05/01/2023

1

Association & Causation A BASIC CONCEPT IN EPIDEMIOLOGYDR SHYAM ASHTEKAR, SMBT MED COLLEGE DEC 2016

05/01/2023

2Is Nota-bandi cause of Q deaths?

05/01/2023

3A chain of Events

Demonetization of

high value notes

No funds

at home

Long queues

Heart attack

Man died

05/01/2023

4Contributory causes

Demonetization

Long queue

Heart attack

Old age

Man died

05/01/2023

5On deaths ‘due to’ 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

05/01/2023

6The questions

Did it 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?

05/01/2023

7What we shall learn in this?

About ‘variables’

Proving causation in Epidemiology

Association to causation

05/01/2023

8

It is all about Variables/Factors/Events

05/01/2023

9The relation of variables!

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

05/01/2023

10Factors…Risk factors (variables)

Predisposing

Enabling/disabling Precipitatin

g Reinforcing

05/01/2023

11Confounding -factors that confuse/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

Diabetes

Type2

05/01/2023

12

About Association & CausationIMPORTANT CONCEPTS

05/01/2023

13Why 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)

05/01/2023

14Association & 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)

05/01/2023

15Types of Association

Association

Causal

Direct Indirect Interaction

Non-causal

ChanceBias/

Confounding

Ecological

05/01/2023

16Spurious Association

Spurious (not true) association

Not real, only apparentExample1: 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.

05/01/2023

17Direct Causation

Independent variable 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.

05/01/2023

18Indirect causation

Some factor leads to another factors/event and through that the disease event.

Streptococcal sore throat

Rheumatic fever

Rheumatic carditis/valve

damage

05/01/2023

19Interaction of causative factors-

Synergy-both factors work together- IHD

BMI Smoking

Protective (negative) effect of a factor--IHD

Physical work Aging

05/01/2023

20Conditional factors

Sometimes/Often another factor is necessary for a causative factor to lead to disease.

Viral Fever in child Aspirin

Rey’s syndrome (rapidly

progressing encephalitis

05/01/2023

21Necessary AND sufficient cause

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

05/01/2023

22Necessary but not sufficient

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

??

05/01/2023

23One 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.

alcoholism

Liver cirrhosis

neuritis

Gastritis

dementia

05/01/2023

24Multiple –multifactorial-causes ..

In most non-communicable diseases ,multiple factors have a varying role to play..cancers, ischemic heart disease, aging etc

IHDBMI

Stress

Hypertension

Smoking

??

05/01/2023

25Multifactorial causation-Often true of NCDs

Ageing

Obesity

High Calorie diets

Insulin Resistance

Lack of exercise

Genetic traits

Diabetes Type 2

05/01/2023

26Multiple variables in causation

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/

emissions

05/01/2023

27Multi-factorial cause—Epidemiological Triangle

Disease Agent factors

Host/Group factors

Time

Environmental

factors

05/01/2023

28Summary of Causal Models

Caus

al m

odel

s 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)

05/01/2023

29

Proving association/causation

05/01/2023

30Problems of proving causal 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.

05/01/2023

31Steps for Establishing Causality 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)

05/01/2023

32Evidence for a causal 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

05/01/2023

33Hill’s Modified Criteria of causation

Temporal precedence (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!

05/01/2023

34Temporal relationship

Exposure to 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)

05/01/2023

35Temporal relationship cont.

05/01/2023

36Strength 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

05/01/2023

37Dose 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

05/01/2023

38Dose response relationship cont.

05/01/2023

39Replication 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

05/01/2023

40Biologic plausibility

Coherence with 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

05/01/2023

41Cessation 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

05/01/2023

42Consistency with other knowledge

05/01/2023

43Specificity of the association

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

05/01/2023

44Basic methods of arriving 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.

05/01/2023

45Recap-keywords

VariablesIndependent or exposure variableDependent or outcome variablePre-disposing factorsContributing factorsEnabling factorsPrecipitating factorsRisk factorsConfounding variables

Association & CausationAssociation, CausationDirect Causation, Indirect causationMultifactorial causationEpidemiological triadInteraction of factors, SynergisticConditional causationConfounding variablesSpurious relationNecessary Cause, Sufficient cause

Proving CausationTake care of BIAS/ERRORSHills Modified criteriaStrength of Association (Relative Risk/Odds ratio)Temporality Specificity ConsistencyStudy designEvidence Experimental proofDose-Response relationCoherenceAgreement, difference, analogy, residual