Epidemiology

1. Epidemiology Chapter 2 Causal Concepts Gerstman Chapter 2 Chapter 2 1

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 Chapter 2

3. Natural History of Disease Definition: Progression of disease in an individual over time Fig 2.1 (p. 37) Gerstman Chapter 2 Chapter 2 3

4. Natural History of HIV/AIDS Stages: • Susceptibility • Subclinical • Clinical Gerstman Chapter 2 Chapter 2 4

5. 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 Chapter 2 Chapter 2 5

6. 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 Gerstman Chapter 2 Chapter 2 6

7. 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 (causalinteraction) Ken Rothman (contemporary epidemiologist) Gerstman Chapter 2 Chapter 2 7

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 Gerstman Chapter 2 Chapter 2 8

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 complementis “susceptibility” Gerstman Chapter 2 Chapter 2 9

10. 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 Shank Illustration genetics trait yellow corn Are cancers environmental or genetic diseases? Chapter 2 10

11. Causal Web Causal factors act in a hierarchal web Gerstman Chapter 2 Chapter 2 11

12. Epidemiologic Triad Agent, host, environmental interaction Gerstman Chapter 2 Chapter 2 12

13. A H A H E E H A The proportion of susceptibles in population decreases Agent becomes more pathogenic E At equilibrium Steady rate A H E E H A Environmental changes that favor the host Environmental changes that favor the agent Homeostatic Balance Chapter 2 13 Gerstman Chapter 2

14. Types of Agents (Table 2.2) Chapter 2

15. Host Factors • Physiological • Anatomical • Genetic • Behavioral • Occupational • Constitutional • Cultural • etc! Chapter 2

16. Environmental Factors • Physical, chemical, biological • Social, political, economic • Population density • Cultural • Env factors that affect presence and levels of agents Chapter 2

17. §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 Chapter 2

18. 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 Chapter 2

19. Opposing View: Discovery of Preventive Measure May Predate Identification of Definitive Cause What if we waited until the mechanism was known before employing citrus? Chapter 2

20. 1964 Surgeon General’s Report • Epi data must be coupled with clinical, pathological, and experimental data • Epi data must consider multiple variables • Multiple studiesmust be considered • Statistical methodsalone cannot establish proof [Link to Surgeon General’s report] Chapter 2

21. 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, 295-300. full text Chapter 2

22. 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 Chapter 2

23. 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 Chapter 2

24. 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) Chapter 2

25. 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? Chapter 2

26. or this? Chapter 2

27. Element 5: Biological Gradient Increases in exposure dose  dose-response in risk Chapter 2

28. 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 Chapter 2

29. 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. Chapter 2

30. 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 Chapter 2

31. 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 Chapter 2