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Association & Causation. Framework DefinitionsDefinitions IntroductionIntroduction Historical theories of causation of diseaseHistorical theories of causation.

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Presentation on theme: "Association & Causation. Framework DefinitionsDefinitions IntroductionIntroduction Historical theories of causation of diseaseHistorical theories of causation."— Presentation transcript:

1 Association & Causation

2 Framework DefinitionsDefinitions IntroductionIntroduction Historical theories of causation of diseaseHistorical theories of causation of disease Current conceptsCurrent concepts Factors in causationFactors in causation From association to causationFrom association to causation How to establish the cause of a disease?How to establish the cause of a disease? Analytical approachAnalytical approach Modern concept of causationModern concept of causation

3 Definitions Association: define as occurrence of two variable more often than would be expected by chanceAssociation: define as occurrence of two variable more often than would be expected by chance Causal association: when cause and effect relation is seen.Causal association: when cause and effect relation is seen.

4 Historical Theories “Supernatural causes”& Karma“Supernatural causes”& Karma Theory of humors (humor means fluid)Theory of humors (humor means fluid) The miasmatic theory of diseaseThe miasmatic theory of disease Theory of contagionTheory of contagion Germ theoryGerm theory Koch’s postulatesKoch’s postulates

5 Koch’s postulates The organism must be present in every case of the disease;The organism must be present in every case of the disease; The organism must be able to be isolated and grown in pure culture;The organism must be able to be isolated and grown in pure culture; The organism must, when inoculated into a susceptible animal, cause the specific disease;The organism must, when inoculated into a susceptible animal, cause the specific disease; The organism must then be recovered from the animal and identified.The organism must then be recovered from the animal and identified.

6 Limitations of Koch postulate Non communicable diseaseNon communicable disease One to one relation are rare biology.One to one relation are rare biology. Disease production may require co cofactors.Disease production may require co cofactors. Always it is not possible to isolate organism from disease personAlways it is not possible to isolate organism from disease person Viruses cannot be cultured like bacteria because viruses need living cells in which to grow.Viruses cannot be cultured like bacteria because viruses need living cells in which to grow. Always infection does not produce diseaseAlways infection does not produce disease Pathogenic microbes may be present without clinical disease (sub sub-clinical infections, carrier states).Pathogenic microbes may be present without clinical disease (sub sub-clinical infections, carrier states).

7 Single or Multiple cause? One to one association Epidemiological triad Sufficient & Necessary cause (Specificity) Multi factorial causation Web of causation Interaction

8 Epidemiological triad

9 Agent Factors Physical Agents Chemical Agents Biological Agents Nutritional agents Host Factors Socio-demographic Factors Psycho-social Factors Intrinsic Characteristics Environmental Factors Physical Environment Biological Environment Social Environment

10 From association to causation A.Spurious association B. Indirect association C. Direct (Causal) association 1. One –to- one causal association 2. Multifactorial causation Sufficient & necessary cause Sufficient & necessary cause Web of causation (Interaction) Web of causation (Interaction)

11 Spurious association Not real e.g. More perinatal deaths in hospital delivery than home delivery. The cause of spurious association is poor control of Biases in study.

12 Direct Vs indirect cause High cholesterol Artery thickening Hemostatic factors Myocardial infarction Indirect  F508 Polymorphism Cystic Fibrosis Direct

13 Indirect association: Statistical association due to presence of another factor, known or unknown that is common both the characteristics & disease i.e. Confounding factors.Statistical association due to presence of another factor, known or unknown that is common both the characteristics & disease i.e. Confounding factors. ExampleExample Smoking Pancreatic cancer Coffee drinking e.g. 1. Altitude & endemic goiter 2. Sucrose & CHD

14 Direct ( Causal) association 1.One –to- one causal association 2. Multifactorial causation Sufficient & necessary cause Web of causation (Interaction)

15 One-to-one causal association A causing BA causing B e.g. Measlese.g. Measles CriticsCritics Haemolytic Streptococci Streptococal tonsilitis Scarlet fever Erysipelas

16 ii) Multifactorial causation Multiple factor leads to the diseasesMultiple factor leads to the diseases Common in non-communicable diseasesCommon in non-communicable diseases e.g. e.g. Smoking Air pollution Reaction at cellular level Lung cancer Exposure to asbestos

17 b. Interaction of multiple individual causes Smoking + Air pollution Reaction at cellular level Lung cancer + Exposure to asbestos Table 1: Age-standardized lung cancer death rates (per population) in relation to tobacco use and occupational exposure to asbestos dust

18 Web of causation Change in life style Stress Abundance of food Smoking Emotional Aging & D Disturbance other factor Obesity Lack of physical activity Hypertension Hyperlidemia Increase catacholamine Changes in walls of arteries thrombotic activity Coronory atherosclerosis Coronary occlusion Myocardial Infarction

19 INTERACTION Disease Factor A Factor B - Positive: Synergistic, Transadditivity, Negative: Antagonist, Subadditivity I+ - I- - I- + ?

20 INTERACTION Lung cancer Asbestos 1 0 I 11 I 10 I 01 I 00 1 Smoking 0 0 – Factor absent 1 – Factor present I 11 = I 01 I 11 =I 10 No interaction

21 Sufficient & necessary cause Sufficient & necessary cause Necessary cause is without this disease/outcome never develops. Sufficient cause: presence of this factor disease always develops. Component cause: Supporting causes, per se they can not develop ds Necessary causes + Component causes = Sufficient cause

22 Sufficient & necessary cause Sufficient & necessary cause A U B C N Known components (causes) – A, B, C, N Unknown component (cause)- U N – Necessary cause Known components + Unknown component cause + Necessary cause = Sufficient cause

23 Figure 1: Causes of tuberculosis Infection Tubercu -losis Susceptible host

24 There may be number of sufficient causes for single disease in various combination of component causes, necessary causes U A B U A E U A B Disease A U B E N A U D C N A U B C N

25 How to establish the cause of a disease? Could it be due to selection or measurement bias? Could it be due to confounding? Could it be causal? Could it be a result of chance? No Probably not Apply guidelines and make judgment OBSERVED ASSOCIATION? No

26 Appling guidelines (Hills criteria/Guidelines for causation) and making judgment regarding causation Temporal relationDoes the cause precede the effect? (essential) Plausibility Is the association consistent with other knowledge? (mechanism of action; evidence from experimental animals) ConsistencyHave similar results been shown in other studies? Strength What is the strength of the association between the cause and the effect? (relative risk) Dose–response relationship Is increased exposure to the possible cause associated with increased effect? Reversibility Does the removal of a possible cause lead to reduction of disease risk? Study designIs the evidence based on a strong study design? Judging the evidence How many lines of evidence lead to the conclusion?

27 1. Temporal relationship (Relationship with time) Cause must precede the effect. (Essential)Cause must precede the effect. (Essential) Which is cart & Which hourse? Drinking contaminated water occurrence of diarrhea However many chronic cases, because of insidious onset and ignorance of precise induction period, it become hard to establish a temporal sequence as which comes first -the suspected agent or disease.

28 2.. Plausibility ( Biological plausibility) Consistent with biological knowledge of dayConsistent with biological knowledge of day Smoking causing lung cancerSmoking causing lung cancer Smoking causes skin cancer?Smoking causes skin cancer? Lack of plausibility may simply reflect lack of scientific knowledgeLack of plausibility may simply reflect lack of scientific knowledge

29 3. Consistency of association Different persons, in Different places, in Different circumstances & times by Different method (by various types studies) is established the Same result by several studies.Different persons, in Different places, in Different circumstances & times by Different method (by various types studies) is established the Same result by several studies. Cigarette smoking and lung cancer. More than 50 retrospective studies and at least nine prospective studiesCigarette smoking and lung cancer. More than 50 retrospective studies and at least nine prospective studies

30 Meta-analysis of the relative risk of cleft palate in the offspring of mothers who smoked during pregnancy compared with the offspring of mothers who did not smoke

31 4. Strength of association Relative risks/Odds ratio greater than 2 can be considered strongRelative risks/Odds ratio greater than 2 can be considered strong Risk ratioInterpretation < 1 Protective No association Weak Causal association moderate causal association >2.6Strong causal association

32 5. Dose – response relationship ( The Biological gradient ) Death rates from lung cancer (per 1000) by number of cigarettes smoked, British male doctors, 1951 –1961 Death rates from lung cancer (per 1000) by number of cigarettes smoked, British male doctors, 1951 –1961

33 6. Specificity 6. Specificity One to one associationOne to one association CriticsCritics Haemolytic Streptococal tonsilitis Streptococci Scarlet fever Erysipelas

34 7. Reversibility Fig 7: Stopping works: cumulative risk of lung cancer mortalityFig 7: Stopping works: cumulative risk of lung cancer mortality Critics eg Infection of HIV/ AIDS

35 8. Study design Relative ability of different types of study to “prove” causationRelative ability of different types of study to “prove” causation

36 9 9. Analogy (= Similarity, = reasoning from parallel cases) Judging by analogyJudging by analogy known effect of drug thalidomide & rubella in pregnancyknown effect of drug thalidomide & rubella in pregnancy accepting slighter but similar evidence with another drug or another viral disease accepting slighter but similar evidence with another drug or another viral disease

37 10. Coherence of association & Judging the evidence Based on available evidence or should be coherence with known facts that are thought to be relevant: uncertainty always remainsBased on available evidence or should be coherence with known facts that are thought to be relevant: uncertainty always remains Correct temporal relationship is essential; then greatest weight may be given to plausibility, consistency and the dose–response relationship. The likelihood of a causal association is heightened when many different types of evidence lead to the same conclusion.Correct temporal relationship is essential; then greatest weight may be given to plausibility, consistency and the dose–response relationship. The likelihood of a causal association is heightened when many different types of evidence lead to the same conclusion.

38 Critics on Hill’s guideline on causation Criteria Vs Guidelines Vs considerationCriteria Vs Guidelines Vs consideration Except for temporality, none of the Hill’s criteria is absolute for establishing a causal relationExcept for temporality, none of the Hill’s criteria is absolute for establishing a causal relation

39 Analytical Methods Measures of association /strength of association Measures of association /strength of association Testing hypothesis of associationTesting hypothesis of association Controlling confoundersControlling confounders

40 Measures of association / strength of association 1. Ratio measures - Relative risk - Odds ratio 2. Difference measures 2. Difference measures -Attributable risk -Attributable risk -Population Attributable risk -Population Attributable risk

41 Testing hypothesis of association Null Hypothesis Null Hypothesis Rejecting Accepted Causal association Not causal association

42 Controlling confounders At time designing of epidemiological study or while carrying study Randomization Restriction Matching At analysis stage Stratification Adjustment Statistical modeling

43 Modern concepts in causation Counterfactual ModelCounterfactual Model Causal diagramCausal diagram

44 Counterfactual model (Potential outcome model) When we are interested to measure effect of a particular cause, we measure effect in a population who are exposed Imagine amount of effect which would have been observed, if the same population would not have been exposed to that cause, all other conditions remaining identical. We calculate risk ratios & risk differences based on this model The difference of the two effect measures is the effect due the cause we are interested in.

45 Example Will smoking ban decrease the rate of lung cancer in 10 yrs beyond what can be expected in absence of ban?Will smoking ban decrease the rate of lung cancer in 10 yrs beyond what can be expected in absence of ban?

46 Disease 1 0 R 11 R 10 R 01 R 00 1 Exp 0 1 – Factor present/ Ds present 0 – Factor absent /Ds absent Risk difference Risk ratio R 11 -R 10 R 11 /R 10 R 01- R 00 R 01 /R 00 Risk difference R 11 -R 01 R 10 -R 00 Risk ratioR 11 /R 00 R 01 /R 00

47 Causal Diagram Confounding is complex phenomenon – Need to understand relation Useful for analysis of confounders Conceptual definition of variable involved Directionality of causal association Need some level of understanding (Knowledge & hypothetical) – relation between risk factor, confounders & outcome. Directed Acyclic Graph (DAG)

48 Causal Diagram X Y Z U

49 EXAMPLE ASPIRIN PLATELET AGGREGATION CHD ? Collider Backdoor path

50 X Y Z U X Y Z U Causal AssumptionIndependencyMarginal association Conditional association X & Y are each direct cause of Y (Direct with respect to other variable in Diagram) X & Y are independent (only path between them is blocked by the collider) X & Y are associated X & U are associated conditional on Y (Conditional on a collider unblocks the path) Y is direct cause of ZX & Y are independent conditioned on Y (Conditioning on Y blocks the path between X & Z) U & Y are associated X & U are associated conditional on Z (Z is a descendent of collider) X is not a direct cause of Z, but X is an indirect cause of Z via Y U & Z are independent conditional on Y Y & Z are associated No 2 variable in diagram(X,U, Y, Z) share prior cause not shown in the diagram eg. No variable causes both X & Y, or both X & U X & Z are associated U & Z are associated

51 References : Hill AB. The environment and disease: association or causation? Proc R Soc Med 1965;58: Hill AB. Bradford Hill ’ s Principle of Medical statistics. Ed first Indian addition New Delhi: B. I. Publication pvt limited. Detels R, McEwen J, Beaglhole R, Tanaka H. Oxford textbook of public health. 4 th ed. New York: Oxford university press; Beaglehole R, Bonita R. Basic epidemiology. Delhi: AITBS publisher & distributor; Park K. Park’s textbook of preventive & social medicine. 19 th ed. Jabalpur: M/s Bhanarsidas Bhanot publishers; Galea S, Riddle M, Kaplan GA. Causal thinking & complex system approach in epidemiology. International journal of epidemiology Feb; 39(1): Rothman KJ, Greenland S, Lash TL. Modern epidemiology. 3 rd ed. New Delhi: Wolter kluwar (India) pvt; 2009.


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