Transcript John Snow

Epidemiology
Chapter 2
Causal Concepts
Gerstman
Chapter 2
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Chapter Outline
2.1 Natural History of Disease
• Stages of Disease
• Stages of Prevention
2.2 Variability in the Expression of Disease
• Spectrum of Disease
• The Epidemiologic Iceberg
2.3 Causal Models
• Definition of Cause
• Component Cause (Causal Pies)
• Causal Web
• Agent, Host, and Environment
2.4 Causal Inference
• Introduction
• Types of Decisions
• Philosophical Considerations
• Report of the Advisory Committee to the U.S. Surgeon General, 1964
• Hill’s Framework for Causal Inference
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Natural History of Disease
Definition: Progression of disease in an individual over time
Fig 2.1 (p. 37)
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Natural History of HIV/AIDS
Stages:
Susceptibility
Subclinical
Clinical
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Spectrum of Disease
• Most diseases
demonstrate a range of
manifestations and
severities
• For infectious diseases,
this is called the gradient
of infection
• Example: Polio
– 95%: subclinical
– 4%: flu-like
– 1%: paralysis
Gerstman
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flu-like
paralysis
Subclinic
al
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Epidemiological Iceberg
• Only the tip of the iceberg
may be detectable
• “Dog bite” example
– 3.73 million dog bites
annually
– 451,000 medically
treated
– 334,000 emergency
room visits
– 13,360 hospitalizations
– 20 deaths
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Definition of Cause
• Any event, act, or condition
• preceding disease or illness
• without which disease would
not have occurred
• or would have occurred at a
later time
Disease results from the
cumulative effects of multiple
causal factors acting together
(causal interaction)
Gerstman
Chapter 2
Ken Rothman
(contemporary epidemiologist)
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“Causal Pie” Terminology
• Necessary cause
≡ found in all cases
• Contributing
cause ≡ needed in
some cases
• Sufficient cause ≡
the constellation of
necessary &
contributing causes
that make disease
inevitable in an
individual
Gerstman
A disease can have multiple
sufficient causal mechanisms
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Causal Complement
• Causal complement ≡ the
set of factors that
completes a sufficient
causal mechanism
• Example: tuberculosis
• Mycobacterium
tuberculosis is necessary
but not sufficient
• Most general causal
complement
is “susceptibility”
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Yellow Shank Illustration
• Yellow shank disease (avian
disease) occurs only in
susceptible chicken strains
when fed yellow corn
• What would a farmer think if he
started feeding yellow corn to a
susceptible flock?
• What would the same farmer
think if he added susceptible
chickens to a flock already
being fed yellow corn?
• Is yellow shank disease an
environmental or genetic
disease?
yellow
corn
genetics
trait
Are cancers environmental or genetic diseases?
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Causal Web
Causal factors act in a hierarchal web
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Epidemiologic Triad
Agent, host, environmental interaction
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Homeostatic Balance
A
H
A
H
E
E
Agent becomes more
pathogenic
H
A
The proportion of susceptibles
in population decreases
E
H
At equilibrium
Steady rate
A
H
A
E
Environmental changes that
favor the agent
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E
Environmental changes that
favor the host
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Types of Agents (Table 2.2)
Biological
Chemical
Physical
Helminths
Foods
Heat
Protozoans
Poisons
Light / radiation
Fungi
Drugs
Noise
Bacteria
Allergens
Vibration
Rickettsia
Objects
Viral
Prion
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Host Factors
•
•
•
•
•
•
•
•
Physiological
Anatomical
Genetic
Behavioral
Occupational
Constitutional
Cultural
etc!
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Environmental Factors
• Physical, chemical,
biological
• Social, political,
economic
• Population density
• Cultural
• Env factors that
affect presence and
levels of agents
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§2.4 Causal Inference
• Causal inference 
the process of
deriving cause-andeffect conclusions by
reasoning from
knowledge and
factual evidence
• “Proof” is
impossible in
empirical sciences but
causal statements
can be made strong
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Understanding causal mechanisms
Understanding causal
mechanisms is essential for
effective public health
intervention
Told ya’
Consider the case of miasmas
and cholera (from Chapter 1)
“For want of knowledge,
efforts which have been made
to oppose [cholera] have often
had contrary effect.”
– John Snow
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Opposing View: Discovery of Preventive Measure
May Predate Identification of Definitive Cause
What if we waited until the mechanism was known before employing citrus?
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1964 Surgeon General’s
Report
• Epi data must be coupled with clinical,
pathological, and experimental data
• Epi data must consider multiple variables
• Multiple studies must be considered
• Statistical methods alone cannot establish
proof
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[Link to Surgeon
Chapter 2General’s report]
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Hill’s Inferential Framework
1.
2.
3.
4.
5.
6.
7.
8.
Consistency
Specificity
Temporality
Biological gradient
Plausibility
Coherence
Experimentation
Analogy
A. Bradford Hill
(1897–1991)
* Hill, A. B. (1965). The environment and disease: association or causation?
Gerstman of the Royal Society of Medicine,
Chapter 2 58, 295-300. full text
Proceedings
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Element 1: Strength
• Stronger associations are
less easily explained away by
confounding than weak
associations
• Ratio measures (e.g., RR, OR)
quantify the strength of an
association
• Example: An RR of 10 provides
stronger evidence than an RR
of 2
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Element 2: Consistency
• Consistency ≡ similar conclusions
from diverse methods of study in
different populations under a
variety of circumstances
• Example: The association between
smoking and lung cancer was
supported by ecological, cohort,
and case-control done by
independent investigators on
different continents
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Element 3: Specificity
• Specificity ≡ the exposure is
linked to a specific effect or
mechanism
• Example: Smoking is not
specific for lung cancer (it
causes many other ailments,
as well)
Aristotle
(384 – 322 BCE)
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Element 4: Temporality
Temporality ≡ exposure precedes disease in
time
Mandatory, but not easy to prove. For example, is
the relationship between lead consumption and
encephalopathy this?
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or this?
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Element 5: Biological Gradient
Increases in exposure dose  dose-response in risk
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Element 6: Plausibility
• Plausibility ≡
appearing worthy of
belief
• The mechanism must
be plausible in the
face of known
biological facts
• However, all that is
plausible is not
always true
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Element 7: Coherence
• Coherence ≡ facts stick
together to form a coherent
whole.
• Example: Epidemiologic,
pharmacokinetic,
laboratory, clinical, and
biological data create a
cohesive picture about
smoking and lung cancer.
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Element 8: Experimentation
• Experimental evidence
supports observational
evidence
• Both in vitro and in vivo
experimentation
• Experimentation is not
often possible in
humans
• Animal models of
human disease can
help establish causality
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Element 9: Analogy
• Similarities among things
that are otherwise different
• Considered a weak form of
evidence
• Example: Before the HIV
was discovered,
epidemiologists noticed that
AIDS and Hepatitis B had
analogous risk groups,
suggesting similar types of
agents and transmission
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