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Is for Epi Epidemiology basics for non-epidemiologists.

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Presentation on theme: "Is for Epi Epidemiology basics for non-epidemiologists."— Presentation transcript:

1 is for Epi Epidemiology basics for non-epidemiologists

2 Session III Part II Descriptive and Analytic Epidemiology

3 Analytic Epidemiology Hypotheses and Study Designs

4 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

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

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

7 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

8 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

9 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

10 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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12 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

13 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

14 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

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

16 Cross-Sectional Studies Exposure and outcome status are determined at the same time Examples include: –Behavioral Risk Factor Surveillance System (BRFSS) - –National Health and Nutrition Surveys (NHANES) - Also include most opinion and political polls

17 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

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

19 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

20 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

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

22 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

23 Cohort versus Case-Control Study

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

25 Analytic Epidemiology Measures of Association and Statistical Tests

26 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)

27 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

28 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

29 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

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

31 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

32 Odds Ratio Example Autism MMR Vaccine?YesNo Total Yes No Total a x d 130 x 135 OR = == 1.27 b x c 115 x 120

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

34 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

35 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

36 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

37 95% CI Example DiseaseOdds Ratio95% CI Gonorrhea – 4.4 Trichomonas – 2.8 Yeast – 1.7 Other vaginitis – 2.7 Herpes – 1.8 Genital warts – 1.0 Grodstein F, Goldman MB, Cramer DW. Relation of tubal infertility to history of sexually transmitted diseases. Am J Epidemiol Mar 1;137(5):577-84

38 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

39 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

40 p-value Example DiseaseOdds Ratio95% CIp-value Gonorrhea – Trichomonas – Yeast – Other vaginitis – Herpes – Genital warts – Grodstein F, Goldman MB, Cramer DW. Relation of tubal infertility to history of sexually transmitted diseases. Am J Epidemiol Mar 1;137(5):577-84

41 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

42 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.

43 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.

44 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. 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. 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 pdf 000.pdf


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