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Association & Causation

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1 Association & Causation

2 Framework Definitions Introduction
Historical theories of causation of disease Current concepts Factors in causation From association to causation How to establish the cause of a disease? Analytical approach Modern concept of causation

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

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

5 Koch’s postulates 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, when inoculated into a susceptible animal, cause the specific disease; The organism must then be recovered from the animal and identified.

6 Limitations of Koch postulate
Non communicable disease One to one relation are rare biology. Disease production may require co cofactors. Always 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. Always infection does not produce disease 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 Epidemiological triad
Agent Factors Physical Agents Chemical Agents Biological Agents Nutritional agents Agent Factors Physical Agents : (as heat, cold, vibrations, electricity, mechanical forces etc.). Chemical Agents : (as acids, alkalies, heavy metals, allergens, etc.). Biological Agents : (as viruses, bacteria, parasites, etc.). Nutritional agents : These are truly a part of chemical agents but often described as a separate category because of the Host Factors Socio-demographic Factors : Such as age, sex, occupation, education, marital status, etc. Psycho-social Factors : Such as attitudes, practices, behavioural patterns, life style, etc. Intrinsic Characteristics : Intrinsic Characteristics or the “biological, Immunological and Genetic factors” in a human being - e.g. genetic factors, HLA types, biochemical and physiological characteristics, etc. Environmental Factors Physical Environment : Such as seasons, climate, altitude, rainfall, etc. Biological Environment : e.g. arthropod vectors of diseases like mosquitoes, animal reservoirs like canines and rodents, etc. Social Environment : e.g. community attitudes, beliefs, practices and cultural factors affecting disease; level of socioeconomic development; availability of health services, etc. Environmental Factors Physical Environment Biological Environment Social Environment Host Factors Socio-demographic Factors Psycho-social Factors Intrinsic Characteristics

10 From association to causation
Spurious association B. Indirect association C. Direct (Causal) association 1. One –to- one causal association 2. Multifactorial causation Sufficient & necessary cause 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
F508 Polymorphism High cholesterol Artery thickening Hemostatic factors Myocardial infarction Cystic Fibrosis

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. Example Smoking Pancreatic cancer e.g. 1. Altitude & endemic goiter 2. Sucrose & CHD Coffee drinking

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

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

16 ii) Multifactorial causation
Multiple factor leads to the diseases Common in non-communicable diseases 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 Positive: Synergistic, Transadditivity,
Negative: Antagonist, Subadditivity Disease Factor A + Factor B - ? I- + I+ - I- -

20 I11 I10 I01 I00 No interaction INTERACTION I11= I01 I11=I10
0 – Factor absent 1 – Factor present Lung cancer Asbestos I11 I10 I01 I00 I11= I01 I11=I10 No interaction 1 Smoking

21 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 Necessary cause: necessary is factor without this disease/outcome never develops. Each sufficient cause has a necessary cause as a component. Sufficient cause: A cause is termed sufficient when it inevitably produces or initiates an outcome, that is in the presence of this factor disease always develops.

22 Sufficient & necessary cause
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
Susceptible host Infection Tubercu-losis

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

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

26 Appling guidelines (Hills criteria/Guidelines for causation) and making judgment regarding causation
Temporal relation Does the cause precede the effect? (essential) Plausibility Is the association consistent with other knowledge? (mechanism of action; evidence from experimental animals) Consistency Have 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 design Is 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) 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 day Smoking causing lung cancer Smoking causes skin cancer? Lack 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. Cigarette 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 strong Risk ratio Interpretation < 1 Protective No association Weak Causal association moderate causal association >2.6 Strong 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, –1961

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

34 7. Reversibility Fig 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” causation

36 9. Analogy (= Similarity, = reasoning from parallel cases)
Judging by analogy known effect of drug thalidomide & rubella in pregnancy 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 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.

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

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

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

41 Testing hypothesis of association
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 Model Causal 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?

46 R11 R10 R01 R00 R11-R10 R11/R10 R01-R00 R01/R00 Risk difference
1 – Factor present/ Ds present 0 – Factor absent /Ds absent Disease R11 R10 R01 R00 Risk difference Risk ratio R11-R10 R11/R10 R01-R00 R01/R00 1 Exp Risk difference R11-R01 R10-R00 Risk ratio R11/R00 R01/R00

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

Backdoor path

50 X Y Z U Causal Assumption Independency Marginal 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 Z X & 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. 4th ed. New York: Oxford university press; 2004. Beaglehole R, Bonita R. Basic epidemiology. Delhi: AITBS publisher & distributor; 2006. Park K. Park’s textbook of preventive & social medicine. 19th ed. Jabalpur: M/s Bhanarsidas Bhanot publishers; 2007. 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. 3rd ed. New Delhi: Wolter kluwar (India) pvt; 2009.

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