Introduction: Causal theories and interrelationships between measures of disease occurrence - PowerPoint PPT Presentation

Loading...

PPT – Introduction: Causal theories and interrelationships between measures of disease occurrence PowerPoint presentation | free to download - id: 6f6cf9-MDAxN



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

Introduction: Causal theories and interrelationships between measures of disease occurrence

Description:

Introduction: Causal theories and interrelationships between measures of disease occurrence Lydia B. Zablotska, MD, PhD Associate Professor Department of Epidemiology ... – PowerPoint PPT presentation

Number of Views:189
Avg rating:3.0/5.0
Slides: 104
Provided by: Lyd74
Learn more at: http://rds.epi-ucsf.org
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Introduction: Causal theories and interrelationships between measures of disease occurrence


1
Introduction Causal theories and
interrelationships between measures of disease
occurrence
  • Lydia B. Zablotska, MD, PhD
  • Associate Professor
  • Department of Epidemiology and Biostatistics

2
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

3
Practice of Epidemiology
  • Example
  • Study of the association between fiber intake and
    risk of colorectal cancer
  • Incidence rates of colorectal cancer per year in
    the U.S.
  • SEER 2008 SEER 2012
  • Males 60 per 100,000 54 per 100,000
  • Females 43 per 100,000 40 per 100,000

Howlader et al. 2012
4
0
5
0
6
0
7
Saga continues
0
  • 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

8
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...

9
2010 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.
10
2011 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.
11
2013 never ending
  • Br J Nutr. 2012 Sep108(5)820-31. A review of
    the potential mechanisms for the lowering of
    colorectal oncogenesis by butyrate. Fung KY et
    al.
  • Result Foods containing dietary fibre are
    protective to a degree that the World Cancer
    Research Fund classifies the evidence supporting
    their consumption as 'convincing'.It is emerging
    that fermentable fibres, including resistant
    starch (RS), are particularly important. RS
    fermentation induces SCFA production, in
    particular, relatively high butyrate levels, and
    in vitro studies have shown that this acid has
    strong anti-tumorigenic properties.
  • Lancet Oncol. 2012 Dec13(12)1242-1249.
    Long-term effect of resistant starch on cancer
    risk in carriers of hereditary colorectal cancer
    an analysis from the CAPP2 randomised controlled
    trial. Mathers JC et al.
  • Results In the CAPP2 study, 918 individuals with
    Lynch syndrome were randomly assigned to receive
    30 g resistant starch or starch placebo, for up
    to 4 years.
  • CONCLUSIONS Resistant starch had no detectable
    effect on cancer development in carriers of
    hereditary colorectal cancer. Dietary
    supplementation with resistant starch does not
    emulate the apparently protective effect of diets
    rich in dietary fibre against colorectal cancer.

12
2014 genetic studies are on the horizon
13
Epidemiology in the news
  • Jennifer Kelsey on diet and nutrition articles in
    The New York Times, week after week of cause
    after cause.

14
Why worry about causes?
  • So that we can intervene
  • So that we can reduce or prevent disease

15
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

16
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

17
0
  • 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

18
the study of the distribution and determinants
of disease frequency in human populations
0
MacMahon and Pugh (1970)
19
the study of the distribution and determinants
of disease frequency in human populations
0
MacMahon and Pugh (1970)
We also add
  • AND the application of this study to
  • control health problems
  • improve public health

20
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

21
Societal origins of epidemiology
0
  • 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

22
And since it is the purpose of epidemiology to
  • Identify factors that cause the distribution of
    disease

23
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

24
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
25
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
26
Causal Inference Henle-Koch postulates for
causation
  • 1890
  • 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)

Jacob Henle
27
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
28
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
29
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

30
Theories of causal inference
  • Deductive reasoning
  • Inductivism
  • Refutationism
  • Bayesianism

RGL2008 Ch 2
31
Deductive reasoning
0
  • George Simenons Inspector Maigret
  • Arthur Conan Doyles Sherlock Holmes
  • Agatha Christie's Hercule Poirot
  • Modern disease detectives Sandro Galea

Pull the clues together, arrive at
generalization, i.e. deduct the answer
32
Inductive reasoning Conditional Inductive
Tree (1620) formulate laws based on limited
observations of recurring phenomenal patterns
0
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)
Sir Francis Bacon
33
Deductive vs. inductive reasoning
  • Deductive reasoning applies general principles to
    reach specific conclusions

34
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.

35
Causal models
RGL2008 Ch 2
36
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.

Kenneth Rothman
37
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.

38
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.

39
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

40
Sufficient and component causes
A sufficient cause is a set of minimal conditions
or events that inevitably produce disease
41
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
42
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
43
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
44
For example Tuberculosis
M. tuberculosis
M. tuberculosis
Immuno- suppression
Poor nutrition
Sufficient Cause 1
Sufficient Cause 2
45
Causing a myocardial infarction
Potato chips
Y
W
No exercise
46
Causing a myocardial infarction
Potato chips
Y
W
Obesity
No exercise
A
47
Causing a myocardial infarction
Potato chips
Y
W
Obesity
No exercise
A
NO EFFECT
48
Causing a myocardial infarction
Potato chips
Y
W
Obesity
No exercise
A
C
Genes
49
Causing a myocardial infarction
Potato chips
Y
W
Obesity
No exercise
High cholesterol
A
C
Genes
50
Causing a myocardial infarction
Potato chips
Y
W
Obesity
No exercise
High cholesterol
A
C
Genes
NO EFFECT
51
Causing a myocardial infarction
Potato chips
Y
W
Obesity
No exercise
High cholesterol
A
C
Genes
Smoking
Stress
52
Causing a myocardial infarction
Potato chips
Y
W
Obesity
No exercise
High cholesterol
A
C
Genes
Smoking
Stress
53
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

54
Causing a myocardial infarction
Potato chips
Y
W
Obesity
No exercise
High cholesterol
A
C
Genes
Smoking
Stress
55
Causing a myocardial infarction
Potato chips
Y
W
Obesity
No exercise
High cholesterol
A
C
Genes
Smoking
Stress
56
Causing a myocardial infarction
Potato chips
Y
W
Obesity
No exercise
High cholesterol
A
C
Genes
Smoking
Stress
57
Causing a myocardial infarction
Potato chips
Y
W
Obesity
No exercise
High cholesterol
A
C
Genes
Smoking
Stress
58
Counterfactual model (Greenland-Morgenstern,
2001)
  • a causal effect is a counterfactual contrast
    between the outcomes of a single unit under
    different treatment possibilities

Hal Morgenstern
Sander Greenland
59
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
  • Can be applied at individual or population levels
    of analysis
  • Specifies what would happen to individuals or
    populations under alternative possible patterns
    of interventions or exposures
  • Force researchers to think about operational
    definitions of cases and controls, sampling
    schemes, and other important design questions
  • One of the two conditions in the definitions of
    the effect measures must be contrary to fact
    exposures or treatment vs. a reference condition

60
Restrictions of Counterfactuals
  • effects are defined only for comparisons of
    treatment levels
  • causes refer to factors that can be potentially
    manipulated, such as drug treatments, but not to
    fixed personal attributes such as gender and race
  • implicit in most discussions of potential
    outcomes is that the outcome for a given unit
    under a specific treatment does not depend on the
    treatment given to any other unit, i.e. the
    stability assumption. This assumption is likely
    to be violated when the outcome is contagious or
    the exposure represents a set of social
    conditions.

61
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)

RGL2008 Ch 4
62
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
63
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
64
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

65
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
66
Measures of attributable risk
  • Formula for attributable fraction (excess
    fraction)
  • For dichotomous exposure
  • Risk difference/ Risk in exposed(RR-1)/RR
  • For categorical (ngt2) exposure
  • (AFi x Pi)
  • Different from etiologic fraction, which refers
    to Rothmans causal heuristic adds up to more
    than 100

67
Causal diagrams
  • Provide a unified framework for evaluating design
    and analysis strategies for any causal question
    under any set of causal assumptions
  • intuitive device for deducing the statistical
    associations implied by causal relations.
    Conversely, given a set of observed statistical
    relations, a researcher armed with causal graph
    theory can systematically characterize all causal
    structures compatible with the observations.

RGL2008 Ch 12
68
Example Comparison 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 ?

69
(No Transcript)
70
Example Comparison 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

71
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



--
72
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

RGL2008 Ch 2
73
Causal Inference A. 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

A. Bradford Hill
RGL2008 Ch 2
74
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
75
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

76
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

77
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

78
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

79
Tronko et al. JNCI 2006
80
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.
E. Hall, Radiobiology for the Radiologist, 2000.
81
Threshold effect?
E. Hall, Radiobiology for the Radiologist, 2000.
82
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

83
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

84
Radiation-associated risks of chronic lymphocytic
leukemia vs. other types of leukemia
Romanenko et al. Rad Res 2008
Zablotska et al. EHP 2012.
85
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

86
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

87
9. Analogy
Zablotska et al. EHP 2008
88
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

89
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?

90
Summary When is an association causal?
91
Summary When is an association causal?
Smoking is a carcinogen
92
Summary When is an association causal?
Smoking is a carcinogen
Smoking causes lung cancer
93
Summary When is an association causal?
Smoking is a carcinogen
Smoking causes lung cancer
Prospective cohort study
94
Summary When is an association causal?
Smoking is a carcinogen
Smoking causes lung cancer
Prospective cohort study
Recruit 10,000 doctors, follow for 10 years
95
Summary When 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
96
Practice of Epidemiology
  • Review of the previous example
  • Study of the association between fiber intake and
    risk of colorectal cancer

97
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

98
Summary When is an association causal?
99
Example A few well known causes of disease
  • Smoking
  • High cholesterol
  • M. tuberculosis
  • S. viridans
  • Head injury
  • ? Poverty

100
Example A 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

101
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?

102
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?

103
0
November 26, 2001, The New Yorker.
About PowerShow.com