Introduction: Causal theories and interrelationships between measures of disease occurrence

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Introduction Causal theories and
interrelationships between measures of disease
occurrence

• Lydia B. Zablotska, MD, PhD
• Associate Professor
• Department of Epidemiology and Biostatistics

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Learning Objectives

• Discuss how causal inference is central to the
  role of epidemiology
• Brief history of causal thinking through the
  years
• Theories of causal inference
• Causal models
• Sufficient-component cause model
• Describe (and critique) Rothmans causal
  heuristic
• Counterfactual model
• Counterfactual effect measures rate ratios, risk
  ratios and odds ratios
• Effect measures vs. measures of association
• Measures of attributable risk
• Causal diagrams (eg., directed acyclic graphs)
• Discuss how epidemiologic thinking leads to
  causal inference
• Discuss and critique Bradford Hills causal
  criteria

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Practice of Epidemiology

• Example
• Study of the association between fiber intake and
  risk of colorectal cancer
• SEER 2008
• Incidence rates of colorectal cancer per year in
  the U.S.
• Males 60 per 100,000
• Females 43 per 100,000

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Saga continues
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• Cancer Causes Control. 2005 Apr16(3)225-33.
  Dietary intakes of fruit, vegetables, and fiber,
  and risk of colorectal cancer in a prospective
  cohort of women (United States). Lin J et al.
• CONCLUSIONS Our data offer little support for
  associations between intakes of fruit,
  vegetables, and fiber, and colorectal cancer
  risk. However, our data suggest that legume fiber
  and/or other related sources may reduce risk of
  colorectal cancer.
• Int J Cancer. 2006 Oct119(12)2938-2942 Dietary
  intake of calcium, fiber and other micronutrients
  in relation to colorectal cancer risk Results
  from the Shanghai Women's Health Study. Shin A et
  al.
• CONCLUSIONS No apparent associations were found
  for fiber, total vitamin A, carotene, vitamins
  B1, B2, B3, C and E with colorectal cancer risk.
  Our results suggest that calcium may be
  protective against colorectal cancer development
  

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and continues

• Am J Clin Nutr 2007851353 60.Dietary fiber and
  whole-grain consumption in relation to colorectal
  cancer in the NIH-AARP Diet and Health Study15.
  A. Schatzkin et al.
• CONCLUSIONS Total dietary fiber intake was not
  associated with colorectal cancer. In analyses of
  fiber from different food sources, only fiber
  from grains was associated with a lower risk of
  colorectal cancer... Whole-grain intake was
  inversely associated with colorectal cancer
  risk...

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and then continues some more
Scand J Gastroenterol. 2010 Oct45(10)1223-31.
Dietary fiber, source foods and colorectal cancer
risk the Fukuoka Colorectal Cancer Study. K.
Uchuda et al. Results Total, soluble and
insoluble dietary fibers were not measurably
associated with overall risk or subsite-specific
risk of colorectal cancer. By contrast, rice
consumption was associated with a decreased risk
of colorectal cancer (trend p 0.03),
particularly of distal colon and rectal cancer
(trend p 0.02), and high intake of non-rice
cereals tended to be related to an increased risk
of colon cancer (trend p 0.07). There was no
association between vegetable consumption and
colorectal cancer, whereas individuals with the
lowest intake of fruits tended to have an
increased risk of colorectal cancer.
CONCLUSIONS The present study did not
corroborate a protective association between
dietary fiber and colorectal cancer, but
suggested a decreased risk of distal colorectal
cancer associated with rice consumption.
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and just does not stop
BMJ. 2011 Nov 10343d6617. doi
10.1136/bmj.d6617. Dietary fibre, whole grains,
and risk of colorectal cancer systematic review
and dose-response meta-analysis of prospective
studies. D. Aune et al. Results 25 prospective
studies were included in the analysis. The
summary relative risk of developing colorectal
cancer for 10 g daily of total dietary fibre (16
studies) was 0.90 (95 confidence interval 0.86
to 0.94, I(2) 0), for fruit fibre (n 9) was
0.93 (0.82 to 1.05, I(2) 23), for vegetable
fibre (n 9) was 0.98 (0.91 to 1.06, I(2) 0),
for legume fibre (n 4) was 0.62 (0.27 to 1.42,
I(2) 58), and for cereal fibre (n 8) was
0.90 (0.83 to 0.97, I(2) 0). CONCLUSIONS A
high intake of dietary fibre, in particular
cereal fibre and whole grains, was associated
with a reduced risk of colorectal cancer. Further
studies should report more detailed results,
including those for subtypes of fibre and be
stratified by other risk factors to rule out
residual confounding. Further assessment of the
impact of measurement errors on the risk
estimates is also warranted.
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genetic studies are on the horizon
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Epidemiology in the news

• Jennifer Kelsey on diet and nutrition articles in
  The New York Times, week after week of cause
  after cause.

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Why worry about causes?

• So that we can intervene
• So that we can reduce or prevent disease

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What is a cause?

• A cause is something that makes a difference.
  Insofar as epidemiology is a science...that
  aims to discover the cause of health states, the
  search includes all determinants of health
  outcomes. These may be both active agents... and
  static conditions such as the attributes of
  persons and places.
• Mervyn Susser

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What is a cause?

• A cause is something that makes a difference.
  Insofar as epidemiology is a science...that
  aims to discover the cause of health states, the
  search includes all determinants of health
  outcomes. These may be both active agents... and
  static conditions such as the attributes of
  persons and places.
• Mervyn Susser

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• Back to basics
• Epidemiology is
• science that focuses on the occurrence of
  disease rather than on the natural history or
  some other aspect of the disease
• K. Rothman

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the study of the distribution and determinants
of disease frequency in human populations
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MacMahon and Pugh (1970)
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the study of the distribution and determinants
of disease frequency in human populations
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MacMahon and Pugh (1970)
We also add

• AND the application of this study to
• control health problems
• improve public health

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Epidemiology defined

• Aims to find causes of diseases and to explain
  varying patterns of disease occurrence across
  populations and groups
• The basic science or one of the pillars of public
  health
• Way of thinking and logically structuring
  scientific inquiry in public health
• Scientific discipline with roots in biology,
  medicine, logic, and the philosophy of science

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Societal origins of epidemiology
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• Epidemiology affects the daily lives of most
  people
• Comes from the Greek words epi and demos, meaning
  the study of people
• Originated in the Sanitary Era (XIX century) out
  of necessity to improve the economic productivity
  by decreasing squalor of the industrial slums
• Epidemiology is the result of the evolution of
  progressive thinking and our understanding of the
  basic human rights

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And since it is the purpose of epidemiology to

• Identify factors that cause the distribution of
  disease

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And since it is the purpose of epidemiology to

• Identify factors that cause the distribution of
  disease
• This must be the most important lecture of the
  course

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Historical developments in the understanding of
causes of diseases

• 1. Sanitary era (paradigm miasma)
• Miasma theory of Sydenham
• foul emanations from soil, water and air cause
  all diseases
• poverty is at the core of all ills, it is a cause
  rather then a consequence of disease
• The Public Health Act of 1848
• Decaying organic matter ?insanitation ? foul
  emanations ?diseases ?poverty ? high birth
  rates among poor

Edwin Chadwick
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Historical developments in the understanding of
causes of diseases

• 2. Infectious disease era (paradigm germ theory)
• Discovery of causal agents of anthrax,
  tuberculosis and cholera by R. Koch
• Bacillus anthracis (1877)
• Mycobacterium tuberculosis (1882)
• Vibrio cholerae (1883)

Robert Koch
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Causal Inference Henle-Koch postulates for
causation

• 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 generates,
  produces the disease (in experimental animals)

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Historical developments in the understanding of
causes of diseases

• 3. Risk factor epidemiology or chronic disease
  era(paradigm black box)
• Web of causation (MacMahon 1960)
• All factors are at the same level
• Diseases can be prevented by cutting a few
  strands of the web
• Does not elucidate societal forces or their
  relation to health
• too much statistics takes away all the
  pleasure and the message of epidemiology.

Brian MacMahon
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Historical developments in the understanding of
causes of diseases

• 4. Ecoepidemiology (paradigm Chinese (nesting)
  boxes)
• Eras in Epidemiology The Evolution of Ideas
  (Susser 2009)
• Conceptual approach combining molecular,
  societal, and population-based aspects to study a
  health-related problem.
• People are not only individuals but also members
  of communities (social context)
• Helps to recognize broad dynamic patterns and
  disease in its social context
• Places exposure, outcome and risk in societal
  context.

Mervyn Susser
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Causal inference

• Goal of epidemiology
• learn causes of diseases and factors that could
  prevent or delay disease development
• Causal inference
• a process of determining causal and preventive
  factors

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Theories of causal inference

• Deductive reasoning
• Inductivism
• Bayesianism

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• George Simenons Inspector Maigret
• Arthur Conan Doyles Sherlock Holmes
• Agatha Christie's Hercule Poirot

Pull the clues together, arrive at
generalization, i.e. deduct the answer
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In Epidemiology we use inductive
reasoning Francis Bacon (XVI century) suggested
the conditional inductive tree
formulate laws based on limited observations of
recurring phenomenal patterns
Specification of alternative hypotheses
Design of crucial experiments to test these
hypotheses
Exclusion of some alternatives
Adoption of what is left (for the time being)
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Deductive vs. inductive reasoning

• Deductive reasoning applies general principles to
  reach specific conclusions
• Wikipedia

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Deductive vs. inductive reasoning

• Deductive reasoning applies general principles to
  reach specific conclusions, whereas inductive
  reasoning examines specific information, perhaps
  many pieces of specific information, to derive a
  general principle.
• Wikipedia

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Causal models
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What is a cause? (Rothman)

• A cause of a specific disease event is an
  antecedent event, condition or characteristic
  that was necessary for the disease at the moment
  it occurred, given that other conditions are
  fixed.
• A cause of a disease is an event, condition, or
  characteristic that preceded the disease event
  and without which the disease event would not
  have occurred at all or would not have occurred
  until some later time.

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What is a cause? (Rothman)

• A cause of a specific disease event is an
  antecedent event, condition or characteristic
  that was necessary for the disease at the moment
  it occurred, given that other conditions are
  fixed.
• A cause of a disease is an event, condition, or
  characteristic that preceded the disease event
  and without which the disease event would not
  have occurred at all or would not have occurred
  until some later time.

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What is a cause? (Rothmans sufficient-component
cause model)

• A cause of a specific disease event is an
  antecedent event, condition or characteristic
  that was necessary for the disease at the moment
  it occurred, given that other conditions are
  fixed.
• A cause of a disease is an event, condition, or
  characteristic that preceded the disease event
  and without which the disease event would not
  have occurred at all or would not have occurred
  until some later time.

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Types of causal relationships(Rothmans
sufficient-component cause model)

• If a relationships is indeed causal, then
• Necessary and sufficient
• E.g., rabies, HIV exposure in AIDS
• Necessary but not sufficient
• Multiple factors acting in a specific temporal
  sequence
• E.g., multistage carcinogenesis
• Sufficient but not necessary
• E.g., both ionizing radiation and benzene
  exposure cause leukemia independently
• Neither sufficient nor necessary
• Many different pathways of getting the same
  disease

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Sufficient and component causes
A sufficient cause is a set of minimal conditions
or events that inevitably produce disease
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Sufficient and component causes
Component causes
Sufficient Cause 1
Sufficient Cause 2
A sufficient cause is a set of minimal conditions
or events that inevitably produce disease
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Sufficient and component causes
A component cause is any one of a set of
conditions which are necessary for the completion
of a sufficient cause
Component causes
Sufficient Cause 1
Sufficient Cause 2
A sufficient cause is a set of minimal conditions
or events that inevitably produce disease
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Sufficient and component causes
A necessary component cause is a component cause
that is a member of every sufficient cause
Sufficient Cause 1
Sufficient Cause 2
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For example Tuberculosis
M. tuberculosis
M. tuberculosis
Immuno-suppression
Poornutrition
Sufficient Cause 1
Sufficient Cause 2
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Causing a myocardial infarction
Potato chips
Y
W
No exercise
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Causing a myocardial infarction
Potato chips
Y
W
Obesity
No exercise
A
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Causing a myocardial infarction
Potato chips
Y
W
Obesity
No exercise
A
NO EFFECT
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Causing a myocardial infarction
Potato chips
Y
W
Obesity
No exercise
A
C
Genes
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Causing a myocardial infarction
Potato chips
Y
W
Obesity
No exercise
High cholesterol
A
C
Genes
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Causing a myocardial infarction
Potato chips
Y
W
Obesity
No exercise
High cholesterol
A
C
Genes
NO EFFECT
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Causing a myocardial infarction
Potato chips
Y
W
Obesity
No exercise
High cholesterol
A
C
Genes
Smoking
Stress
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Causing a myocardial infarction
Potato chips
Y
W
Obesity
No exercise
High cholesterol
A
C
Genes
Smoking
Stress
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The trouble with Rothman

• Omits discussion of origins of causes, focuses on
  proximal causes and ignores induction period
• Specific components but not linkages among them
• Ignores indirect effects (effects of some
  component causes mediated by other component
  causes in the model)
• Causes of disease in individuals but not in
  populations
• Does not consider factors that control
  distribution of risk factors
• Ignores dynamic non-linear relations

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Causing a myocardial infarction
Potato chips
Y
W
Obesity
No exercise
High cholesterol
A
C
Genes
Smoking
Stress
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Causing a myocardial infarction
Potato chips
Y
W
Obesity
No exercise
High cholesterol
A
C
Genes
Smoking
Stress
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Causing a myocardial infarction
Potato chips
Y
W
Obesity
No exercise
High cholesterol
A
C
Genes
Smoking
Stress
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Causing a myocardial infarction
Potato chips
Y
W
Obesity
No exercise
High cholesterol
A
C
Genes
Smoking
Stress
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Counterfactual model (potential-outcome)

• Ideal comparison to obtain a measure of effect
  would be of study subjects with themselves in
  both an exposed and an unexposed state
• One of the two conditions in the definitions of
  the effect measures must be contrary to fact
  exposures or treatment vs. a reference condition

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Effect measures vs. measures of association

• Effect is
• The endpoint of the causal mechanism
• Change in a population characteristic that is
  caused by the factor being at one level versus
  another
• Effect measures
• Can never achieve counterfactual ideal
• Logically impossible to observe the population
  under both conditions
• Measures of association
• Compares what happens in two distinct populations
• Constructed to equal the effect measure of
  interest
• Absolute differences in occurrence measures
  (rate or risk difference)
• Relative ratios of occurrence measures (rate or
  risk ratio, relative risk, odds ratio)

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Comparison of absolute and relative effect
measures (Rothman 2002)
Measure Numerical Range Dimensionality
Risk difference -1, 1 None
Risk ratio 0, ? None
Incidence rate difference - ?, ? 1/Time
Incidence rate ratio 0, ? None
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Interrelationships between relative measures of
disease occurrence

• Rare disease assumption (Cornfield 1951)
• If disease is rare, the odds ratio approximates
  the risk ratio
• ORodds of disease among exposed/odds of disease
  among unexposed
• (A/B) / (C/D)
• RRrisk in exposed/ risk in unexposed
• (A/(AB)) / (C/(CD))

Disease Disease
-
Exposure A B
Exposure - C D
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Interrelationships between relative measures of
disease occurrence

• Exposure only negligibly affects the person-time
  at risk (T1?T0)
• IRRincidence rate among exposed/ incidence rate
  among unexposed
• (IR1xT1) / (IR0xT0)
• RRrisk in exposed/ risk in unexposed
• (A/(AB)) / (C/(CD))R1/R0

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Interrelationships between relative measures of
disease occurrence

• If R1gtR0, then A/Cgt1, i.e. OR overestimates
  association and is larger than RR and further
  away from the null
• If R1gtR0, then T1ltT0 and T1/T0lt1, i.e.
  IR1/IR0gtR1/R0, i.e. rate ratio fall between the
  risk ratio and the odds ratio
• 1 lt RR lt IRR lt OR

Disease Disease
-
Exposure A B
Exposure - C D
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Measures of attributable risk

• In Rothmans model, the fraction of disease
  attributable to a single component cause cannot
  exceed 100, but attributable fractions for
  individuals could sum far more than 100
• Formula for attributable fraction
• For dichotomous exposure
• Risk difference/ Risk in exposed(RR-1)/RR
• For categorical (ngt2) exposure
• ? (AFi x Pi)

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Causal diagrams

• Provide a unified framework for evaluating design
  and analysis strategies for any causal question
  under any set of causal assumptions

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ExampleComparison of mortality in Sweden and
Panama

• Our prediction
• Standard of living in Sweden is generally higher
  than in Panama
• Panama has more limited health care and higher
  poverty rates compared to Sweden
• Proportion of the population that dies each year
  is higher in ?

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ExampleComparison of mortality in Sweden and
Panama

• Our prediction
• Standard of living in Sweden is generally higher
  than in Panama
• Panama has more limited health care and higher
  poverty rates compared to Sweden
• Proportion of the population that dies each year
  is higher in ?
• Actual findings
• Death rates are lower for people of the same age
  in Sweden
• In both countries older people die at a greater
  rate than younger people
• Proportion of older people is greater in Sweden

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Causal diagrams

• Provide a unified framework for evaluating design
  and analysis strategies for any causal question
  under any set of causal assumptions
• Older population
• SES Proportion of
  dead each year

--
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Practice of causal inference

• Is the observed association valid? Is it true?
• Association appears causal but is due to
• Bias or systematic error (misclassification of E
  or D)
• Confounding (other variable causes the D and this
  variable correlates with E)
• Chance or random error (just this once)
• Did the exposure actually cause the disease?
• Use causal guidelines to decide if association is
  truly causal
• The most important is temporality

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Causal InferenceA. Bradford Hill Criteria for
Causal Inference (1965)

1. Strength
2. Consistency
3. Specificity
4. Temporality
5. Biological gradient
6. Plausibility
7. Coherence
8. Experiment
9. Analogy

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1. Strength of association

• Strong associations are less likely to be caused
  by chance or bias
• A strong association means a very high or very
  low relative risk

CAVEAT Environmental associations with very low
relative risks
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2. Consistency

• Replication of findings in different populations
  under different circumstances, in different
  times, with different study designs

• CAVEAT
• Lack of consistency does not rule out a causal
  association, because some effects are produced by
  their causes only under unusual circumstances
• Publication bias
• Contradictory findings across different studies
  are not unusual in studies of weak effects

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3. Specificity of the association

• Specific exposure associated with only one
  disease
• Arises from old Henle-Koch postulates for
  causation
• Effect has one cause, not multiple causes

• CAVEATS
• Many exposures are linked to multiple diseases
• Many diseases have multiple causes

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4. Temporality

• Exposure must precede disease (cause must precede
  effect)
• Levels of evidence
• Cross-sectional studies (exposure and disease
  measured at the same time)
• e.g., NHANES (National Health and Nutrition
  Examination Survey) looking at the link between
  obesity and coronary artery disease
• Case-control studies (compare exposures and risk
  factors among people with and without the
  disease)
• e.g., case-control study investigating the link
  between radiation exposure among Chornobyl
  clean-up workers and leukemia
• Cohort studies (follow-up exposed and unexposed
  to see who will develop the disease)
• e.g., cohort study of children with thyroid
  activity measurements taken within 6 weeks after
  the Chornobyl accident and development of thyroid
  cancer 15-22 years later)
• In disease with long latency periods, exposures
  must precede latency period
• In chronic diseases, often need long-term
  exposure for disease induction

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5. Biologic gradient (dose-response relationship)

• Presence of a dose-response or exposure-response
  curve with an expected shape
• Changes in exposure are related to trend in risk
  of disease
• Strong evidence for causal relation suggesting
  biologic relation

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Tronko et al. JNCI 2006
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Study of the survivors of atomic bombings in
Hiroshima (LSS)
Association between radiation dose received in
1945 and risk of developing cancer later in life.
Source E. Hall, Radiobiology for the
Radiologist, 2000.
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Threshold effect?
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5. Biologic gradient (dose-response relationship)

• Presence of a dose-response or exposure-response
  curve with an expected shape
• Changes in exposure are related to trend in risk
  of disease
• Strong evidence for causal relation suggesting
  biologic relation

• CAVEAT
• Thresholds, i.e., no disease past a certain level
  of exposure

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6. Plausibility

• The proposed mechanism should be biologically
  (etiologically) plausible
• Reference to a coherent body of knowledge

• CAVEAT
• New diseases and new causes
• Theoretical plausibility

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Radiation-associated risks of chronic lymphocytic
leukemia vs. other types of leukemia
Romanenko et al. Rad Res 2008
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7. Coherence with established facts

• A cause-and-effect interpretation for an
  association does not conflict with what is known
  of the natural history and biology of disease
• Implications
• If a relation is causal, would expect observed
  findings to be consistent with other data
• Hypothesized causal relations need to be
  consistent with epidemiologic and biologic
  knowledge

• CAVEATS
• Data may not be available yet to directly support
  proposed mechanism
• Science must be prepared to reinterpret existing
  understanding of disease process in the face of
  new evidence

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8. Experiment

• In Hills article, refers to cessation of
  exposure, i.e., elimination of putative harmful
  exposure results in the decrease of the frequency
  of disease

• CAVEATS
• If the pathogenic process has already started,
  removal of cause does not reduce disease risk
• Reduction in disease frequency might not be for
  etiologic reason hypothesized

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9. Analogy
Zablotska et al. Environ Health Perspect 2008
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9. Analogy

• Similar exposures can cause similar effects,
  e.g., medications and infectious agents may cause
  other birth defects

• CAVEAT
• Limited by the current knowledge

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Overall caveats to criteria

• None of my ... criteria can bring undisputable
  evidence for or against the cause-and-effect
  hypothesis and none can be required as a sine qua
  non.
• Sir Austin Bradford Hill (1965)
• Temporality?

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Summary When is an association causal?
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SummaryWhen is an association causal?
Smoking is a carcinogen
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SummaryWhen is an association causal?
Smoking is a carcinogen
Smoking causes lung cancer
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SummaryWhen is an association causal?
Smoking is a carcinogen
Smoking causes lung cancer
Prospective cohort study
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SummaryWhen is an association causal?
Smoking is a carcinogen
Smoking causes lung cancer
Prospective cohort study
Recruit 10,000 doctors, follow for 10 years
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SummaryWhen is an association causal?
Smoking is a carcinogen
Smoking causes lung cancer
Prospective cohort study
Recruit 10,000 doctors, follow for 10 years
High RR of lung cancer in smokers
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Practice of Epidemiology

• Review of the previous example
• Study of the association between fiber intake and
  risk of colorectal cancer

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Application of A. Bradford Hills Guidelines for
Causal Inference

1. Strength - Yes
2. Consistency - Questionable
3. Specificity - No
4. Temporality - Yes
5. Biological gradient - Yes
6. Plausibility - Yes
7. Coherence - Possible
8. Experiment - Yes
9. Analogy - Yes

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Summary When is an association causal?
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ExampleA few well known causes of disease

• Smoking
• High cholesterol
• M. tuberculosis
• S. viridans
• Head injury
• ? Poverty

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ExampleA few well known causes of disease

• Smoking Lung Cancer
• High cholesterol Cardiovascular Disease
• M. tuberculosis Tuberculosis
• S. viridans Endocarditis
• Head injury Subarachnoid hemorrhage
• ? Poverty All-cause mortality

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Ethics and the public health balance

• When is there enough evidence to say something is
  a cause?
• When should we decide that something is a cause
  and act on it?
• Does first do no harm always apply at the
  population level?
• Are there different guidelines for solutions
  where we have to DO something vs. solutions where
  we try to remove something?

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Therefore, causal inference

• Causal inference is not a simple (or quick)
  process
• No single study is sufficient in establishing
  causal inference
• Requires critical judgment and interpretation
• Can one prove causal associations?

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0
November 26, 2001, The New Yorker.