1

ASSOCIATION & CAUSATION

DR. PRIYANKA SHARMA

III YEAR M.D.S

DEPARTMENT OF PUBLIC HEALTH DENTISTRY

JSS DENTAL COLLEGE & HOSPITAL

2CONTENTS INTRODUCTION

APPROACHES FOR STUDYING DISEASE ETIOLOGY

HISTORY

WHAT IS ASSOCIATION

TYPES OF ASSOCIATION

WHAT IS CAUSE

GENERAL MODELS OF CAUSATION

TYPES OF CAUSAL RELATIONSHIP

CRITERIA FOR A CAUSAL RELATIONSHIP

GUIDELINES FOR JUDGING WHETHER THE ASSOCIATION IS CAUSAL

EVIDENCE FOR A CAUSAL RELATIONSHIP

DERIVING CAUSAL INFERENCES: EXAMPLE

MODIFIED GUIDELINES FOR EVALUATING THE EVIDENCE OF A CAUSAL RELATIONSHIP

MEASURES OF ASSOCIATION

CONCLUSION

REFERENCES

3

INTRODUCTION

4

In The Magic Years, Fraiberg (1959) characterized every toddler

as a scientist, busily fulfilling an earnest mission to develop a

logical structure for the strange objects and events that make up

the world that he or she inhabits.

Each person develops and tests an inventory of causal

explanations that brings meaning to the events that are

perceived and ultimately leads to increasing power to control

those events.

The fruit of such scientific labours is a working knowledge of the

essential system of causal relations that enables each of us

to navigate our complex world.

5

In epidemiological studies, ascertainment of cause-effect

relationships is one of the central and most difficult tasks of all

scientific activities.

Epidemiological principles stand on two basic assumptions:

Human disease does not occur at random.

The disease and its cause as well as preventive factors can be

identified by a thorough investigation of population.

Hence, identification of causal relationship between a disease and

suspected risk factors forms part of epidemiological research.

6

APPROACHES FOR STUDYING DISEASE

ETIOLOGY

7Strength of evidence of

studies

Systematic review or meta-analysis of RCTs

Double-blind RCTs

Single-blind RCTs

Randomized, controlled trials (RCTs)

Non-randomized / uncontrolled experimental studies

cohort studies

Case-control studies

Ecological studies

Cross-sectional studies

Expert opinions, anecdotal reports

Approach for

studying disease

etiology

8

Conceptually, a two-step process is followed in carrying out studies

and evaluating evidence:

1. Determine whether there is an association between an exposure or

characteristic and the risk of a disease. To do so, we use:   

a. Studies of group characteristics: ecologic studies   

b. Studies of individual characteristics: case-control and cohort studies   

2. If an association is demonstrated, we determine whether the observed

association is likely to be a causal one or not.

9Ecologic Studies

The first approach in determining whether an association exists might be to conduct studies of group characteristics, called ecologic studies.

ECOLOGICAL FALLACY : Eg.relationship between breast cancer incidence and average dietary fat consumption in each country

ECOLOGICAL INFERENCE FALLACY: Eg.areas with high concentrations of farm animals are also the areas with lowest concentrations of childhood asthma.

It’s a fallacy to then assume that a child who has asthma must not live near any farm animals

10So? Do You Have Enough Info To Inform The

Patient?

11

Recognizing the limitations discussed above of ecologic studies

that use only group data, we turn next to studies of individual

characteristics: case-control and cohort studies.

In case-control or cohort studies, for each subject we have

information on both exposure (whether or not and, often, how

much exposure occurred) and disease outcome (whether or not

the person developed the disease in question).

12

HISTORY

13Historical Theories of

disease causation

• “Supernatural causes”& Karma

• Theory of humors (humor means fluid)

• The miasmatic theory of disease

• Theory of contagion

• Germ theory

• Koch’s postulates

14EVIDENCE FOR A CAUSAL

RELATIONSHIP

In 1840, Henle proposed postulates for causation that were expanded by Koch

in the 1880s.The postulates for causation were as follows:   

1.    The organism is always found with the disease.   

2.    The organism is not found with any other disease.   

3.    The organism, isolated from one who has the disease, and cultured

through several generations, produces the disease (in experimental

animals).

Koch added that “Even when an infectious disease cannot be transmitted to

animals, the ‘regular’ and ‘exclusive’ presence of the organism [postulates

1 and 2] proves a causal relationship.”

15

These postulates, though not perfect, proved very useful for

infectious diseases

However, as apparently noninfectious diseases assumed

increasing importance toward the middle of the 20th century,

The issue arose as to what would represent strong evidence of

causation in diseases that were generally not of infectious

origin.

16

ASSOCIATION

17Association

Syn: Correlation, Covariation, Statistical dependence, Relationship

Defined as occurrence of two variables more often than would be

expected by chance.

An association is present if probability of occurrence of a variable

depends upon one or more variable.

(A dictionary of

Epidemiology by John M. Last)

18

If two attributes say A and B are found to co-exit more often than an ordinary chance.

It is useful to consider the concept of correlation.

Correlation indicates the degree of association between two variables

Causal association: when cause and effect relation is seen.

19

Pyramid Of Associations

Raj Bhopal : Cause and effect: the epidemiological approach

Causal

Non-causal

Confounded

Spurious

Positive /negative

20

Positive: Occurrence of higher value of a predictor variable is

associated with occurrence of higher value of another dependent

variable. Ex- education and suicide.

Negative: Occurrence of higher value of a predictor variable is

associated with lower value of another dependent variable.

Ex - Female literacy and IMR

21

Causal: Independent variable must cause change in dependent

variable.

Definite condition of causal associations are time and direction

Ex – salt intake and hypertension

Non-causal: Non-directional association between two variables.

Ex – alcohol use and smoking

22

Spurious Association

(Spurious= not real, artificial, fortuitous, false, non-causal associations due to chance, bias or confounding)

Observed association between a disease and suspected factor may not be real.

This is due to selection bias

Eg: Increased water intake and crime rate in summer.

The ringing of alarm clocks and rising of the sun.

Cock’s crow causes sun to rise.

23

Ex : Neonatal mortality was observed to be more in the newborns born

in a hospital than those born at home. This is likely to lead to a

conclusion that home delivery is better for the health of newborn.

However, this conclusion was not drawn in the study

because the proportion of “high risk” deliveries was found to be

higher in the hospital than in home.

24

Indirect Association

It is a statistical association between a characteristic of interest

and a disease due to the presence of another factor i.e. common

factor (confounding variable).

So the association is due to the presence of another factor which is common to both, known as CONFOUNDING factor.

Ex:

1.Rahul is a friend with Suma, and Suma is Shoba’s friend, so

Shoba is Rahul ’s friend too but indirectly. The common friend is

Suma.

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2. Altitude and endemic goiter confounding factor is iodine

deficiency.

3. Glucose and CHD ,confounding factor is cigarette smoking(it

increase the of cups of coffee and amount of sugar u consume)

26

Direct Association

The association between the two attributes is not through the third attributes.

When the disease is present, the factor must also be present.

27

Direct (Causal) association:

1. One –to- one causal association

2. Multifactorial causation

Sufficient & necessary cause

Web of causation (Interaction)

28

One-to-one Casual Relationship

The variables are stated to be casual related (AB) if a change in A is followed by a change in B.

When the disease is present, the factor must also be present.

A single factor (cause) may lead to more than one outcome.

But its not always that simple , as some causes can cause more than 1 disease like streptococci

Hemolytic Streptococci

Streptococcal tonsillitisScarlet feverErysipelas

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Multifactorial causation

Multiple factor leads to the disease.

Common in non-communicable diseases

Alternative causal factors each acting independently.

Ex: In lung cancer more than one factor (e.g. air pollution, smoking,

heredity) can produce the disease independently.

Either the causes are acting

Independently OR Cumulatively

30

Smoking Air pollution Reaction at cellular level Lung cancer Exposure to asbestos

Smoking +Air pollution Reaction at cellular level Lung cancer + Exposure to asbestos

Independently

Cumulatively

31

CAUSATION

32WHAT IS CAUSE

The word cause is the one in general usage in connection with

matters considered in this study, and it is capable of conveying the

notion of a significant, effectual relationship between an agent and

an associated disorder or disease in the host.”

1964 Surgeon General Report

33

General Models of Causation

The most widely applied models are:

– The epidemiological triad (triangle),

– The web

– The wheel and

– The sufficient cause and component causes models

(Rothman’s component causes model)

34Epidemiological 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

35Web of Causation

DISEASEbehaviour

Unkno

wn

fact

orsgenes

phenotyp

e

workplace

microbes

environment

36

Web of Causation - CHD

CHD

Unkno

wn

fact

ors

gender

inflamm

ation

med

icat

ions

lipids

physical activityblo

od

pre

ssure

stress

37

Social Environment

Genetic Core

Biological Environment

Host (human

)

Physical Environment

Wheel of Causation

38

Sufficient & Necessary Cause

NECESSARY cause - causal factor whose presence is required for the occurrence of the effect. If disease does not develop without the factor being present, then we term the causative factor “necessary”.

Ex: Agent in Malaria: Plasmodium falciparum parasite is necessary factor- always

present.

SUFFICIENT cause - “minimum set of conditions, factors or events

needed to produce a given outcome. Usually there’s no sufficient factor

“rare”.

The factors or conditions that form a sufficient cause are called

component causes.

Necessary causes + Component causes = Sufficient cause

39Rothman’s Component Causes and

Causal Pies Model

• Rothman's model has emphasised that the causes of disease

comprise a collection of factors.

• These factors represent pieces of a pie, the whole pie (combinations

of factors) are the sufficient causes for a disease.

• It shows that a disease may have more that one sufficient cause, with

each sufficient cause being composed of several factors

40

• The factors represented by the pieces of the pie in this model are called

component causes.

• Each single component cause is rarely a sufficient cause by itself, But

may be necessary cause.

• Control of the disease could be achieved by removing one of the

components in each "pie" and if there were a factor common to all

"pies“ (necessary cause) the disease would be eliminated by removing

that alone. AU B

C N

41

AU B

C N

Known components (causes) – A, B, C Unknown component (cause) - U

N – Necessary cause

Known components causes +Unknown component cause = Sufficient cause + Necessary cause

42Causes of tuberculosis

Infection

Tubercu-losis

Susceptible host

43

TYPES OF CAUSAL RELATIONSHIPS

44

If a relationship is causal, four types of causal relationships are possible:

(1) Necessary And Sufficient

(2) Necessary, But Not Sufficient

(3) Sufficient, But Not Necessary

(4) Neither Sufficient Nor Necessary

45

Necessary and Sufficient

A factor is both necessary and sufficient for producing the disease.

Without that factor, the disease never develops and in the presence of that factor, the disease always develops

Types of causal relationships I:

Each factor is both necessary and sufficient

FACTOR A DISEASE

46

Necessary, But Not Sufficient

Each factor is necessary, but not, in itself, sufficient to cause the disease .

Thus, multiple factors are required, often in a specific temporal sequence.

Ex: Carcinogenesis is considered to be a multistage process involving both

initiation and promotion. A promoter must act after an initiator has acted.

Action of an initiator or a promoter alone will not produce a cancer

47

Types of causal relationships: Each factor is necessary, but not sufficient

48

Sufficient But Not Necessary

The factor alone can produce the disease, but so can other factors that are acting

alone

Either radiation or benzene exposure can each produce leukemia without the presence

of the other.

Even in this situation, however, cancer does not develop in everyone who has

experienced radiation or benzene exposure, so although both factors are not needed,

other cofactors probably are. Thus, the criterion of sufficient is rarely met by a single

factor.

49

Each factor is sufficient, but not necessary

50

Neither Sufficient Nor Necessary

A factor by itself, is neither sufficient nor necessary to produce disease

This is a more complex model, which probably most accurately represents the causal relationships that operate in most chronic diseases.

Types of causal relationships: IV. Each factor is neither sufficient nor necessary

51When we can say that this association is likely to be

causation??

We have certain criteria that should be present:

– Temporal association

– Strength of association

– Specificity of association

– Consistency of association

– Biological plausibility

– Coherence of association

52

53Guidelines for Judging Whether an Association Is Causal (Leon Gordis)

1.    Temporal relationship   

2.    Strength of the association   

3.    Dose-response relationship   

4.    Replication of the findings   

5.    Biologic plausibility   

6.    Consideration of alternate explanations   

7.    Cessation of exposure   

8.    Consistency with other knowledge   

9.    Specificity of the association

54

Temporal association

The causal attribute must precede the disease or unfavorable outcome.

Exposure to the factor must have occurred before the disease developed.

Length of interval between exposure and disease very important .

Its more obvious in acute disease more than in chronic disease

55

Temporal relationship (Relationship with time)

• Cause must precede the effect.

Drinking contaminated water occurrence of diarrhea

However in 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.

56

Strength Of The Association

Relationship between cause and outcome could be strong or

weak.

With increasing level of exposure to the risk factor an increase in

incidence of the disease is found.

Strong associations are more likely to be causal than weak.

Weaker associations are more likely to be explained by

undetected bias.

But weaker association does not rule out causation.

57

• Strength of association can be estimated by relative risk, attributable risk etc.

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

Dose-Response Relationship ( The Biological gradient )

As the dose of exposure increases, the risk of disease also increases

If a dose-response relationship is present, it is strong evidence for a causal relationship.

However, the absence of a dose-response relationship does not necessarily rule out a causal relationship.

In some cases in which a threshold may exist, no disease may develop up to a certain level of exposure (a threshold); above this level, disease may develop

58

59Death rates from lung cancer (per 1000) by number of cigarettes smoked, British male

doctors, 1951 –1961

60

Biologic Plausibility Of The Association

The association must be consistent with the other knowledge (viz

mechanism of action, evidence from animal experiments etc).

Sometimes the lack of plausibility may simply be due to the lack

of sufficient knowledge about the pathogenesis of a disease.

It is too often not based on logic or data but only on prior beliefs.

It is difficult to demonstrate where the confounder itself exhibits

a biological gradient in relation to the outcome.

61

Consideration of Alternate Explanations

Interprets an observed association in regard to whether a

relationship is causal or is the result of confounding.

In judging whether a reported association is causal, the extent

to which the investigators have taken other possible

explanations into account and the extent to which they have

ruled out such explanations are important considerations.

Cessation of Exposure

If a factor is a cause of a disease, we would

expect the risk of the disease to decline when

exposure to the factor is reduced or eliminated

62

63

Consistency Of The Association

Consistency is the occurrence of the association at some other time and place repeatedly unless there is a clear reason to expect different results.

If a relationship is causal, the findings should be consistent with other data. Lack of consistency however does not rule out a causal association.

Repeated observation of an association in different populations under different circumstances.

Specificity Of The Association

The weakest of the criteria. (should probably be eliminated)

Specific exposure is associated with only one disease.

Specificity implies a one to one relationship between the cause and effect.

It’s the most difficult to occur for 2 reasons:

Single cause or factor can give rise to more than 1 disease

Most diseases are due to multiple factors.

Ex: Smoking is associated with many diseases.

• Not everyone who smokes develops cancer

• Not every one who develop cancer has smoke

64

65

Analogy (Similarity, reasoning from parallel cases)

• Provides a source of more elaborate hypotheses about the

associations under study.

• Absence of such analogies only reflects lack of imagination or

experience , not falsity of the hypothesis.

Ex: Known effect of drug Thalidomide & Rubella in pregnancy

• Accepting slighter but similar evidence with another drug or another

viral disease

Coherence of the association and 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.

66

67

Deriving causal inferences: example

Assessment of the Evidence Suggesting Helicobacter pylori Ulcers as a Causative Agent of Duodenal   

1.    Temporal relationship.   

•    Helicobacter pylori is clearly linked to chronic gastritis. About 11% of chronic gastritis patients will go on to have duodenal ulcers over a 10-year period.   

2.    Strength of the relationship.   

•    Helicobacter pylori is found in at least 90% of patients with duodenal ulcer.

68

3. Dose-response relationship.   

•    Density of Helicobacter pylori per square millimeter of gastric mucosa is higher in patients with duodenal ulcer than in patients without duodenal ulcer

4.    Replication of the findings.(consistency)   

•    Many of the observations regarding Helicobacter pylori have been replicated repeatedly

5.    Consideration of alternate explanations.   

•    Data suggest that smoking can increase the risk of duodenal ulcer in Helicobacter pylori-infected patients but is not a risk factor in patients in whom Helicobacter pylori has been eradicated

69

6.    Biologic plausibility.   

•  Originally it was difficult to envision a bacterium that infects the stomach antrum causing ulcers in the duodenum, but is now recognized that Helicobacter pylori has binding sites on antral cells and can follow these cells into the duodenum.   

•    Helicobacter pylori also induces mediators of inflammation.   

•  Helicobacter pylori-infected mucosa is weakened and is susceptible to the damaging effects of acid.   

7.    Cessation of exposure.   

•  Eradication of Helicobacter pylori heals duodenal ulcers at the same rate as histamine receptor antagonists.   

•  Long-term ulcer recurrence rates were zero after Helicobacter pylori was eradicated using triple-antimicrobial therapy,.

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8.    Specificity of the association.   

•  Prevalence of Helicobacter pylori in patients with duodenal ulcers is 90% to 100%.

9.    Consistency with other knowledge.   

• Prevalence of Helicobacter pylori infection is the same in men as in women. The incidence of duodenal ulcer, which in earlier years was believed to be higher in men than in women, has been equal in recent years.   

•  The prevalence of ulcer disease is believed to have peaked in the latter part of the 19th century, and the prevalence of Helicobacter pylori may have been much higher at that time because of poor living conditions.

71Modified Guidelines for Evaluating the Evidence of a Causal Relationship. (In

each category, studies are listed in descending priority order.) 1990

1.    Major criteria   

a.    Temporal relationship: An intervention can be considered evidence of a reduction in risk of disease or abnormality only if the intervention was applied before the time the disease or abnormality would have developed.   

b.    Biological plausibility: A biologically plausible mechanism should be able to explain why such a relationship would be expected to occur.   

72

c.    Consistency:

Single studies are rarely definitive. Study findings that are replicated in different populations and by different investigators carry more weight than those that are not. If the findings of studies are inconsistent, the inconsistency must be explained.   

d.    Alternative explanations (confounding):

The extent to which alternative explanations have been explored is an important criterion in judging causality

73

2.    Other considerations   

a.    Dose-response relationship:

If a factor is the cause of a disease, usually the greater the exposure to the factor, the greater the risk of the disease. Such a dose-response relationship may not always be seen because many important biologic relationships are dichotomous, and reach a threshold level for observed effects.   

b.    Strength of the association:

Usually measured by the extent to which the relative risk or

odds depart from unity.

c.    Cessation effects:

If an intervention has a beneficial effect, then the benefit should cease when it is removed from a population.

74

Modern concepts in causation

• Counterfactual Model

• Causal diagram

75Counterfactual model (Potential

outcome model)

When we are interested to measure effect of a particular cause, we measure effect in a population who are exposed.

• 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.

76

Causal Diagram

• Confounding is complex phenomenon.

• 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)

77

ANALYTICAL METHOD –

ASSOCIATION MEASURES

78

Analytical Methods

• Measures of association /strength of association

• Testing hypothesis of association

• Controlling confounders

79

Measures of association

Ratio measures

Measures of association in which relative differences between groups being compared

Difference measures

Difference measures are measures of association in which absolute differences between groups being compared .

80

Absolute differences:(difference measures )

Main goal is often an absolute reduction in the risk of an undesirable outcome.

When outcome of interest is continuous, the assessment of mean absolute differences between exposed and unexposed individuals may be an appropriate method for the determination of association.

Relative differences: ( ratio measures)

Can be assessed for discrete outcomes.

To assess causal associations

81

82

Relative risk

If an association exist, then how strong is it?

What is the ratio of the risk of disease in exposed individuals to the risk of disease in unexposed individual?

Incidence among exposed

Relative risk =

Incidence among unexposed

It is direct measure of the strength of association.

83

84

Relative risk of developing the disease is expressed as the ratio of the risk(incidence) in exposed individuals (q+) to that in unexposed individual(q-)

Total

exposed = a+b

Total

unexposed = c+d

85

Incidence among exposed Relative risk =

Incidence among unexposed a/a+b

RR = q+/q- = c/c+d

86Odds ratio in a cohort study

• Odds that an exposed person develop disease = a/b

• Odds that an unexposed persondevelop disease = c/d

Odds ratio = (a/b ) / (c/d) = ad/bc

Develop disease

Do not develop disease

Exposed a b

Unexposed c d

What are the odds that the disease will develop in an exposed person?

87Relationship between OR and RR

OR is a valid measure of association in its own right and it

is often used as an approximation of the relative risk’.

Use of OR as an estimate of the relative risk biases it in a

direction opposite to the null hypothesis, i.e. it tends to

exaggerate the magnitude of the association.

88 ATTRIBUTABLE RISK (AR)

AR is defined as the amount of proportion of disease incidence (or

disease risk) that can be attributed to a specific exposure.

Based on the absolute difference between two risk estimates.

Used to imply a cause-effect relationship and should be interpreted

as a true etiologic fraction only when there is a reasonable

certainty of a causal connection between exposure and outcome.

89AR in exposed individuals

• It is merely a difference between the risk estimates of different exposure levels and a reference exposure level.

• If q+ = risk in exposed individual.

q- = risk in unexposed individual.

• ARexp = q+ - q-

• It measures the excess risk for a given exposure category associated with the exposure

90

Percent AR exposure

When AR is expressed as a percentage

Interpretation:The percentage of the total risk in the exposed attributable to the

exposure.

100

q

qq

91

POPULATION ATTRIBUTABLE RISK

What proportion of the disease incidence can be attributed to a specific exposure in a total population .

To know the PAR , we need to know incidence in total population =a

incidence in unexposed group(background risk)=b

PAR= a-b ÷ a

92

Various correlation tests

• Pearsson’s product-moment correlation

• Spearmans rank order correlation

• Kendall correlation

• Point biserial correlation

• Tetrachoric correlation

• Phi correlation

93Types of correlation

Based on linearity of correlation

94Based on direction of correlation

Positive correlation:As X increases ,Y also increases,ex: As height increases, so does weight.

Negative correlation: As X increases ,Y decreases.ex: As time of watching TV increases , grade scores decreases.

95

Perfect positive

Moderately positive

Zero correlation

Moderately negative

Perfectly Negative

Based on degree of correlation

96

97

98

99REGRESSION

It can also be used in measuring association.

They are the measure of the mean changes to be expected in the

dependent variable for a unit change in the value of the

independent variable.

When more than 1 independent variable is associated with the

dependent variable, multiple regression analysis will indicate how

much of the variation observed in the dependent variable can be

accounted for, by one or a combination of independent variables.

100PROBLEMS IN ESTABLISHING

CAUSALITY

The existence of correlation/ association does not necessarily

imply causation.

Concept of single cause concept of multiple causation

Koch’s postulates cannot be used for non-infectious diseases.

The period between exposure to a factor and appearance of

clinical diseases is long in non-infectious diseases.

Specificity established in one disease does not apply on others.

101

Confounders associated with disease tend to distort relationship with the suspected factors.

Systematics errors/ bias can produce spurious association.

No statistical method can differentiate between causal and non-causal.

Because of these many uncertainties, the terms : Causal inference, causal possibility, or likelihood are preferred to causal conclusion.

This helps in formulating policy rather than waiting for the unequivocal proof ( Unattainable in several disease conditions)

102

Results from epidemiological studies are often used as inputs for policy and judicial decisions.

It is thus important for public health and policy makers to understand the fundamentals of causal inference.

Association does not imply causation.

Apart from outbreak investigations, no single study is capable of establishing a causal relation or fully informing either individual or policy decisions.

Those decisions should be based on a carefull consideration of the entire relevant scientific and policy literature

Conclusion

103

[1] Park K. Textbook of Preventive and Social Medicine. 23rd ed.

[2]Gordis, Leon Epidemiology / Leon Gordis.—5th ed.

[3]Roger Detels et al. Oxford Text Book of Public Health. 5th ed. New york(U.S.A): Oxford University Press; 201

WHO research methodology. Second edition.

AFMC WHO – Text book of Public Health and Community Medicine – Rajvir Balwar – 1st edition

Soben peters – Text book of Community Dentistry – 5th edi

Raj Bhopal : Cause and effect: the epidemiological approach : Google book source

REFERENCES

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THANK YOU