![Page 1: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/1.jpg)
2004 Public Health Training and
Information Network (PHTIN) Series
![Page 2: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/2.jpg)
Site Sign-in Sheet
Please mail or fax your site’s sign-in sheet to:
Linda WhiteNC Office of Public Health Preparedness and ResponseCooper Building1902 Mail Service CenterRaleigh, NC 27699
FAX: (919) 715 - 2246
![Page 3: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/3.jpg)
Outbreak Investigation Methods
From Mystery to Mastery
![Page 4: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/4.jpg)
![Page 5: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/5.jpg)
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
![Page 6: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/6.jpg)
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)
![Page 7: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/7.jpg)
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/
![Page 8: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/8.jpg)
Session V
“Analyzing Data”
![Page 9: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/9.jpg)
Today’s Presenters
Michelle Torok, MPH
Graduate Research Assistant and Doctoral Student, NCCPHP
Sarah Pfau, MPH
Consultant, NCCPHP
![Page 10: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/10.jpg)
“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
![Page 11: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/11.jpg)
“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
![Page 12: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/12.jpg)
“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
![Page 13: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/13.jpg)
Analyzing Data
Overview
![Page 14: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/14.jpg)
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
![Page 15: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/15.jpg)
Analysis Planning
![Page 16: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/16.jpg)
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
![Page 17: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/17.jpg)
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
![Page 18: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/18.jpg)
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
![Page 19: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/19.jpg)
Analysis Planning
Three key considerations as you plan your analysis:
1. Work backwards from the research question(s) to design the most efficient data collection instrument
2. Study design will determine which statistical tests and measures of association you evaluate in the analysis output
3. Consider the need to present, graph, or map data
![Page 20: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/20.jpg)
Analysis Planning
1. 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!
![Page 21: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/21.jpg)
Analysis Planning
2. 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
![Page 22: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/22.jpg)
Analysis Planning
3. 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
![Page 23: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/23.jpg)
Basic Steps of an Outbreak Investigation
1. Verify the diagnosis and confirm the outbreak
2. Define a case and conduct case finding
3. Tabulate and orient data: time, place, person
4. Take immediate control measures
5. Formulate and test hypotheses
6. Plan and execute additional studies
7. Implement and evaluate control measures
8. Communicate findings
![Page 24: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/24.jpg)
Descriptive Epidemiology
![Page 25: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/25.jpg)
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)
![Page 26: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/26.jpg)
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
![Page 27: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/27.jpg)
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
![Page 28: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/28.jpg)
Data Cleaning:Distribution of Variables
Illness Onset for Outbreak of Gastrointestinal Illness at a Nursing Home
0
2
4
6
8
Day of Onset
Nu
mb
er o
f C
ases
“Outlier”
![Page 29: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/29.jpg)
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
![Page 30: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/30.jpg)
Data Cleaning:Frequency Distributions
![Page 31: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/31.jpg)
Data Cleaning:Data Categories
• Which variables are continuous versus categorical?
• Collapse existing categories into fewer?
• Create categories from continuous? (e.g., age)
![Page 32: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/32.jpg)
Descriptive Epidemiology
• Comprehensively describes the outbreak– Time– Place– Person
![Page 33: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/33.jpg)
Descriptive Epidemiology
Time
![Page 34: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/34.jpg)
Descriptive Epidemiology: Time
• Time– Display time trends– Epidemic curves
![Page 35: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/35.jpg)
Descriptive Epidemiology: Time
02468
101214161820
Day
# o
f C
ases
![Page 36: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/36.jpg)
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
![Page 37: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/37.jpg)
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
![Page 38: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/38.jpg)
Epidemic Curves
Patterns of Spread
![Page 39: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/39.jpg)
Epidemic Curves
• The overall shape of the epi curve can reveal the type of outbreak
– Common source• Intermittent• Continuous• Point source
– Propagated
![Page 40: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/40.jpg)
Epidemic Curves:Common Source
• People are exposed to a common harmful source
• Period of exposure may be brief (point source), long (continuous) or intermittent
![Page 41: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/41.jpg)
Epi Curve: Common Source Outbreak with Intermittent Exposure
![Page 42: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/42.jpg)
Epi Curve: Common Source Outbreak with Continuous Exposure
![Page 43: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/43.jpg)
Epi Curve: Point Source Outbreak
![Page 44: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/44.jpg)
Epi Curve: Propagated Outbreak
![Page 45: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/45.jpg)
Epidemic Curves
Outbreak Magnitude
![Page 46: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/46.jpg)
Epidemic Curves
![Page 47: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/47.jpg)
Epidemic Curves
Outbreak Time Trend
![Page 48: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/48.jpg)
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
![Page 49: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/49.jpg)
Epidemic Curves
![Page 50: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/50.jpg)
Epidemic Curves
Period of Exposure / Incubation Period
![Page 51: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/51.jpg)
Epidemic Curves
• If the timing of the exposure is known, epi curves can be used to estimate the incubation period of the disease
• The time between the exposure and the peak of the epi curve represents the median incubation period
![Page 52: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/52.jpg)
Epidemic Curves
• In common source outbreaks with known incubation periods, epi curves can help determine the average period of exposure
– Find the average incubation period for the organism and count backwards from the peak case on the epi curve
![Page 53: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/53.jpg)
Epidemic Curves
• This can also be done to find the minimum incubation period
– Find the minimum incubation period for the organism and count backwards from the earliest case on the epi curve
![Page 54: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/54.jpg)
Exposure / Outbreak Incubation Period
• Average and minimum incubation periods should be close and should represent the probable period of exposure
• Widen the estimated exposure period by 10% to 20%
![Page 55: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/55.jpg)
Onset of illness among cases of E. coli O157:H7 Infection, Massachusetts, December, 1998.
Onset of illness among cases of E. coli O157:H7 Infection, Massachusetts, December, 1998.
Calculating Incubation Period
![Page 56: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/56.jpg)
Epidemic Curves
Creating an Epidemic Curve
![Page 57: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/57.jpg)
Creating an Epidemic Curve
Provide a descriptive titleLabel each axisPlot the number of cases of disease
reported during an outbreak on the y-axisPlot the time or date of illness onset on the
x-axisInclude the pre-epidemic period to show
the baseline number of cases
![Page 58: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/58.jpg)
Epi Curve for a Common Source Outbreak with Continuous Exposure
Y-
Axi
s
X - Axis
![Page 59: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/59.jpg)
Creating an Epidemic CurveX-axis considerations
Choice of time unit for x-axis depends upon the incubation period
• Begin with a unit approximately one quarter the length of the incubation period
Example: 1. Mean incubation period for influenza = 36 hours2. 36 x ¼ = 93. Use 9-hour intervals on the x-axis for an outbreak
of influenza lasting several days
![Page 60: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/60.jpg)
Creating an Epidemic Curve
X-axis considerations
• If the incubation period is not known, graph several epi curves with different time units
• Usually the day of illness onset is the best unit for the x-axis
![Page 61: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/61.jpg)
Epi Curve X-Axis Considerations
05
101520253035404550
10/1-10/7 10/8-10/14 10/15-10/21 10/22-10/28
Week of Onset
# o
f C
ases
0123456789
10
10/2
/200
2
10/4
/200
2
10/6
/200
2
10/8
/200
2
10/1
0/20
02
10/1
2/20
02
10/1
4/20
02
10/1
6/20
02
10/1
8/20
02
10/2
0/20
02
10/2
2/20
02
10/2
4/20
02
10/2
6/20
02
10/2
8/20
02
10/3
0/20
02
Day of Onset#
of
Cas
es
X-axis unit of time = 1 week X-axis unit of time = 1 day
![Page 62: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/62.jpg)
Descriptive Epidemiology
Place
![Page 63: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/63.jpg)
Descriptive Epidemiology: Place
• Provides information on the geographic boundaries of the outbreak
• May highlight outbreak patterns
![Page 64: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/64.jpg)
Descriptive Epidemiology: Place
• Spot map
– Shows where cases live, work, spend time
– If population size varies between locations being compared, use location-specific attack rates instead of number of cases
![Page 65: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/65.jpg)
Descriptive Epidemiology: Place
Source: http://www.phppo.cdc.gov/PHTN/catalog/pdf-file/LESSON4.pdf
![Page 66: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/66.jpg)
Descriptive Epidemiology
Person
![Page 67: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/67.jpg)
Descriptive Epidemiology: Person
• Data summarization for descriptive epidemiology of the population– Line listings– Graphs
• Bar graphs• Histograms
![Page 68: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/68.jpg)
Line Listing Signs/
SymptomsLab Demograph
ics
Case #
Report Date
Onset Date
Physician
Diagnosis
N V J HAIgM
Sex
Age
1 10/12/02 10/5/02 Hepatitis A
1 1 1 1 M 37
2 10/12/02 10/4/02 Hepatitis A
1 0 1 1 M 62
3 10/13/02 10/4/02 Hepatitis A
1 0 1 1 M 38
4 10/13/02 10/9/02 NA 0 0 0 NA F 44
5 10/15/02 10/13/02 Hepatitis A
1 1 0 1 M 17
6 10/16/02 10/6/02 Hepatitis A
0 0 1 1 F 43
![Page 69: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/69.jpg)
Bar Graph
![Page 70: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/70.jpg)
Histogram
Epidemic Curve for Outbreak of Gastrointestinal Illness in a Nursing Home, 2002
![Page 71: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/71.jpg)
5 minute break
![Page 72: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/72.jpg)
Descriptive Epidemiology
Measures of Central Tendency
![Page 73: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/73.jpg)
Descriptive Epidemiology
– Measures of central tendency• Mean• Median• Mode• Range
![Page 74: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/74.jpg)
Measures of Central Tendency
Mean (Average)The sum of all values divided by the number of values
Example:
1.Cases 7,10, 8, 5, 5, 37, 9 years old
2.Mean = (7+10+8+5+5+37+9)/7
3.Mean = 11.6 years of age
![Page 75: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/75.jpg)
Measures of Central Tendency
Median (50th percentile)
The value that falls in the middle position when the measurements are ordered from smallest to largest
Example:
1.Ages 7,10, 8, 5, 5, 37, 9
2.Ages sorted: 5, 5, 7, 8, 9,10, 37
3.Median age = 8
![Page 76: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/76.jpg)
Calculate a Median ValueIf the number of measurements is odd:
Median = value with rank (n+1) / 2• 5, 5, 7, 8, 9,10, 37 • n = 7, (n+1) / 2 = (7+1) / 2 = 4• The 4th value = 8
Where n = the number of values
![Page 77: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/77.jpg)
Calculate a Median Value
If the number of measurements is even:
Median=average of the two values with a.rank of n / 2 and b.(n / 2) + 1Where n = the number of values
• 5, 5, 7, 8, 9,10, 37 • n = 7; (7 / 2) = 3.5. So “8” is the first value• (7 / 2) + 1 = 4.5, so “9” is the second value• (8 + 9) / 2 = 8.5• The Median value = 8.5
![Page 78: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/78.jpg)
Measures of Central TendencyMode [Modal Value]
• The value that occurs the most frequently– Example: 5, 5, 7, 8, 9,10, 37
Mode= 5
• It is possible to have more than one mode– Example: 5, 5, 7,8,10,10, 37
Modes= 5 and 10
![Page 79: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/79.jpg)
Measures of Central Tendency
Mode [Modal Value]:
The value for the variable in which the greatest frequency of records fall
Epi Info limitation: If multiple values share the same frequency that is also the highest frequency, Epi Info will identify only the first value it encounters as “Mode” as it scans the table in ascending order
![Page 80: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/80.jpg)
Measures of Central Tendency Mode Software Limitation
The ages 11, 17, 35, and 62 all qualify for the status of “mode,” but Epi Info identifiesAge 11 as the mode in analysis output for MEANS AGE in viewOswego.
Modal Values
![Page 81: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/81.jpg)
Measures of Central Tendency
3 7711 36.836.0Min MaxMode
50th percentile
Median Mean(average)
![Page 82: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/82.jpg)
Activity:Calculate Mean and Median
Completion time: 5 minutes
![Page 83: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/83.jpg)
Calculate Mean and Median AgeCase # Age (Years)1 5
2 9
3 7
4 6
5 8
6 5
For an even number of measurements, Median = the average of two values ranked:
a. N / 2b. (n / 2) + 1
![Page 84: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/84.jpg)
Calculate Mean and Median Age
Mean age:• 5+9+7+6+8+5=40• 40 / 6 = 6.67 years
Median age:• 5,5,6,7,8,9• Average of values ranked (n/2) and (n/2)+1• =(6/2) and (6/2) +1 = average of 6 and 7• =(6+7) / 2 = 6.5 years
![Page 85: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/85.jpg)
Attack Rates
![Page 86: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/86.jpg)
Attack Rates (AR)AR
# of cases of a disease
# of people at risk (for a limited period of time)
Food-specific AR# people who ate a food and became ill
# people who ate that food
![Page 87: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/87.jpg)
Food-Specific Attack Rates
CDC. Outbreak of foodborne streptococcal disease. MMWR 23:365, 1974.
Consumed
ItemDid Not Consume
Item
Item Ill Total AR(%) Ill Total AR(%)
Chicken 12 46 26 17 29 59
Cake 26 43 61 20 32 63
Water 10 24 42 33 51 65
Green Salad 42 54 78 3 21 14
Asparagus 4 6 67 42 69 61
![Page 88: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/88.jpg)
Stratified Attack Rates
Ill Well Total AR(%)
Women 13 16 29 45
Men 5 27 32 16
Attack rate in women: 13 / 29 = 45%
Attack rate in men: 5 / 32 = 16%
![Page 89: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/89.jpg)
Question & Answer Opportunity
![Page 90: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/90.jpg)
Hypothesis Generation vs. Hypothesis Testing
![Page 91: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/91.jpg)
Hypothesis Generation vs. Hypothesis Testing
Step 5a. Formulate hypotheses– Occurs after having spoken with some case –
patients and public health officials – Based on information form literature review– Based on descriptive epidemiology (step #3)
Step 5b. Test hypotheses– Occurs after hypotheses have been generated– Based on analytic epidemiology
![Page 92: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/92.jpg)
Descriptive Epidemiology
Analytic Epidemiology
Search for clues Clues available
Formulate hypotheses Test hypotheses
No comparison group Comparison group
Answers: How much, who, what, when, where
Answers: How, why
![Page 93: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/93.jpg)
5 minute break
![Page 94: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/94.jpg)
Analytic Epidemiology
![Page 95: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/95.jpg)
Analytic Epidemiology
• Measures of Association– Risk Ratio (cohort study)– Odds Ratio (case-control study)
![Page 96: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/96.jpg)
Cohort versus Case-Control Study
![Page 97: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/97.jpg)
Cohort versus Case-Control Study
![Page 98: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/98.jpg)
Cohort Study
Measure of Association
![Page 99: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/99.jpg)
Risk Ratio
![Page 100: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/100.jpg)
Risk Ratio
Ill Not Ill Total
Exposed A B A+B
Unexposed C D C+D
Risk Ratio [A/(A+B)]
[C/(C+D)]
![Page 101: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/101.jpg)
Risk Ratio Example
Ill Well Total
Ate alfalfa sprouts 43 11 54
Did not eat alfalfa sprouts
3 18 21
Total 46 29 75
RR = (43 / 54) / (3 / 21) = 5.6
![Page 102: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/102.jpg)
Interpreting a Risk Ratio
• RR=1.0 = no association between exposure and disease
• RR>1.0 = positive association
• RR<1.0 = negative association
![Page 103: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/103.jpg)
Case-Control Study
Measure of Association
![Page 104: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/104.jpg)
Odds Ratio
![Page 105: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/105.jpg)
Odds Ratio
Cases Controls
Exposed A B
Unexposed C D
Odds Ratio (A/C)/(B/D)=(A*D)/(B*C)
![Page 106: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/106.jpg)
Odds Ratio Example
Case Control Total
Ate at restaurant X 60 25 85
Did not eat at restaurant X
18 55 73
Total 78 80 158
OR = (60 / 18) / (25 / 55) = 7.3
![Page 107: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/107.jpg)
Interpreting an Odds Ratio
The odds ratio is interpreted in the same way as a risk ratio:
• OR=1.0 = no association between exposure and disease
• OR>1.0 = positive association
• OR<1.0 = negative association
![Page 108: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/108.jpg)
What to do with a Zero CellCase Control Total
Ate at restaurant X 60 0 60
Did not eat at restaurant X
18 55 73
Total 78 55 133
•Try to recruit more study participants
•Add 1 to each cell*
*Remember to document / report this!
![Page 109: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/109.jpg)
Confidence Intervals
![Page 110: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/110.jpg)
Confidence Intervals• Allow the investigator to:
– Evaluate statistical significance
– Assess the precision of the estimate (the odds ratio or risk ratio)
• Consist of a lower bound and an upper bound
– Example: RR=1.9, 95% CI: 1.1-3.1
![Page 111: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/111.jpg)
Confidence Intervals• Provide information on precision of
estimate
– Narrow confidence intervals =more precise
– Wide confidence intervals =less precise
• Example: OR=10, 95% CI: 0.9 - 44.0
• Example: OR=10, 95% CI: 9.0 - 11.0
![Page 112: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/112.jpg)
Analysis Output
![Page 113: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/113.jpg)
Step 6: Plan and Execute Additional Studies
• To gather more specific info– Example: Salmonella muenchen
• Interventional study – Example: implement intensive hand-washing
![Page 114: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/114.jpg)
Question & Answer Opportunity
![Page 115: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/115.jpg)
Epi Info Analysis
Case Study
Download Epi Info software for free at:
http://www.cdc.gov/epiinfo
![Page 116: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/116.jpg)
Case Study Overview
• Oswego County, New York: 1940
• 80 people attended a church supper on 4/18
• 46 people who attended the supper suffered from gastrointestinal illness beginning 4/18 and ending 4/19
• 75 people (ill and non-ill) interviewed
• Investigation focus: church supper as source of infection
![Page 117: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/117.jpg)
Church Supper Menu
![Page 118: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/118.jpg)
Case Study
Descriptive Epidemiology
![Page 119: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/119.jpg)
Case Study
• Investigators needed to determine:
a) The type of outbreak occurring;
b) The pathogen causing the acute gastrointestinal illness; and
c) The source of infection
![Page 120: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/120.jpg)
Data Cleaning
Know your data! Know the:
• Number of records• Field formats and contents• Special properties• Table relationships
![Page 121: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/121.jpg)
Data Cleaning
Tell Epi Info which records to include in analyses
![Page 122: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/122.jpg)
Case Study: Line Listing
• Organize and review data about time, person, and place that were collected via hypothesis generating interviews.
![Page 123: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/123.jpg)
Epi Info Demonstration
•Display Variables•Line Listing•Means
![Page 124: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/124.jpg)
Case Study: Line Listing
DO try this at home!
![Page 125: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/125.jpg)
Case Study: Means
![Page 126: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/126.jpg)
Case Study: Frequency Distributions
• Gender
• Age
![Page 127: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/127.jpg)
Epi Info Demonstration
•Frequency Table•Recode data•Graph data
![Page 128: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/128.jpg)
Frequency by Gender
![Page 129: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/129.jpg)
Frequency by AGE Category
![Page 130: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/130.jpg)
AGE Distribution among Cases
![Page 131: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/131.jpg)
Case Study:Epidemic Curve
Variable of Interest:
DATEONSET (date of onset of illness)
– Entered into database in mm/dd/yyyy/hh/mm/ss/AM PM field format
![Page 132: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/132.jpg)
Case Study: Epidemic Curve
![Page 133: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/133.jpg)
Point-Source Outbreak
‘Textbook’ distributionCase Study distribution
![Page 134: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/134.jpg)
Case Study: Epidemic Curve
Maximum incubation period
Overlap
Average incubation period
![Page 135: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/135.jpg)
Using Epi Info to Create Epi Curves
To create an epi curve with Epi Info1. Open the “Analyze data” component
2. Use the Read command to use the outbreak data
3. Click on the “Graph” command
4. Choose “Histogram” as the “Graph Type”
5. Choose date / time of illness onset variable as the x- axis main variable
![Page 136: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/136.jpg)
Using Epi Info to Create Epi Curves
To create an epi curve with Epi Info:6. Choose “count” from the “Show value of”
option beneath the y-axis option
7. Choose weeks, days, hours, or minutes for the x-axis interval from the “interval” dropdown menu
8. Type in graph title where it says “Page title”
9. Click “OK”
![Page 137: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/137.jpg)
Determine Incubation Period
Create a temporary variable called “Incubation” in Analyze Data:
INCUBATION = DATEONSET – TIMESUPPER
Where field format is identical:
Date / time – mm/dd/yyyy/hh/mm/ss/AM PM
![Page 138: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/138.jpg)
Mean Incubation
![Page 139: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/139.jpg)
Calculate Mean Incubationin Epi Info
![Page 140: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/140.jpg)
Identify the Pathogen. . .
![Page 141: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/141.jpg)
Potential Enteric Agents
Viruses Bacteria Parasites Toxins
Norwalk CampylobacterCyrptospor-idium parvum
Clostridium botulinum
Norwalk-like viruses (caliciviruses)
E. coli CyclosporaStaph. aureus
RotavirusSalmonella spp.
GiardiaMushroom toxins
Hepatitis A ShigellaEntamoeba histolytica
Fish/Shellfish toxins
![Page 142: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/142.jpg)
Pathogen IdentificationResource
CDC’s Foodborne Outbreak Response and Surveillance Unit
“Guide to Confirming the Diagnosis in Foodborne Diseases”
http://www.cdc.gov/foodborneoutbreaks/guide_fd.htm
![Page 143: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/143.jpg)
Verify the Diagnosis: Find Plausible Agents
Evaluate:
predominant signs and symptoms
incubation period
duration of symptoms
suspected food
laboratory testing of stool, blood, or vomitus
![Page 144: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/144.jpg)
Case Study:Attack Rates
Obtain the information that you need to calculate food-specific attack rates via:
A. Stratified Frequency TablesB. Line Listings
Food-specific AR# people who ate a food and became ill
# people who ate that food
![Page 145: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/145.jpg)
Stratified Frequency Tables
AR for people who consumed cake: 27 / 40 = 67.5%
40 people ate cake; 27 people who ate cake are ill.
AR for people who did not consume cake:
19 / 35 = 54.2%
35 people did not eat cake;19 of those people are ill.
![Page 146: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/146.jpg)
Line Listings
13 + 27 people ate cakes
27 people who ate cake are ill
AR for people whoConsumed cake: 27 / 40 = 67.5%
![Page 147: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/147.jpg)
Case Study Attack Rates Consumed
ItemDid Not Consume
Item
Item Ill Total AR(%) Ill Total AR(%)
Baked Ham 29 46 63% 17 29 59%
CabbageSalad
18 28 64% 28 47 60%
Cakes 27 40 68% 19 35 54%
Chocolate Ice Cream
25 47 53% 20 27 74%
VanillaIce Cream
43 54 80% 3 21 14%
![Page 148: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/148.jpg)
Generate and Testa Hypothesis!
![Page 149: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/149.jpg)
Generate and Test a Hypothesis!
• The epi curve is indicative of a Point-Source outbreak
• Based on the incubation period, we suspect Staphylococcus aureus as the pathogen
• The food-specific attack rates lead us to believe that vanilla ice cream may be the source of infection
![Page 150: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/150.jpg)
Case Study
Analytic Epidemiology
![Page 151: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/151.jpg)
Case Study
![Page 152: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/152.jpg)
Epi Info Demonstration
Tables command
![Page 153: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/153.jpg)
Tables Analysis Output
2 x 2 Table Shell Epi Info 2 x 2 Table
![Page 154: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/154.jpg)
Tables Analysis Output
“The risk of becoming ill was more than five times greater for peoplewho consumed vanilla ice cream than for
people who did not consume vanilla ice cream.”
![Page 155: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/155.jpg)
Case StudyAnalytic Results
- Point-Source Outbreak
- Staphylococcus aureus is suspected pathogen based on 4.3 hour average incubation period
- Vanilla ice cream as suspected source of infection (highest food-specific AR of 80%)
- Vanilla ice cream RR = 5.6
- Vanilla ice cream C.I. = 1.9 – 16.0
![Page 156: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/156.jpg)
Question & AnswerOpportunity
![Page 157: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/157.jpg)
Session V Summary
Analysis planning can: be an invaluable investment of time; help you select the most appropriate epidemiologic methods; and 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.
As you plan your analysis: 1) Work backwards from the research question(s) to design the most efficient data collection instrument; 2) Consider your study design to guide which statistical tests and measures of association you evaluate in the analysis output; and 3) Consider the need to present, graph, or map data
![Page 158: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/158.jpg)
Session V SummaryDescriptive epidemiology: 1) Familiarizes the investigator with data about time, place, and person; 2) Comprehensively describes the outbreak; and 3) Is essential for hypothesis generation.
Data cleaning is the first step in preparing to generate descriptive statistics, as it contributes to the accuracy and completeness of the data.
Measures of central tendency provide a means of assessing the distribution of data. Measures include mean, median, mode, and range.
Epi curves, spot maps, and line listings are all ways in which you can generate and review the time, place, and person elements – respectively – of descriptive statistics.
![Page 159: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/159.jpg)
Session V Summary
Attack rates are descriptive statistics that are useful for comparing the risk of disease in groups with different exposures (e.g., consumption of individual food items).
Analytic epidemiology allows you to test the hypotheses generated via review of descriptive statistics and the medical literature.
The measures of association for case control and cohort analytic studies, respectively, are odds ratios and risk ratios.
Confidence intervals that accompany measures of association evaluate the statistical significance of the measures and assess the
precision of the estimates.
![Page 160: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/160.jpg)
Next Session November 9th10:00 a.m. - Noon
Topic: “Techniques for Review of Surveillance Data”
![Page 161: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/161.jpg)
Session V Slides
Following this program, please visit one of the web sites below to access and download a copy of today’s slides:
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/
![Page 162: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/162.jpg)
Site Sign-in Sheet
Please mail or fax your site’s sign-in sheet to:
Linda WhiteNC Office of Public Health Preparedness and ResponseCooper Building1902 Mail Service CenterRaleigh, NC 27699
FAX: (919) 715 - 2246
![Page 163: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/163.jpg)
References and ResourcesCenters for Disease Control and Prevention (1992).
Principles of Epidemiology, 2nd ed. Atlanta, GA: Public Health Practice Program Office.
Division of Public Health Surveillance and Informatics, Epidemiology Program Office, Centers for Disease Control and Prevention (January 2003). Epi Info Support Manual. [included with installation of the software, which can be found at: http://www.cdc.gov/epiinfo/index.htm]
Gordis L. (1996). Epidemiology. Philadelphia, WB Saunders.
![Page 164: 2004 Public Health Training and Information Network (PHTIN) Series](https://reader035.vdocument.in/reader035/viewer/2022070411/568146bb550346895db3ea40/html5/thumbnails/164.jpg)
References and Resources
Rothman KJ. Epidemiology: An Introduction. New York, Oxford University Press, 2002.
Stehr-Green, J. and Stehr-Green, P. (2004). Hypothesis Generating Interviews. Module 3 of a Field Epidemiology Methods course being developed in the NC Center for Public Health Preparedness, UNC Chapel Hill.
Torok, M. (2004). FOCUS on Field Epidemiology. “Epidemic Curves”. Volume 1, Issue 5. NC Center for Public Health Preparedness