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Welcome to. 3. Teach Epidemiology. Professional Development Workshop. Centers for Disease Control and Prevention July 14-18, 2008. Time Check 9:00 AM 15 Minutes. Teach Epidemiology. Teach Epidemiology. Is Epidemiology in Your Future?.

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Teach Epidemiology


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Presentation Transcript
slide1

Welcome to

3

Teach Epidemiology

Professional Development Workshop

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

slide2

Time Check

9:00 AM

15 Minutes

slide3

Teach Epidemiology

Teach Epidemiology

slide5

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

slide6

Time Check

9:15 PM

45 Minutes

slide8

Teach Epidemiology

Teach Epidemiology

slide9

Time Check

10:00 AM

135 Minutes

slide11

Teach Epidemiology

Teach Epidemiology

slide12

Teach Epidemiology

Young Epidemiology Scholars Professional Development Workshop

July 16, 2008

Diane Marie M. St. George, PhD

enduring understandings 7 9

Enduring Understandings 7-9

Explaining associations

and

judging causation

slide14
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
slide15
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.
slide16
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
reasons for associations
Reasons for associations
  • Confounding
  • Bias
  • Reverse causality
  • Sampling error (chance)
  • Causation
slide18
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.
confounding
Confounding
  • Confounding is an alternate explanation for an observed association of interest.

Number of persons in the home

Osteoporosis

Age

confounding1
Confounding
  • Confounding is an alternate explanation for an observed association of interest.

Exposure

Outcome

Confounder

confounding2
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?
pollution and low vaccination rates
Pollution and low vaccination rates

RR = (79 / 824) / (286 / 8576) = 2.9

pollution and vaccination rates
Pollution and vaccination rates
  • Could there be some other explanation for the observed association?
pollution and vaccination rates1
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?
pollution and vaccination rates access
Pollution and vaccination rates (Access)

RR = (55 / 106) / (5 / 10) = 1.0

pollution and vaccination rates no access
Pollution and vaccinationrates (No access)

RR = (24 / 718) / (281 / 8566) = 1.0

conclusions
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…
confounding3
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
handling confounding
Handling confounding
  • Restriction
  • Matching
  • Random assignment
  • Stratification
  • Statistical adjustment
confounding4
Confounding
  • Confounding is an alternate explanation for an observed association of interest.

Low vaccination rates

Manufacturing waste

No health care access

reasons for associations1
Reasons for associations
  • Confounding
  • Bias
  • Reverse causality
  • Sampling error (chance)
  • Causation
slide34
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
types of bias
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
information bias
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)
birth defects and diet
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.
pesticides and cancer mortality
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.
induced abortion breast ca
Induced abortion & breast CA
  • Positive association found in 5 studies
  • No association found in 6 studies
  • Negative association found in 1 study
minimize bias
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
selection bias
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
slide42

Time Check

12:15 PM

30 Minutes

slide44

Teach Epidemiology

Revised

Teach Epidemiology

slide45

Time Check

12:45 PM

15 Minutes

slide47

Teach Epidemiology

Revised

Teach Epidemiology

slide48

Teach Epidemiology

Revised

Teach Epidemiology

slide49

Time Check

1:30 PM

15 Minutes

slide51

Teach Epidemiology Web Site

http://www.montclair.edu/YESteachingunits/

Teach Epidemiology

slide52

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@collegeboard.org

Teach Epidemiology

slide53

Public Health Workforce Crisis

http://www.asph.org/document.cfm?page=1038

Teach Epidemiology

slide54

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?

slide55

Time Check

1:45 PM

15 Minutes

slide57

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

slide58

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

slide59

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

slide60

Hypothesis

YES Professional Development Workshop

Identifying Patterns and Formulating Hypotheses

Teach Epidemiology

slide61

DZ

E

%

Hypothesis

or

DZ

%

or

Healthy People

Healthy People

DZ

E

-

?

DZ

Risk

Relative Risk

Total

Exposure

Outcome

a b

c d

Turned Up Together

Making Group Comparisons and Identifying Associations

Identifying Patterns and Formulating Hypotheses

Teach Epidemiology

slide62

DZ

E

%

Hypothesis

or

DZ

%

or

Healthy People

Healthy People

DZ

E

-

?

DZ

Risk

Relative Risk

Total

Exposure

Outcome

a b

c d

Turned Up Together

Making Group Comparisons and Identifying Associations

Explaining Associations and Judging Causation

Teach Epidemiology

slide63

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

slide64

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

slide65

Time Check

2:00 PM

15 Minutes

slide67

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 controlof health problems.

(Gordis, 2004)

Teach Epidemiology

slide68

causal, ….

X

… and you avoided or eliminated the hypothesized cause, what would happen to the outcome?

Explaining Associations and Judging Causation

If an association was causal, ….

?

Hypothesized Exposure

Outcome

X

Teach Epidemiology

slide69

found due to confounding, ….

Unobserved Exposure

X

… and you avoided or eliminated the hypothesized cause, what would happen to the outcome?

Explaining Associations and Judging Causation

If the association was found due to confounding, ….

?

Hypothesized Exposure

Outcome

Teach Epidemiology

slide70

found due to reverse time order, ….

X

… and you avoided or eliminated the hypothesized cause, what would happen to the outcome?

Explaining Associations and Judging Causation

If an association was found due to reversed time-order, ….

?

Hypothesized Exposure

Outcome

Teach Epidemiology

slide71

X

… and you avoided or eliminated the hypothesized cause, what would happen to the outcome?

Explaining Associations and Judging Causation

If an association was found due to chance, ….

found due to chance, ….

?

Hypothesized Exposure

Outcome

Teach Epidemiology

slide72

X

… and you avoided or eliminated the hypothesized cause, what would happen to the outcome?

Explaining Associations and Judging Causation

If an association was found due to bias, ….

found due to bias, ….

?

Hypothesized Exposure

Outcome

Teach Epidemiology

slide73

If an association was causal, ….

causal, ….

Hypothesized Exposure

X

Outcome

X

… and you avoided or eliminated the hypothesized cause, what would happen to the outcome?

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 controlof health problems.

Teach Epidemiology

slide74

Explaining Associations and Judging Causation

1.

Cause

2.

Confounding

3.

Bias

Chance

4.

5.

Reverse Time Order

Teach Epidemiology

slide75

Explaining Associations and Judging Causation

1.

Cause

2.

Confounding

3.

Bias

Chance

4.

5.

Reverse Time Order

Teach Epidemiology

slide76

Time Check

2:15 PM

30 Minutes

slide78

Outcome

No Outcome

Outcome

a

b

Exposure

c

d

Exposure

No

Exposure

Explaining Associations and Judging Causation

Things that are associated are linked in some way that makes them turn up together.

Teach Epidemiology

slide79

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 Links Iron Deficiency to Math Scores

Study Concludes: Movies Influence Youth Smoking

Outcome

Exposure

a

b

c

d

Lack of High School Diploma Tied to US Death Rate

No Exposure

Study Links Spanking to Aggression

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

slide80

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

slide82

Explaining Associations and Judging Causation

Teach Epidemiology

YES Teaching Units Professional Development Workshop

slide83

Explaining Associations and Judging Causation

Diagram

2x2 Table

DZ

DZ

X

a

b

c

d

X

YES Teaching Units Professional Development Workshop

Teach Epidemiology

slide84

Explaining Associations and Judging Causation

Diagram

2x2 Table

DZ

DZ

X

a

b

c

d

X

Teach Epidemiology

slide86

X

X

X

X

X

X

X

DZ

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

Necessary and Sufficient

Diagram

2x2 Table

DZ

DZ

X

X

a

b

c

d

X

Teach Epidemiology

slide87

X

X

X

X

X

X

X

X

X

+

X

+

X

DZ

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

Necessary but Not Sufficient

Diagram

2x2 Table

DZ

DZ

X

X

a

b

c

d

X

Teach Epidemiology

slide88

X

X

X

X

X

X

DZ

X

X

X

X

X

X

X

X

X

X

X

X

Not Necessary but Sufficient

Diagram

2x2 Table

DZ

DZ

X

X

X

a

b

c

d

X

Teach Epidemiology

slide89

X

X

X

X

+

X

+

X

X

X

X

X

+

X

+

X

DZ

X

X

X

X

+

X

+

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

Not Necessary and Not Sufficient

Diagram

2x2 Table

DZ

DZ

X

X

a

b

c

d

X

Teach Epidemiology

slide90

NoHeart Attack

Heart Attack

Lack of Fitness

No Lack of Fitness

Explaining Associations and Judging Causation

a bc d

Teach Epidemiology

slide91

NoLead Poisoning

Lead Poisoning

Lack of Supervision

No Lack of Supervision

Explaining Associations and Judging Causation

a bc d

Teach Epidemiology

slide94

Time Check

2:45 PM

15 Minutes

slide97

Explaining Associations and Judging Causation

1.

Cause

2.

Confounding

3.

Bias

Chance

4.

5.

Teach Epidemiology

slide98

Sample

A selection of people from a population

Inference

Process of predicting from what is observed in a sample to what is not observed in a population

Explaining Associations and Judging Causation

Population

All the people in a particular group

Teach Epidemiology

slide99

Explaining Associations and Judging Causation

Deck of 100 cards

Teach Epidemiology

slide100

a

b

c

d

25 cards

25 cards

25 cards

25 cards

Explaining Associations and Judging Causation

Teach Epidemiology

slide101

Population

a

b

c

d

No Marijuana

No Marijuana

Odd #

25 cards

25 cards

25 cards

Even #

25 cards

Explaining Associations and Judging Causation

Total

a

b

=

=

c

d

Teach Epidemiology

slide102

a

b

c

d

No Marijuana

No Marijuana

50

25

25

Odd #

25 cards

25 cards

50

25

25

25 cards

Even #

25 cards

Explaining Associations and Judging Causation

Population

Total

=

=

Teach Epidemiology

slide103

Population

a

b

c

d

No Marijuana

No Marijuana

No Flu

Flu

50

50

25

25

25

25

Odd #

M&M’s

25 cards

25 cards

50

50

25

25

25

25

No M&M’s

25 cards

Even #

25 cards

Explaining Associations and Judging Causation

Total

=

=

=

Total

Teach Epidemiology

slide104

Population

a

b

c

d

No Marijuana

No Marijuana

50

25

25

Odd #

25 cards

25 cards

50

25

25

25 cards

Even #

25 cards

Explaining Associations and Judging Causation

=

=

Total

Risk

25 / 50 or 50%

25 / 50 or 50%

Teach Epidemiology

slide105

Population

a

b

c

d

No Marijuana

No Marijuana

50

25

25

Odd #

25 cards

25 cards

50

25

25

25 cards

Even #

25 cards

Risk

= 1

50 % / 50% =

Explaining Associations and Judging Causation

=

=

Total

Relative Risk

25 / 50 or 50 %

50 %

____

25 / 50 or 50 %

50 %

Teach Epidemiology

slide106

25 cards

25 cards

25 cards

25 cards

Explaining Associations and Judging Causation

Teach Epidemiology

slide109

Population

b

Sample

25 cards

25 cards

25 cards

25 cards

Explaining Associations and Judging Causation

Sample of 20 cards

Teach Epidemiology

slide110

Population

b

Sample

No Marijuana

No Marijuana

10

5

5

Odd #

10

5

5

Even #

25 cards

25 cards

25 cards

25 cards

Explaining Associations and Judging Causation

Sample of 20 cards

Total

Teach Epidemiology

slide111

Population

b

Sample

No Marijuana

No Marijuana

10

5

5

Odd #

10

5

5

Even #

Risk

25 cards

25 cards

25 cards

25 cards

Explaining Associations and Judging Causation

Sample of 20 cards

Total

5 / 10 or 50 %

5 / 10 or 50 %

Teach Epidemiology

slide112

Population

b

Sample

No Marijuana

No Marijuana

10

5

5

Odd #

10

5

5

Even #

Relative Risk

= 1

50 % / 50% =

25 cards

25 cards

25 cards

25 cards

Explaining Associations and Judging Causation

Sample of 20 cards

Total

Risk

5 / 10 or 50 %

50 %

____

5 / 10 or 50 %

50 %

Teach Epidemiology

slide113

b

No Marijuana

No Marijuana

Sample of 20 cards

Odd #

Even #

Risk

Relative Risk

5 / 10 = 50 %

50 1

5 / 10 = 50 %

Explaining Associations and Judging Causation

By Chance

Total

%

___

%

=

Teach Epidemiology

slide114

= 1

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

5

5

10

5 / 10 or 50 %

Odd #

50 %

____

5

5

10

5 / 10 or 50 %

50 %

Even #

By Chance

Teach Epidemiology

slide115

Explaining Associations and Judging Causation

Relative Risks

Less than 1

Less than 1

Greater than 1

Teach Epidemiology

slide116

Explaining Associations and Judging Causation

Study Links Having an Odd Address to Marijuana Use

Teach Epidemiology

slide117

Explaining Associations and Judging Causation

Relative Risks

Less than 1

Less than 1

Greater than 1

Teach Epidemiology

slide118

Explaining Associations and Judging Causation

Study Links Having an Even Address to Marijuana Use

Teach Epidemiology

slide119

1

By Chance

By Chance

25 cards

25 cards

25 cards

25 cards

Explaining Associations and Judging Causation

Relative Risks

Less than 1

Greater than 1

Teach Epidemiology

slide120

Time Check

3:00 PM

15 Minutes

slide122

Explaining Associations and Judging Causation

1.

Cause

2.

Confounding

3.

Bias

Chance

4.

5.

Reverse Time Order

Teach Epidemiology

slide123

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

slide124

DZ

DZ

E

DZ

E

Healthy People

Healthy People

DZ

E

DZ

DZ

Random Assignment

DZ

DZ

E

E

DZ

E

DZ

Healthy People

DZ

DZ

E

Healthy People

E

E

E

Explaining Associations and Judging Causation

Case-Control Study

Controlled Trial

Time

Time

Cohort Study

Cross-Sectional Study

Time

Time

Teach Epidemiology

slide127

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

slide128

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

slide130

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

slide131

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 Links Iron Deficiency to Math Scores

Study Concludes: Movies Influence Youth Smoking

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

slide135

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 controlof health problems.

(Gordis, 2004)

Teach Epidemiology

slide136

Explaining Associations and Judging Causation

Does evidence from an aggregate of studies support a cause-effect relationship?

Causal or Not Causal?

Guilt or Innocence?

Teach Epidemiology

slide137

Explaining Associations and Judging Causation

Handouts

Association Found Between Coffee and Pancreatic Cancer

Teach Epidemiology

slide138

Explaining Associations and Judging Causation

Things that are associated are linked in some way that makes themturn up together.

4

Teach Epidemiology

slide139

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

slide140

Explaining Associations and Judging Causation

“In what circumstances can we pass from this observed association to a verdict of causation?”

Teach Epidemiology

slide141

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

slide142

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

slide143

Explaining Associations and Judging Causation

Stress causes ulcers.

Helicobacter pylori causes ulcers.

Teach Epidemiology

slide144

*

*

*

*

*

*

*

*

*

Explaining Associations and Judging Causation

Teach Epidemiology

slide146

Time Check

3:30 PM

15 Minutes

slide148

Teach Epidemiology

Revised

Teach Epidemiology

slide149

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

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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

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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

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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

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Teach Epidemiology Rules

Teach Epidemiology

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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

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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

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4 Basic Epidemiological Study Designs

Case-Control Study

Controlled Trial

Cohort Study

Cross-Sectional Study

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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

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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

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4 Basic Epidemiological Study Designs

4 Basic Epidemiological Study Designs

Case-Control Study

Controlled Trial

Cohort Study

Cross-Sectional Study

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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 year’s 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

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4 Basic Epidemiological Study Designs

4 Basic Epidemiological Study Designs

Case-Control Study

Controlled Trial

Cohort Study

Cross-Sectional Study

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Case-Control Study

Controlled Trial

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Cohort Study

Cross-Sectional Study

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4 Basic Epidemiological Study Designs