Lecture 20 - Assessing Causality

Lecture 20 - Assessing Causality - Learning Objectives...

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Learning Objectives After attending this lecture and reading the core material, the student should be able to: 1. Define the following types of associations: 1. Artifactual 2. Noncausal 3. Causal 2. Distinguish between association and causation and list criteria that support a causal inference Lecture Outline Determining Causation Different Types of Associations Threats to Causality Types of Causal Relationships Causal Factors Guidelines for Assessing Causality First step in determining causation: Understanding disease etiology Experimental studies 1. in vitro systems 2. animal studies in controlled environments 3. Allows for 1. control of precise dose 2. control of environmental conditions 3. loss to follow up kept to a minimum 4. Problems with 1. extrapolating data to human populations 2. human diseases with no good animal models Clinical pathologies Two step process to evaluate evidence in Human Populations Here’s where Epidemiology is important…. Epidemiology capitalizes on “natural” or “unplanned” experiments. We take advantage of groups who have been exposed for non-study purposes. All of the study designs are important here and provide different evidence for or against a causal hypothesis. Two step process to evaluate evidence in Human Populations Step 1: Determine if an association is present (i.e. conduct the study) - Ecologic studies: studies of group characteristics - Cross-sectional studies: studies at one particular time - Case-control or cohort studies: studies of individual characteristics. Ecologic studies A study in which the units of analysis are populations or groups of people, rather than individuals. Usually takes advantage of pre-existing data collected for other purposes - an efficient and economical study design No time element - a “snapshot” of populations - think cross-sectional studies of populations, not individuals. Is there a relationship between HUS incidence and dairy cattle density? What is the limitation (flaw) with this data? - What is the limitation (flaw) with this data? - The density of cattle in the different areas of france (the cause) - Here’s the incidence in different areas of france - You don’t have individual level of information à you are given 300,000 you have the # of incidence and # of density of cattle - You just get the big numbers and not if the individual were even exposed to the cattle - We are interested in the individual units Why do an ecologic study? HYPOTHESIS BUILDING! - It’s cheap, and easy to obtain information - The data is easy to obtain, no follow-up or individual contact is needed.
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