Epidemiology in Action
Unit II.
Types of Epidemiology Studies
•
Experimental vs. Observational
•
Descriptive and Analytic Studies
Activities:
1.
Study the STATS
2.
Name That Study
Epidemiology in Action
Experimental
Researchers have control over key
variables in the study.
Observational
Researchers do not have control over key
variables in the study.
2
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3
EXPERIMENTAL
Studies in which researchers control key
variables. These include intervention and clinical
trials that attempt to prove cause and effect.
Vaccine trials, for
example. One
population receives
the vaccine (an
independent variable
that the researchers
control), the other
doesn’t (or is given a
placebo to “blind”
subjects) and serves
as the control group.
The groups are
compared in terms of
how well/if the
vaccine works.
Source: CDC/1963 Health workers administering the polio vaccine to children.
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EXPERIMENTAL
This is a hypothetical example of a study where one group of juveniles recovering
from substance abuse is treated through in-house drug treatment while the other is
not. Study tracks rate of relapse over time.
16
14
12
10
treatment
no treatment
8
6
4
Total #
who
2
relapsed
0
3
6
9
Months After Treatment
12
Epidemiology in Action
EXPERIMENTAL
Advantages
•
Gold standard for proving causation, because
you control the variables you’re studying
•
Risk factors can be isolated
Disadvantages
•
Can be difficult to isolate variables
•
Ethical issues
5
Epidemiology in Action
OBSERVATIONAL STUDIES:
DESCRIPTIVE
Studies which describe the
FREQUENCY and PATTERNS of
occurrence of a condition or factor
by time, place, and person – for
example, these studies on exercise
by U.S. state.
Link: Behavioral Risk Factor
Surveillance System CDC
SOURCE: Centers for Disease Control
and Prevention (CDC). Behavioral Risk Factor
Surveillance System Survey Data. Atlanta: CDC/U.S.
Department of Health and Human Services, 2000.
6
Epidemiology in Action
DESCRIPTIVE
Advantages
•
Can be conducted using widely available data
•
Can be conducted on large populations
Disadvantages
•
Difficult to infer causation
•
Can be expensive and time-consuming
•
Susceptible to bias (surveys, for example)
7
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DESCRIPTIVE Studies are used to characterize a health problem
and its risk factors. These studies also COMPARE groups of people in
terms of changes in health conditions over time, place, and
exposure to risk factors.
For example, many
organizations keep ongoing
statistics on teen
pregnancy. They may
DESCRIBE the health status
(pregnancy) of teen
females, and/or COMPARE
the RATE of pregnancy for
POPULATIONS ( a 1975
group to a 1985 group).
They may compare rates to
RISK FACTORS
(race/ethnicity, socioeconomic status, etc.)
Link: Teen Pregnancy.org
Epidemiology in Action
A study like
the one at
right
DESCRIBES
the health
status of a
population.
Link: Teen Pregnancy.org
9
Number of Teen Pregnancies, 1996 (AGI)
10
Nearly 1 million teen pregnancies occurred in 1996. To put it another way, more than 100 U.S.
teens become pregnant each hour. Forty percent of these pregnancies were to girls under age 18,
and 60 percent were to girls aged 18-19.
Total: 905,000
542,640
337,530
15-17
37%
18-19
60%
24,830
under 15
3%
The Alan Guttmacher Institute. (1999). Special report: U.S. teenage pregnancy statistics with comparative statistics for women aged 20-24.
New York: Author. Link: The Alan Guttmacher Institute: Home Page
This study DESCRIBES and COMPARES rates by age.
11
Teen Pregnancy Rates, Girls Aged 15-17 (AGI)
(number of pregnancies per 1,000 girls)
After increasing 19 percent between 1972 and 1989 (including 7 percent between 1986 and
1989), the teen pregnancy rate for girls aged 15-17 decreased 17 percent between 1989 and 1996
to its lowest rate ever recorded.
80
74.4
75
72.5
70
69.6
65
60
55
62.4
61.5
Link: Teen Pregnancy.org
50
1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996
The Alan Guttmacher Institute. (1999). Special report: U.S. teenage pregnancy statistics with comparative statistics for women aged 20-24.
New York: Author. Link: The Alan Guttmacher Institute: Home Page
This study COMPARES two populations, girls/1972 to girls/1996.
Number of teen births, 1999*
12
Among teens aged 15-19, more births occur to non-Hispanic White teens than to
any other racial/ethnic group.
400,000
350,000
337,323
300,000
250,000
213,223
200,000
150,000
121,262
124,352
100,000
50,000
White (total)
White (non-Hispanic)
African American
Native American
Asian/Pacific Islander
Hispanic (any race)
7,905
9,255
0
teen girls aged 15-19
* Data for 1999 are preliminary. Curtin, S.C., & Martin, J.A. (2000). Births: Preliminary data for 1999. National Vital
Statistics Reports 48(14).
Link: Teen Pregnancy.org
This study COMPARES rates to the RISK FACTOR of Race/Ethnicity.
13
Teen Pregnancy Rates, Racial/Ethnic Subgroups (AGI)
(number of pregnancies per 1,000 girls aged 15-19)
Teen pregnancy rates vary substantially among the three largest racial/ethnic subgroups. Between 1990 and
1996, the rate for African-American teens declined 20 percent and the rate for non-Hispanic White teens
declined 24 percent. The teen pregnancy rate for Hispanics increased between 1990 and 1994, but then
declined 6 percent between 1994 and 1996.
240
Non-Hispanic Black
200
160
224.3
Hispanic (any race)
178.9
175.1
163.4
164.6
120
87.3
Non-Hispanic White
80
66.1
40
0
1990
1991
1992
1993
1994
1995
1996
Darroch, J.E., & Singh, S. (1999). Why is teenage pregnancy declining? The roles of abstinence, sexual activity and contraceptive use.
Occasional Report 1. New York: The Alan Guttmacher Institute.
Link: The Alan Guttmacher Institute: Home Page
This study COMPARES rates, in terms of race/ethnicity, and over time.
14
You may have noticed that data on teen pregnancy can be
presented in many different ways. When INTERPRETING results
from studies, it is important to pay attention to these details. For
example:
• WHAT is the study measuring, in terms of
AGE
TIME
GEOGRAPHY
RISK FACTORS?
• WHERE did the data come from?
• Who collected it, and how was it collected?
The data on the next three screens is similar, but comes from
different sources and tracks different years and ages.
Link: Teen Pregnancy.org
Link: The Alan Guttmacher Institute: Home Page
15
Teen Pregnancy Rates, Girls Under 15 (AGI)
(number of pregnancies per 1,000 girls)
After increasing 30 percent between 1973 and 1988, the teen pregnancy rate for girls aged 14 or
younger decreased 24 percent between 1988 and 1996 to the lowest rate ever recorded.
18
17.6
17
16
15
14
13
13.5
13.3
12
11
10
1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995
Note: denominator used is the population of girls aged 14. The Alan Guttmacher Institute. (1999). Special report: U.S. teenage pregnancy
statistics with comparative statistics for women aged 20-24. New York: Author.
Link: The Alan Guttmacher Institute: Home Page
16
Teen Pregnancy Rates, Girls Under 15 (NCHS)
(number of pregnancies per 1,000 girls)
After increasing 16 percent between 1982 and 1985 and remaining constant between 1985 and 1986, the teen
pregnancy rate for girls aged 14 or younger decreased 22 percent between 1986 and 1996 to the lowest rate
ever recorded.
3.75
3.6
3.50
3.25
3.2
3.1
3.00
2.8
2.75
2.50
2.25
2.00
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
Note: denominator used is the population of girls aged 10-14. Ventura, S.J., Mosher, W.D., Curtin, S.C., Abma, J.C., & Henshaw, S.
(2000). Trends in pregnancies and pregnancy rates by outcome: Estimates for the United States, 1976-96. Vital and Health Statistics
21(56).
Link: The Alan Guttmacher Institute: Home Page
1996
17
Teen Pregnancy Rates, Girls Under 15 (NCCDPHP)
(number of pregnancies per 1,000 girls)
The teen pregnancy rate for girls aged 14 or younger decreased 11 percent between 1995 and
1997.
10
8
7.2
6.8
6.4
6
4
2
0
1995
1996
Note: denominator used is the population of girls aged 13-14. Centers for Disease Control and Prevention. (2000). National and statespecific pregnancy rates among adolescents – United States, 1995-1997. MMWR, 49(27), 605-11.
Link: The Alan Guttmacher Institute: Home Page
1997
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Data Sources
Teen pregnancy data are released by three national groups:
•
•
•
The Alan Guttmacher Institute
The National Center for Health Statistics (NCHS), Centers for
Disease Control and Prevention (CDC), U.S. Department of Health
and Human Services (DHHS)
The National Center for Chronic Disease Prevention and Health
Promotion (NCCDPHP)
Each group collects different information, from different sources,
in different ways. These three groups DO tend to operate very similarly,
thus the similarity of their results. Other studies, however, will have
marked differences in terms of collection and presentation of data, and
it can affect things significantly.
Link: The Alan Guttmacher Institute: Home Page
Link: National Center for Health Statistics
Link: National Center for Chronic Disease Prevention
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• How data is gathered,
• by whom, and
• how it is analyzed
are all factors that can
introduce BIAS
into research.
Library of Congress 1908 Sioux Indian smoking
Do we look at research by
cigarette manufacturers on the
health dangers of smoking, for
example, the same as research
done by an independent,
unbiased organization?
We’re BIASED when we play favorites in terms of choosing study
subjects or in assessing exposure. Bias is dangerous, because it can
invalidate a study.
20
For example, how could a researcher make the US statistics look even worse?
International Pregnancy Rates, Teens 15-19 (AGI)
The United States has much higher pregnancy and birth rates than other fully industrialized
countries. U.S. pregnancy rates are nearly twice as high as rates in Canada and England and
seven to eight times as high as rates in Japan and the Netherlands.
120
100
80
60
40
20
0
United
States
(1996)
Canada
(1995)
Denmark
(1995)
England &
Wales
(1995)
France
(1995)
Pregnancy Rate
Japan
(1995)
Netherlands
(1992)
Norway
(1996)
Sweden
(1996)
Birth Rate
Singh, S., & Darroch, J.E. (2000). Adolescent pregnancy and childbearing: Levels and trends in developed countries. Family Planning Perspectives 32(1), 14-23.
Pregnancy rates calculated as the sum of births, abortions, and estimated miscarriages (20 percent of births plus 10 percent of miscarriages).
21
120
100
80
60
40
20
0
United
States
(1996)
Canada
(1995)
Denmark
(1995)
England &
Wales
(1995)
France
(1995)
Pregnancy Rate
By only presenting
data on 4 countries,
that’s how.
(Granted, it’s bad,
but is it that bad?)
Japan
(1995)
Netherlands
(1992)
Norway
(1996)
Sweden
(1996)
Birth Rate
100
90
80
70
60
50
40
30
20
10
0
Pregnancy
Birth Rate
US
JAPAN
FR
NETH
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This is an example of BIAS, and it happens all the time when
politicians, advertisers, and other organizations organize their statistics
in ways that best persuade, no matter what the real “truth” is. When
conducting solid research, we want to reach what’s called the “true”
value – the pure statistics, untainted by bias.
For an interesting look at how mangled research can get when bias is
allowed to reign, check out STATS, a non-partisan, non-profit research
organization in Washington, D.C. devoted to the accurate use of
scientific and social research in public policy debate. In their “news
clips” section, for example, you’ll see articles such as “Good News!
More People are Reporting Crimes!,” that tell the real story behind the
faulty use of statistics.
LINK: Statistical Assessment Service (STATS)
Source: Statistical Assessment Service
2100 L. St. NW Suite 300
Washington, D.C. 20037
Epidemiology in Action
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Problems to watch out for in epidemiological studies:
• Selection Bias – when subjects chosen are not
representative of the target population about which
conclusions are to be drawn.
• Information Bias – errors in measuring exposure/risk
or disease/condition
• Confounding Variable – A variable that is related to the
condition/disease under study and is associated with, but
NOT a consequence of, the exposure/risk factor under
investigation.
24
STUDENT ASSIGNMENT: STUDY THE STATS
DIRECTIONS: In groups of two or three, students are to find two sets of data
done on a similar population, from two different sources. Use any of the
sources we’ve been introduced to so far as starting points, or find your own
sources. Suggested study health topics include:
Cancer, smoking, drugs/alcohol, teen violence, health/nutrition issues, etc.
Then….. find out how both studies got their information – how was it
collected? When was it collected? Who collected it? What instruments (such
as a survey) did they use to collect information? How did they analyze and
present their data? Compare/contrast the two studies, in terms of the
Who/What/When/Where/How.
• Determine how the studies DIFFERED in terms of what they measured
(age/time/risk factors/etc.)
• Decide how the researchers may have introduced BIAS into their research,
and discuss possible CONFOUNDING variables that may be affecting results.
Give at least three examples of places where BIAS could be lurking in this
study, and two examples of potential CONFOUNDING variables that may call
the results into question.
• Present your findings to the class in a written report, a PowerPoint
presentation, or both.
400,000
350,000
337,323
300,000
250,000
213,223
200,000
150,000
121,262
124,352
100,000
50,000
7,905
9,255
0
teen girls aged 15-19
White (total)
White (non-Hispanic)
African American
Native American
Asian/Pacific Islander
Hispanic (any race)
STUDENT ASSIGNMENT: STUDY THE STATS
Suggested GENERAL Resources:
25
400,000
350,000
337,323
300,000
250,000
213,223
200,000
150,000
121,262
124,352
100,000
Link: National Center for Health Statistics
Link: National Center for Chronic Disease Prevention
Link: NIH: Health Information
Link: National Center for Educational Statistics
Link: Census Bureau Home Page
OTHER Resources:
Try searching by topic for research statistics. For example:
•Teen smoking research statistics
•Cancer research statistics
•Juvenile crime research statistics
•Teen driving research statistics
50,000
7,905
9,255
0
teen girls aged 15-19
Epidemiology in Action
Observational Studies: Analytic
Three types of ANALYTIC studies:
•
Cohort
•
Case-Control
•
Cross-sectional
26
Epidemiology in Action
27
COHORT Studies
compare groups of people who have been exposed to
suspected risk factors to groups of people who have not
been exposed to determine who develops a disease or
other outcome.
For example, an ongoing study of nuns has found a link between low language skills
and development of Alzheimer’s disease in later life. In this study:
Exposure
Disease, y/n ?
Exposed=had
low language
ability
Not
Exposed=did
not have low
language
ability
Also See: How to Investigate an Outbreak for excellent
lesson on cohort studies
Link: The Nun Study
Epidemiology in Action
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COHORT Studies
Summary of the Nun Study on Linguistic Ability:
Summary:The Nun Study indicates that low linguistic ability in early life has a strong
association with dementia and premature death in old age. Researchers investigated the
relationship of linguistic ability in early life to the neuropathology of Alzheimer’s disease
and cerebrovascular disease. They analyzed 74 Nun Study participants’ handwritten
autobiographies, written between ages 19 – 37. Idea density of the journals was
measured, and after death their brains were autopsied and investigated for evidence of
Alzheimer’s disease pathology in the neocortex. Low idea density scores from early life
were associated with the severity of Alzheimer’s disease at the time of death.
Source: : Linguistic ability in early life and the neuropathology of Alzheimer's disease and cerebrovascular disease: Findings from the
Nun Study In: Vascular factors in Alzheimer's disease, Volume 903, Kalaria RN, Ince P, eds., pp. 34-38, New York: New York Academy of
Scicences, 2000.
Link: The Nun Study
Note: This site is filled with interesting statistics, study abstracts, and
other materials on a wide variety of risk factors. Examples of more
advanced statistical analyses than those covered in this curriculum are in
plentiful supply, and easily accessible to students.
Epidemiology in Action
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COHORT
Advantages
•
Exposure precedes health outcome, necessary to
infer causation
•
Less subject to bias because exposure is evaluated
before health outcome is known
•
Can examine multiple health results to exposure
Disadvantages
•
Expensive, time consuming
•
Can infer cause, not prove
•
Not as useful when the total population at risk is not
well-defined
Epidemiology in Action
30
CASE CONTROL Studies
compare people with a condition (cases) to a
people without the condition (controls) to study
risk factors.
For example, the first
studies that pointed
out the strong
connection between
smoking and lung
cancer were CaseControl Studies.
Source: CDC/ gross pathology of lung from smoking
This link describes some interesting current studies on teens and smoking:
Epidemiology of Youth Drug Abuse - Research Findings 2/00
Epidemiology in Action
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CASE CONTROL Studies
compare people with a condition (cases)
to people without the condition (controls)
to study risk factors.
Smoking and Carcinoma of the Lung
Disease
Status
# of smokers
# of
nonsmokers
Males
Lung cancer
647
(99.7%)
2
(0.3%
Males
Controls
622
(95.8%)
27
(4.2%)
Females
Lung cancer
41
(46.7%)
19
31.7%)
Females
Controls
28
(46.7%)
32
53.3%
P-value
0.00000064
0.016
Doll R. Bradford, Hill A. Smoking and carcinoma of the lung: preliminary report. British
Medical Journal 1950, 2: 739-748.
Epidemiology in Action
CASE CONTROL Studies
Advantages
•
Can study rare health outcomes quickly/less
expensively
•
Can study multiple exposures for a single
outcome
•
Good for cases where the total population at
risk is not well defined
•
Easier to conduct
Disadvantages
•
Greater potential for bias because of smaller
sample size and because selection is done after
risk and outcome have occurred
32
Epidemiology in Action
CROSS-SECTIONAL Studies
compare current health and exposure status
of groups AT THE SAME TIME.
Uses a large sample so that inferences can be made
regarding the whole population
For example, survey a large
number of college students in
terms of heavy alcohol
consumption, a risk factor.
(Exposure/Non-exposure).
Also survey how many students in
the Exposed/Not Exposed
populations are suffering from a
stomach complaint, and compare
the rates.
Links to recent studies on college-age youth and alcohol use:
Research Matters: College Alcohol Study
Boozing, "binge" drinking, and violence among college students over time: Dr. Ruth Engs
33
34
CROSS-SECTIONAL
Studies
25
20
15
heavy drinkers
not heavy drinkers
10
5
0
1st Qtr
PROPORTION
reporting
stomach
ailment
The PROPORTION of heavy drinkers
with a stomach ailment at the time
of the study is higher than the
proportion of not-heavy drinkers.
Epidemiology in Action
CROSS-SECTIONAL
Advantages
•
Easy to conduct because you don’t have to wait
for outcome to occur
•
Good for examining relationship between a risk
factor and outcome
•
Disadvantages
A cause can’t be inferred because only current
health and exposure are being studied
•
Not good for conditions that are long-term
35
Epidemiology in Action
36
Student Assignment:
Your worksheet provides descriptions of a
number of studies. In groups of two or three,
based on what you’ve learned about types of
studies, classify each study according to type,
discuss advantages and disadvantages of each,
and describe a hypothetical study that could be
done that would be suited to this study design.
Name
That
Study
16
14
12
10
treatmen
no treat
8
6
4
2
0
3
6
9
12
37
FOR
EXAMPLE:
Name
That
Study
CDC/Dr. Demetri Vacalis 1999 line graph
1.
Decide if this study can be best categorized as EXPERIMENTAL, DESCRIPTIVE,
COHORT, CASE-CONTROL, CROSS-SECTIONAL, or a combination.
2.
Defend your choice with two reasons.
3.
How is this design an ADVANTAGE/DISADVANTAGE in a study of THIS population?
4.
What would be a hypothetical study you could do on a population, that would be
suited to this type of study design? Explain WHY this design would be best for this
type of study.
38
Link/Source: NCADI: The
National Treatment
Improvement Evaluation Study
(NTIES) - Changes in Physical &
Mental Health
Another
EXAMPLE:
Name
That
Study
1.
Decide if this study can be best categorized as EXPERIMENTAL, DESCRIPTIVE, COHORT,
CASE-CONTROL, CROSS-SECTIONAL, or a combination.
2.
Defend your choice with two reasons.
3.
How is this design an ADVANTAGE/DISADVANTAGE in a study of THIS population?
4.
What would be a hypothetical study you could do on a population, that would be suited
to this type of study design? Explain WHY this design would be best for this type of
study.