Public Health Information Network PHIN Series II

1
Public Health Information Network (PHIN) Series
II

• Outbreak Investigation Methods
• From Mystery to Mastery

2
(No Transcript)
3
Access Series Files Online http//www.vdh.virgini
a.gov/EPR/Training.asp

• Session slides
• Session activities (when applicable)
• Session evaluation forms
• Speaker biographies
• Alternate Web site http//www.sph.unc.edu/nccphp/
  phtin/index.htm

4
Site Sign-in Sheet

• Please submit your site sign-in sheet and
• session evaluation forms to
• Suzi Silverstein
• Director, Education and Training
• Emergency Preparedness Response Programs
• FAX (804) 225 - 3888

5
Series IISession VI

• Data Analysis

6
Series II Sessions

• Recognizing an Outbreak
• Risk Communication
• Study Design
• Designing Questionnaires
• Interviewing Techniques
• Data Analysis
• Writing and Reviewing Epidemiological
  Literature

7
Todays Presenters

• Amy Nelson, PhD
• Consultant
• NC Center for Public Health Preparedness
• Sarah Pfau, MPH
• Consultant
• NC Center for Public Health Preparedness

8
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

9
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

10
Analyzing Data Learning Objectives (contd.)

• Know why and how to generate measures of
  association for cohort and case-control studies
• 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

11
Lecturer

• Amy Nelson, PhD
• Consultant,
• NC Center for Public Health Preparedness

12
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

13
Analysis Planning
14
Analysis Planning

• An invaluable investment of time
• Helps you select the most appropriate
  epidemiologic methods
• Helps 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

15
Analysis Planning

• Several factors influenceand sometimes
  limityour approach to data analysis
• Research question
• Exposure and outcome variables
• Study design
• Sample population

16
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

17
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!

18
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

19
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

20
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

21
Descriptive Epidemiology
22
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)

23
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

24
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

25
Data CleaningDistribution of Variables
Outlier
26
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
  

27
Data CleaningFrequency Distributions
28
Data CleaningData Categories

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

29
Descriptive Epidemiology

• Comprehensively describes the outbreak
• Time
• Place
• Person

30
Descriptive Epidemiology

• Time

31
Descriptive Epidemiology Time
32
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

33
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

34
Epidemic Curves

• The overall shape of the epi curve can reveal
  the type of outbreak (the pattern of spread)
• Common source
• Intermittent
• Continuous
• Point source
• Propagated

35
Epidemic CurvesCommon Source

• People are exposed to a common harmful source
• Period of exposure may be brief (point source),
  long (continuous) or intermittent

36
Epi Curve Common Source Outbreak with
Intermittent Exposure
Pattern of Spread
37
Epi Curve Common Source Outbreak with
Continuous Exposure
Pattern of Spread
38
Epi Curve Point Source Outbreak
Pattern of Spread
39
Epi Curve Propagated Outbreak
Pattern of Spread
40
Epidemic Curves
Magnitude
41
Epidemic CurvesTime Trend

• 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

42
Epidemic Curves
Time Trend
43
Epidemic CurvesIncubation Period

• 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

44
Epidemic CurvesIncubation Period

• 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

45
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

46
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

47
Calculating Incubation Period
Onset of illness among cases of E. coli O157H7
Infection, Massachusetts, December, 1998.
48
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

49
Epi Curve for a Common Source Outbreak with
Continuous Exposure
Y- Axis
X - Axis
50
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

51
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

52
Epi Curve X-Axis Considerations
X-axis unit of time 1 week
X-axis unit of time 1 day
53
Descriptive Epidemiology

• Place

54
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

55
Descriptive Epidemiology Place
Source http//www.phppo.cdc.gov/PHTN/catalog/pdf-
file/LESSON4.pdf
56
Descriptive Epidemiology

• Person

57
Descriptive Epidemiology Person

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

58
Line Listing
59
Bar Graph
60
Descriptive Epidemiology

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

61
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

62
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

63
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

64
Calculate a Median Value

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

65
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

66
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

67
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.
68
Measures of Central Tendency
50th percentile
3
77
11
36.8
36.0
Min
Max
Mode
Median
Mean (average)
69
ActivityCalculate Mean and Median

• Completion time 5 minutes

70
Calculate Mean and Median Age

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

71
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

72
Question Answer Opportunity
73
5 minute break
74
Attack Rates
75
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

76
Food-Specific Attack Rates
CDC. Outbreak of foodborne streptococcal disease.
MMWR 23365, 1974.
This food is probably not the source of infection
77
Stratified Attack Rates
Attack rate in women 13 / 29 45 Attack rate
in men 5 / 32 16
78
Hypothesis Generation vs. Hypothesis Testing
79
Hypothesis Generation vs. Hypothesis Testing

• 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)
• Test hypotheses
• Occurs after hypotheses have been generated
• Based on analytic epidemiology

80
(No Transcript)
81
Analytic Epidemiology
82
Analytic Epidemiology

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

83
Cohort versus Case-Control Study

84
Cohort versus Case-Control Study
85
Analysis Output
86
Cohort Study

• Measure of Association

87
Risk Ratio
88
Risk Ratio Example
RR (43 / 54) / (3 / 21) 5.6
89
Interpreting a Risk Ratio

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

90
Case-Control Study

• Measure of Association

91
Odds Ratio
92
Odds Ratio Example
OR (60 / 18) / (25 / 55) 7.3
93
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

94
What to do with a Zero Cell

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

95
Confidence Intervals
96
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

97
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

98
Plan and Execute Additional Studies

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

99
Question Answer Opportunity
100
5 minute break
101
Epi Info Analysis

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

102
Oswego Tutorial

• 1. Epi Info Main Menu
• 2. Help
• 3. Tutorials
• 4. Oswego Tutorial

103
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

104
Church Supper

• Supper held in the church basement. 
• Foods contributed by numerous families. 
• Supper from 600 PM to 1100 PM, so food consumed
  over a period of several hours.

105
Case StudyDescriptive Epidemiology

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

106
Data Cleaning

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

107
Data Cleaning
Tell Epi Info which records to include in
analyses
Set command in Analyze Data
108
Case Study Line Listing

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

109
Case Study Line Listing
Code for generating output
110
Line Listing Windows Commands

• 1. Read (viewOswego in Sample.MDB)
• 2. Sort (on AGE, in ascending order)
• 3. Select (only the cases where ILLYes)
• 4. List (generate a line listing with the fields
  AGE, SEX, and DATEONSET)

111
Case Study Means
Code
Windows command
Means (of AGE)
112
Distribution Frequency by Gender
Windows command Frequencies (by SEX)
113
Case StudyEpidemic Curve

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

114
Case Study Epidemic Curve
115
Point-Source Outbreak
Textbook distribution
Case Study distribution
116
Case Study Epidemic Curve
Average incubation period
Maximum incubation period
Overlap
Outlier?
117
Using Epi Info to Create Epi Curves

• Step-by-Step Instructions
• Open the Analyze Data component
• Use the Read command to access your data table
• Click on the Graph command
• Choose Histogram as the Graph Type
• Choose your date / time of illness onset variable
  as the x- axis main variable

118
Using Epi Info to Create Epi Curves

• Step-by-Step Instructions
• 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

119
Determine Incubation Period

• Alternative 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

120
Means INCUBATIONAnalysis Output
121
Calculate Mean Incubationin Epi Info
122
Identify the Pathogen. . .
123
Identify the Pathogen. . .

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

124
Case Study Attack Rates

• Obtain the information that you need to
  calculate food-specific attack rates via
• Stratified Frequency Tables
• Line Listings
• 2 x 2 Tables
• Food-specific AR
• people who ate a food and became ill
• people who ate that food

125
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
Frequencies CAKE Stratify by ILL
126
Line Listings
13 27 people ate cakes
27 people who ate cake are ill
AR for people who Consumed cake 27 / 40 67.5
Not Ill
Ill
127
Tables Analysis Output
2 x 2 Table
Windows command Tables (Exposure CAKES
Outcome ILL)
128
Activity Interpreting Output
What percentage of people who ate cake did not
get ill?
129
Activity Interpreting Output
Exposure Outcome
Answer 32.5 of the people who ate cake did not
get ill.
130
Case Study Attack Rates
We should further investigate the association of
vanilla ice cream consumption and illness
131
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

132
Case Study
133
Tables Analysis Output
Epi Info 2 x 2 Table
2 x 2 Table Shell
Windows command Tables (for VANILLA)
134
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.
135
Case StudyAnalytic Results

• - Point-Source Outbreak
• - Staphylococcus aureus suspected pathogen based
  on 4.3 hr average incubation period
• - Vanilla ice cream 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

136
Online Epi Info Instruction

• http//www.sph.unc.edu/nccphp/training/all_trainin
  gs/at_epi_info.htm
• 8 Self-Instructional Training Modules for
  various screen components, functions, and
  commands in Analyze Data

137
Question AnswerOpportunity
138
Next Session December 1st, 100 p.m. 300 p.m.

• Topic Writing and Reviewing Epidemiological
  Literature

139
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.

140
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.

141
Session V Summary

• Attack rates are descriptive statistics that are
  useful for comparing the risk of disease in
  groups with different exposures (such as
  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.

142
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.

143
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