2004 Public Health Training and Information Network (PHTIN) Series

2004 Public Health Training and

Information Network (PHTIN) Series

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

Outbreak Investigation Methods

From Mystery to Mastery

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

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)

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/

Session V

“Analyzing Data”

Today’s Presenters

Michelle Torok, MPH

Graduate Research Assistant and Doctoral Student, NCCPHP

Sarah Pfau, MPH

Consultant, NCCPHP

“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

“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

“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

Analyzing Data

Overview

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

Analysis Planning

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

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

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

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

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!

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

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

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

Descriptive Epidemiology

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)

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

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

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”

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

Data Cleaning:Frequency Distributions

Data Cleaning:Data Categories

• Which variables are continuous versus categorical?

• Collapse existing categories into fewer?

• Create categories from continuous? (e.g., age)


• Comprehensively describes the outbreak– Time– Place– Person


Time

Descriptive Epidemiology: Time

• Time– Display time trends– Epidemic curves

Descriptive Epidemiology: Time

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101214161820

Day

# o

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ases

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

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

Epidemic Curves

Patterns of Spread

Epidemic Curves

• The overall shape of the epi curve can reveal the type of outbreak

– Common source• Intermittent• Continuous• Point source

– Propagated

Epidemic Curves:Common Source

• People are exposed to a common harmful source

• Period of exposure may be brief (point source), long (continuous) or intermittent

Epi Curve: Common Source Outbreak with Intermittent Exposure

Epi Curve: Common Source Outbreak with Continuous Exposure

Epi Curve: Point Source Outbreak

Epi Curve: Propagated Outbreak

Epidemic Curves

Outbreak Magnitude

Epidemic Curves

Epidemic Curves

Outbreak Time Trend

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

Epidemic Curves

Epidemic Curves

Period of Exposure / Incubation Period

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

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

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

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%

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

Epidemic Curves

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

Epi Curve for a Common Source Outbreak with Continuous Exposure

Y-

Axi

s

X - Axis

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


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

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

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10/1

2/20

02

10/1

4/20

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10/1

6/20

02

10/1

8/20

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10/2

0/20

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10/2

2/20

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4/20

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10/2

6/20

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10/2

8/20

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10/3

0/20

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Day of Onset#

of

Cas

es

X-axis unit of time = 1 week X-axis unit of time = 1 day


Place

Descriptive Epidemiology: Place

• Provides information on the geographic boundaries of the outbreak

• May highlight outbreak patterns


• 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


Source: http://www.phppo.cdc.gov/PHTN/catalog/pdf-file/LESSON4.pdf


Person

Descriptive Epidemiology: Person

• Data summarization for descriptive epidemiology of the population– Line listings– Graphs

• Bar graphs• Histograms

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

Bar Graph

Histogram

Epidemic Curve for Outbreak of Gastrointestinal Illness in a Nursing Home, 2002

5 minute break


Measures of Central Tendency


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


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


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

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

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

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


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

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


3 7711 36.836.0Min MaxMode

50th percentile

Median Mean(average)

Activity:Calculate Mean and Median

Completion time: 5 minutes

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

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

Attack Rates

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

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

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%

Question & Answer Opportunity

Hypothesis Generation vs. Hypothesis Testing

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


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

5 minute break



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

Cohort versus Case-Control Study

Cohort versus Case-Control Study

Cohort Study

Measure of Association

Risk Ratio

Risk Ratio

Ill Not Ill Total

Exposed A B A+B

Unexposed C D C+D

Risk Ratio [A/(A+B)]

[C/(C+D)]

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

Interpreting a Risk Ratio

• RR=1.0 = no association between exposure and disease

• RR>1.0 = positive association

• RR<1.0 = negative association

Case-Control Study

Measure of Association

Odds Ratio

Odds Ratio

Cases Controls

Exposed A B

Unexposed C D

Odds Ratio (A/C)/(B/D)=(A*D)/(B*C)

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

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

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!

Confidence Intervals

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

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

Analysis Output

Step 6: Plan and Execute Additional Studies

• To gather more specific info– Example: Salmonella muenchen

• Interventional study – Example: implement intensive hand-washing

Question & Answer Opportunity

Epi Info Analysis

Case Study

Download Epi Info software for free at:

http://www.cdc.gov/epiinfo

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

Church Supper Menu

Case Study


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

Data Cleaning

Know your data! Know the:

• Number of records• Field formats and contents• Special properties• Table relationships

Data Cleaning

Tell Epi Info which records to include in analyses

Case Study: Line Listing

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

Epi Info Demonstration

•Display Variables•Line Listing•Means

Case Study: Line Listing

DO try this at home!

Case Study: Means

Case Study: Frequency Distributions

• Gender

• Age


•Frequency Table•Recode data•Graph data

Frequency by Gender

Frequency by AGE Category

AGE Distribution among Cases

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

Case Study: Epidemic Curve

Point-Source Outbreak

‘Textbook’ distributionCase Study distribution

Case Study: Epidemic Curve

Maximum incubation period

Overlap

Average incubation period

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

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”

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

Mean Incubation

Calculate Mean Incubationin Epi Info

Identify the Pathogen. . .

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

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

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

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

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.

Line Listings

13 + 27 people ate cakes

27 people who ate cake are ill

AR for people whoConsumed cake: 27 / 40 = 67.5%

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%

Generate and Testa Hypothesis!

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

Case Study


Case Study


Tables command

Tables Analysis Output

2 x 2 Table Shell Epi Info 2 x 2 Table

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.”

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

Question & AnswerOpportunity

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

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.

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.

Next Session November 9th10:00 a.m. - Noon

Topic: “Techniques for Review of Surveillance Data”

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/

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

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.

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

Documents

2004 Public Health Training and Information Network (PHTIN) Series