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Day 3. Teach Epidemiology. Professional Development Workshop. The Health Education Center at Lankenau Hospital 100 Lancaster Avenue, Wynnewood, PA 19096 July 20-24, 2009. Teach Epidemiology. Teach Epidemiology. Time Check 9:15 AM. Teach Epidemiology. Teach Epidemiology.

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slide1

Day

3

Teach Epidemiology

Professional Development Workshop

The Health Education Center at Lankenau Hospital100 Lancaster Avenue, Wynnewood, PA 19096July 20-24, 2009

slide2

Teach Epidemiology

Teach Epidemiology

slide3

Time Check

9:15 AM

slide5

Teach Epidemiology

Teach Epidemiology

slide6

Teaching Epidemiology

Class 1

Pages 16-21)

Group 3

Teach Epidemiology

slide7

Time Check

10:00 AM

slide9

Teach Epidemiology

Teach Epidemiology

slide10

Teaching Epidemiology

Group 1

Pages 35-36

Teach Epidemiology

slide11

Time Check

10:45 AM

slide13

Teach Epidemiology

Teach Epidemiology

slide14

Time Check

11:00 AM

slide16

Teach Epidemiology

Teach Epidemiology

enduring understandings 7 9

Enduring Understandings 7-9

Explaining associations

and

judging causation

slide18
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.
slide19
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.
slide20
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.
reasons for associations
Reasons for associations
  • Confounding
  • Bias
  • Reverse causality
  • Sampling error (chance)
  • Causation
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
  • YES confounding module example:
    • Cohort study
    • 9,400 elderly in the hospital
    • RQ: Are bedsores related to mortality among elderly patients with hip fractures?
bedsores and mortality
Bedsores and Mortality

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

bedsores and mortality1
Bedsores and Mortality
  • Avoid bedsores…Live forever!!
  • Could there be some other explanation for the observed association?
bedsores and mortality2
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 severe
Bedsores and Mortality (Severe)

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

bedsores and mortality not severe
Bedsores and Mortality (Not severe)

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

bedsores
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
confounding3
Confounding
  • Confounding is an alternate explanation for an observed association of interest.

Bedsores

Death in the hospital

Severity of medical problems

controlling confounding
Controlling confounding
  • Study design phase
    • Matching
    • Restriction
    • Random assignment
  • Study analysis phase
    • Stratification
    • Statistical adjustment
reasons for associations1
Reasons for associations
  • Confounding
  • Bias
  • Reverse causality
  • Sampling error (chance)
  • Causation
slide37
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
selection bias
Selection bias
  • People who are working are likely to be healthier than non-workers
  • People who 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
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 (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.
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
reasons for associations2
Reasons for associations
  • Confounding
  • Bias
  • Reverse causality
  • Sampling error (chance)
  • Causation
reverse causality
Reverse causality
  • Suspected disease actually precedes suspected cause
  • Pre-clinical disease  Exposure  Disease
    • For example: Memory deficits  Reading cessation  Alzheimer’s
  • Cross-sectional study
    • For example: Sexual activity/Marijuana
minimize effect of reverse causality
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
slide48

Time Check

12:15 AM

slide50

Teach Epidemiology

Teach Epidemiology

slide51

Time Check

12:45 PM

slide53

Teach Epidemiology

Teach Epidemiology

slide54

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.

54

slide55

causal, ….

X

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

Control of Health Problems

If an association was causal, ….

?

Hypothesized Exposure

Outcome

X

55

slide56

found due to confounding, ….

Unobserved Exposure

X

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

Control of Health Problems

If the association was found due to confounding, ….

?

Hypothesized Exposure

Outcome

56

slide57

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

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

?

Hypothesized Exposure

Outcome

57

slide58

X

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

Control of Health Problems

If an association was found due to chance, ….

found due to chance, ….

?

Hypothesized Exposure

Outcome

58

slide59

X

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

Control of Health Problems

If an association was found due to bias, ….

found due to bias, ….

?

Hypothesized Exposure

Outcome

59

slide60

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?

Control of Health Problems

... 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.

60

slide61

Control of Health Problems

... 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.

1.

Cause

2.

Confounding

3.

Reverse Time Order

Chance

4.

5.

Bias

61

slide62

Ties, Links, Relationships, and Associations

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

3.

Reverse Time Order

Study Links Spanking to Aggression

Chance

4.

Depressed Teens More Likely to Smoke

Snacks Key to Kids’ TV- Linked Obesity: China Study

5.

Bias

Pollution Linked with Birth Defects in US Study

Kids Who Watch R-Rated Movies More Likely to Drink, Smoke

62

slide65

Ties, Links, Relationships, and Associations

Suicide Higher in Areas with Guns

Family Meals Are Good for Mental Health

Study Links Iron Deficiency to Math Scores

Study Concludes: Movies Influence Youth Smoking

Lack of High School Diploma Tied to US Death Rate

Study Links Spanking to Aggression

Depressed Teens More Likely to Smoke

Snacks Key to Kids’ TV- Linked Obesity: China Study

Pollution Linked with Birth Defects in US Study

Kids Who Watch R-Rated Movies More Likely to Drink, Smoke

slide66

Ties, Links, Relationships, and Associations

Suicide Higher in Areas with Guns

Family Meals Are Good for Mental Health

Study Links Iron Deficiency to Math Scores

Study Concludes: Movies Influence Youth Smoking

Lack of High School Diploma Tied to US Death Rate

Study Links Spanking to Aggression

Depressed Teens More Likely to Smoke

Snacks Key to Kids’ TV- Linked Obesity: China Study

Pollution Linked with Birth Defects in US Study

Kids Who Watch R-Rated Movies More Likely to Drink, Smoke

slide67

Possible Explanations for Finding an Association

1.

Cause

2.

Confounding

3.

Reverse Time Order

Chance

4.

5.

Bias

slide68

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.

slide69

Possible Explanations for Finding an Association

1.

Cause

2.

Confounding

3.

Reverse Time Order

Chance

4.

5.

Bias

slide70

Possible Explanations for Finding an Association

Cause

A factor that produces a change in another factor.

William A. Oleckno, Essential Epidemiology: Principles and Applications, Waveland Press, 2002.

slide73

Types of Causal Relationships

Diagram

2x2 Table

DZ

DZ

X

a

b

c

d

X

slide74

Types of Causal Relationships

Diagram

2x2 Table

DZ

DZ

X

a

b

c

d

X

slide77

X1

X1

X1

X1

X1

X1

X1

DZ

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

Necessary and Sufficient

Diagram

2X12 Table

DZ

DZ

X1

a

b

c

d

X1

slide78

X1

X1

X1

X1

X1

X1

X1

X1

X1

+

X2

+

X3

DZ

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

Necessary but Not Sufficient

Diagram

2X12 Table

DZ

DZ

X1

a

b

c

d

X1

slide79

X1

X1

X1

X1

X1

X2

DZ

X1

X3

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

Not Necessary but Sufficient

Diagram

2X12 Table

DZ

DZ

X1

X1

a

b

c

d

X1

slide80

X1

X1

X1

X1

+

X2

+

X3

X1

X1

X1

X4

+

X5

+

X6

DZ

X1

X1

X1

X7

+

X8

+

X9

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

X1

Not Necessary and Not Sufficient

Diagram

2X12 Table

DZ

DZ

X1

a

b

c

d

X1

slide81

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

slide82

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

slide83

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

slide84

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

slide85

NoHeart Attack

Heart Attack

Lack of Fitness

No Lack of Fitness

Lack of fitness and physical activity causes heart attacks.

a bc d

slide86

NoLead Poisoning

Lead Poisoning

Lack of Supervision

No Lack of Supervision

Lack of supervision of small children causes lead poisoning.

a bc d

slide89

Ties, Links, Relationships, and Associations

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

3.

Reverse Time Order

Study Links Spanking to Aggression

Chance

4.

Depressed Teens More Likely to Smoke

Snacks Key to Kids’ TV- Linked Obesity: China Study

5.

Bias

Pollution Linked with Birth Defects in US Study

Kids Who Watch R-Rated Movies More Likely to Drink, Smoke

slide91

Possible Explanations for Finding an Association

1.

Cause

2.

Confounding

3.

Reverse Time Order

Chance

4.

5.

Bias

slide92

Possible Explanations for Finding an Association

Population

All the people in a particular group.

slide93

Possible Explanations for Finding an Association

Sample

A selection of people from a population.

slide94

Possible Explanations for Finding an Association

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.

slide95

Observed

Not Observed

Inference

Population

Sample

Process of predicting from what is observed

to what is not observed.

slide96

Population

Deck of 100 cards

slide97

a

b

c

d

25 cards

25 cards

25 cards

25 cards

Population

slide98

Population

a

b

c

d

No Marijuana

No Marijuana

Odd #

25 cards

25 cards

25 cards

Even #

25 cards

Population

Total

a

b

=

=

c

d

slide99

a

b

c

d

No Marijuana

No Marijuana

50

25

25

Odd #

25 cards

25 cards

50

25

25

25 cards

Even #

25 cards

Population

Population

Total

=

=

slide100

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

Population

Total

=

=

=

Total

slide101

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

Population

=

=

Total

Risk

25 / 50 or 50%

25 / 50 or 50%

slide102

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% =

Population

=

=

Total

Relative Risk

25 / 50 or 50 %

50 %

____

25 / 50 or 50 %

50 %

slide103

25 cards

25 cards

25 cards

25 cards

Population

slide104

Possible Explanations for Finding an Association

Chance

To occur accidentally.

To occur without design.

A coincidence.

slide107

Population

b

Sample

25 cards

25 cards

25 cards

25 cards

Sample

Sample of 20 cards

slide108

Population

b

Sample

No Marijuana

No Marijuana

10

5

5

Odd #

10

5

5

Even #

25 cards

25 cards

25 cards

25 cards

Sample

Sample of 20 cards

Total

slide109

Population

b

Sample

No Marijuana

No Marijuana

10

5

5

Odd #

10

5

5

Even #

Risk

25 cards

25 cards

25 cards

25 cards

Sample

Sample of 20 cards

Total

5 / 10 or 50 %

5 / 10 or 50 %

slide110

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

Sample

Sample of 20 cards

Total

Risk

5 / 10 or 50 %

50 %

____

5 / 10 or 50 %

50 %

slide111

b

No Marijuana

No Marijuana

Sample of 20 cards

Odd #

Even #

Risk

Relative Risk

5 / 10 = 50 %

50 1

5 / 10 = 50 %

Sample

CDC

By Chance

Total

%

___

%

=

slide112

= 1

Chance

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

slide113

Chance

Relative Risks

Greater than 1

Less than 1

slide114

Ties, Links, Relationships, and Associations

Study Links Having an Odd Address to Marijuana Use

slide115

Possible Explanations for Finding an Association

Relative Risks

Greater than 1

Less than 1

slide116

Ties, Links, Relationships, and Associations

Study Links Having an Even Address to Marijuana Use

slide117

1

By Chance

By Chance

25 cards

25 cards

25 cards

25 cards

Chance

Relative Risks

Greater than 1

Less than 1

slide118

No Marijuana

No Marijuana

Odd #

Even #

Risk

Relative Risk

5 / 10 = 50 %

50

5 / 10 = 50 %

Different Sample Sizes

b

Sample of 20 cards

50

Total

%

___

%

=

slide119

1

By Chance

By Chance

25 cards

25 cards

25 cards

25 cards

Chance

50 cards

Relative Risks

Greater than 1

Less than 1

slide120

No Marijuana

No Marijuana

Odd #

Even #

Risk

Relative Risk

5 / 10 = 50 %

50

5 / 10 = 50 %

Different Sample Sizes

b

Sample of 20 cards

75

Total

%

___

%

=

slide121

1

By Chance

By Chance

25 cards

25 cards

25 cards

25 cards

Chance

75 cards

Relative Risks

Greater than 1

Less than 1

slide122

No Marijuana

No Marijuana

Odd #

Even #

Risk

Relative Risk

5 / 10 = 50 %

50 1

5 / 10 = 50 %

Different Sample Sizes

b

Sample of 20 cards

99

Total

%

___

%

=

slide123

1

By Chance

By Chance

25 cards

25 cards

25 cards

25 cards

Chance

99 cards

Relative Risks

Greater than 1

Less than 1

slide124

Ties, Links, Relationships, and Associations

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

Study Links Spanking to Aggression

3.

Reverse Time Order

Chance

4.

Depressed Teens More Likely to Smoke

Snacks Key to Kids’ TV- Linked Obesity: China Study

5.

Bias

Kids Who Watch R-Rated Movies More Likely to Drink, Smoke

slide126

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

Where are we?

126

slide129

Explaining Associations and Judging Causation

Coffee and Cancer of the Pancreas

1.

Cause

2.

Confounding

3.

Reverse Time Order

Chance

4.

5.

Bias

Teach Epidemiology

slide131

Explaining Associations and Judging Causation

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

Causal or Not Causal?

Guilt or Innocence?

131

Teach Epidemiology

slide132

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

slide133

Explaining Associations and Judging Causation

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

133

Teach Epidemiology

slide134

Explaining Associations and Judging Causation

“Here then are nine different viewpoints from all of which we should study association before we cry causation.”

134

Teach Epidemiology

slide135

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

slide137

O

O

E

O

E

Healthy People

Healthy People

O

E

O

O

Random Assignment

O

O

E

E

O

E

O

Healthy People

Healthy People

O

O

E

E

E

E

Timeline

Timeline

Timeline

Timeline

Explaining Associations and Judging Causation

Case-Control Study

Randomized Controlled Trial

Cohort Study

Cross-Sectional Study

Teach Epidemiology

slide139

Explaining Associations and Judging Causation

Stress causes ulcers.

Helicobacter pylori causes ulcers.

Teach Epidemiology

slide140

*

*

*

*

*

*

*

*

*

Explaining Associations and Judging Causation

Teach Epidemiology

slide143

Explaining Associations and Judging Causation

Handout

“Does Playing Video Games Cause Asthma?”

Teach Epidemiology

slide145

Time Check

3:30 PM

slide147

Teach Epidemiology

Teach Epidemiology

slide148

Teaching Epidemiology

  • Rules
  • Teach epidemiology
  • As a group, create a 20-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 20-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.
  • Be certain that the lesson is taught in 20 minutes or less.
  • Teach epidemiology.

Teach Epidemiology

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Teaching Epidemiology

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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Teaching Epidemiology

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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Teaching Epidemiology

Group 2

Pages 32-36

Teach Epidemiology

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Teaching Epidemiology

Procedures 2, 4, and 5

Group 3

Teach Epidemiology