Summary of lecture notes

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13 Jan 2011
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HMB342HIS Lecture 1, Jan. 11, 2011: Introduction & Abnormality
Epidemiology: the study of patterns of health & disease in human populations.
Clinical epidemiology: patients in a clinical setting, defined medically.
Evidence-based medicine: (Sackett) use of clinical epidemiology to generate results to questions that
guide clinical practice.
Common clinical questions:
1. Is the patient sick? (Abnormality)
2. How accurate are the diagnostic tests for the disease? (Diagnosis)
3. How often does the disease occur? (Frequency)
4. What factors are associated with increased risk for the disease? (Risk & causation)
5. What are the consequences of having the disease? (Prognosis)
6. How does the treatment alter the disease course? (Treatment)
7. Does intervention/early detection help to avert or improve the disease course? (Prevention)
Components of an Epidemiology Study:
1) Health Outcomes (5D):
Death, Disease, Discomfort, Disability, Dissatisfaction
2) Variables
Independent: Cause, exposure or predictor variable.
Dependent: The possible effect or outcome variable; influenced by the independent and covariables.
Co-variables: Other variables measured that may impact the relationship between dependent &
independent variables. (e.g. age, sex, height)
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Document Summary

Hmb342his lecture 1, jan. 11, 2011: introduction & abnormality. Epidemiology: the study of patterns of health & disease in human populations. Clinical epidemiology: patients in a clinical setting, defined medically. Evidence-based medicine: (sackett) use of clinical epidemiology to generate results to questions that guide clinical practice. Components of an epidemiology study: health outcomes (5d): Dependent: the possible effect or outcome variable; influenced by the independent and covariables. Co-variables: other variables measured that may impact the relationship between dependent & independent variables. (e. g. age, sex, height) Bias: a process at any stage of study that produces sample results that is different from population results. Selection: the patient groups differ on a factor other than the predictor of interest. Measurement: measurement methods between the 2 patient groups are not the same. Confounding: a variable that is related to both the predictor and the outcome but impacts the outcome. (e. g. smoking is related to coffee drinking and impacts lung cancer rates).

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