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Centers for Disease Control and Prevention July 14-18, 2008

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Title: Centers for Disease Control and Prevention July 14-18, 2008


1
Welcome to
3
Teach Epidemiology
Professional Development Workshop
Centers for Disease Control and Prevention
July
14-18, 2008
2
Time Check 900 AM 15 Minutes
3
Teach Epidemiology
Teach Epidemiology
4
Is Epidemiology in Your Future?
Teach Epidemiology
5
Making Group Comparisons and Identifying
Associations
Ralph Cordell, Ph.D. Acting Associate Director of
Science
Division of Partnership and Strategic Alliances
National Center for Health
Marketing
Teach Epidemiology
6
Time Check 915 PM 45 Minutes
7
(No Transcript)
8
Teach Epidemiology
Teach Epidemiology
9
Time Check 1000 AM 135 Minutes
10
(No Transcript)
11
Teach Epidemiology
Teach Epidemiology
12
Teach Epidemiology
Young Epidemiology Scholars
Professional Development Workshop
July 16, 2008 Diane Marie M. St. George, PhD
13
Enduring Understandings 7-9
  • Explaining associations
  • and
  • judging causation

14
  • EU7 One possible explanation for finding an
    association is that the exposure causes the
    outcome. Because studies are complicated by
    factors not controlled by the observer, other
    explanations also must be considered, including
    confounding, chance, and bias.
  • The Not everything that glitters is gold
    Principle

15
  • EU8 Judgments about whether an exposure causes a
    disease are developed by examining a body of
    epidemiologic evidence, as well as evidence from
    other scientific disciplines.

16
  • EU9 While a given exposure may be necessary to
    cause an outcome, the presence of a single factor
    is seldom sufficient. Most outcomes are caused
    by a combination of exposures that may include
    genetic make-up, behaviors, social, economic, and
    cultural factors and the environment.
  • The Just because your friend sleeps in class and
    never fails her courses does not mean that
    sleeping in class does not cause F grades
    Principle

17
Reasons for associations
  • Confounding
  • Bias
  • Reverse causality
  • Sampling error (chance)
  • Causation

18
  • Osteoporosis risk is higher among women who live
    alone.
  • Death rates are low in AK and high in FL.
  • Those who work on farms are more likely to have a
    heart attack than those who do not.
  • In GA, African American women have the lowest
    mammography screening rates.

19
Confounding
  • Confounding is an alternate explanation for an
    observed association of interest.

Number of persons in the home
Osteoporosis
Age
20
Confounding
  • Confounding is an alternate explanation for an
    observed association of interest.

Exposure
Outcome
Confounder
21
Confounding
  • Hypothetical cohort study
  • 9400 newborns followed for 10 yrs
  • RQ Is exposure to manufacturing chemical
    by-products related to low vaccination rates
    among children?

22
Pollution and low vaccination rates
RR (79 / 824) / (286 / 8576) 2.9
23
Pollution and vaccination rates
  • Could there be some other explanation for the
    observed association?

24
Pollution and vaccination rates
  • If health care access had been the reason for the
    association between pollution and vaccination
    rates, what might the RR be if all children had
    no access?
  • What about if the children all had health care
    access?

25
Pollution and vaccination rates
26
Pollution and vaccination rates (Access)
RR (55 / 106) / (5 / 10) 1.0
27
Pollution and vaccination rates (No access)
RR (24 / 718) / (281 / 8566) 1.0
28
Conclusions
  • Exposure to manufacturing waste is unrelated to
    vaccination rates among children with no health
    care access.
  • Exposure to manufacturing waste is unrelated to
    vaccination rates among children with health care
    access.
  • So

29
Confounding
  • Exists when confounder related to exposure
  • Exists when confounder related to outcome
  • Confounders can be risk factors (not just
    nuisance factors), e.g. lung CA

30
Handling confounding
  • Restriction
  • Matching
  • Random assignment
  • Stratification
  • Statistical adjustment

31
Confounding
  • Confounding is an alternate explanation for an
    observed association of interest.

Low vaccination rates
Manufacturing waste
No health care access
32
Reasons for associations
  • Confounding
  • Bias
  • Reverse causality
  • Sampling error (chance)
  • Causation

33
Cohort study
34
Bias
  • Errors are mistakes that are
  • randomly distributed
  • not expected to impact the MA
  • less modifiable
  • Biases are mistakes that are
  • not randomly distributed
  • may impact the MA
  • more modifiable

35
Types of bias
  • Selection bias
  • The process for selecting/keeping subjects causes
    mistakes
  • Information bias
  • The process for collecting information from the
    subjects causes mistakes

36
Information bias
  • Misclassification, e.g. non-exposed as exposed or
    cases as controls
  • Recall bias
  • Cases are more likely than controls to recall
    past exposures
  • Interviewer bias
  • Interviewers probe cases more than controls
    (exposed more than unexposed)

37
Birth defects and diet
  • In a study of birth defects, mothers of children
    with and without infantile cataracts are asked
    about dietary habits during pregnancy.

38
Pesticides and cancer mortality
  • In a study of the relationship between home
    pesticide use and cancer mortality, controls are
    asked about pesticide use and family members are
    asked about their loved ones usage patterns.

39
Induced abortion breast CA
  • Positive association found in 5 studies
  • No association found in 6 studies
  • Negative association found in 1 study

40
Minimize bias
  • Can only be done in the planning and
    implementation phase
  • Standardized processes for data collection
  • Masking
  • Clear, comprehensive case definitions
  • Incentives for participation/retention

41
Selection bias
  • People who agree to participate in a study may be
    different from people who do not
  • People who drop out of a study may be different
    from those who stay in the study

42
Time Check 1215 PM 30 Minutes
43
(No Transcript)
44
Teach Epidemiology
Revised
Teach Epidemiology
45
Time Check 1245 PM 15 Minutes
46
(No Transcript)
47
Teach Epidemiology
Revised
Teach Epidemiology
48
Teach Epidemiology
Revised
Teach Epidemiology
49
Time Check 130 PM 15 Minutes
50
(No Transcript)
51
Teach Epidemiology Web Site
http//www.montclair.edu/YESteachingunits/
Teach Epidemiology
52
Is Epidemiology in Your Future?
Robin Casanova National Recognition and
Scholarship Programs The College Board 11911
Freedom Drive, Suite 300 Reston, VA 20190 Tel
(571) 262-5927 Fax (703) 464-8407 rcasanova_at_coll
egeboard.org
Teach Epidemiology
53
Public Health Workforce Crisis
http//www.asph.org/document.cfm?page1038
Teach Epidemiology
54
Disease Detectives
Detectives Investigate crimes Look for clues at a
crime scene Judge quality of evidence Form
hypotheses Identify suspects Present evidence in
court Help control crime
Epidemiologists Investigate diseases Look
for clues in the community Judge
quality of
evidence Form hypotheses Identify suspected
causes Present evidence in scientific journals
and at scientific meetings Help control disease
Detectives in the Classroom - Investigation 1-1
Why Are These Students Getting Sick?
55
Time Check 145 PM 15 Minutes
56
(No Transcript)
57
Enduring Understandings
Enduring Epidemiological Understandings the big
ideas that reside at the heart of epidemiology
and have lasting value outside the
classroom.
they can distinguish between foundational
concepts and
elaborations or illustrations of those ideas.
Ken Bain, What the Best College Teachers Do
Teach Epidemiology
58
Enduring Understandings
Enduring Epidemiological Understandings the big
ideas that reside at the heart of epidemiology
and have lasting value outside the
classroom.
they can distinguish between foundational
concepts and
elaborations or illustrations of those ideas.
Ken Bain, What the Best College Teachers Do
Teach Epidemiology
59
Enduring Understandings
Enduring Epidemiological Understandings the big
ideas that reside at the heart of epidemiology
and have lasting value outside the
classroom.
they can distinguish between foundational
concepts and
elaborations or illustrations of those ideas.
Ken Bain, What the Best College Teachers Do
Teach Epidemiology
60
YES Professional Development Workshop
Identifying Patterns and Formulating Hypotheses
Teach Epidemiology
61
Identifying Patterns and Formulating Hypotheses
Making Group Comparisons and Identifying
Associations
Teach Epidemiology
62
Making Group Comparisons and Identifying
Associations
Explaining Associations and Judging Causation
Teach Epidemiology
63
Enduring Understandings
Enduring Epidemiological Understandings the big
ideas that reside at the heart of epidemiology
and have lasting value outside the
classroom.
they can distinguish between foundational
concepts and
elaborations or illustrations of those ideas.
Ken Bain, What the Best College Teachers Do
Teach Epidemiology
64
Enduring Understandings
Enduring Epidemiological Understandings the big
ideas that reside at the heart of epidemiology
and have lasting value outside the
classroom.
they can distinguish between foundational
concepts and
elaborations or illustrations of those ideas.
Ken Bain, What the Best College Teachers Do
Teach Epidemiology
65
Time Check 200 PM 15 Minutes
66
(No Transcript)
67
Explaining Associations and Judging Causation
... the study of the distribution and
determinants of health-related states or events
in specified populations and the application of
this study to the control of health
problems.   (Gordis, 2004)
Teach Epidemiology
68
Explaining Associations and Judging Causation
If an association was causal, .
?
Outcome
Hypothesized Exposure
X
Teach Epidemiology
69
Explaining Associations and Judging Causation
If the association was found due to confounding,
.
?
Outcome
Hypothesized Exposure
Teach Epidemiology
70
Explaining Associations and Judging Causation
If an association was found due to reversed
time-order, .
?
Hypothesized Exposure
Outcome
Teach Epidemiology
71
Explaining Associations and Judging Causation
If an association was found due to chance, .
found due to chance, .
?
Outcome
Hypothesized Exposure
Teach Epidemiology
72
Explaining Associations and Judging Causation
If an association was found due to bias, .
found due to bias, .
?
Outcome
Hypothesized Exposure
Teach Epidemiology
73
Explaining Associations and Judging Causation
... the study of the distribution and
determinants of health-related states or events
in specified populations and the application of
this study to the control of health problems.
Teach Epidemiology
74
Explaining Associations and Judging Causation
1.
Cause
2.
Confounding
3.
Bias
Chance
4.
5.
Reverse Time Order
Teach Epidemiology
75
Explaining Associations and Judging Causation
1.
Cause
2.
Confounding
3.
Bias
Chance
4.
5.
Reverse Time Order
Teach Epidemiology
76
Time Check 215 PM 30 Minutes
77
(No Transcript)
78
Explaining Associations and Judging Causation
Things that are associated are linked in some way

that makes them turn up together.
Teach Epidemiology
79
Explaining Associations and Judging Causation
Smoking Linked to Youth Eating Disorders
Suicide Higher in Areas with Guns
Family Meals Are Good for
Mental Health
No Outcome
Study Concludes Movies Influence
Youth Smoking
Study Links Iron
Deficiency to Math
Scores
Outcome
a
b
Exposure
c
d
Lack of High School Diploma Tied to
US Death Rate
Study Links Spanking
to Aggression
No Exposure
Depressed Teens More Likely to Smoke
Snacks Key to Kids TV- Linked Obesity China
Study
Breakfast Each Day May Keep Colds Away
Pollution Linked with Birth Defects in US Study
Kids Who Watch R-Rated Movies More Likely to
Drink, Smoke
Teach Epidemiology
80
Explaining Associations and Judging Causation
Cause A factor that produces a change in another
factor
William A. Oleckno, Essential Epidemiology
Principles and Applications, Waveland Press, 2002.
Teach Epidemiology
81
Explaining Associations and Judging Causation
Teach Epidemiology
82
Explaining Associations and Judging Causation
YES Teaching Units Professional Development
Workshop
Teach Epidemiology
83
Explaining Associations and Judging Causation
Diagram
2x2 Table
DZ
DZ
X
a
b
c
d
X
YES Teaching Units Professional Development
Workshop
Teach Epidemiology
84
Explaining Associations and Judging Causation
Diagram
2x2 Table
DZ
DZ
X
a
b
c
d
X
Teach Epidemiology
85
Handout
86
Necessary and Sufficient
Diagram
2x2 Table
DZ
DZ
X
X
a
b
c
d
X
Teach Epidemiology
87
Necessary but Not Sufficient
Diagram
2x2 Table
DZ
DZ
X
X
a
b
c
d
X
Teach Epidemiology
88
Not Necessary but Sufficient
Diagram
2x2 Table
DZ
DZ
X
X
X
a
b
c
d
X
Teach Epidemiology
89
Not Necessary and Not Sufficient
Diagram
2x2 Table
DZ
DZ
X
X
a
b
c
d
X
Teach Epidemiology
90
Explaining Associations and Judging Causation
a b c d
Teach Epidemiology
91
Explaining Associations and Judging Causation
a b c d
Teach Epidemiology
92
Explaining Associations and Judging Causation
Teach Epidemiology
93
Explaining Associations and Judging Causation
Teach Epidemiology
94
Time Check 245 PM 15 Minutes
95
(No Transcript)
96
Explaining Associations and Judging Causation
Teach Epidemiology
97
Explaining Associations and Judging Causation
1.
Cause
2.
Confounding
3.
Bias
Chance
4.
5.
Teach Epidemiology
98
Explaining Associations and Judging Causation
Population
All the people in a particular group
Teach Epidemiology
99
Explaining Associations and Judging Causation
Deck of 100 cards
Teach Epidemiology
100
Explaining Associations and Judging Causation
Teach Epidemiology
101
Explaining Associations and Judging Causation
Total
a
b


c
d
Teach Epidemiology
102
Explaining Associations and Judging Causation
Population
Total


Teach Epidemiology
103
Explaining Associations and Judging Causation
Total



Total
Teach Epidemiology
104
Explaining Associations and Judging Causation


Total
Risk
25 / 50 or 50
25 / 50 or 50
Teach Epidemiology
105
Explaining Associations and Judging Causation


Total
Relative Risk
25 / 50 or 50
50
____
25 / 50 or 50
50
Teach Epidemiology
106
Explaining Associations and Judging Causation
Teach Epidemiology
107
Explaining Associations and Judging Causation
Teach Epidemiology
108
Explaining Associations and Judging Causation
Teach Epidemiology
109
Explaining Associations and Judging Causation
Sample of 20 cards
Teach Epidemiology
110
Explaining Associations and Judging Causation
Sample of 20 cards
Total
Teach Epidemiology
111
Explaining Associations and Judging Causation
Sample of 20 cards
Total
5 / 10 or 50
5 / 10 or 50
Teach Epidemiology
112
Explaining Associations and Judging Causation
Sample of 20 cards
Total
Risk
5 / 10 or 50
50
____
5 / 10 or 50
50
Teach Epidemiology
113
Explaining Associations and Judging Causation
By Chance
Total
___

Teach Epidemiology
114
Explaining Associations and Judging Causation
How many students picked a sample with 5 people
in each cell?
No Marijuana
No Marijuana
Total
Risk
Relative Risk
10
5
5
5 / 10 or 50
Odd
50
____
10
5
5
5 / 10 or 50
50
Even
By Chance
Teach Epidemiology
115
Explaining Associations and Judging Causation
Relative Risks
Less than 1
Greater than 1
Less than 1
Teach Epidemiology
116
Explaining Associations and Judging Causation
Study Links Having an Odd Address to Marijuana Use
Teach Epidemiology
117
Explaining Associations and Judging Causation
Relative Risks
Less than 1
Greater than 1
Less than 1
Teach Epidemiology
118
Explaining Associations and Judging Causation
Study Links Having an Even Address to Marijuana
Use
Teach Epidemiology
119
Explaining Associations and Judging Causation
Relative Risks
Greater than 1
Less than 1
Teach Epidemiology
120
Time Check 300 PM 15 Minutes
121
(No Transcript)
122
Explaining Associations and Judging Causation
1.
Cause
2.
Confounding
3.
Bias
Chance
4.
5.
Reverse Time Order
Teach Epidemiology
123
Explaining Associations and Judging Causation
A situation
in
which the hypothesized time order
of an exposure and an
outcome is reversed and
the outcome actually came before the exposure.
Teach Epidemiology
124
Explaining Associations and Judging Causation
Controlled Trial
Case-Control Study
Time
Time
Cohort Study
Cross-Sectional Study
Time
Time
Teach Epidemiology
125
Explaining Associations and Judging Causation
Teach Epidemiology
126
Explaining Associations and Judging Causation
Teach Epidemiology
127
Explaining Associations and Judging Causation
Violent Video Games
Violent Video Games Can Increase Aggression
Cross Sectional Study
No Violent Video Games
Playing violent video games often may well cause
increases in aggressive behavior.
It could be that highly aggressive individuals
are especially attracted to violent video games.
Aggression
No Aggression
Violent Video Games
Aggression
Time
Teach Epidemiology
128
Explaining Associations and Judging Causation
Enduring Epidemiological Understandings the big
ideas that reside at the heart of epidemiology
and have lasting value outside the
classroom.
they can distinguish between foundational
concepts and
elaborations or illustrations of those ideas.
Ken Bain, What the Best College Teachers Do
Teach Epidemiology
129
(No Transcript)
130
Explaining Associations and Judging Causation
Enduring Epidemiological Understandings the big
ideas that reside at the heart of epidemiology
and have lasting value outside the
classroom.
they can distinguish between foundational
concepts and
elaborations or illustrations of those ideas.
Ken Bain, What the Best College Teachers Do
Teach Epidemiology
131
Explaining Associations and Judging Causation
Association is not necessarily causation.
Suicide Higher in Areas with Guns
Family Meals Are Good for
Mental Health
1.
Cause
Study Concludes Movies Influence
Youth Smoking
Study Links Iron
Deficiency to Math
Scores
2.
Confounding
Lack of High School Diploma Tied to
US Death Rate
Snacks Key to Kids TV- Linked Obesity China
Study
3.
Bias
Chance
4.
Depressed Teens More Likely to Smoke
Study Links Spanking
to Aggression
5.
Reverse Time Order
Kids Who Watch R-Rated Movies More Likely to
Drink, Smoke
Teach Epidemiology
132
(No Transcript)
133
Explaining Associations and Judging Causation
Teach Epidemiology
134
Explaining Associations and Judging Causation
Teach Epidemiology
135
Explaining Associations and Judging Causation
... the study of the distribution and
determinants of health-related states or events
in specified populations and the application of
this study to the control of health
problems.   (Gordis, 2004)
Teach Epidemiology
136
Explaining Associations and Judging Causation
Does evidence from an aggregate of studies
support a cause-effect relationship?
Guilt or Innocence?
Causal or Not Causal?
Teach Epidemiology
137
Explaining Associations and Judging Causation
Handouts
Association Found
Between Coffee and Pancreatic Cancer
Teach Epidemiology
138
Explaining Associations and Judging Causation
Things that are associated are linked in some way

that makes them turn up together.
4
Teach Epidemiology
139
Explaining Associations and Judging Causation
Handout
Sir Austin Bradford Hill
The Environment and Disease
Association or Causation?
Proceedings of the Royal Society of Medicine
January 14, 1965
Teach Epidemiology
140
Explaining Associations and Judging Causation
In what circumstances can we pass
from this observed association
to a verdict of causation?
Teach Epidemiology
141
Explaining Associations and Judging Causation
Here then are nine different viewpoints
from all of which we should study association
before we cry causation.
Teach Epidemiology
142
Explaining Associations and Judging Causation
Study Links Coffee Use to Pancreatic Cancer
Does evidence from an aggregate of studies
support a
cause-effect relationship?
  1.   What is the strength of the association
between the risk factor and the disease? 2.  
Can a biological gradient be demonstrated? 3.  
Is the finding consistent? Has it been
replicated by others in other places? 4.   Have
studies established that the risk factor precedes
the disease? 5.   Is the risk factor associated
with one disease or many different
diseases? 6.   Is the new finding coherent with
earlier knowledge about the risk factor and the
m disease? 7.   Are the implications of the
observed findings biological sensible? 8.   Is
there experimental evidence, in humans or
animals, in which the disease has m been
produced by controlled administration of the risk
factor?
Teach Epidemiology
143
Explaining Associations and Judging Causation
Stress causes ulcers.
Helicobacter pylori causes ulcers.
Teach Epidemiology
144
Explaining Associations and Judging Causation
Teach Epidemiology
145
Explaining Associations and Judging Causation
Teach Epidemiology
146
Time Check 330 PM 15 Minutes
147
(No Transcript)
148
Teach Epidemiology
Revised
Teach Epidemiology
149
YES Teaching Units
Group 1 Teenage Births (Class 1, pages
6-12) Group 2 Casualties of War (Questions
11-21)
Group 3 TV and Aggressive Acts (pages 1-33)
Group 4 Case Control - (Class 1, pages
16-21) Group 5 Cross-Sectional Studies (pages
35-39)
Group 6 Confounding (pages 32-36)
Teach Epidemiology
150
Your Teach Epidemiology Stories
Teach Epidemiology
151
Teach Epidemiology Rules
  • Teach epidemiology
  • As a group, create a 30-minute lesson during
    which we will develop a deeper understanding of
    an enduring epidemiological understanding.
  • Focus on the portion of the unit that is
    assigned. Use that portion of the unit as the
    starting point for creating your 30-minute
    lesson.
  • When teaching assume the foundational
    epidemiological knowledge from the preceding days
    of the workshop.
  • Try to get us to uncover the enduring
    epidemiological understanding. Try to only tell
    us something when absolutely necessary.
  • End each lesson by placing it in the context of
    the appropriate enduring epidemiological
    understanding.
  • Teach epidemiology.
  • After the lesson, metacognitate about your
    preparation for and teaching of the lesson.

Teach Epidemiology
152
Teach Epidemiology Rules
Metacognition
They can then use that ability to think about
their own thinking to grasp
how other people might learn.
They know what
has to come first,
and they can
distinguish between foundational concepts
and elaborations or
illustrations of those ideas. They realize
where people are likely to face
difficulties developing
their own comprehension,
and
they can use that understanding
to
simplify and clarify complex topics for others,
tell the right story, or raise a powerfully
provocative question. Ken Bain, What the Best
College Teachers Do
Teach Epidemiology
153
Teach Epidemiology Rules
To create a professional community

that discusses new teacher materials and
strategies and
that supports the risk taking and struggle
entailed in
transforming practice.
Teach Epidemiology
154
Teach Epidemiology Rules
Teach Epidemiology
155
(No Transcript)
156
5 - Minute Papers
157
Study Designs
"Modafinil and Cocaine Treatment
Modafinil, a drug used to treat narcolepsy and
boost alertness, seemed to help cocaine addicts
avoid using the drug during a recent
study. University of Pennsylvania researchers
found that a study group taking modafinil
(marketed as Provigil) were twice as likely to
remain abstinent for a week as a control group
given a placebo. Over a three-week period, the
modafinil group was three times more likely to
avoid cocaine use. Addicts seem to like the fact
that modafinil makes them alert, and the drug
curbs the impulsiveness often associated with
cocaine use. Still, a high number of cocaine
users in the study continued to use the drug,
meaning that modafinil might be most useful if
targeted at certain types of users.
Randomized Controlled Trial
158
Study Designs
Smoking During Pregnancy and Cleft Lip
British researchers found that women who smoke
during pregnancy are more likely to have babies
who are born with a cleft lip. In examining
records from 1997 to 2000 that contained
information on smoking and on birth defects, the
researchers found that mothers who smoked during
the first trimester of pregnancy were slightly
more at risk for having a baby with a cleft lip.
The results were consistent with prior North
American and European research. Although
researchers collected information about
secondhand-smoke exposure, there was no definite
association between secondhand smoke and cleft
lip in newborns.
Case-Control Study
159
5 - Minute Papers
160
4 Basic Epidemiological Study Designs
Controlled Trial
Case-Control Study
Cohort Study
Cross-Sectional Study
161
Study Designs
"Tattoos and Substance Use"
This study is based on information from almost
6,000 adolescents who participated in the
National Survey of Adolescent Health. The survey
interviewed adolescents in 1995 and then again in
1996. In 1995, 270 of the 5,869 adolescents said
they had a permanent tattoo, Roberts and his
colleagues report in the journal Pediatrics.
Then, in 1996, the researchers asked the same
adolescents several questions about risky
behaviors they had engaged in during the past
year.
Cohort Study
162
Study Designs
Prescription Heroin and Employment
Heroin-assisted substitution treatment for
severely opioid-dependent drug users has been
available in Switzerland since 1994. Our aim was
to ascertain the feasibility, safety, and
efficacy of this treatment. We assessed
opioid-dependent drug users, who began
heroin-assisted substitution treatment between
January 1, 1994, and March 31, 1995, and who
stayed with the programme for at least 18 months,
to ascertain admission and discharge patterns,
and patient characteristics. We used
questionnaires, interviews, and medical
examinations to assess somatic and mental health,
social integration, and treatment outcomes.
Cohort Study
Controlled Trial
163
5 - Minute Papers
164
4 Basic Epidemiological Study Designs
4 Basic Epidemiological Study Designs
Controlled Trial
Case-Control Study
Cohort Study
Cross-Sectional Study
165
4 Basic Epidemiological Study Designs
166
4 Basic Epidemiological Study Designs
167
Study Designs
Marijuana Use
The data collection method used involves
in-person interviews with sample persons,
incorporating procedures that would be likely to
increase respondents' cooperation and willingness
to report honestly about their illicit drug use
behavior. Confidentiality is stressed in all
written and oral communications with potential
respondents. Respondents' names are not
collected with the data and computer-assisted
interviewing methods, including audio
computer-assisted self-interviewing, are used to
provide a private and confidential setting to
complete the interview. The results of this
years survey demonstrate that anti-drug messages
inside and outside of school, participation in
religious and other activities, parental
disapproval of substance use and positive
attitudes about school are linked to lower rates
of youth marijuana use. 
Cross-Sectional Study
168
5 - Minute Papers
169
4 Basic Epidemiological Study Designs
4 Basic Epidemiological Study Designs
Controlled Trial
Case-Control Study
Cohort Study
Cross-Sectional Study
170
5 - Minute Papers
171
4 Basic Epidemiological Study Designs
172
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