Association & Causation

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

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.

Historical Theories

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

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.

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).

Single or Multiple cause?

One to one association Epidemiological triad Sufficient & Necessary cause

(Specificity) Multi factorial causation Web of causation Interaction

Epidemiological triad

Epidemiological triad Agent FactorsPhysical Agents Chemical Agents Biological Agents Nutritional agents

Host FactorsSocio-demographic Factors Psycho-social Factors Intrinsic Characteristics

Environmental FactorsPhysical Environment Biological Environment Social Environment

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

Web of causation (Interaction)

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.

Direct Vs indirect cause

High cholesterol

Artery thickening

Hemostatic factors

Myocardial infarction

Indirect

F508 Polymorphism

Cystic Fibrosis

Direct

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

Coffee drinking e.g. 1. Altitude & endemic goiter 2. Sucrose & CHD

Direct ( Causal) association

1. One –to- one causal association

2. Multifactorial causation Sufficient & necessary cause Web of causation (Interaction)

One-to-one causal association

• A causing B• e.g. Measles

• Critics

Haemolytic Streptococci

Streptococal tonsilitisScarlet feverErysipelas

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

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 100 000 population) in relation to tobacco use and occupational exposure to asbestos dust

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

Myocardial Infarction

INTERACTION

Disease

Factor A + -

+Factor B -

Positive: Synergistic, Transadditivity, Negative: Antagonist, Subadditivity

I+ - I- - I- + ?

INTERACTION

Lung cancer

Asbestos 1 0

I11 I10

I01 I00

1Smoking 0

0 – Factor absent 1 – Factor present

I11= I01 I11=I10

No interaction

 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

 Sufficient & necessary cause

AU 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

Figure 1: Causes of tuberculosis

Infection

Tubercu-losis

Susceptible host

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

AU B

E N

AU D

C N

AU B

C N

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

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?

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.

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

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

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

4 . Strength of association

• Relative risks/Odds ratio greater than 2 can be considered strong

Risk ratio Interpretation< 1 Protective 0.9-1.1 No association 1.2- 1.6 Weak Causal association1.7- 2.5 moderate causal association>2.6 Strong causal association

5. Dose – response relationship ( The Biological gradient )

Death rates from lung cancer (per 1000) by number of cigarettes smoked, British male doctors, 1951 –1961

 6. Specificity       

• One to one association

• Critics

Haemolytic Streptococal tonsilitisStreptococci

Scarlet fever Erysipelas

7. Reversibility• Fig 7: Stopping works: cumulative risk of lung cancer mortality

Critics• eg Infection of HIV/ AIDS

8. Study design

• Relative ability of different types of study to “prove” causation

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

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.

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

Analytical Methods

• Measures of association /strength of association

• Testing hypothesis of association

• Controlling confounders

Measures of association / strength of association

1. Ratio measures

- Relative risk

- Odds ratio

2. Difference measures

-Attributable risk

-Population Attributable risk

Testing hypothesis of association

• Null Hypothesis

Rejecting Accepted

Causal association Not causal association

Controlling confounders

At time designing of epidemiological study or while carrying study• Randomization• Restriction• Matching

At analysis stage• Stratification• Adjustment• Statistical modeling

Modern concepts in causation

• Counterfactual Model• Causal diagram

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.

Example

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

Disease 1 0

R11 R10

R01 R00

1Exp 0

1 – Factor present/ Ds present 0 – Factor absent /Ds absent

Risk difference

Risk ratio

R11-R10 R11/R10

R01-R00 R01/R00

Risk difference

R11-R01 R10-R00

Risk ratio R11/R00 R01/R00

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)

Causal Diagram

X

Y Z

U

EXAMPLE

ASPIRIN

PLATELET AGGREGATION

CHD

?

GENETIC VARIENT

Collider

Backdoor path

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

References :•Hill AB. The environment and disease: association or causation? Proc R Soc Med 1965;58:295-300.

•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. 2010 Feb; 39(1):97-106.

•Rothman KJ, Greenland S, Lash TL. Modern epidemiology. 3rd ed. New Delhi: Wolter kluwar (India) pvt; 2009.