2004 Public Health Training and Information Network (PHTIN) Series

1. 2004 Public Health Training and Information Network (PHTIN) Series

2. Site Sign-in Sheet Please mail or fax your site’s sign-in sheet to: Linda White NC Office of Public Health Preparedness and Response Cooper Building 1902 Mail Service Center Raleigh, NC 27699 FAX: (919) 715 - 2246

3. Outbreak Investigation Methods From Mystery to Mastery

5. 2004 PHTIN Training Development Team Pia MacDonald, PhD, MPH - Director, NCCPHP Jennifer Horney, MPH - Director, Training and Education, NCCPHP Anjum Hajat, MPH – Epidemiologist, NCCPHP Penny Padgett, PhD, MPH Amy Nelson, PhD - Consultant Sarah Pfau, MPH - Consultant Amy Sayle, PhD, MPH - Consultant Michelle Torok, MPH - Doctoral student Drew Voetsch, MPH - Doctoral Candidate Aaron Wendelboe, MSPH - Doctoral student

6. Upcoming PHTIN Sessions November 9th. . . “Techniques for Review of Surveillance Data” December 14th. . . “Risk Communication” 10:00 am - 12:00 pm (with time for discussion)

7. Session I – VI Slides After the airing of each session, NCCPHP will post PHTIN Outbreak Investigation Methods series slides on the following two web sites: NCCPHP Training web site: http://www.sph.unc.edu/nccphp/phtin/index.htm North Carolina Division of Public Health, Office of Public Health Preparedness and Response http://www.epi.state.nc.us/epi/phpr/

8. Session V “Analyzing Data”

9. Today’s Presenters Michelle Torok, MPH Graduate Research Assistant and Doctoral Student, NCCPHP Sarah Pfau, MPH Consultant, NCCPHP

10. “Analyzing Data” Learning Objectives Upon completion of this session, you will: • Understand what an analytic study contributes to an epidemiological outbreak investigation • Understand the importance of data cleaning as a part of analysis planning

11. “Analyzing Data” Learning Objectives • Know why and how to generate descriptive statistics to assess trends in your data • Know how to generate and interpret epi curves to assess trends in your outbreak data • Understand how to interpret measures of central tendency

12. “Analyzing Data” Learning Objectives (cont’d.) • Know why and how to generate measures of association for a cohort or case-control study • Understand how to interpret measures of association (risk ratios, odds ratios) and corresponding confidence intervals • Know how to generate and interpret selected descriptive and analytic statistics in Epi Info software

13. Analyzing Data Overview

14. Analyzing Data: Session Overview • Analysis planning • Descriptive epidemiology • Epi curves • Spot maps • Measures of central tendency • Attack rates • Analytic epidemiology • Measures of association • Case study analysis using Epi Info software

15. Analysis Planning

16. Analysis Planning • Regardless of the data analysis software program you use, you will have access to numerous data manipulation and analysis commands • However, you need to understand the function of each command to determine when and why to use one

17. Analysis Planning Several factors influence—and sometimes limit—your approach to data analysis: • Your research question • Which variables will function as exposure and outcome • Which study design you use • How you select your sample population • How you collect and code information obtained from study participants

18. Analysis Planning Analysis planning can: • Be an invaluable investment of time • Help you select the most appropriate epidemiologic methods • Help assure that the work leading up to analysis yields a database structure and content that your preferred analysis software needs to successfully run analysis programs

19. Analysis Planning Three key considerations as you plan your analysis: • Work backwards from the research question(s) to design the most efficient data collection instrument • Study design will determine which statistical tests and measures of association you evaluate in the analysis output • Consider the need to present, graph, or map data

20. Analysis Planning • Work backwards from the research question(s) to design the most efficient data collection instrument • Develop a sound data collection instrument • Collect pieces of information that can be counted, sorted, and recoded or stratified • Analysis phase is not the time to realize that you should have asked questions differently!

21. Analysis Planning • Study design will determine which statistical tools you will use. • Use risk ratio (RR) with cohort studies and odds ratio (OR) with case-control studies; need to know which to evaluate, because both are generated simultaneously in Epi Info and SAS • Some sampling methods (e.g., matching in case-controls studies) require special types of analysis

22. Analysis Planning • Consider the need to present, graph, or map data • Even if you collect continuous data, you may later categorize it so you can generate a bar graph and assess frequency distributions • If you plan to map data, you may need X-and Y-coordinate or denominator data

23. Basic Steps of an Outbreak Investigation • Verify the diagnosis and confirm the outbreak • Define a case and conduct case finding • Tabulate and orient data: time, place, person • Take immediate control measures • Formulate and test hypotheses • Plan and execute additional studies • Implement and evaluate control measures • Communicate findings

24. Descriptive Epidemiology

25. Step 3: Tabulate and orient data: time, place, person Descriptive epidemiology: • Familiarizes the investigator with the data • Comprehensively describes the outbreak • Is essential for hypothesis generation (step #5)

26. Data Cleaning • Check for accuracy • Outliers • Check for completeness • Missing values • Determine whether or not to create or collapse data categories • Get to know the basic descriptive findings

27. Data Cleaning:Outliers • Outliers can be cases at the very beginning and end that may not appear to be related • First check to make certain they are not due to a collection, coding or data entry error • If they are not an error, they may represent • Baseline level of illness • Outbreak source • A case exposed earlier than the others • An unrelated case • A case exposed later than the others • A case with a long incubation period

28. Data Cleaning:Distribution of Variables “Outlier”

29. Data Cleaning:Missing Values • The investigator can check into missing values that are expected versus those that are due to problems in data collection or entry • The number of missing values for each variable can also be learned from frequency distributions

30. Data Cleaning:Frequency Distributions

31. Data Cleaning:Data Categories • Which variables are continuous versus categorical? • Collapse existing categories into fewer? • Create categories from continuous? (e.g., age)

32. Descriptive Epidemiology • Comprehensively describes the outbreak • Time • Place • Person

33. Descriptive Epidemiology Time

34. Descriptive Epidemiology: Time • Time • Display time trends • Epidemic curves

35. Descriptive Epidemiology: Time

36. Descriptive Epidemiology:Time • What is an epidemic curve and how can it help in an outbreak? • An epidemic curve (epi curve) is a graphical depiction of the number of cases of illness by the date of illness onset

37. Descriptive Epidemiology:Time • An epi curve can provide information on the following characteristics of an outbreak: • Pattern of spread • Magnitude • Outliers • Time trend • Exposure and / or disease incubation period

38. Epidemic Curves Patterns of Spread

39. Epidemic Curves • The overall shape of the epi curve can reveal the type of outbreak • Common source • Intermittent • Continuous • Point source • Propagated

40. Epidemic Curves:Common Source • People are exposed to a common harmful source • Period of exposure may be brief (point source), long (continuous) or intermittent

41. Epi Curve: Common Source Outbreak with Intermittent Exposure

42. Epi Curve: Common Source Outbreak with Continuous Exposure

43. Epi Curve: Point Source Outbreak

44. Epi Curve: Propagated Outbreak

45. Epidemic Curves Outbreak Magnitude

46. Epidemic Curves

47. Epidemic Curves Outbreak Time Trend

48. Epidemic Curves Provide information about the time trend of the outbreak • Consider: • Date of illness onset for the first case • Date when the outbreak peaked • Date of illness onset for the last case

49. Epidemic Curves

50. Epidemic Curves Period of Exposure / Incubation Period