13b-DataAnalCausalInf-2

13b-DataAnalCausalIn - Principles of Epidemiology for Public Health(EPID600 Data analysis and causal inference 2 Victor J Schoenbach PhD home page

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4/19/2011 Data analysis and causal inference 1 Data analysis and causal inference – 2 Victor J. Schoenbach, PhD home page Department of Epidemiology Gillings School of Global Public Health University of North Carolina at Chapel Hill www.unc.edu/epid600/ Principles of Epidemiology for Public Health (EPID600)
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7/1/2009 Data analysis and causal inference 2 Causal relations and public health Many public health questions hinge on causal relations, e.g. Does dietary fiber prevent colon cancer? Do abstinence-only sex education programs raise the age of sexual debut? What level of arsenic in drinking water is harmful? Does higher patient volume reduce knee replacement complication rates? Does male circumcision prevent HIV infection?
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12/30/2001 Data analysis and causal inference 3 Conceptual issues in causal relations In general we cannot “see” causal relations but must infer their existence. “Proving” causation means creating a belief – our own and others’. Causal inference is therefore a social process. What we regard as “causes” depends on our conceptual framework.
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12/30/2001 Data analysis and causal inference 4 Pre-20th century causal discoveries Food poisoning from shellfish, pork Plumbism from wine kept in lead-glazed pottery (Romans) Contagion (isolation, quarantine) Scurvy and citrus fruit (James Lind) Scrotal cancer in chimney sweeps (Percival Pott)
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12/30/2001 Data analysis and causal inference 5 Pre-20th century causal discoveries Smallpox vaccination Cowpox vaccination (Edwin Jenner) Waterborne transmission of typhoid fever (William Budd) and cholera (John Snow) Person-to-person transmission of measles (Peter Panum) Puerperal fever and handwashing (Ignaz Semmelweis)
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7/29/2009 Data analysis and causal inference 6 Rise of the germ theory Invention of the microscope enabled direct observation of microorganisms Seeing microbes ≠ Seeing microbes cause disease Henle-Koch postulates for proving that a microorganism causes a disease
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Even seeing involves inference I LQVF FRIDEMIQLQCX 4/19/2011 Data analysis and causal inference 7
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Inference that is not always correct I LQVF FRIDEMIQLQCX 4/19/2011 Data analysis and causal inference 8
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Inference that is not always correct van der Helm’s “Kaleidoscope Motion” (from Michael’s “Visual Phenomena & Optical Illusions”) www.michaelbach.de/ot/ www.michaelbach.de/ot/mot_feet_lin/ www.michaelbach.de/ot/mot_kaleidoscope/ 4/19/2011 Data analysis and causal inference 9
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4/22/2002 Data analysis and causal inference 10 Henle-Koch postulates 1. The parasite must be present in all who have the disease; 2. The parasite can never occur in healthy persons; 3. The parasite can be isolated, cultured and capable of passing the disease to others
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12/30/2001 Data analysis and causal inference 11 E.H. Carr – What is history ? “History … is ‘a selective system’ … of causal
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This note was uploaded on 03/12/2012 for the course EPID 600 taught by Professor Staff during the Spring '08 term at UNC.

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13b-DataAnalCausalIn - Principles of Epidemiology for Public Health(EPID600 Data analysis and causal inference 2 Victor J Schoenbach PhD home page

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