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Association & causation (2016)
The document discusses key concepts in establishing causation in epidemiology, including the difference between association and causation. It explains that causation requires determining if a factor A truly causes outcome B rather than being a spurious relationship. Several of Hill's criteria for establishing a causal relationship are described, such as strength of association, consistency of findings, specificity of the relationship, and examining alternative explanations through study design and accounting for potential confounding factors. The document emphasizes that multiple factors often interact to cause outcomes, and that proving causation involves considering the strength and consistency of evidence rather than any single study.
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Association & causation (2016)
- 1. Association & Causation ABASIC CONCEPT IN EPIDEMIOLOGY DR SHYAM ASHTEKAR, SMBT MED COLLEGE DEC 2016 1
- 2. 12/21/2016 2 Is Nota-bandi cause ofQ deaths?
- 3. A chain ofEvents Demonetiz ation of high value notes No funds at home Long queues Heart attack Man died 12/21/2016 3
- 4. Contributory causes Demonetizatio n Long queue Heart attack Oldage Man died 12/21/2016 4
- 5. On deaths ‘dueto’ demonetization! The deaths were due to heart disease, old age Long queues, stress and waiting. Could be due to cold of night or hot weather of afternoon. Because nobody helped the dying It was demonetization that killed. Could be all of these factors. They could have died even at home.. So no link to demonetization 12/21/2016 5
- 6. The questions Didit happen by chance/error? Is their a bias in saying event A caused event B Is there a true relation between A as cause to event B? Is the relation of A to B strong enough? Are their confounding/confusing variables involved? 12/21/2016 6
- 7. What we shalllearn in this? About ‘variables’ Proving causation in Epidemiology Association to causation 12/21/2016 7
- 8. It is allabout Variables/Factors/Events 8
- 9. The relation ofvariables! Independent, dependent, and confounding variables We have fundamentally two variables to measure/monitor—(a) the exposure/INDEPENDENT variable-often on X axis and (b) the dependent or the OUTCOME variable-usually Y axis But there are OTHER variables that can influence the independent and dependent variables. These are called CONFOUNDING variables Relation between BMI (X axis) and MAC (Y axis): correlation (0.9) close to 1 12/21/2016 9
- 10. Factors…Risk factors (variables) Predisposing Enabling/ disablingPrecipitating Reinforcing 12/21/2016 10
- 11. Confounding -factors thatconfuse/mix up/hide Influences both cause and effect differentially For instance, increasing AGE is associated with type2 Diabetes. But BMI is a confounding factor. BMI increases with age and BMI also independently predisposes to diabetes. So you have to account for BMI in this relation –hidden factor in both cause and effect Confounding means a hidden factor, a factor that is mixed up etc. BMI Aging Diabete s Type2 12/21/2016 11
- 12. About Association & Causation IMPORTANTCONCEPTS 12
- 13. Why is Association& Causation important? To decide if a factor A causes disease B or not! Is the link true or only facile? Is it true or by chance? If we know the cause(s) we can cure/treat /prevent/minimize the illness. (in a patient or the society) 12/21/2016 13
- 14. Association & Causation Association Relation between two or more variables Generally found in snapshot (cross-sectional) studies Things found together! Relationships can be positive or negative Correlation! (factors moving together– like poverty and under nutrition) Causation A variable (s) lead to another variable that is dependent/ outcome/ event/disease So it suggests Etiology of disease We need analytical studies to find out/prove cause(s) 12/21/2016 14
- 15. Types of Association Association Causal DirectIndirect Interactio n Non- causal Chance Bias/Conf ounding Ecologica l 12/21/2016 15
- 16. Spurious Association Spurious (nottrue) association Not real, only apparent Example1: Incomes and alcohol consumption are strongly associated (Is it true?) Exapmle2: Fire and Fire Brigade may be found together in a snapshot--but Fire brigade is not the cause of FIRE. 12/21/2016 16
- 17. Direct Causation Independentvariable A leads to dependent variable B, without help of any other factor. This is rare in life. Cyanide poisoning and death is an example. This happens more with infectious diseases that are highly virulent and there is no immunity-like smallpox, anthrax, rabies. 12/21/2016 17
- 18. Indirect causation Somefactor leads to another factors/event and through that the disease event. Streptococcal sore throat Rheumatic fever Rheumatic carditis/valve damage 12/21/2016 18
- 19. Interaction of causativefactors- Synergy-both factors work together- IHD BMI Smoking Protective (negative) effect of a factor--IHD Physical work Aging 12/21/2016 19
- 20. Conditional factors Sometimes/Oftenanother factor is necessary for a causative factor to lead to disease. Viral Fever in child Aspirin Rey’s syndrome (rapidly progressing encephalitis 12/21/2016 20
- 21. Necessary AND sufficientcause Cyanide poison alone can cause death..no other factor is necessary! Another is rabies infection leading death! Without that factor the diseases never develops, and in its presence the disease always develops Death Cyanide 12/21/2016 21
- 22. Necessary but notsufficient Common Situation The causative variable factor is always necessary but often not enough to cause disease by itself It needs other variable/ factor(s) to cause the disease This is more common in health and medicine Example TB disease Malnutrition TB infection ?? 12/21/2016 22
- 23. One cause ,many effects Some causes/factors can cause multiple effects. Common examples are malnutrition, smoking, alcoholism etc Diabetes can cause multiple organ damage-heart, kidneys, eyes, nerves etc So it is wiser to curb these factors to maximize health gains. 12/21/2016 23 alcoholis m Liver cirrhosis neuritis Gastritis dementia
- 24. Multiple –multifactorial-causes .. In most non- communicable diseases ,multiple factors have a varying role to play..cancers, ischemic heart disease, aging etc 12/21/2016 24 IHDBMI Stress Hypertension Smoking ??
- 25. Multifactorial causation-Often true of NCDs Ageing Obesity HighCalorie diets Insulin Resistance Lack of exercise Genetic traits Diabetes Type 2 12/21/2016 25
- 26. Multiple variables incausation Often the relationships are not linear-or chain like They can be a complex web of causative factors An example is the Pollution hazard of Delhi in Nov2016 has following factors: winter, diwali crackers, vehicular emissions, coal-power plants, burning of rice-stubs in UP, Haryana and Punjab, winds flowing into Delhi from east-west- north-south etc, construction activity, dust raised because of stopping of rains, etc. Stub- burning Winds/ emissi ons Winte r 12/21/2016 26
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- 28. Summary of CausalModels Causalmodels 1 Causal Direct (A causes B) HIV causes AIDS One cause-multiple effects ( A causes B,C,D) Smoking causes cancer, IHD, Bronchial disease etc Multiple causes (A, B, C together cause D) Hypertension caused by age, BMI, smoking etc 2 Effect Modification Synergistic (B helps A to cause C) Obesity hastens knee arthritis with age Negative/Protective (B protects from effect C to cause D) Exercise can protect against effects of ageing on IHD 3 Conditional causation (A can cause B only in presence C) Rh-ve mother will have abortions only if father is Rh+ve 4 Indirect causal (A causes B only through C) Ageing causes hypertension through BMI 5.Confounding association (factor B influences both A and C) 12/21/2016 28
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- 30. Problems of provingcausal relation Correlation may not be equal to CAUSATION-it could be coincidence! There could be multiple causes of an effect/event Factors operating in Communicable and Non-communicable diseases are different May be a time lag between cause and effect– eg occupational chemical exposures. (or Silicosis) Bias in study design--selecting wrong sample! Confounders--factors that influence cause and effect/underlying factors There is no statistical method to prove cause from association, we suggest only probability and strength of association. 12/21/2016 30
- 31. Steps for EstablishingCausality between- exposure and outcome variables Look for chance variation (probability-take enough and proper sample) Rule out bias-tilt/obliqueness in sample taking, observation, Follow correct methods of measurements, comparing Look & account for confounding variables Look for Hill’s criteria, confirmatory criteria (specific) 12/21/2016 31
- 32. Evidence for acausal relationship-Now not followed due to limitations Infectious diseases: Henle assumptions 1840 – which was expanded by Koch in 1880s: The organism is always found with the disease The organism is not found with any other disease The organism, isolated from one who has the disease, and cultured through several generations, produces the disease (in experimental animals) NCDs, no organism to detect and culture --- causal relationship more complex 12/21/2016 32
- 33. Hill’s Modified Criteria of causation Temporalprecedence (must happen before the disease) Strength of association (Higher Risk) Specificity (event A should lead to event B) Consistent (should be found again & again) Coherence (must fit in existing knowledge/observations) Dose response relationship (more exposure-more disease) Strength of study design Biological plausibility (biologically possible) Should be proven by experiment (??)-eg in animals! Existing Evidence! 12/21/2016 33
- 34. Temporal relationship Exposureto the factor must occur before the disease developed It is easy to establish a temporal relationship in a prospective cohort study than case control and retrospective cohort Length of the interval between the exposure and disease (asbestos in lung cancer) 12/21/2016 34
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- 36. Strength of association Strength of association is measured by Relative Risk or Odds Ratio/attributable risk or risk difference The stronger the association, the more likely the relation is causal Exposed to silica dust Non exposed to silica dust 12/21/2016 36
- 37. Dose response relationship As the dose of exposure increase, the risk of disease also increases If a dose response relationship is present, it is strong evidence for a causal relationship In some cases a threshold may exist Sometimes it could be a J shaped relation 12/21/2016 37
- 38. Dose response relationshipcont. 12/21/2016 38
- 39. Replication of findings If the relationship is causal, we would expect to find it consistently in different studies and in different population It is expected to be present in subgroups of the population 12/21/2016 39
- 40. Biologic plausibility Coherencewith the current body of biologic knowledge Sometimes, epidemiological observation preceded biologic knowledge E.g. Gregg’s observation on Rubella and congenital cataracts preceded any knowledge of teratogenic viruses If epidemiological findings are not consistent with the existing knowledge – interpreting the meaning of observed association might be difficult 12/21/2016 40
- 41. Cessation of exposure If a factor is a cause of a diseases, the risk of the disease to decline when exposure to the factor is reduced or eliminated 12/21/2016 41
- 42. Consistency with otherknowledge 12/21/2016 42
- 43. Specificity of theassociation An association is specific when a certain exposure is associated with only one disease This is the weakest point of the Hills criteria – Smoking is linked with lung, pancreatic & bladder cancers; hearth disease, emphysema … Specificity of an association provides additional support for a causal inference 12/21/2016 43
- 44. Basic methods ofarriving at ‘The Cause’ Agreement ..common factor points to ‘cause’ (e.g in food poisoning episode, the food item common to meals of all affected is most suspect cause) Difference: In similar situations, the ‘only difference’ points to probable cause of a disease. (Polished rice vs unpolished rice caused beriberi in the first group, not the other) Analogy: parallel example to help suggest a cause (Kyasnur Forest Disease cause found by analogy to Yellow fever) Concomitant variation (seasonal changes in diseases)-more allergies in flowering seasons Residual or elimination method. 12/21/2016 44
- 45. Recap-keywords Variables Independent or exposurevariable Dependent or outcome variable Pre-disposing factors Contributing factors Enabling factors Precipitating factors Risk factors Confounding variables Association & Causation Association, Causation Direct Causation, Indirect causation Multifactorial causation Epidemiological triad Interaction of factors, Synergistic Conditional causation Confounding variables Spurious relation Necessary Cause, Sufficient cause Proving Causation Take care of BIAS/ERRORS Hills Modified criteria Strength of Association (Relative Risk/Odds ratio) Temporality Specificity Consistency Study design Evidence Experimental proof Dose-Response relation Coherence Agreement, difference, analogy, residual 12/21/2016 45
Editor's Notes
- #43 The absence of such consistency would not completely rule out this hypothesis