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is for Epi Epidemiology basics for non-epidemiologists

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Session III Part II Descriptive and Analytic Epidemiology

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Analytic Epidemiology Hypotheses and Study Designs

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Descriptive vs. Analytic Epidemiology Descriptive epidemiology deals with the questions: Who, What, When, and Where Analytic epidemiology deals with the remaining questions: Why and How

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Analytic Epidemiology Used to help identify the cause of disease Typically involves designing a study to test hypotheses developed using descriptive epidemiology

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Borgman, J (1997). The Cincinnati Enquirer. King Features Syndicate.

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Exposure and Outcome A study considers two main factors: exposure and outcome Exposure refers to factors that might influence one’s risk of disease Outcome refers to case definitions

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Case Definition A set of standard diagnostic criteria that must be fulfilled in order to identify a person as a case of a particular disease Ensures that all persons who are counted as cases actually have the same disease Typically includes clinical criteria (lab results, symptoms, signs) and sometimes restrictions on person, place, and time

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Developing Hypotheses A hypothesis is an educated guess about an association that is testable in a scientific investigation Descriptive data provide information to develop hypotheses Hypotheses tend to be broad initially and are then refined to have a narrower focus

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Example Hypothesis: People who ate at the church picnic were more likely to become ill –Exposure is eating at the church picnic –Outcome is illness – this would need to be defined, for example, ill persons are those who have diarrhea and fever Hypothesis: People who ate the egg salad at the church picnic were more likely to have laboratory- confirmed Salmonella –Exposure is eating egg salad at the church picnic –Outcome is laboratory confirmation of Salmonella

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Types of Studies Two main categories: 1.Experimental 2.Observational 1.Experimental studies – exposure status is assigned 2.Observational studies – exposure status is not assigned

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Experimental Studies Can involve individuals or communities Assignment of exposure status can be random or non-random The non-exposed group can be untreated (placebo) or given a standard treatment Most common is a randomized clinical trial

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Experimental Study Examples Randomized clinical trial to determine if giving magnesium sulfate to pregnant women in preterm labor decreases the risk of their babies developing cerebral palsy Randomized community trial to determine if fluoridation of the public water supply decreases dental cavities

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Observational Studies Three main study designs: 1.Cross-sectional study 2.Cohort study 3.Case-control study

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Cross-Sectional Studies Exposure and outcome status are determined at the same time Examples include: –Behavioral Risk Factor Surveillance System (BRFSS) - http://www.cdc.gov/brfss/http://www.cdc.gov/brfss/ –National Health and Nutrition Surveys (NHANES) - http://www.cdc.gov/nchs/nhanes.htm http://www.cdc.gov/nchs/nhanes.htm Also include most opinion and political polls

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Cohort Studies Study population is grouped by exposure status Groups are then followed to determine if they develop the outcome ExposureOutcome ProspectiveAssessed at beginning of study Followed into the future for outcome RetrospectiveAssessed at some point in the past Outcome has already occurred

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Cohort Studies DiseaseNo Disease Study Population Exposed Non-exposed No DiseaseDisease Exposure is self selected Follow through time

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Cohort Study Examples Study to determine if smokers have a higher risk of lung cancer Study to determine if children who receive influenza vaccination miss fewer days of school Study to determine if the coleslaw was the cause of a foodborne illness outbreak

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Case-Control Studies Study population is grouped by outcome Cases are persons who have the outcome Controls are persons who do not have the outcome Past exposure status is then determined

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Case-Control Studies Had ExposureNo Exposure Study Population Cases Controls No ExposureHad Exposure

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Case-Control Study Examples Study to determine an association between autism and vaccination Study to determine an association between lung cancer and radon exposure Study to determine an association between salmonella infection and eating at a fast food restaurant

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Cohort versus Case-Control Study

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Classification of Study Designs Source: Grimes DA, Schulz KF. Lancet 2002; 359: 58

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Analytic Epidemiology Measures of Association and Statistical Tests

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Measures of Association Assess the strength of an association between an exposure and the outcome of interest Indicate how more or less likely a group is to develop disease as compared to another group Two widely used measures: 1.Relative risk (a.k.a. risk ratio, RR) 2.Odds ratio (a.k.a. OR)

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2 x 2 Tables Used to summarize counts of disease and exposure in order to do calculations of association Outcome ExposureYesNoTotal Yesaba + b Nocdc + d Totala + cb + da + b + c + d

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2 x 2 Tables a = number who are exposed and have the outcome b = number who are exposed and do not have the outcome c = number who are not exposed and have the outcome d = number who are not exposed and do not have the outcome ****************************************************************** a + b = total number who are exposed c + d = total number who are not exposed a + c = total number who have the outcome b + d = total number who do not have the outcome a + b + c + d = total study population ab cd Outcome Yes No Yes Exposure No

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Relative Risk The relative risk is the risk of disease in the exposed group divided by the risk of disease in the non-exposed group RR is the measure used with cohort studies a a + b RR = c c + d ab cd Outcome Yes No Total Yes Exposure No a + b c + d Risk among the exposed Risk among the unexposed

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Relative Risk Example Escherichia coli? Pink hamburgerYesNo Total Yes231033 No76067 Total3070100 a / (a + b) 23 / 33 RR = == 6.67 c / (c + d) 7 / 67

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Odds Ratio In a case-control study, the risk of disease cannot be directly calculated because the population at risk is not known OR is the measure used with case-control studies a x d OR = b x c

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Odds Ratio Example Autism MMR Vaccine?YesNo Total Yes130115245 No120135255 Total250 500 a x d 130 x 135 OR = == 1.27 b x c 115 x 120

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Interpretation Both the RR and OR are interpreted as follows: = 1 - indicates no association > 1 - indicates a positive association < 1 - indicates a negative association

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Interpretation If the RR = 5 –People who were exposed are 5 times more likely to have the outcome when compared with persons who were not exposed If the RR = 0.5 –People who were exposed are half as likely to have the outcome when compared with persons who were not exposed If the RR = 1 –People who were exposed are no more or less likely to have the outcome when compared to persons who were not exposed

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Tests of Significance Indication of reliability of the association that was observed Answers the question “How likely is it that the observed association may be due to chance?” Two main tests: 1.95% Confidence Intervals (CI) 2.p-values

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95% Confidence Interval (CI) The 95% CI is the range of values of the measure of association (RR or OR) that has a 95% chance of containing the true RR or OR One is 95% “confident” that the true measure of association falls within this interval

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95% CI Example DiseaseOdds Ratio95% CI Gonorrhea2.41.3 – 4.4 Trichomonas1.91.3 – 2.8 Yeast1.31.0 – 1.7 Other vaginitis1.71.0 – 2.7 Herpes0.90.5 – 1.8 Genital warts0.40.2 – 1.0 Grodstein F, Goldman MB, Cramer DW. Relation of tubal infertility to history of sexually transmitted diseases. Am J Epidemiol. 1993 Mar 1;137(5):577-84

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Interpreting 95% Confidence Intervals To have a significant association between exposure and outcome, the 95% CI should not include 1.0 A 95% CI range below 1 suggests less risk of the outcome in the exposed population A 95% CI range above 1 suggests a higher risk of the outcome in the exposed population

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p-values The p-value is a measure of how likely the observed association would be to occur by chance alone, in the absence of a true association A very small p-value means that you are very unlikely to observe such a RR or OR if there was no true association A p-value of 0.05 indicates only a 5% chance that the RR or OR was observed by chance alone

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p-value Example DiseaseOdds Ratio95% CIp-value Gonorrhea2.41.3 – 4.40.004 Trichomonas1.91.3 – 2.80.001 Yeast1.31.0 – 1.70.04 Other vaginitis1.71.0 – 2.70.04 Herpes0.90.5 – 1.80.80 Genital warts0.40.2 – 1.00.05 Grodstein F, Goldman MB, Cramer DW. Relation of tubal infertility to history of sexually transmitted diseases. Am J Epidemiol. 1993 Mar 1;137(5):577-84

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Summary Descriptive Epidemiology –Answers: Who, what, where, when –Key Terms: Prevalence, person, place, time –Hypothesis-generating Analytic Epidemiology –Answers: Why, how –Key Terms: Measure of association –Hypothesis-testing

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References and Resources Centers for Disease Control and Prevention (1992). Principles of Epidemiology: 2 nd Edition. Public Health Practice Program Office: Atlanta, GA. Gordis, L. (2000). Epidemiology: 2 nd Edition. W.B. Saunders Company: Philadelphia, PA. Gregg, M.B. (2002). Field Epidemiology: 2 nd Edition. Oxford University Press: New York. Hennekens, C.H. and Buring, J.E. (1987). Epidemiology in Medicine. Little, Brown and Company: Boston/Toronto.

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References and Resources Last, J.M. (2001). A Dictionary of Epidemiology: 4 th Edition. Oxford University Press: New York. McNeill, A. (January 2002). Measuring the Occurrence of Disease: Prevalence and Incidence. Epid 160 lecture series, UNC Chapel Hill School of Public Health, Department of Epidemiology. Morton, R.F, Hebel, J.R., McCarter, R.J. (2001). A Study Guide to Epidemiology and Biostatistics: 5 th Edition. Aspen Publishers, Inc.: Gaithersburg, MD. University of North Carolina at Chapel Hill School of Public Health, Department of Epidemiology, and the Epidemiologic Research & Information Center (June 1999). ERIC Notebook. Issue 2. http://www.sph.unc.edu/courses/eric/eric_notebooks.htm http://www.sph.unc.edu/courses/eric/eric_notebooks.htm

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References and Resources University of North Carolina at Chapel Hill School of Public Health, Department of Epidemiology, and the Epidemiologic Research & Information Center (July 1999). ERIC Notebook. Issue 3. http://www.sph.unc.edu/courses/eric/eric_notebooks.htm http://www.sph.unc.edu/courses/eric/eric_notebooks.htm University of North Carolina at Chapel Hill School of Public Health, Department of Epidemiology, and the Epidemiologic Research & Information Center (September 1999). ERIC Notebook. Issue 5. http://www.sph.unc.edu/courses/eric/eric_notebooks.htm http://www.sph.unc.edu/courses/eric/eric_notebooks.htm University of North Carolina at Chapel Hill School of Public Health, Department of Epidemiology (August 2000). Laboratory Instructor’s Guide: Analytic Study Designs. Epid 168 lecture series. http://www.epidemiolog.net/epid168/labs/AnalyticStudExerInstGuid2 000.pdf http://www.epidemiolog.net/epid168/labs/AnalyticStudExerInstGuid2 000.pdf

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