Centers for Disease Control and PreventionMorgantown, West Virginia
June 20-24, 2011
Teach EpidemiologyProfessional Development Workshop
Day4
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Teach Epidemiology
http://www.cdc.gov/
MMWR
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Time Check
8:15 AM
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8Teach Epidemiology
Teach Epidemiology
Teach EpidemiologyDay 4Morgantown, WVDiane Marie M St. George, PhDUniversity of MD School of MedicineDept of Epidemiology and Public Health
EU7: One possible explanation for EU7: One possible explanation for finding an association is that the finding an association is that the exposure causes the outcome. exposure causes the outcome. Because studies are complicated by Because studies are complicated by factors not controlled by the observer, factors not controlled by the observer, other explanations also must be other explanations also must be considered, including confounding, considered, including confounding, chance, and bias.chance, and bias.
EU8: Judgments about whether an exposure EU8: Judgments about whether an exposure causes a disease are developed by causes a disease are developed by examining a body of epidemiologic examining a body of epidemiologic evidence, as well as evidence from other evidence, as well as evidence from other scientific disciplines.scientific disciplines.
EU9: While a given exposure may be EU9: While a given exposure may be necessary to cause an outcome, the necessary to cause an outcome, the presence of a single factor is seldom presence of a single factor is seldom sufficient. Most outcomes are caused sufficient. Most outcomes are caused by a combination of exposures that may by a combination of exposures that may include genetic make-up, behaviors, include genetic make-up, behaviors, social, economic, and cultural factors social, economic, and cultural factors and the environment. and the environment.
Reasons for associations
Confounding Bias Reverse causality Sampling error (chance) Causation
Confounding in our lives
Age-adjusted rates of… Rates of lung cancer adjusted for smoking
Osteoporosis risk is higher among women who live alone than among women who live with others.
Confounding
Confounding is an alternate explanation for an observed association of interest.
Number of persons in the
homeOsteoporosis
Age
Confounding
Confounding is an alternate explanation for an observed association of interest.
Exposure Outcome
Confounder
Confounding
YES confounding module example:Cohort study9,400 elderly in the hospitalRQ: Are bedsores related to
mortality among elderly patients with hip fractures?
Bedsores and Mortality
D+ D-
E+ 79 745 824
E- 286 8290 8576
365 9035 9400
RR = (79 / 824) / (286 / 8576) = 2.9
Bedsores and Mortality
Avoid bedsores…Live forever!!
Could there be some other explanation for the observed association?
Bedsores and mortality
If severity of medical problems had been the reason for the association between bedsores and mortality, what might the RR be if all study participants had very severe medical problems?
What about if the participants all had problems of very low severity?
Bedsores and Mortality
Died Did not die
Bedsores 55 severe
24 not
51 severe
694 not
824
No bedsores
5 severe
281 not
5 severe
8285 not
8576
365 9035 9400
Bedsores and Mortality (Severe)
Died Did not die
Bedsores 55 51 106
No bedsores
5 5 10
60 56 116
RR = (55 / 106) / (5 / 10) = 1.0
Bedsores and Mortality (Not severe)
Died Did not die
Bedsores 24 694 718
No bedsores
281 8285 8566
305 8979 9284
RR = (24 / 718) / (281 / 8566) = 1.0
Bedsores and Mortality stratified by Medical SeveritySEVERE+ Died Didn’t die
Bedsores a b
No sores c d
RR = 1.0
SEVERE- Died Didn’t die
Bedsores a b
No sores c d
RR = 1.0
Bedsores
Bedsores are unrelated to mortality among those with severe problems.
Bedsores are unrelated to mortality among those with problems of less severity.
Adjusted RR = 1, and the unadjusted RR = 2.9
Controlling confounding
Study design phaseMatchingRestrictionRandom assignment
Study analysis phaseStratificationStatistical adjustment
Reasons for associations
Confounding Bias Reverse causality Sampling error (chance) Causation
Bias Case Studies
In groups, review the assigned case studies.
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 of cases are asked about their loved ones’ usage patterns.
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.
Types of bias
Selection biasThe process for selecting/keeping subjects
causes mistakes Information bias
The process for collecting information from the subjects causes mistakes
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
Hospital controls may not represent the source population for the cases
Information bias
Misclassification, e.g. non-exposed as exposed or cases as controls
Cases are more likely than controls to recall past exposures
Interviewers probe cases more than controls (or probe exposed more than unexposed)
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
Reasons for associations
Confounding Bias Reverse causality Sampling error (chance) Causation
Reverse causality
Suspected disease actually precedes suspected cause
Pre-clinical disease Exposure DiseaseFor example: Memory deficits Reading
cessation Alzheimer’s Cross-sectional study
For example: Sexual activity/Marijuana
Minimize effect of reverse causality
Done in the planning and implementation phase of a study
Pick study designs in which exposure is measured before disease onset
Assess disease status with as much accuracy as possible
Reasons for associations
Confounding Bias Reverse causality Sampling error (chance) Causation
Sampling error/chance
E and D are associated in a sample, but not in the population from which the sample was drawn.
RR in the populationRR in the population
D+D+ D-D-
E+E+ 5050 5050 100100
E-E- 5050 5050 100100
100100 100100 200200
RR in sample 1RR in sample 1
D+D+ D-D-
E+E+ 2525 2525 5050
E-E- 2525 2525 5050
5050 5050 100100
RR in sample 2RR in sample 2
D+D+ D-D-
E+E+ 4545 55 5050
E-E- 1515 3535 5050
5050 5050 100100
RR in sample 3RR in sample 3
D+D+ D-D-
E+E+ 2020 3030 5050
E-E- 3030 2020 5050
5050 5050 100100
Minimize sampling error (chance)
Random selection Adequate sample size
46
Time Check
9:45 AM
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Time Check
10:00 AM
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Time Check
11:00 AM
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Time Check
11:30 AM
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Hypothesis
Total Risk Relative Risk
a b
c d
or %
or %Exposure Outcome
?Turned Up Together
Healthy People
-
Healthy People
E
E
DZ
DZ
DZ
DZ
Teach Epidemiology
Where are we?
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60Teach Epidemiology
Enduring Epidemiological Understandings
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Enduring Epidemiological Understandings
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63
Suicide Higher in Areas with Guns
Family Meals Are Good for Mental Health
Lack of High School Diploma Tied to US Death
Rate
Study Links
Spanking to
Aggression
Study Concludes: Movies Influence
Youth Smoking
Study Links Iron
Deficiency to Math
Scores
Kids Who Watch R-Rated Movies More Likely to Drink, Smoke
Pollution Linked with Birth Defects in US Study
Ties, Links, Relationships, and Associations
Snacks Key to Kids’ TV- Linked Obesity: China
Study
Depressed Teens More
Likely to Smoke
64
Suicide Higher in Areas with Guns
Family Meals Are Good for Mental Health
Lack of High School Diploma Tied to US Death
Rate
Study Links
Spanking to
Aggression
Study Concludes: Movies Influence
Youth Smoking
Study Links Iron
Deficiency to Math
Scores
Kids Who Watch R-Rated Movies More Likely to Drink, Smoke
Pollution Linked with Birth Defects in US Study
Snacks Key to Kids’ TV- Linked Obesity: China
Study
Depressed Teens More
Likely to Smoke
Ties, Links, Relationships, and Associations
65
1. Cause
2. Confounding
3. Reverse Time Order
4. Chance
5. Bias
Possible Explanations for Finding an Association
66
Epidemiology
Epidemiology
... 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.
Leon Gordis, Epidemiology, 3rd Edition, Elsevier Saunders, 2004.
67
1. Cause
2. Confounding
3. Reverse Time Order
4. Chance
5. Bias
Possible Explanations for Finding an Association
68
Cause
A factor that produces a change in another factor.
William A. Oleckno, Essential Epidemiology: Principles and Applications, Waveland Press, 2002.
Possible Explanations for Finding an Association
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Sample of 100
70
Sample of 100, 25 are Sick
71
Diagram
2x2 Table
DZ DZ
X
X
a bc d
Types of Causal Relationships
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DZ DZ
X
X
a bc d
Diagram
2x2 Table
Types of Causal Relationships
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Handout
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75
X1
X1
X1
X1
X1
X1
X1
X1
X1 X1
X1
X1X1X1
X1X1
X1
X1
X1
X1
X1
X1
X1
X1
X1
X1 DZ
DZ DZ
X1
X1
a bc d
Diagram
2X12 Table
Necessary and Sufficient
X1
76
DZ DZ
a bc d
X1 DZX2 X3+ +X1
X1
X1
X1
X1
X1
X1
X1
X1 X1
X1
X1X1X1
X1X1
X1
X1
X1
X1
X1
X1
X1
X1
X1
X1
X1
X1
X1
X1
X1
X1
X1
X1X1
Diagram
2X12 Table
Necessary but Not Sufficient
X1
X1
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X1
X1
X1
X1
X1
X1
X1
X1 X1
X1
X1X1
X1
X1
X1
X1
DZ DZ
a bc d
X2 DZ
X1
X3
Diagram
2X12 Table
Not Necessary but Sufficient
X1
X1
78
DZ DZ
a bc d
X1
X1
X1
X1
X1
X1
X1 X1
X1X1X1
X1
X1
X1
X1
X1
X1
X1
X1
X1
X1
X1
X1
X1
X1
X1
X1
X1
X1X1
X4
X1
X7
DZX5 X6+ +
X2 X3+ +
X8 X9+ +
Not Necessary and Not Sufficient
Diagram
2X12 Table
X1
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X
X
X
X
X
X
X
X
X X
X
XXX
XX
X
X
X
X
X
X
X
X
X
X DZ
DZ DZ
X
X
a bc d
X
Diagram
2x2 Table
Necessary and Sufficient
80
DZ DZ
X
X
a bc d
X DZX X+ +
X
X
X
X
X
X
X
X
X
X X
X
XXX
XX
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
XX
Diagram
2x2 Table
Necessary but Not Sufficient
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X
X
X
X
X
X
X
X X
X
XX
X
X
X
X
DZ DZ
X
X
a bc d
X
X DZ
X
X
Diagram
2x2 Table
Not Necessary but Sufficient
82
DZ DZ
X
X
a bc d
X
X
X
X
X
X
X
X X
XXX
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
XX
X
X
X
DZX X+ +
X X+ +
X X+ +
Not Necessary and Not Sufficient
Diagram
2x2 Table
83
a b
c d
Heart Attack
NoHeart Attack
Lack of Fitness
No Lack of Fitness
Lack of fitness and physical activity causes heart attacks.
84
a b
c d
Lead Poisoning
NoLead
Poisoning
Lack of Supervision
No Lack of
Supervision
Lack of supervision of small children causes lead poisoning.
85
Is the association causal?
86
Suicide Higher in Areas with Guns
Family Meals Are Good for Mental Health
Lack of High School Diploma Tied to US Death
Rate
Study Links
Spanking to
Aggression
Study Concludes: Movies Influence
Youth Smoking
Study Links Iron
Deficiency to Math
Scores
Kids Who Watch R-Rated Movies More Likely to Drink, Smoke
Pollution Linked with Birth Defects in US Study
Ties, Links, Relationships, and Associations
1. Cause
2. Confounding
3. Reverse Time Order
4. Chance
5. Bias
Snacks Key to Kids’ TV- Linked Obesity: China
Study
Depressed Teens More
Likely to Smoke
87Teach Epidemiology
Enduring Epidemiological Understandings
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Time Check
Noon AM
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Teach Epidemiology
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Time Check
1:00 PM
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Teach Epidemiology
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1. Cause
2. Confounding
3. Reverse Time Order
4. Chance
5. Bias
Possible Explanations for Finding an Association
95
All the people in a particular group.
Population
Possible Explanations for Finding an Association
96
A selection of people from a population.
Sample
Possible Explanations for Finding an Association
97
Inference
Process of predicting from what is observed in a sample to what is not observed in a population.
To generalize back to the source population.
Possible Explanations for Finding an Association
98
Sample
Population
Process of predicting from what is observed
to what is not observed.
Observed
Not Observed
Inference
99
Deck of
100 cards
Population
100
a
25 cards
b
25 cards
c
25 cards
25 cards
d
Population
101
=
Population
a
25 cards
b c d
25 cards25 cards25 cards
=a b
c d
Odd #
Even #
No Marijuana
No Marijuana
Population
Total
102
=
Population
a
25 cards
b c d
25 cards25 cards25 cards
= 2525
25 25
50
50
Total
Odd #
Even #
No Marijuana
No Marijuana
Population
103
=
Population
=M&M’s
No M&M’s
FluNo
Flu
2525
25 25
50
50
Total
=
2525
25 25
50
50
Total
a
25 cards
b c d
25 cards25 cards25 cards
Odd #
Even #
No Marijuana
No Marijuana
Population
104
=
Population
=
2525
25 25
50
50
Total
a
25 cards
b c d
25 cards25 cards25 cards
Risk
25 / 50 or 50%
25 / 50 or 50%
Odd #
Even #
No Marijuana
No Marijuana
Population
105
=
Population
a
25 cards
b c d
25 cards25 cards25 cards
=
2525
25 25
50
50
Total Risk Relative Risk
25 / 50 or 50 %
25 / 50 or 50 %50 % / 50% = = 1
50 %
50 %
____Odd #
Even #
No Marijuana
No Marijuana
Population
106
25 cards
25 cards
25 cards
25 cards
Population
107
To occur accidentally.
To occur without design.
Chance
A coincidence.
Possible Explanations for Finding an Association
108
Chance
109
Chance
110
Population
Sample
b
Sample
of
20 cards25 cards25 cards25 cards25 cards
Sample
111
Population
Sample
b
Sample
of
20 cards25 cards25 cards25 cards25 cards
10
10
Total
55
5 5Odd #
Even #
No Marijuana
No Marijuana
Sample
112
Population
Sample
b
Sample
of
20 cards25 cards25 cards25 cards25 cards
10
10
Total
55
5 5
Risk
5 / 10 or 50 %
5 / 10 or 50 %Odd #
Even #
No Marijuana
No Marijuana
Sample
113
Population
Sample
b
Sample
of
20 cards25 cards25 cards25 cards25 cards
10
10
Total
55
5 5
Risk
5 / 10 or 50 %
5 / 10 or 50 %Odd #
Even #
No Marijuana
No Marijuana
Sample
Relative Risk
50 % / 50% = = 150 %
50 %
____
114
b
Sample
of
20 cards
TotalRisk
5 / 10 = 50 %
5 / 10 = 50 %
50 1
Relative Risk
By Chance CDC
% ___
%
=Odd #
Even #
No Marijuana
No Marijuana
Sample
115
10
10
Total
55
5 5
Risk
5 / 10 or 50 %
5 / 10 or 50 %
Relative Risk
How many students picked a sample with 5 people in each cell?
= 150 %
50 %
____
Odd #
Even #
No Marijuana
No Marijuana
Chance
By Chance
116
Relative Risks
Greater than 1 Less than 1
Chance
117
Study Links Having an Odd Address to Marijuana Use
Ties, Links, Relationships, and Associations
118
Relative Risks
Greater than 1 Less than 1
Possible Explanations for Finding an Association
119
Study Links Having an Even Address to Marijuana Use
Ties, Links, Relationships, and Associations
120
Relative Risks
Greater than 1 Less than 1
1
By ChanceBy Chance
25 cards25 cards25 cards25 cards
Chance
121
b
Sample
of
20 cards
TotalRisk
5 / 10 = 50 %
5 / 10 = 50 %
50
Relative Risk
50
%___
%
=Odd #
Even #
No Marijuana
No Marijuana
Different Sample Sizes
122
Relative Risks
Greater than 1 Less than 1
1
By ChanceBy Chance
25 cards25 cards25 cards25 cards
Chance
50 cards
123
b
Sample
of
20 cards
TotalRisk
5 / 10 = 50 %
5 / 10 = 50 %
50
Relative Risk
75
%___
%
=Odd #
Even #
No Marijuana
No Marijuana
Different Sample Sizes
124
Relative Risks
Greater than 1 Less than 1
1
By ChanceBy Chance
25 cards25 cards25 cards25 cards
Chance
75 cards
125
b
Sample
of
20 cards
TotalRisk
5 / 10 = 50 %
5 / 10 = 50 %
50 1
Relative Risk
99
%___
%
=Odd #
Even #
No Marijuana
No Marijuana
Different Sample Sizes
126
Relative Risks
Greater than 1 Less than 1
1
By ChanceBy Chance
25 cards25 cards25 cards25 cards
Chance
99 cards
127
Suicide Higher in Areas with Guns
Family Meals Are Good for Mental Health
Lack of High School Diploma Tied to US Death
Rate
Study Links
Spanking to
Aggression
Study Concludes: Movies Influence
Youth Smoking
Study Links Iron
Deficiency to Math
Scores
Kids Who Watch R-Rated Movies More Likely to Drink, Smoke
1. Cause
2. Confounding
3. Reverse Time Order
4. Chance
5. Bias
Snacks Key to Kids’ TV- Linked Obesity: China
Study
Depressed Teens More
Likely to Smoke
Association is not necessarily causation.
Ties, Links, Relationships, and Associations
128Teach Epidemiology
Enduring Epidemiological Understandings
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130Teach Epidemiology
Enduring Epidemiological Understandings
Teach Epidemiology
Explaining Associations and Judging Causation
Handout
Teach Epidemiology
Explaining Associations and Judging Causation
1. Cause
2. Confounding
3. Reverse Time Order
4. Chance
5. Bias
Teach Epidemiology
Explaining Associations and Judging Causation
Coffee and Cancer of the Pancreas
134
135
Guilt or Innocence?Causal or Not Causal?
Does evidence from an aggregate of studies support a cause-effect relationship?
Teach Epidemiology
Explaining Associations and Judging Causation
136
Sir Austin Bradford Hill “The Environment and Disease:
Association or Causation?” Proceedings of the Royal Society of Medicine
January 14, 1965
Teach Epidemiology
Explaining Associations and Judging Causation
Handout
137
“In what circumstances can we pass from this observed association
to a verdict of causation?”
Teach Epidemiology
Explaining Associations and Judging Causation
138
“Here then are nine different viewpoints from all of which we should study association
before we cry causation.”
Teach Epidemiology
Explaining Associations and Judging Causation
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 biologically 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
Explaining Associations and Judging Causation
Timeline
Cohort Study
Randomized Controlled Trial
Timeline
Case-Control Study
Timeline
Cross-Sectional Study
Timeline
E
E
O
O
O
O
E
E
E
E
Healthy PeopleHealthy People
E
Random Assignment
E
O
O
O
O
Healthy PeopleHealthy People
E
E
O
O
O
O
Teach Epidemiology
Explaining Associations and Judging Causation
Teach Epidemiology
Explaining Associations and Judging Causation
Handout
142
Stress causes ulcers.
Helicobacter pylori causes ulcers.
Teach Epidemiology
Explaining Associations and Judging Causation
143
*
*
*
**
*
*
*
*
Teach Epidemiology
Explaining Associations and Judging Causation
144Teach Epidemiology
Explaining Associations and Judging Causation
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146
Epidemiology
Epidemiology
... 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.
Leon Gordis, Epidemiology, 3rd Edition, Elsevier Saunders, 2004.
147
Outcome
If an association was causal, ….
Hypothesized Exposure XX
… and you avoided or eliminated the hypothesized cause, what would happen to the outcome?
causal, ….
?
Control of Health Problems
148
Outcome
If the association was found due to confounding, ….
Hypothesized Exposure
Unobserved Exposure
X… and you avoided or eliminated the hypothesized cause, what would
happen to the outcome?
?
found due to confounding, ….
Control of Health Problems
149
Hypothesized Exposure
Outcome
If an association was found due to reversed time-order, ….found due to reversed time order, ….
X… and you avoided or eliminated the hypothesized cause, what would
happen to the outcome?
?
Control of Health Problems
150
Outcome
If an association was found due to chance, ….
Hypothesized Exposure
found due to chance, ….
X… and you avoided or eliminated the hypothesized cause, what would
happen to the outcome?
?
Control of Health Problems
151
Outcome
If an association was found due to bias, ….
Hypothesized Exposure
?
found due to bias, ….
X… and you avoided or eliminated the hypothesized cause, what would
happen to the outcome?
Control of Health Problems
152
Outcome
If an association was causal, ….
Hypothesized Exposure XX
… and you avoided or eliminated the hypothesized cause, what would happen to the outcome?
causal, ….
... 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.
Control of Health Problems
153
1. Cause
2. Confounding
3. Reverse Time Order
4. Chance
5. Bias
... 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.
Control of Health Problems
154
Suicide Higher in Areas with Guns
Family Meals Are Good for Mental Health
Lack of High School Diploma Tied to US Death
Rate
Study Links
Spanking to
Aggression
Study Concludes: Movies Influence
Youth Smoking
Study Links Iron
Deficiency to Math
Scores
Kids Who Watch R-Rated Movies More Likely to Drink, Smoke
Pollution Linked with Birth Defects in US Study
1. Cause
2. Confounding
3. Reverse Time Order
4. Chance
5. Bias
Snacks Key to Kids’ TV- Linked Obesity: China
Study
Depressed Teens More
Likely to Smoke
Ties, Links, Relationships, and Associations
155Teach Epidemiology
Enduring Epidemiological Understandings
156
157
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
Your Teach Epidemiology Stories
158
Welcome to
Teach Epidemiology
Your Teach Epidemiology Stories
To create “… a professional community that discusses new teacher materials and strategies and that supports the risk taking and struggle entailed in transforming
practice.”
Centers for Disease Control and PreventionMorgantown, West Virginia
June 20-24, 2011
Teach EpidemiologyProfessional Development Workshop
160
Time Check
2:45 PM
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Teach Epidemiology
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Time Check
3:00 PM
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Time Check
3:30 PM
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Enduring Understandings
171Teach Epidemiology
Enduring Understandings
Enduring Understandings
… the big ideas that reside at the heart of a discipline and have lasting value outside the classroom.
Enduring Epidemiological Understandings
… the big ideas that reside at the heart of epidemiology and have lasting value outside the classroom.
172Teach Epidemiology
Enduring Epidemiological Understandings
Enduring Epidemiological Understandings
… the big ideas that reside at the heart of epidemiology and have lasting value outside the classroom.
173Teach Epidemiology
Enduring Epidemiological Understandings
… the big ideas that reside at the heart of epidemiology and have lasting value outside the classroom.
Enduring Epidemiological Understandings
174Teach Epidemiology
Enduring Epidemiological Understandings
… the big ideas that reside at the heart of epidemiology and have lasting value outside the classroom.
Enduring Epidemiological Understandings
175Teach Epidemiology
Enduring Epidemiological Understandings
… the big ideas that reside at the heart of epidemiology and have lasting value outside the classroom.
Enduring Epidemiological Understandings
176Teach Epidemiology
Enduring Epidemiological Understandings
… the big ideas that reside at the heart of epidemiology and have lasting value outside the classroom.
Enduring Epidemiological Understandings
177
“… to see past the surface features of any problem to
the deeper, more fundamental principles of
the discipline.”
National Research Council Learning and Understanding
Enduring Understandings
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Epidemiology
Hypothesis
Total Risk Relative Risk
a b
c d
or %
or %Exposure Outcome
?Turned Up Together
Healthy People
-
Healthy People
E
E
O
O
O
O
180
Total Risk Relative Risk
a b
c d
or %
or %Exposure Outcome
?Associated
Turned Up Together
1.
2.
3.
4.
5.
Cause
Confounding
Reverse Time Order
Chance
Bias
?
Epidemiology
Give people fish, they have food for a day,
Teach people how to fish, they have food for a lifetime.
Teach Epidemiology
Enduring Epidemiological Understandings
182
183
Explore Public Health Career Paths
http://www.asph.org/document.cfm?page=1038
Teach Epidemiology
What do you mean - Teach Epidemiology?
184
Explore Public Health Career Paths
http://pathwaystopublichealth.org/
Teach Epidemiology
What do you mean - Teach Epidemiology?
185
Leverage the Science Olympiad Competition
http://soinc.org/
Teach Epidemiology
What do you mean - Teach Epidemiology?
186
Create and Teach a New Epidemiology Lesson
Teach Epidemiology
What do you mean - Teach Epidemiology?
187
Infuse Epidemiology into Existing Lesson about Something Else
Teach Epidemiology
What do you mean - Teach Epidemiology?
188
Infuse Epidemiology into Existing Lesson about Something Else
Teach Epidemiology
What do you mean - Teach Epidemiology?
189
Infuse Epidemiology into Existing Lesson about Something Else
Teach Epidemiology
What do you mean - Teach Epidemiology?
190
Infuse Epidemiology into Existing Lesson about Something Else
Teach Epidemiology
What do you mean - Teach Epidemiology?
191
Infuse Epidemiology into Existing Lesson about Something Else
Teach Epidemiology
What do you mean - Teach Epidemiology?
192
Infuse Epidemiology into Existing Lesson about Something Else
Teach Epidemiology
What do you mean - Teach Epidemiology?
193
Infuse Epidemiology into Existing Lesson about Something Else
Teach Epidemiology
What do you mean - Teach Epidemiology?
194Teach Epidemiology
What do you mean - Teach Epidemiology?
Teaching Existing Epidemiology Lessons
195
Teaching Existing Epidemiology Lessons
http://ccnmtl.columbia.edu/projects/epiville/
Teach Epidemiology
What do you mean - Teach Epidemiology?
196
Teaching Existing Epidemiology Lessons
http://www.diseasedetectives.org/
Teach Epidemiology
What do you mean - Teach Epidemiology?
197
Teaching Existing Epidemiology Lessons
http://www.cdc.gov/excite/
Teach Epidemiology
What do you mean - Teach Epidemiology?
198
Teaching Existing Epidemiology Lessons
http://www2a.cdc.gov/epicasestudies/
Teach Epidemiology
What do you mean - Teach Epidemiology?
199
Teaching Existing Epidemiology Lessons
http://www.cdc.gov/excite/ScienceAmbassador/ScienceAmbassador.htm
Teach Epidemiology
What do you mean - Teach Epidemiology?
200
Teaching Existing Epidemiology Lessons
http://www.buffetbusters.ca/
Teach Epidemiology
What do you mean - Teach Epidemiology?
201
Teaching Existing Epidemiology Lessons
http://www.montclair.edu/Detectives/
Teach Epidemiology
What do you mean - Teach Epidemiology?
202
Teaching Existing Epidemiology Lessons
http://www.montclair.edu/drugepi/
Teach Epidemiology
What do you mean - Teach Epidemiology?
203
Teaching Existing Epidemiology Lessons
Teach Epidemiology
What do you mean - Teach Epidemiology?
http://www.collegeboard.com/yes/ft/iu/units.html
204
View a News Item from an Epidemiologic Perspective
http://www.nationalacademies.org/headlines/
Teach Epidemiology
What do you mean - Teach Epidemiology?
205
206
1.
2.
3.
4.
5.
6.
7.
8.
.
Empowers students to be scientifically literate participants in the democratic decision-making process concerning public health policy.
Empowers students to make more informed personal health-related decisions.
Increases students’ media literacy and their understanding of public health messages.
Increases students’ understanding of the basis for determining risk.
Improves students’ mathematical and scientific literacy.
Expands students’ understanding of scientific methods and develops their critical thinking skills.
Provides students with another mechanism for exploring important, real world questions about their health and the health of others.
Introduces students to an array of career paths related to the public’s health.
Top 8 Reasons to Teach / Learn about Epidemiology
Teach Epidemiology
207
208Teach Epidemiology
Innovation
… an idea, practice or object that is perceived as new by an individual or other unit of adoption.
Everett M. Rogers, Diffusion of Innovations
Workshop Goal
209
Diffusion
The process by which an innovation is communicated through certain channels over time among the members of a social system
(with the aim being to maximize the exposure and reach of innovations, strategies, or programs.)
Everett M. Rogers, Diffusion of Innovations
Teach Epidemiology
Workshop Goal
210
Workshop Goal
Teach Epidemiology
To increase the frequency with which epidemiology is taught to students in grades 6-12
211
212
Post-Workshop Assessment
Teach Epidemiology
213Teach Epidemiology
Handout
Workshop Evaluation
214
Centers for Disease Control and PreventionMorgantown, West Virginia
June 20-24, 2011
Teach EpidemiologyProfessional Development Workshop
Thank You
216
Time Check
4:00 PM