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Epidemiology

Chapter 2

Causal Concepts

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Chapter Outline2.1 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 DiseaseProgression of disease in an individual Progression of disease in an individual over timeover time

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Natural History of HIV/AIDSIdentify stages:SusceptibilitySubclinicalClinical

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Spectrum of Disease• Most diseases

demonstrate a range of manifestations and severities

• For infectious diseases, this called the gradient of infection

• Example: Polio– 95%: subclinical– 4%: flu-like– 1%: paralysis

flu-likeparalysi

s

Subclin

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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 CauseDefinition 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

Ken Rothman (contemporary epidemiologist)

Disease results from the Disease results from the cumulative effects of multiple cumulative effects of multiple causes acting together causes acting together ((causalcausal interactioninteraction))

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Types of Causes (Causal Pies)• 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 given disease can have multiple sufficient mechanisms

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Causal Complement(Causal Pie)

• Causal complement ≡ the set of factors that completes a sufficient causal mechanism

• Example: tuberculosis– Necessary agent

Mycobacterium tuberculosis

– Causal complement“Susceptibility”

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Yellow Shank Illustration• Yellow shank disease (an

avian disease) occurs only in susceptible chicken strains fed yellow corn

• What would the farmer think if he started feeding yellow corn to a susceptible flock?

• What would the farmer think if he added susceptible chickens to a flock being fed yellow corn?

• Is yellow shank disease an environmental or genetic disease?

How does this concept apply to environmental and genetic causes of cancer?How does this concept apply to environmental and genetic causes of cancer?

geneticstraityellow

corn

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Causal WebCausal factors act in a hierarchal web

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Epidemiologic TriadAgent, host, and environmental interaction

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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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Types of Host Factors

• Physiological • Anatomical• Genetic • Behavioral• Occupational• Constitutional• Cultural• etc!

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Types of Environmental Factors

• Physical, chemical, biological

• Social, political, economic

• Population density• Cultural• Env factors that

affect presence and levels of agents

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Homeostatic Balance

E

A H

At equilibriumSteady rate

E

HA

The proportion of susceptibles in population decreases

Environmental changes that favor the agent

EA

H

Environmental changes that favor the host

E

H

A

E

AH

Agent becomes more pathogenic

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§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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Understanding causal mechanisms

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

Told ya’

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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

[Link to Surgeon General’s report]

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Hill’s Inferential Framework1. Consistency2. Specificity3. Temporality4. Biological gradient5. Plausibility6. Coherence7. Experimentation8. Analogy

* Hill, A. B. (1965). The environment and disease: association or causation? Proceedings of the Royal Society of Medicine, 58, 295-300. full text

A. Bradford Hill(1897–1991)

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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: TemporalityTemporality ≡ 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 GradientIncreases 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