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is for Epi

is for Epi. Epidemiology basics for non-epidemiologists. Session III Part II. Descriptive and Analytic Epidemiology. Analytic Epidemiology. Hypotheses and Study Designs. Descriptive vs. Analytic Epidemiology. Descriptive epidemiology deals with the questions: Who, What, When, and Where

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is for Epi

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  1. is for Epi Epidemiology basics for non-epidemiologists

  2. Session IIIPart 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

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

  12. 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

  13. 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

  14. Observational Studies Three main study designs: • Cross-sectional study • Cohort study • Case-control study

  15. 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/ • National Health and Nutrition Surveys (NHANES) - http://www.cdc.gov/nchs/nhanes.htm • Also include most opinion and political polls

  16. Cohort Studies • Study population is grouped by exposure status • Groups are then followed to determine if they develop the outcome

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

  18. 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

  19. 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

  20. Case-Control Studies Study Population Controls Cases Had Exposure No Exposure Had Exposure No Exposure

  21. 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

  22. Cohort versus Case-Control Study

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

  24. Analytic Epidemiology Measures of Association and Statistical Tests

  25. 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: • Relative risk (a.k.a. risk ratio, RR) • Odds ratio (a.k.a. OR)

  26. 2 x 2 Tables Used to summarize counts of disease and exposure in order to do calculations of association

  27. 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 Outcome Yes No Yes Exposure No

  28. 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 Outcome Yes No Total Risk among the exposed Yes Exposure No a + b c + d Risk among the unexposed

  29. Relative Risk Example a / (a + b) 23 / 33 RR = = = 6.67 c / (c + d) 7 / 67

  30. 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

  31. Odds Ratio Example a x d 130 x 135 OR = = = 1.27 b x c 115 x 120

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

  33. 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

  34. 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: • 95% Confidence Intervals (CI) • p-values

  35. 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

  36. 95% CI Example 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

  37. 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

  38. 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

  39. p-value Example 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

  40. 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

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

  42. References and Resources • Last, J.M. (2001). A Dictionary of Epidemiology: 4th 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: 5th 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

  43. 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 • 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 • 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/AnalyticStudExerInstGuid2000.pdf

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