroneous? Because they over-simplify the true picture. They fail to consider that all our biological status is an interaction between the phenotype and the physical environment. Lifestyle is a way of saying “different environments” and no more. However, it’s a useful phrase because when people talk about environmental factors, they usually refer to things external to the body, such as solar radiation, air pollution, smoking and so forth. Lifestyle incorporates these and includes other things found to be important, such as activity level, diet and behaviour, which are more “interactive” aspects between phenotype and environment.
- To take a specific, evolutionary perspective, genes encode information to make proteins. That’s all. Nothing more. Now, a specific protein may provide some advantage to an organism such as a cell, in terms of efficiency of metabolism, or absorption or excretion, or it may not. It might provide a disadvantage to that cell. Whether a protein is advantageous, neutral or disadvantageous is solely dependent on the interaction the cell has with its immediate surroundings, other proteins, and so forth. In environments that confer advantage, the cell is more likely to survive and reproduce, and hence the numbers of cells carrying that particular gene will also increase - the frequency of the gene will increase in the population. If the environmental conditions change, there may be a selective disadvantage against cells carrying that gene, so the gene frequency will decline with each cell that fails to reproduce. Useful genes are usually “conserved”, that is, they may not be expressed, or only be partly expressed under conditions where there is no advantage. Disadvantageous genes under one type of environment may prove to advantageous in a different environment. A gene that alters the structure of the red blood cell membrane so that malarial parasites are less likely to successfully develop will do two things: it makes it more difficult for the cells to function normally, but it doesn’t kill you. Malaria, on the other hand, can kill you, so it is to the organism’s advantage to be a little weaker, but alive to reproduce, than to be strong, perfect and dead before reaching puberty. So it is the interaction of a specific phenotype with the range of selection pressures presented by environment and lifestyle that leads to the development of specific advantageous and disadvantageous outcomes. The disadvantageous ones we now call disease, but we have simply tended to ignore the advantageous ones, so far. A key point here to remember is that, from a population perspective, gene frequencies only change over generational time, taking hundreds, if not thousands of years to increase or decline.
- Follow the web-links, there are three, to see three different views of this question. We can think of different levels of cause. Normal variations in the human genome, some of which provide advantage and some disadvantage, such as height, eye colour and proportion. Taller people are more successful in life, they are more likely to get better jobs, earn more money and so are advantaged. Their offspring are more likely to get a good start and hence survive, ensuring that trait is maintained. The opposite will also be the case, namely that there are normal variants which are disadvantageous in the present environment of C21st Urban humankind. If there was a sudden change, such as the earth being struck by a large lump of space debris, these disadvantageous genes might come in very useful, ensuring that, while different individuals lived or died, the species as a whole would continue. This variation is the vital genetic flexibility of our species. This includes inherited genes that confer strong disadvantage, such as that for Huntington’s chorea. These tend to be very rare, but because they are only expressed after reproduction, carriers are not selected against by failure of reproduction. Germ line mutations. New changes or polymorphisms in the germ line, (which are heritable). The ones drawn to the attention of the medical profession are usually those that disadvantage the child or adult. Few parents take their child to a doctor because the child is too intelligent for school, or taller than their peers, or better at sports, or can play the piano at 3 years of age. Somatic mutations. New changes to the somatic line (non-inheritable). This includes errors of DNA copying when cells divide, and is implicated as the basis of ageing and some if not most cancer.
- There are a great many problems associated with attributing cause to diseases. Cancer is considered the “jewel in the crown” of medical cures. S/he who can cure cancer will undoubtedly win the Nobel Prize and be as famous as Jenner and Pasteur. However, it is seriously problematic from the point of view of cause. At a recent guest lecture to HKU, the Chairman of the Imperial Cancer Research Fund, the oldest and wealthiest in the UK, the director, Sir Walter Bodmer, showed a slide indicating the only around 10% of cancers were due to heritable factors, which he considered at great length. He failed to discuss the other 90% of the most common diseases called cancer. With the exceptions of very rare cancers, such as retinoblastoma, most cancers arise only in the presence of certain environmental factors which have impacted over a lifetime. The longer a person lives and their environment determines their exposure. Their body’s ability to detoxify their environment is partly genotypically determined, but also depends on factors life income (poor can’t afford adequate nutrition, such as fresh fruits, vegetables, clean air), diet, and behaviour (toxin load, from smoking, alcohol use etc.) “True” genetic cancers are very rare for a very good reason. They are selected against.
- This table outlines some interesting data. The first column on the left gives the proportion of deaths attributable to the six top causes of mortality in the USA in 1973, listed in the second column (Heart dis. =all heart disease; CVD, cerebro-vascular disease; pneumon = pneumonia; Respiratory= all other respiratory diseases). The third to sixth columns list the proportional contribution to the disease estimated to arise from each of the four columns of: 1. medical care (how much medical care does to prevent mortality from the particular diseases in column two), 2. lifestyle, 3. environmental and 4.biological contributions. These proportions are derived from epidemiological studies which What can you conclude about the predominant sources of cause or prevention of these conditions? If we look at the fourth most important cause of death in the USA in 1973, accidents, we see that about 85% of the variance in accident rates arises from life style and environmental factors, with biology being largely irrelevant as a contributor (2%) and subsequent medical care accounting for little more than one sixth (13%) of the variation in mortality from accidents. Other causes, like cancer,have a larger estimated contribution from biology, but less from treatment.
- Compare this table with the previous one. This table details the expenditure in the USA in 1974-6 on the four different areas or components listed in the previous table (medical care, lifestyle, environment and biology). The right hand column lists the total estimated contribution to overall mortality from each of the four areas. The middle column gives the proportion of government expenditure allocated to each of these four areas in 1974-1976. While these data are getting dated now, the pattern of spending remains fairly unchanged. They do indicate one thing quite clearly when you consider this table with the data in the previous table. Can you say what this is?
- A study of 44,788 pairs of twins in Swedish, Danish and Finnish twin registries (NEJM July 2000) estimated that heritable factors accounted for 42% of risk for prostate cancer, 35% for colorectal cancer and 27% for breast cancer. At present, current genes identified account for much lower proportions of risk in these diseases. So there remain major gaps in knowledge of the genetics of cancer, but still the majority of risk variation is accountable by other, non-heritable factors. Few diseases are caused purely by genes. Genes are only information carriers. External factors can and do disrupt every level from the genes themselves to their final phenotype. At the other end of the spectrum, injuries sustained in a road traffic accident (RTA) have a genetic component, but you wouldn’t want to look for a gene predisposing to RTAs. To do so would be considered silly. Lifestyle is the term used to describe the range of exposures a person receives over their life. These exposures may be in the form of toxins, they may be in the form of microbiological agents, they may be in the form of a pattern of behaviour, dictated by a culture or family which modulates exposure to other toxins, such as tobacco or hazards such as work. Lifestyle is the interaction between heritable and non-heritable influences on a person. Genes exist because they serve some purpose. Polymorphisms are conserved because they provide some advantages under certain environmental conditions; for example it may be a “tolerable” side effect of some other advantageous feature, such as HLA typology, and important immune feature, or may confer some advantage in times of dietary stress. An example is that thalassemias and sickle-cell disease provide some benefit in areas of high malarial prevalence, though are disadvantageous at other times and places. Type 2 Diabetes “genes” are likely to be advantageous when nourishment is scarce, not when plentiful.
- Finally, the uterine environment seems to “set” the embryo’s metabolism for what to expect after birth. In lean times, a poorly nourished mother may produce a baby whose metabolism is set to be efficient with energy extraction and storage. Fifty years later, if food has been plentiful, a person’s risk of heart disease, and probably their weight, is much increased. Similar uterine effects have been proposed for other disease, such as breast cancer. This model is called Barker’s Hypothesis.