Public Health Information Network PHIN Series I

1
Public Health Information Network (PHIN) Series
I
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(No Transcript)
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Series Overview

• Introduction to
• The history of Epidemiology
• Specialties in the field
• Key terminology, measures, and resources
• Application of Epidemiological methods

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Series I Sessions
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What to Expect. . .

• Today
• Understand the basic terminology and measures
  used in descriptive and analytic Epidemiology

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Session I V Slides

• VDH will post PHIN series slides on the
  following Web site
• http//www.vdh.virginia.gov/EPR/Training.asp
• NCCPHP Training Web site
• http//www.sph.unc.edu/nccphp/training

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Site Sign-in Sheet

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

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Series ISession III

• Descriptive and Analytic Epidemiology

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Todays Presenter

• Kim Brunette, MPH
• Epidemiologist
• North Carolina Center for Public Health
  Preparedness, Institute for Public Health, UNC
  Chapel Hill

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Session Overview

• Define descriptive epidemiology
• Define incidence and prevalence
• Discuss examples of the use of descriptive data
• Define analytic epidemiology
• Discuss different study designs
• Discuss measures of association
• Discuss tests of significance

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Todays Learning Objectives

• Understand the distinction between descriptive
  and analytic Epidemiology, and their utility in
  surveillance and outbreak investigations
• Recognize descriptive and analytic measures used
  in the Epidemiological literature
• Know how to interpret data analysis output for
  measures of association and common statistical
  tests

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Descriptive Epidemiology

• Prevalence and Incidence

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What is Epidemiology?

• Study of the distribution and determinants of
  states or events in specified populations, and
  the application of this study to the control of
  health problems
• Study risk associated with exposures
• Identify and control epidemics
• Monitor population rates of disease and exposure

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What is Epidemiology?

• Looking to answer the questions
• Who?
• What?
• When?
• Where?
• Why?
• How?

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Case Definition

• A case definition is a set of standard diagnostic
  criteria that must be fulfilled in order to
  identify a person as a case of a particular
  disease
• Ensures that all persons who are counted as cases
  actually have the same disease
• Typically includes clinical criteria (lab
  results, symptoms, signs) and sometimes
  restrictions on time, place, and person

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Descriptive vs. Analytic Epidemiology

• Descriptive Epidemiology deals with the
  questions Who, What, When, and Where
• Analytic Epidemiology deals with the remaining
  questions Why and How

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Descriptive Epidemiology

• Provides a systematic method for characterizing a
  health problem
• Ensures understanding of the basic dimensions of
  a health problem
• Helps identify populations at higher risk for the
  health problem
• Provides information used for allocation of
  resources
• Enables development of testable hypotheses

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Descriptive EpidemiologyWhat?

• Addresses the question How much?
• Most basic is a simple count of cases
• Good for looking at the burden of disease
• Not useful for comparing to other groups or
  populations

http//www.vdh.virginia.gov/epi/Data/race03t.pdf
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Prevalence

• The number of affected persons present in the
  population divided by the number of people in the
  population
• of cases
• Prevalence -------------------------------------
  ----
• of people in the population

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Prevalence Example

• In 1999, Virginia reported an estimated 253,040
  residents over 20 years of age with diabetes.
  The US Census Bureau estimated that the 1999
  Virginia population over 20 was 5,008,863.
• 253,040
• Prevalence 0.051
• 5,008,863
• In 1999, the prevalence of diabetes in Virginia
  was 5.1
• Can also be expressed as 51 cases per 1,000
  residents over 20 years of age

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Prevalence

• Useful for assessing the burden of disease within
  a population
• Valuable for planning
• Not useful for determining what caused disease

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Incidence

• The number of new cases of a disease that occur
  during a specified period of time divided by the
  number of persons at risk of developing the
  disease during that period of time
• of new cases of disease over a
  specific period of time
• Incidence --------------------------------------
  -----
• of persons at risk of disease
  over that specific period of time

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Incidence Example

• A study in 2002 examined depression among persons
  with dementia. The study recruited 201 adults
  with dementia admitted to a long-term care
  facility. Of the 201, 91 had a prior diagnosis
  of depression. Over the first year, 7 adults
  developed depression.
• 7
• Incidence 0.0636
• 110
• The one year incidence of depression among adults
  with dementia is 6.36
• Can also be expressed as 63.6 (64) cases per
  1,000 persons with dementia

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Incidence

• High incidence represents diseases with high
  occurrence low incidence represents diseases
  with low occurrence
• Can be used to help determine the causes of
  disease
• Can be used to determine the likelihood of
  developing disease

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Prevalence and Incidence

• Prevalence is a function of the incidence of
  disease and the duration of disease

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Prevalence and Incidence
Prevalence
prevalent cases
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Prevalence and Incidence
New prevalence
Incidence
Old (baseline) prevalence
No cases die or recover
prevalent cases
incident cases
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Prevalence and Incidence
prevalent cases
incident cases
deaths or recoveries
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Time for you to try it!!!
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Descriptive Epidemiology

• Person, Place, Time

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Descriptive EpidemiologyWho? When? Where?

• Related to Person, Place, and Time
• Person
• May be characterized by age, race, sex,
  education, occupation, or other personal
  variables
• Place
• May include information on home, workplace,
  school
• Time
• May look at time of illness onset, when exposure
  to risk factors occurred

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Person Data

• Age and Sex are almost always used in looking at
  data
• Age data are usually grouped intervals will
  depend on what type of disease / event is being
  looked at
• May be shown in tables or graphs
• May look at more than one type of person data at
  once

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Data Characterized by Person
http//www.vahealth.org/civp/Injury20in20Virgini
a_Report_2004.pdf
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Data Characterized by Person
http//www.vdh.virginia.gov/std/AnnualReport2003.p
df
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Data Characterized by Person
http//www.vdh.virginia.gov/epi/cancer/Report99.pd
f
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Data Characterized by Person
http//www.vahealth.org/chronic/Data_Report_Part_3
.pdf
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Time Data

• Usually shown as a graph
• Number / rate of cases on vertical (y) axis
• Time periods on horizontal (x) axis
• Time period will depend on what is being
  described
• Used to show trends, seasonality, day of week /
  time of day, epidemic period

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Data Characterized by Time
http//www.dhhs.state.nc.us/docs/ecoli.htm
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Data Characterized by Time
http//www.vdh.virginia.gov/std/HIVSTDTrends.pdf
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Data Characterized by Time
http//www.cdc.gov/mmwr/preview/mmwrhtml/mm5153a1.
htm
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Data Characterized by Time
http//www.health.qld.gov.au/phs/Documents/cdu/127
76.pdf
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Place Data

• Can be shown in a table usually better presented
  pictorially in a map
• Two main types of maps used
• choropleth and spot
• Choropleth maps use different shadings/colors to
  indicate the count / rate of cases in an area
• Spot maps show location of individual cases

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Data Characterized by Place
http//www.vdh.virginia.gov/epi/Data/region03t.pdf
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Data Characterized by Place
http//www.vdh.virginia.gov/epi/Data/Maps2002.pdf
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Data Characterized by Place
http//www.vahealth.org/civp/preventsuicideva/epip
lan202004.pdf
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Data Characterized by Place
http//www.vahealth.org/civp/preventsuicideva/epip
lan202004.pdf
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Data Characterized by Place
Source Olsen, S.J. et al. N Engl J Med. 2003
Dec 18 349(25)2381-2.
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5 Minute Break
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Analytic Epidemiology

• Hypotheses and Study Designs

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Descriptive vs. Analytic Epidemiology

• Descriptive Epidemiology deals with the
  questions Who, What, When, and Where
• Analytic Epidemiology deals with the remaining
  questions Why and How

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Analytic Epidemiology

• Used to help identify the cause of disease
• Typically involves designing a study to test
  hypotheses developed using descriptive
  epidemiology

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Borgman, J (1997). The Cincinnati Enquirer.
King Features Syndicate.
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Exposure and Outcome

• A study considers two main factors
• exposure and outcome
• Exposure refers to factors that might influence
  ones risk of disease
• Outcome refers to case definitions

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Case Definition

• A set of standard diagnostic criteria that must
  be fulfilled in order to identify a person as a
  case of a particular disease
• Ensures that all persons who are counted as cases
  actually have the same disease
• Typically includes clinical criteria (lab
  results, symptoms, signs) and sometimes
  restrictions on time, place, and person

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Developing Hypotheses

• A hypothesis is an educated guess about an
  association that is testable in a scientific
  investigation
• Descriptive data provide information to develop
  hypotheses
• Hypotheses tend to be broad initially and are
  then refined to have a narrower focus

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Example

• Hypothesis People who ate at the church picnic
  were more likely to become ill
• Exposure is eating at the church picnic
• Outcome is illness this would need to be
  defined, for example, ill persons are those who
  have diarrhea and fever
• Hypothesis People who ate the egg salad at the
  church picnic were more likely to have
  laboratory-confirmed Salmonella
• Exposure is eating egg salad at the church picnic
• Outcome is laboratory confirmation of Salmonella

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(No Transcript)
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Types of Studies

• Two main categories
• Experimental
• Observational
• Experimental studies exposure status is
  assigned
• Observational studies exposure status is not
  assigned

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Experimental Studies

• Can involve individuals or communities
• Assignment of exposure status can be random or
  non-random
• The non-exposed group can be untreated (placebo)
  or given a standard treatment
• Most common is a randomized clinical trial

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Experimental Study Examples

• Randomized clinical trial to determine if giving
  magnesium sulfate to pregnant women in preterm
  labor decreases the risk of their babies
  developing cerebral palsy
• Randomized community trial to determine if
  fluoridation of the public water supply decreases
  dental cavities

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Observational Studies

• Three main types
• Cross-sectional study
• Cohort study
• Case-control study

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Cross-Sectional Studies

• Exposure and outcome status are determined at the
  same time
• Examples include
• Behavioral Risk Factor Surveillance System
  (BRFSS) - http//www.cdc.gov/brfss/
• National Health and Nutrition Surveys (NHANES) -
  http//www.cdc.gov/nchs/nhanes.htm
• Also include most opinion and political polls

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Cohort Studies

• Study population is grouped by exposure status
• Groups are then followed to determine if they
  develop the outcome

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Cohort Studies
Study Population
Exposure is self selected
Non-exposed
Exposed
Follow through time
Disease
No Disease
No Disease
Disease
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Cohort Study Examples

• Study to determine if smokers have a higher risk
  of lung cancer
• Study to determine if children who receive
  influenza vaccination miss fewer days of school
• Study to determine if the coleslaw was the cause
  of a foodborne illness outbreak

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Case-Control Studies

• Study population is grouped by outcome
• Cases are persons who have the outcome
• Controls are persons who do not have the outcome
• Past exposure status is then determined

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Case-Control Studies
Study Population
Controls
Cases
Had Exposure
No Exposure
No Exposure
Had Exposure
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Case-Control Study Examples

• Study to determine an association between autism
  and vaccination
• Study to determine an association between lung
  cancer and radon exposure
• Study to determine an association between
  salmonella infection and eating at a fast food
  restaurant

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Cohort versus Case-Control Study

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Classification of Study Designs
Source Grimes DA, Schulz KF. Lancet 2002 359
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Time for you to try it!!!
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5 Minute Break
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Analytic Epidemiology

• Measures of Association
• and
• Statistical Tests

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Measures of Association

• Assess the strength of an association between an
  exposure and the outcome of interest
• Indicate how more or less likely one is to
  develop disease as compared to another
• Two widely used measures
• Relative risk (a.k.a. risk ratio, RR)
• Odds ratio (a.k.a. OR)

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2 x 2 Tables

• Used to summarize counts of disease and exposure
  in order to do calculations of association

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2 x 2 Tables

• a number who are exposed and have the outcome
• b number who are exposed and do not have the
  outcome
• c number who are not exposed and have the
  outcome
• d number who are not exposed and do not have
  the outcome
• 
  
• a b total number who are exposed
• c d total number who are not exposed
• a c total number who have the outcome
• b d total number who do not have the outcome
• a b c d total study population

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Relative Risk

• The relative risk is the risk of disease in the
  exposed group divided by the risk of disease in
  the non-exposed group
• RR is the measure used with cohort studies
• a
• a b
• RR
• c
• c d

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Relative Risk Example
a / (a c) 23 / 33 RR
6.67 c / (c d) 7 / 67
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Odds Ratio

• In a case-control study, the risk of disease
  cannot be directly calculated because the
  population at risk is not known
• OR is the measure used with case-control studies
• a x d
• OR
• b x c

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Odds Ratio Example
a x d 130 x 135 OR 1.27 b
x c 115 x 120
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Interpretation

• Both the RR and OR are interpreted as follows
• 1 - indicates no association
• 1 - indicates a positive association

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Interpretation

• If the RR 5
• People who were exposed are 5 times more likely
  to have the outcome when compared with persons
  who were not exposed
• If the RR 0.5
• People who were exposed are half as likely to
  have the outcome when compared with persons who
  were not exposed
• If the RR 1
• People who were exposed are no more or less
  likely to have the outcome when compared to
  persons who were not exposed

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Tests of Significance

• Indication of reliability of the association that
  was observed
• Answers the question How likely is it that the
  observed association may be due to chance?
• Two main tests
• 95 Confidence Intervals (CI)
• p-values

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95 Confidence Interval (CI)

• The 95 CI is the range of values of the measure
  of association (RR or OR) that has a 95 chance
  of containing the true RR or OR
• One is 95 confident that the true measure of
  association falls within this interval

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95 CI Example
Grodstein F, Goldman MB, Cramer DW. Relation of
tubal infertility to history of sexually
transmitted diseases. Am J Epidemiol. 1993 Mar
1137(5)577-84
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Interpreting 95 Confidence Intervals

• To have a significant association between
  exposure and outcome, the 95 CI should not
  include 1.0
• A 95 CI range below 1 suggests less risk of the
  outcome in the exposed population
• A 95 CI range above 1 suggests a higher risk of
  the outcome in the exposed population

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p-values

• The p-value is a measure of how likely the
  observed association would be to occur by chance
  alone, in the absence of a true association
• A very small p-value means that you are very
  unlikely to observe such a RR or OR if there was
  no true association
• A p-value of 0.05 indicates only a 5 chance that
  the RR or OR was observed by chance alone

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p-value Example
Grodstein F, Goldman MB, Cramer DW. Relation of
tubal infertility to history of sexually
transmitted diseases. Am J Epidemiol. 1993 Mar
1137(5)577-84
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Time for you to try it!!!
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Questions???
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Epidemiology Pocket GuideQuick Review Any Time!

• Measures of Disease Frequency
• Classification of Study Designs
• 2 x 2 Tables
• Measures of Association
• Tests of Significance
• http//www.vdh.virginia.gov/EPR/Training.asp

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Session III Slides

• Following this program, please visit the Web
  site below to access and download a copy of
  todays slides
• http//www.vdh.virginia.gov/EPR/Training.asp

93
Site Sign-in Sheet

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

94
References and Resources

• Centers for Disease Control and Prevention
  (1992). Principles of Epidemiology 2nd Edition.
  Public Health Practice Program Office Atlanta,
  GA.
• Gordis, L. (2000). Epidemiology 2nd Edition.
  W.B. Saunders Company Philadelphia, PA.
• Gregg, M.B. (2002). Field Epidemiology 2nd
  Edition. Oxford University Press New York.
• Hennekens, C.H. and Buring, J.E. (1987).
  Epidemiology in Medicine. Little, Brown and
  Company Boston/Toronto.

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References and Resources

• Last, J.M. (2001). A Dictionary of Epidemiology
  4th Edition. Oxford University Press New York.
• McNeill, A. (January 2002). Measuring the
  Occurrence of Disease Prevalence and Incidence.
  Epid 160 lecture series, UNC Chapel Hill School
  of Public Health, Department of Epidemiology.
• Morton, R.F, Hebel, J.R., McCarter, R.J. (2001).
  A Study Guide to Epidemiology and Biostatistics
  5th Edition. Aspen Publishers, Inc.
  Gaithersburg, MD.
• University of North Carolina at Chapel Hill
  School of Public Health, Department of
  Epidemiology, and the Epidemiologic Research
  Information Center (June 1999). ERIC Notebook.
  Issue 2. http//www.sph.unc.edu/courses/eric/eric_
  notebooks.htm

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References and Resources

• University of North Carolina at Chapel Hill
  School of Public Health, Department of
  Epidemiology, and the Epidemiologic Research
  Information Center (July 1999). ERIC Notebook.
  Issue 3. http//www.sph.unc.edu/courses/eric/eric_
  notebooks.htm
• University of North Carolina at Chapel Hill
  School of Public Health, Department of
  Epidemiology, and the Epidemiologic Research
  Information Center (September 1999). ERIC
  Notebook. Issue 5. http//www.sph.unc.edu/courses
  /eric/eric_notebooks.htm
• University of North Carolina at Chapel Hill
  School of Public Health, Department of
  Epidemiology (August 2000). Laboratory
  Instructors Guide Analytic Study Designs. Epid
  168 lecture series. http//www.epidemiolog.net/epi
  d168/labs/AnalyticStudExerInstGuid2000.pdf

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2005 PHIN Training Development Team

• Pia MacDonald, PhD, MPH
• Director, NCCPHP
• Jennifer Horney, MPH
• Director, Training and Education, NCCPHP
• Kim Brunette, MPH
• Epidemiologist, NCCPHP
• Anjum Hajat, MPH
• Epidemiologist, NCCPHP
• Sarah Pfau, MPH
• Consultant