1. Chapter 2: General Concepts Chapter 2 Causal Concepts
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Epidemiology
Chapter 2
Causal Concepts
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2. 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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3. Natural History of Disease
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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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4. Natural History of HIV/AIDS
Stages:
Susceptibility
Subclinical
Clinical
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5. Chapter 2: General Concepts
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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
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6. Epidemiological Iceberg
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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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7. (contemporary epidemiologist)
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)
Ken Rothman
(contemporary epidemiologist)
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8. “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
A disease can have multiple sufficient causal mechanisms
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9. 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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10. 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?
genetics
trait
yellow
corn
Are cancers environmental or genetic diseases?
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11. Causal factors act in a hierarchal web
Causal Web
Causal factors act in a hierarchal web
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12. Agent, host, environmental interaction
Epidemiologic Triad
Agent, host, environmental interaction
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13. Homeostatic Balance A H E H E A E A H E A H E H A At equilibrium
Agent becomes more pathogenic E H A
The proportion of susceptibles in population decreases E A H
At equilibrium
Steady rate
Environmental changes that favor the agent E A H
Environmental changes that favor the host E H A
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14. 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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15. Host Factors Physiological Anatomical Genetic Behavioral Occupational
Constitutional
Cultural
etc!
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16. Environmental Factors
Physical, chemical, biological
Social, political, economic
Population density
Cultural
Env factors that affect presence and levels of agents
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17. Induction
Sophisticated view of “incubation” needed when considering multicausality
Induction = causal action to initiation
Latency = disease initiation to detection
Empirical induction period = induction + latency
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18. Induction & Initiation Heart Disease Example
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Induction & Initiation Heart Disease Example
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19. §2.4 Causal Inference
Causal inference  the process of deriving cause-and-effect 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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20. Understanding causal mechanisms
Told ya’
Understanding causal mechanisms is essential for effective public health intervention
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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21. 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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22. 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
[Link to Surgeon General’s report]
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23. Hill’s Inferential Framework
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? Proceedings of the Royal Society of Medicine, 58, full text
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24. 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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25. 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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26. 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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27. Temporality ≡ exposure precedes disease in time
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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28. or this?
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29. Element 5: Biological Gradient
Increases in exposure dose  dose-response in risk
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30. 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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31. 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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32. 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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33. 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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