Transcript Public Health Information Network (PHIN) Series I is for Epi

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
“Epidemiology in the Context of
Public Health”
“An Epidemiologist’s Tool Kit”
“Descriptive and Analytic
Epidemiology”
“Surveillance”
“Epidemiology Specialties Applied”
Date
January 12
February 3
March 3
April 7
May 5
What to Expect. . .
Today
Understand 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 Training
Emergency Preparedness & Response Programs
FAX: (804) 225 - 3888
Series I
Session III
“Descriptive and Analytic
Epidemiology”
Today’s Presenter
Kim Brunette, MPH
Epidemiologist
North 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
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
Descriptive Epidemiology
What?
• 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
Black
White
# of Salmonella cases
119
497
http://www.vdh.virginia.gov/epi/Data/race03t.pdf
Pop. size
1,450,675
5,342,532
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
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
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.
Incidence =
7
= 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 and Incidence
• Prevalence is a function of the incidence
of disease and the duration of disease
Prevalence and Incidence
Prevalence
= prevalent cases
Prevalence and Incidence
New
prevalence
Incidence
Old (baseline)
prevalence
No cases die
or recover
= prevalent cases
= incident cases
Prevalence and Incidence
= prevalent cases
= incident cases
= deaths or recoveries
Time for you to try it!!!
Descriptive Epidemiology
Person, Place, Time
Descriptive Epidemiology
Who? 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
Data Characterized by Person
http://www.vdh.virginia.gov/std/AnnualReport2003.pdf
Data Characterized by Person
http://www.vdh.virginia.gov/epi/cancer/Report99.pdf
Data Characterized by Person
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
10
/1
1/
20
05
10
/1
3/
20
05
10
/1
5/
20
05
10
/1
7/
20
05
10
/1
9/
20
05
10
/2
1/
20
05
10
/2
3/
20
05
10
/2
5/
20
05
10
/2
7/
20
05
10
/2
9/
20
05
10
/3
1/
20
05
11
/2
/2
00
5
11
/4
/2
00
5
11
/6
/2
00
5
11
/8
/2
00
11
5
/1
0/
20
05
Number of cases
Data Characterized by Time
Epi Curve for E.Coli outbreak
n=108
12
10
8
6
4
2
0
Date of onset
http://www.dhhs.state.nc.us/docs/ecoli.htm
Data Characterized by Time
http://www.vdh.virginia.gov/std/HIVSTDTrends.pdf
Data Characterized by Time
http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5153a1.htm
Data Characterized by Time
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
Data Characterized by Place
http://www.vdh.virginia.gov/epi/Data/Maps2002.pdf
Data Characterized by Place
http://www.vahealth.org/civp/preventsuicideva/epiplan%202004.pdf
Data Characterized by Place
http://www.vahealth.org/civp/preventsuicideva/epiplan%202004.pdf
Data Characterized by Place
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
Analytic Epidemiology
• 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 laboratoryconfirmed Salmonella
– Exposure is eating egg salad at the church picnic
– Outcome is laboratory confirmation of Salmonella
Types of Studies
Two main categories:
1. Experimental
2. 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
Study
Population
Exposure is
self selected
Non-exposed
Exposed
Follow through
time
Disease
No Disease
Disease
No Disease
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
Study
Population
Cases
Had Exposure
No Exposure
Controls
Had Exposure
No 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
Time for you to try it!!!
5 Minute Break
Analytic Epidemiology
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 Tables
Used 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
a+c
b+d
a+b+c+d
Total
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
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
RR =
a+b
c
c+d
Relative Risk Example
Escherichia coli?
Pink
hamburger
Yes
Total
Yes
23
No
10
33
No
7
60
67
Total
30
70
100
RR =
a / (a + c)
c / (c+ d)
=
23 / 33
7 / 67
= 6.67
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
axd
OR =
bxc
Odds Ratio Example
Autism
MMR
Vaccine?
Yes
Yes
130
No
115
245
No
120
135
255
Total
250
250
500
OR =
axd
bxc
Total
=
130 x 135
115 x 120
= 1.27
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
Time for you to try it!!!
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
http://www.vdh.virginia.gov/EPR/Training.asp
Session III Slides
Following this program, please visit the
Web site below to access and download a
copy of today’s slides:
http://www.vdh.virginia.gov/EPR/Training.asp
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
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/AnalyticStudExerInstGuid2
000.pdf
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