descriptive and analytical epidemiology

Public Health Information Network (PHIN)

Series I

is for Epi

Epidemiology basics for non-epidemiologists

Series Overview

Introduction to:

• The history of Epidemiology

• Specialties in the field

• Key terminology, measures, and resources

• Application of Epidemiological methods

Series I Sessions

Title Date

“Epidemiology in the Context of Public Health”

January 12

“An Epidemiologist’s Tool Kit” February 3

“Descriptive and Analytic Epidemiology”

March 3

“Surveillance” April 7

“Epidemiology Specialties Applied” May 5

What to Expect. . .

TodayUnderstand the basic terminology and measures used in descriptive and analytic Epidemiology

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

Site Sign-in Sheet

Please submit your site sign-in sheet to:

Suzi Silverstein

Director, Education and TrainingEmergency Preparedness & Response Programs

FAX: (804) 225 - 3888

Series ISession III

“Descriptive and Analytic Epidemiology”

Today’s Presenter

Kim Brunette, MPHEpidemiologistNorth Carolina Center for Public Health Preparedness, Institute for Public Health, UNC Chapel Hill

Session Overview

1. Define descriptive epidemiology

2. Define incidence and prevalence

3. Discuss examples of the use of descriptive data

4. Define analytic epidemiology

5. Discuss different study designs

6. Discuss measures of association

7. Discuss tests of significance

Today’s 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

Descriptive Epidemiology

Prevalence and Incidence

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

What is Epidemiology?

• Looking to answer the questions:

– Who?

– What?

– When?

– Where?

– Why?

– How?

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

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


• 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

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

Race # of Salmonella cases Pop. size

Black 119 1,450,675

White 497 5,342,532

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

Prevalence

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

# of casesPrevalence =

-----------------------------------------# of people in the

population

Prevalence ExampleIn 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.

253,040Prevalence= =

0.0515,008,863

• 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

Prevalence

• Useful for assessing the burden of disease within a population

• Valuable for planning

• Not useful for determining what caused disease

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

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.

7Incidence = = 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

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


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


Prevalence

= prevalent cases


Old (baseline) prevalence

= prevalent cases = incident cases

New prevalence

Incidence

No cases die or recover


= prevalent cases = incident cases = deaths or recoveries

Time for you to try it!!!


Person, Place, Time

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

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

Data Characterized by Person

http://www.vahealth.org/civp/Injury%20in%20Virginia_Report_2004.pdf


http://www.vdh.virginia.gov/std/AnnualReport2003.pdf


http://www.vdh.virginia.gov/epi/cancer/Report99.pdf


http://www.vahealth.org/chronic/Data_Report_Part_3.pdf

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

Data Characterized by Time

http://www.dhhs.state.nc.us/docs/ecoli.htm

Epi Curve for E.Coli outbreakn=108

0

2

4

6

8

10

12

Date of onse t

Nu

mb

er o

f ca

ses


http://www.vdh.virginia.gov/std/HIVSTDTrends.pdf


http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5153a1.htm


http://www.health.qld.gov.au/phs/Documents/cdu/12776.pdf

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

Data Characterized by Place

http://www.vdh.virginia.gov/epi/Data/region03t.pdf


http://www.vdh.virginia.gov/epi/Data/Maps2002.pdf


http://www.vahealth.org/civp/preventsuicideva/epiplan%202004.pdf


Source: Olsen, S.J. et al. N Engl J Med. 2003 Dec 18; 349(25):2381-2.

5 Minute Break

Analytic Epidemiology

Hypotheses and Study Designs

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


• Used to help identify the cause of disease

• Typically involves designing a study to test hypotheses developed using descriptive epidemiology

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

Exposure and Outcome

A study considers two main factors: exposure and outcome

• Exposure refers to factors that might influence one’s risk of disease

• Outcome refers to case definitions

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

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

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

Types of Studies

Two main categories:1. Experimental2. Observational

1. Experimental studies – exposure status is assigned

2. Observational studies – exposure status is not assigned

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

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

Observational Studies

Three main types:

1. Cross-sectional study

2. Cohort study

3. Case-control study

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

Cohort Studies• Study population is grouped by exposure

status

• Groups are then followed to determine if they develop the outcome

Exposure Outcome

Prospective Assessed at beginning of study

Followed into the future for outcome

Retrospective Assessed at some point in the past

Outcome has already occurred

Cohort Studies

Disease No Disease

StudyPopulation

Exposed Non-exposed

No DiseaseDisease

Exposure isself selected

Follow throughtime

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

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

Case-Control Studies

Had Exposure No Exposure

StudyPopulation

Cases Controls

No ExposureHad Exposure

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

Cohort versus Case-Control Study

Classification of Study Designs

Source: Grimes DA, Schulz KF. Lancet 2002; 359: 58

5 Minute Break


Measures of Association

and

Statistical Tests

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:1. Relative risk (a.k.a. risk ratio, RR)2. Odds ratio (a.k.a. OR)

2 x 2 TablesUsed to summarize counts of disease and exposure in order to do calculations of association

Outcome

Exposure Yes No Total

Yes a b a + b

No c d c + d

Total a + c b + d a + b + c + d

2 x 2 Tables

a = number who are exposed and have the outcomeb = number who are exposed and do not have the outcomec = number who are not exposed and have the outcomed = number who are not exposed and do not have the outcome

***********************************************************************a + b = total number who are exposedc + d = total number who are not exposeda + c = total number who have the outcomeb + d = total number who do not have the outcomea + b + c + d = total study population

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 + bRR =

c

c + d

Relative Risk Example

Escherichia coli?

Pink hamburger Yes No

Total

Yes 23 10 33

No 7 60 67

Total 30 70 100

a / (a + c) 23 / 33RR = = = 6.67

c / (c+ d) 7 / 67

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

Odds Ratio Example

Autism

MMR Vaccine? Yes No

Total

Yes 130 115 245

No 120 135 255

Total 250 250 500

a x d 130 x 135OR = = = 1.27

b x c 115 x 120

Interpretation

Both the RR and OR are interpreted as follows:

= 1 - indicates no association

> 1 - indicates a positive association

< 1 - indicates a negative association

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

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:1. 95% Confidence Intervals (CI)

2. p-values

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

95% CI Example

Disease Odds Ratio 95% CI

Gonorrhea 2.4 1.3 – 4.4

Trichomonas 1.9 1.3 – 2.8

Yeast 1.3 1.0 – 1.7

Other vaginitis 1.7 1.0 – 2.7

Herpes 0.9 0.5 – 1.8

Genital warts 0.4 0.2 – 1.0

Grodstein F, Goldman MB, Cramer DW. Relation of tubal infertility to history of sexually transmitted diseases. Am J Epidemiol. 1993 Mar 1;137(5):577-84

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

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

p-value Example

Disease Odds Ratio 95% CI p-value

Gonorrhea 2.4 1.3 – 4.4 0.004

Trichomonas 1.9 1.3 – 2.8 0.001

Yeast 1.3 1.0 – 1.7 0.04

Other vaginitis 1.7 1.0 – 2.7 0.04

Herpes 0.9 0.5 – 1.8 0.80

Genital warts 0.4 0.2 – 1.0 0.05

Grodstein F, Goldman MB, Cramer DW. Relation of tubal infertility to history of sexually transmitted diseases. Am J Epidemiol. 1993 Mar 1;137(5):577-84

Questions???

Epidemiology Pocket Guide:Quick Review Any Time!

• Measures of Disease Frequency• Classification of Study Designs• 2 x 2 Tables• Measures of Association• Tests of Significance


Session III Slides

Following this program, please visit the Web site below to access and download a copy of today’s slides:


Site Sign-in Sheet

Please submit your site sign-in sheet to:

Suzi Silverstein

Director, Education and TrainingEmergency Preparedness & Response Programs

FAX: (804) 225 - 3888

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.

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

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 Instructor’s Guide: Analytic Study Designs. Epid 168 lecture series. http://www.epidemiolog.net/epid168/labs/AnalyticStudExerInstGuid2000.pdf

2005 PHIN Training Development Team

Pia MacDonald, PhD, MPH Director, NCCPHP

Jennifer Horney, MPHDirector, Training and Education, NCCPHP

Kim Brunette, MPHEpidemiologist, NCCPHP

Anjum Hajat, MPHEpidemiologist, NCCPHP

Sarah Pfau, MPH Consultant

descriptive and analytical epidemiology

Education

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incidence high incidence

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incidence example

burden of disease

year incidence of depression

particular disease