Transcript Analytic Epidemiology - University of North Carolina at

is for Epi
Epidemiology basics
for non-epidemiologists
Session III
Part II
Descriptive and Analytic
Epidemiology
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 person, place, and time
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 study designs:
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
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 a group
is to develop disease as compared to
another group
•
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
Outcome
a + b + c + d = total study population
Yes
Yes
Exposure
No
a
c
No
b
d
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
Outcome
Yes
No
Yes
Exposure
No
a
b
c
d
a
a+b
Total
a+b
c+d
Risk among
the exposed
Risk among
the unexposed
RR =
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 + b)
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
Summary
• Descriptive Epidemiology
– Answers: Who, what, where, when
– Key Terms: Prevalence, person, place, time
– Hypothesis-generating
• Analytic Epidemiology
– Answers: Why, how
– Key Terms: Measure of association
– Hypothesis-testing
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