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 sites 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

4

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
• 1000 am - 1200 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
Todays 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 (contd.)

• 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 influenceand sometimes
  limityour 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 CleaningOutliers

• 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 CleaningDistribution of Variables
Outlier
29
Data CleaningMissing 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 CleaningFrequency Distributions
31
Data CleaningData 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 EpidemiologyTime

• 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 EpidemiologyTime

• 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 CurvesCommon 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
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Epi Curve Common Source Outbreak with
Continuous Exposure
43
Epi Curve Point Source Outbreak
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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

51
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

52
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

53
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

54
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

55
Calculating Incubation Period
Onset of illness among cases of E. coli O157H7
Infection, Massachusetts, December, 1998.
56
Epidemic Curves

• Creating an Epidemic Curve

57
Creating an Epidemic Curve

• Provide a descriptive title
• Label each axis
• Plot the number of cases of disease reported
  during an outbreak on the y-axis
• Plot the time or date of illness onset on the
  x-axis
• Include the pre-epidemic period to show the
  baseline number of cases

58
Epi Curve for a Common Source Outbreak with
Continuous Exposure
Y- Axis
X - Axis
59
Creating an Epidemic Curve

• X-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
  hours
• 2. 36 x ¼ 9
• 3. Use 9-hour intervals on the x-axis for an
  outbreak of influenza lasting several days

60
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

61
Epi Curve X-Axis Considerations
X-axis unit of time 1 week
X-axis unit of time 1 day
62
Descriptive Epidemiology

• Place

63
Descriptive Epidemiology Place

• Provides information on the geographic boundaries
  of the outbreak
• May highlight outbreak patterns

64
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

65
Descriptive Epidemiology Place
Source http//www.phppo.cdc.gov/PHTN/catalog/pdf-
file/LESSON4.pdf
66
Descriptive Epidemiology

• Person

67
Descriptive Epidemiology Person

• Data summarization for descriptive epidemiology
  of the population
• Line listings
• Graphs
• Bar graphs
• Histograms

68
Line Listing
69
Bar Graph
70
Histogram
Epidemic Curve for Outbreak of Gastrointestinal
Illness in a Nursing Home, 2002
71
5 minute break
72
Descriptive Epidemiology

• Measures of Central Tendency

73
Descriptive Epidemiology

• Measures of central tendency
• Mean
• Median
• Mode
• Range

74
Measures of Central Tendency

• Mean (Average)
• The sum of all values divided by the number of
  values
• Example
• Cases 7,10, 8, 5, 5, 37, 9 years old
• Mean (710855379)/7
• Mean 11.6 years of age

75
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
• Ages 7,10, 8, 5, 5, 37, 9
• Ages sorted 5, 5, 7, 8, 9,10, 37
• Median age 8

76
Calculate a Median Value

• If the number of measurements is odd
• Median value with rank (n1) / 2
• 5, 5, 7, 8, 9,10, 37
• n 7, (n1) / 2 (71) / 2 4
• The 4th value 8
• Where n the number of values

77
Calculate a Median Value

• If the number of measurements is even
• Medianaverage of the two values with
• rank of n / 2 and
• (n / 2) 1
• Where 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

78
Measures of Central Tendency

• Mode 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

79
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

80
Measures of Central Tendency Mode Software
Limitation
Modal Values
The ages 11, 17, 35, and 62 all qualify for the
status of mode, but Epi Info identifies Age 11
as the mode in analysis output for MEANS AGE in
viewOswego.
81
Measures of Central Tendency
50th percentile
3
77
11
36.8
36.0
Min
Max
Mode
Median
Mean (average)
82
ActivityCalculate Mean and Median

• Completion time 5 minutes

83
Calculate Mean and Median Age

• For an even number of measurements,
• Median the average of two values ranked
• N / 2
• (n / 2) 1

84
Calculate Mean and Median Age

• Mean age
• 59768540
• 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
• (67) / 2 6.5 years

85
Attack Rates
86
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

87
Food-Specific Attack Rates
CDC. Outbreak of foodborne streptococcal disease.
MMWR 23365, 1974.
88
Stratified Attack Rates
Attack rate in women 13 / 29 45 Attack rate
in men 5 / 32 16
89
Question Answer Opportunity
90
Hypothesis Generation vs. Hypothesis Testing
91
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

92
(No Transcript)
93
5 minute break
94
Analytic Epidemiology
95
Analytic Epidemiology

• Measures of Association
• Risk Ratio (cohort study)
• Odds Ratio (case-control study)

96
Cohort versus Case-Control Study

97
Cohort versus Case-Control Study
98
Cohort Study

• Measure of Association

99
Risk Ratio
100
Risk Ratio
101
Risk Ratio Example
RR (43 / 54) / (3 / 21) 5.6
102
Interpreting a Risk Ratio

• RR1.0 no association between exposure and
  disease
• RRgt1.0 positive association
• RRlt1.0 negative association

103
Case-Control Study

• Measure of Association

104
Odds Ratio
105
Odds Ratio
106
Odds Ratio Example
OR (60 / 18) / (25 / 55) 7.3
107
Interpreting an Odds Ratio

• The odds ratio is interpreted in the same way as
  a risk ratio
• OR1.0 no association between exposure and
  disease
• ORgt1.0 positive association
• ORlt1.0 negative association

108
What to do with a Zero Cell

• Try to recruit more study participants
• Add 1 to each cell
• Remember to document / report this!

109
Confidence Intervals
110
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 RR1.9, 95 CI 1.1-3.1

111
Confidence Intervals

• Provide information on precision of estimate
• Narrow confidence intervals more precise
• Wide confidence intervals less precise
• Example OR10, 95 CI 0.9 - 44.0
• Example OR10, 95 CI 9.0 - 11.0

112
Analysis Output
113
Step 6 Plan and Execute Additional Studies

• To gather more specific info
• Example Salmonella muenchen
• Interventional study
• Example implement intensive hand-washing

114
Question Answer Opportunity
115
Epi Info Analysis

• Case Study
• Download Epi Info software for free at
  http//www.cdc.gov/epiinfo

116
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

117
Church Supper Menu
118
Case Study

• Descriptive Epidemiology

119
Case Study

• Investigators needed to determine
• The type of outbreak occurring
• The pathogen causing the acute gastrointestinal
  illness and
• The source of infection

120
Data Cleaning

• Know your data! Know the
• Number of records
• Field formats and contents
• Special properties
• Table relationships

121
Data Cleaning
Tell Epi Info which records to include in
analyses
122
Case Study Line Listing

• Organize and review data about time, person, and
  place that were collected via hypothesis
  generating interviews.

123
Epi Info Demonstration

• Display Variables
• Line Listing
• Means

124
Case Study Line Listing
DO try this at home!
125
Case Study Means
126
Case Study Frequency Distributions

• Gender
• Age

127
Epi Info Demonstration

• Frequency Table
• Recode data
• Graph data

128
Frequency by Gender
129
Frequency by AGE Category
130
AGE Distribution among Cases
131
Case StudyEpidemic Curve

• Variable of Interest
• DATEONSET (date of onset of illness)
• Entered into database in mm/dd/yyyy/hh/mm/ss/AM
  PM field format

132
Case Study Epidemic Curve
133
Point-Source Outbreak
Textbook distribution
Case Study distribution
134
Case Study Epidemic Curve
Average incubation period
Maximum incubation period
Overlap
135
Using Epi Info to Create Epi Curves

• To create an epi curve with Epi Info
• Open the Analyze data component
• Use the Read command to use the outbreak data
• Click on the Graph command
• Choose Histogram as the Graph Type
• Choose date / time of illness onset variable as
  the x- axis main variable

136
Using Epi Info to Create Epi Curves

• To create an epi curve with Epi Info
• Choose count from the Show value of option
  beneath the y-axis option
• Choose weeks, days, hours, or minutes for the
  x-axis interval from the interval dropdown menu
• Type in graph title where it says Page title
• Click OK

137
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

138
Mean Incubation
139
Calculate Mean Incubationin Epi Info
140
Identify the Pathogen. . .
141
Potential Enteric Agents
142
Pathogen IdentificationResource

• CDCs Foodborne Outbreak Response and
  Surveillance Unit
• Guide to Confirming the Diagnosis in Foodborne
  Diseases
• http//www.cdc.gov/foodborneoutbreaks/guide_fd.htm
  

143
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

144
Case StudyAttack Rates

• Obtain the information that you need to
  calculate food-specific attack rates via
• Stratified Frequency Tables
• Line Listings
• Food-specific AR
• people who ate a food and became ill
• people who ate that food

145
Stratified Frequency Tables
40 people ate cake 27 people who ate cake are
ill.
AR for people who consumed cake 27 / 40 67.5
35 people did not eat cake 19 of those people
are ill.
AR for people who did not consume cake 19 / 35
54.2
146
Line Listings
13 27 people ate cakes
27 people who ate cake are ill
AR for people who Consumed cake 27 / 40 67.5
147
Case Study Attack Rates
148
Generate and Testa Hypothesis!
149
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

150
Case Study

• Analytic Epidemiology

151
Case Study
152
Epi Info Demonstration
Tables command
153
Tables Analysis Output
Epi Info 2 x 2 Table
2 x 2 Table Shell
154
Tables Analysis Output
The risk of becoming ill was more than five
times greater for people who consumed vanilla ice
cream than for people who did not consume
vanilla ice cream.
155
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

156
Question AnswerOpportunity
157
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

158
Session V Summary

• Descriptive 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.

159
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.

160
Next Session November 9th1000 a.m. - Noon

• Topic Techniques for Review of Surveillance
  Data

161
Session V Slides

• Following this program, please visit one of the
  web sites below to access and download a copy of
  todays 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/

162
Site Sign-in Sheet

• Please mail or fax your sites 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

163
References and Resources

• Centers 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.

164
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