Epidemiology. Chapter 2 Causal Concepts

Transcription

1 Epidemiology Chapter 2 Causal Concepts Gerstman 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 Gerstman Chapter 2 2

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

4 Natural History of HIV/AIDS Stages: Susceptibility Subclinical Clinical Gerstman 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 flu-like paralysis Subclinic al Gerstman 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 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 (causal interaction) Ken Rothman (contemporary epidemiologist) Gerstman 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 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 complement is susceptibility Gerstman Chapter 2 9

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? yellow corn genetics trait Are cancers environmental or genetic diseases? Gerstman Chapter 2 10

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

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

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

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 Gerstman Chapter 2 14

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

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

17 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 Gerstman Chapter 2 19

18 Understanding causal mechanisms Understanding causal mechanisms is essential for effective public health intervention Consider the case of miasmas and cholera (from Chapter 1) Told ya For want of knowledge, efforts which have been made to oppose [cholera] have often had contrary effect. John Snow Gerstman Chapter 2 20

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? Gerstman Chapter 2 21

20 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] Gerstman Chapter 2 22

21 Hill s Inferential Framework 1. Consistency 2. Specificity 3. Temporality 4. Biological gradient 5. Plausibility 6. Coherence 7. Experimentation 8. Analogy A. Bradford Hill ( ) * Hill, A. B. (1965). The environment and disease: association or causation? Proceedings of the Royal Society of Medicine, 58, full text Gerstman Chapter 2 23

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 Gerstman Chapter 2 24

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 Gerstman Chapter 2 25

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 ( BCE) Gerstman Chapter 2 26

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? Gerstman Chapter 2 27

26 or this? Gerstman Chapter 2 28

27 Element 5: Biological Gradient Increases in exposure dose dose-response in risk Gerstman Chapter 2 29

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 Gerstman Chapter 2 30

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. Gerstman Chapter 2 31

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 Gerstman Chapter 2 32

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 Gerstman Chapter 2 33