���M.S.S. in Health Economics�HE 604: Public Health and Epidemiology (PHE)�Causality��

Dr. Aninda Nishat Moitry

MBBS, MPH, MSc, FRSPH

09 April 2023

Overview of presentation

• Approaches for studying disease aetiology
• Types of association
• Types of causal relationships
• Applying guidelines for causal inference

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Applying guidelines

for

causal inference

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If exposure X is associated with outcome Y…..then how do we decide if X is a cause of Y

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If exposure X is associated with outcome Y…..then how do we decide if X is a cause of Y

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Approaches for studying disease aetiology

• Animal study?
• In vitro systems?
• Observations in human populations?
• Experimental studies
• Unplanned or natural experiments

Sequence of studies in human populations

Types of association

• Real association
• Spurious (false) association

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Interpreting real associations

• Causal

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• Due to confounding

Characteristic under study

Disease

Characteristic under study

Disease

Factor X

Observed association

Observed association

Is this association causal?

• Two-stage process:
• Stage I:
• Consider alternative “non-causal explanations” for the association
• In Stage I, we ask ourselves could the association be due to:
• Bias?
• Confounding?
• Chance?

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• Stage II: If the association is unlikely to be due to bias, confounding or chance…
• ….we apply ‘guidelines’ for causal inference

�Assessing a reported association between an exposure and an outcome�

Selection

or measurement bias

Confounding

Chance

Could it be causal?

Could the observed association be due to:

No

No

Probably Not

Stage I

Stage II

Apply Guidelines

for Causal Inference

Types of causal relationships

• Necessary and sufficient
• Necessary, but not sufficient
• Sufficient, but not necessary
• Neither sufficient, nor necessary

Types of causal relationships (Contd.)

• Necessary and sufficient
• Without that factor, the disease never develops (the factor is necessary), and in the presence of that factor, the disease always develops (the factor is sufficient)
• A one-to-one relationship of exposure to disease, which is a consequence of a necessary and sufficient relationship, rarely if ever occurs

Factor A

Disease

Types of causal relationships (Contd.)

• 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
• In tuberculosis, the tubercle bacillus is clearly a necessary factor, even though its presence may not be sufficient to produce the disease in every infected individual

Factor A

Disease

Factor B

Factor C

+

+

Types of causal relationships (Contd.)

• Sufficient, but not necessary
• The factor alone can produce the disease, but so can other factors that are acting alone
• For example, either radiation exposure or benzene exposure can each produce leukemia without the presence of the other
• 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
• The criterion of sufficient is rarely met by a single factor

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Disease

Factor A

Factor B

Factor C

or

or

Types of causal relationships (Contd.)

• Neither necessary, nor sufficient
• 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

Factor A

Factor B

Factor C

Factor E

Factor F

Factor D

+

+

+

or

or

Disease

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Nine ‘aspects of an association’ should be considered before deciding that the most likely interpretation is causation

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“In what circumstances can we pass from an observed association to a verdict of causation? Upon what basis should we proceed to do so?”

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Guidelines for judging whether an observed association is causal

1. Temporal relationship
2. Strength of association
3. Dose-response relationship
4. Replication of study findings
5. Biologic plausibility
6. Consideration of alternate explanations
7. Cessation of exposure
8. Consistency with other knowledge
9. Specificity of the association

Temporality

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• This refers to the necessity for the exposure to precede the outcome (effect) in time

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• Any claim of causation must involve the cause preceding in time the presumed effect

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• Easier to establish in certain study designs
• Prospective cohort study

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• Lack of temporality rules out causality

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Easiest to establish in a cohort study

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Lack of temporality rules out causality

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Exposure

Outcome

TIME

Temporality (Contd.)� British Doctors Cohort Study

• This refers to the necessity for the exposure to precede the outcome (effect) in time

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• Any claim of causation must involve the cause preceding in time the presumed effect

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• Easier to establish in certain study designs
• Prospective cohort study

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Lack of temporality rules out causality

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Exposure

Outcome

TIME

Strength of the association

• “Measures of association”
• used to quantify the strength of the association between an exposure and outcome
• e.g. Relative risk, odds ratio

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• Strong associations are more likely to be causal than weak associations
• The larger the relative risk (RR) or odds ratio (OR), the greater the likelihood that the relationship is causal

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• Weak associations are more likely to be explained by undetected biases or confounders

Strength of the association (Contd.)

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• How large must a relative risk or odds ratio be to be considered ‘strong’:
• 2 ? 4 ? 20 ? …..?

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• No universal agreement regarding what constitutes a ‘strong’ or ‘weak’ association
• An OR or RR > 2.0 is ‘moderately strong’
• An OR or RR > 5.0 is ‘strong’

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• The relationship between smoking and lung cancer is an excellent example of a ‘strong association’
• Odds ratios and relative risks in different studies are in the 4 to 20 range

“For one additional serving of French Fries

per week, the odds ratio for breast cancer

was 1.27” (Michels et al., 2006)

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i.e. a “weak association”

Dose-response relationship

• Dose-response (‘biological gradient’)
• the relationship between the amount of exposure (dose) to a substance and the resulting changes in outcome (response)

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• If an increase in the level of exposure increases the risk of the outcome
• this strengthens the argument for causality

< 5 cigs/day

> 20 cigs/day

0 cigs/day

5 - 20 cigs/day

R

I

S

K

R

I

S

K

R

I

S

K

R

I

S

K

Dose-response relationship (Contd.)

Percentage of people with hearing loss relative to workplace noise exposure

Replication of the findings

• If the relationship is causal, it is expected to be found consistently in different studies and in different population

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(Biological) Plausibility

• Plausibility refers to the biological plausibility of the hypothesized causal relationship between the exposure and the outcome
• Is there a logical and plausible biological mechanism to explain the relationship?

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< 200 mg caffeine/day

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“A high dose of caffeine could constrict a mother’s blood vessels reducing the blood flow to the placenta” (Biological plausibility)

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“There is no accepted biological mechanism to explain the epidemiological results; indeed the relation may be due to chance or confounding”

(Draper et al., 2005)

Biological plausibility (Contd.)

But other researchers have argued that there is a biologically plausible explanation……..

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• EMF can induce currents that might alter the voltages across cell membranes
• Magnetic fields might cause the movement of ferromagnetic particles within cells
• EMF fields might also influence free radicals

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Power lines might deflect and concentrate cosmic rays on people living within their vicinity

Biological plausibility (Contd.)

• It is generally easy to ‘manufacture’ biologically plausible explanations for the findings from epidemiological research

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• Biological plausibility is not a particularly useful viewpoint for assessing a causal relationship

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�Consideration of Alternate Explanations �

• 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

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�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

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Consistency with other knowledge

• Repeated observation of an association in studies conducted on different populations under different circumstances

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• If studies conducted by….
• different researchers
• at different times
• in different settings
• on different populations
• using different study designs

……all produce consistent results, this strengthens the argument for causation

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e.g. The association between cigarette smoking and lung cancer has been consistently demonstrated in a number of different types of epidemiological study (ecological, case-control, cohort)

Consistency with other knowledge (Contd.)

• Repeated observation of an association in studies conducted on different populations under different circumstances

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• If studies conducted by….
• different researchers
• at different times
• in different settings
• on different populations
• using different study designs

……all produce consistent results, this strengthens the argument for causation

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• e.g. The association between cigarette smoking and lung cancer has been consistently demonstrated in a number of different types of epidemiological study (ecological, case-control, cohort)

Is there a causal relationship between fluoride in water and bone fractures?

• 18 studies have investigated the association between hip fractures (outcome) and water fluoride level (exposure)
• 30 separate statistical analyses
• 14 analyses produced a ‘positive association’

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• 13 analyses produced a ‘negative association’

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• 3 ‘no association’

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The inconsistency of these results casts doubt on the hypothesis that there is a causal relationship between fluoride in water and bone fractures

Hankinson SE et al. Obstet Gynecol. 1991;80:708-714.

Hildreth et al, 1981

Rosenberg et al, 1982

La Vecchia et al, 1984

Tzonou et al, 1984

Booth et al, 1989

Hartge et al, 1989

WHO, 1989

Wu et al, 1988

Prazzini et al, 1991

Newhouse et al, 1977

Casagrande et al, 1979

Cramer et al, 1982

Willet et al, 1981

Weiss, 1981

Risch et al, 1983

CASH, 1987

Harlow et al, 1988

Shu et al, 1989

Walnut Creek, 1981

Vessey et al, 1987

Beral et al, 1988

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Relative Risk or Odds Ratio

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Hospital-Based �Case-Control

Community-Based �Case-Control

Cohort

www.contraceptiononline.org

Oral Contraceptive Use and Ovarian Cancer

-ve Association

+ ve Association

Hankinson SE et al. Obstet Gynecol. 1991;80:708-714.

Hildreth et al, 1981

Rosenberg et al, 1982

La Vecchia et al, 1984

Tzonou et al, 1984

Booth et al, 1989

Hartge et al, 1989

WHO, 1989

Wu et al, 1988

Prazzini et al, 1991

Newhouse et al, 1977

Casagrande et al, 1979

Cramer et al, 1982

Willet et al, 1981

Weiss, 1981

Risch et al, 1983

CASH, 1987

Harlow et al, 1988

Shu et al, 1989

Walnut Creek, 1981

Vessey et al, 1987

Beral et al, 1988

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Relative Risk or Odds Ratio

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Hospital-Based �Case-Control

Community-Based �Case-Control

Cohort

www.contraceptiononline.org

Oral Contraceptive Use and Ovarian Cancer (Contd.)

-ve Association

+ ve Association

“….to our knowledge no other data on the association between preschool diet

and breast cancer are available”

(Michels et al., 2006: 751)

Specificity of the association

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

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• Specific exposure is associated with only one disease

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• 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

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• Not everyone who smokes develops cancer
• Not every one who develop cancer has smoke

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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

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Study design�Relative ability of different types of study to ‘prove’ causation�

NB: Assuming study well-designed & conducted & bias etc. minimised

Is this association causal?�

Is this association causal?�

Is this association causal?

NO

Is this association causal?�

Is this association causal?

Yes

When using them, don’t forget Hill’s advice:

“None of these nine viewpoints can bring indisputable evidence for or against a cause and effect hypothesis …. What they can do, with greater or less strength, is to help answer the fundamental question—is there any other way of explaining the set of facts before us, is there any other answer equally, or more, likely than cause and effect?” (Cited in Doll, 1991)

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Thank You