Teach Epidemiology

The Health Education Center at Lankenau Hospital100 Lancaster Avenue, Wynnewood, PA 19096

July 20-24, 2009

Teach EpidemiologyProfessional Development Workshop

Day3

2Teach Epidemiology

Teach Epidemiology

3

Time Check

9:15 AM

4

5Teach Epidemiology

Teach Epidemiology

6Teach Epidemiology

Teaching Epidemiology

Group 3

Class 1 Pages 16-21)

7

Time Check

10:00 AM

8

9Teach Epidemiology

Teach Epidemiology

10Teach Epidemiology


Group 1

Pages 35-36

11

Time Check

10:45 AM

12


Teach Epidemiology

14

Time Check

11:00 AM

15


Teach Epidemiology

Enduring Enduring Understandings 7-9Understandings 7-9

Explaining associations Explaining associations

and and

judging causationjudging causation

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 associationsReasons for associations

Confounding Bias Reverse causality Sampling error (chance) Causation

Osteoporosis risk is higher among women Osteoporosis risk is higher among women who live alone.who live alone.

ConfoundingConfounding

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

Number of persons in the

homeOsteoporosis

Age



Exposure Outcome

Confounder


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 MortalityBedsores and Mortality

D+D+ D-D-

E+E+ 7979 745745 824824

E-E- 286286 82908290 85768576

365365 90359035 94009400

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


Avoid bedsores…Live Avoid bedsores…Live forever!!forever!!

Could there be some other Could there be some other explanation for the observed explanation for the observed association?association?

Bedsores and mortalityBedsores and mortality

If severity of medical problems had been If severity of medical problems had been the reason for the association between the reason for the association between bedsores and mortality, what might the RR bedsores and mortality, what might the RR be if all study participants had very severe be if all study participants had very severe medical problems?medical problems?

What about if the participants all had What about if the participants all had problems of very low severity?problems of very low severity?


DiedDied Did not dieDid not die

BedsoresBedsores 55 severe55 severe

24 not24 not

51 severe51 severe

694 not694 not

824824

No No bedsoresbedsores

5 severe5 severe

281 not281 not

5 severe5 severe

8285 not8285 not

85768576

365365 90359035 94009400

Bedsores and MortalityBedsores and Mortality (Severe) (Severe)


BedsoresBedsores 5555 5151 106106


55 55 1010

6060 5656 116116

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

Bedsores and MortalityBedsores and Mortality (Not severe) (Not severe)


BedsoresBedsores 2424 694694 718718


281281 82858285 85668566

305305 89798979 92849284

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

Bedsores and Mortality Bedsores and Mortality stratifiedstratified by Medical Severity by Medical Severity

SEVERESEVERE++ DiedDied Didn’t dieDidn’t die

BedsoresBedsores aa bb

No soresNo sores cc dd

RR = 1.0RR = 1.0

SEVERE-SEVERE- DiedDied Didn’t dieDidn’t die



RR = 1.0RR = 1.0

SEVERE+SEVERE+ DiedDied Didn’t dieDidn’t die



RR = 2.9RR = 2.9

SEVERE-SEVERE- DiedDied Didn’t dieDidn’t die



RR = 2.9RR = 2.9

BedsoresBedsores

Bedsores are unrelated to mortality among Bedsores are unrelated to mortality among those with severe problems.those with severe problems.

Bedsores are unrelated to mortality among Bedsores are unrelated to mortality among those with problems of less severity.those with problems of less severity.

Adjusted RR = 1, and the unadjusted RR = 2.9Adjusted RR = 1, and the unadjusted RR = 2.9



Bedsores Death in the hospital

Severity of medical problems

Controlling confoundingControlling confounding

Study design phaseStudy design phase MatchingMatching RestrictionRestriction Random assignmentRandom assignment

Study analysis phaseStudy analysis phase StratificationStratification Statistical adjustmentStatistical adjustment


ConfoundingConfounding BiasBias Reverse causalityReverse causality Sampling error (chance)Sampling error (chance) CausationCausation

BiasBias

Errors are mistakes that are:Errors are mistakes that are: randomly distributedrandomly distributed not expected to impact the MAnot expected to impact the MA less modifiableless modifiable

Biases are mistakes that are:Biases are mistakes that are: not randomly distributednot randomly distributed may impact the MAmay impact the MA more modifiablemore modifiable

Types of biasTypes of bias

Selection biasSelection bias The process for The process for selecting/keeping selecting/keeping subjects subjects

causes mistakescauses mistakes

Information biasInformation bias The process for collecting The process for collecting informationinformation from from

the subjects causes mistakesthe subjects causes mistakes

Selection biasSelection bias

People who are working are likely to be People who are working are likely to be healthier than non-workershealthier than non-workers

People who participate in a study may People who participate in a study may be different from people who do notbe different from people who do not

People who drop out of a study may be People who drop out of a study may be different from those who stay in the different from those who stay in the studystudy

Hospital controls may not represent the Hospital controls may not represent the source population for the casessource population for the cases

Information biasInformation bias

Misclassification, e.g. non-exposed Misclassification, e.g. non-exposed as exposed or cases as controlsas exposed or cases as controls

Cases are more likely than controls to Cases are more likely than controls to recall past exposuresrecall past exposures

Interviewers probe cases more than Interviewers probe cases more than controls (exposed more than controls (exposed more than unexposed)unexposed)

Birth defects and dietBirth defects and diet

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

Pesticides and cancer mortalityPesticides and cancer mortality

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

Minimize biasMinimize bias

Can only be done in the planning and Can only be done in the planning and implementation phaseimplementation phase

Standardized processes for data collectionStandardized processes for data collection MaskingMasking Clear, comprehensive case definitionsClear, comprehensive case definitions Incentives for participation/retentionIncentives for participation/retention


ConfoundingConfounding BiasBias Reverse causalityReverse causality Sampling error (chance)Sampling error (chance) CausationCausation

Reverse causalityReverse causality

Suspected disease actually precedes suspected Suspected disease actually precedes suspected causecause

Pre-clinical disease Pre-clinical disease Exposure Exposure Disease Disease For example: Memory deficits For example: Memory deficits Reading Reading

cessation cessation Alzheimer’s Alzheimer’s Cross-sectional studyCross-sectional study

For example: Sexual activity/MarijuanaFor example: Sexual activity/Marijuana

Minimize effect of reverse Minimize effect of reverse causalitycausality

Done in the planning and implementation Done in the planning and implementation phase of a studyphase of a study

Pick study designs in which exposure is Pick study designs in which exposure is measured before disease onsetmeasured before disease onset

Assess disease status with as much Assess disease status with as much accuracy as possibleaccuracy as possible

47

48

Time Check

12:15 AM

49


Teach Epidemiology

51

Time Check

12:45 PM

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Teach Epidemiology

54

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.

55

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

56

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, ….


57


Outcome

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



?


58

Outcome

If an association was found due to chance, ….


found due to chance, ….



?


59

Outcome

If an association was found due to bias, ….


?

found due to bias, ….




60

Outcome

If an association was causal, ….

Hypothesized Exposure XX

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

causal, ….



61

1. Cause

2. Confounding

3. Reverse Time Order

4. Chance

5. Bias



62

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


4. Chance

5. Bias

Snacks Key to Kids’ TV- Linked Obesity: China

Study

Depressed Teens More

Likely to Smoke

Ties, Links, Relationships, and Associations


Enduring Epidemiological Understandings

64

65




Rate

Study Links

Spanking to

Aggression


Youth Smoking

Study Links Iron

Deficiency to Math

Scores





Study


Likely to Smoke

66




Rate

Study Links

Spanking to

Aggression


Youth Smoking

Study Links Iron

Deficiency to Math

Scores




Study


Likely to Smoke


67

1. Cause

2. Confounding


4. Chance

5. Bias

Possible Explanations for Finding an Association

68

Epidemiology

Epidemiology


Leon Gordis, Epidemiology, 3rd Edition, Elsevier Saunders, 2004.

69

1. Cause

2. Confounding


4. Chance

5. Bias


70

Cause

A factor that produces a change in another factor.

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


71

Sample of 100

72

Sample of 100, 25 are Sick

73

Diagram

2x2 Table

DZ DZ

X

X

a bc d

Types of Causal Relationships

74

DZ DZ

X

X

a bc d

Diagram

2x2 Table

Types of Causal Relationships

75

Handout

76

77

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

78

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

79

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

80

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

81

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

82

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

83

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

84

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

85

a b

c d

Heart Attack

NoHeart Attack

Lack of Fitness

No Lack of Fitness

Lack of fitness and physical activity causes heart attacks.

86

a b

c d

Lead Poisoning

NoLead

Poisoning

Lack of Supervision

No Lack of

Supervision

Lack of supervision of small children causes lead poisoning.

87

88

Is the association causal?

89




Rate

Study Links

Spanking to

Aggression


Youth Smoking

Study Links Iron

Deficiency to Math

Scores




1. Cause

2. Confounding


4. Chance

5. Bias


Study


Likely to Smoke

90

91

1. Cause

2. Confounding


4. Chance

5. Bias


92

All the people in a particular group.

Population


93

A selection of people from a population.

Sample


94

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.


95

Sample

Population

Process of predicting from what is observed

to what is not observed.

Observed

Not Observed

Inference

96

Deck of

100 cards

Population

97

a

25 cards

b

25 cards

c

25 cards

25 cards

d

Population

98

=

Population

a

25 cards

b c d

25 cards25 cards25 cards

=a b

c d

Odd #

Even #

No Marijuana

No Marijuana

Population

Total

99

=

Population

a

25 cards

b c d


= 2525

25 25

50

50

Total

Odd #

Even #

No Marijuana

No Marijuana

Population

100

=

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


Odd #

Even #

No Marijuana

No Marijuana

Population

101

=

Population

=

2525

25 25

50

50

Total

a

25 cards

b c d


Risk

25 / 50 or 50%

25 / 50 or 50%

Odd #

Even #

No Marijuana

No Marijuana

Population

102

=

Population

a

25 cards

b c d


=

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

103

25 cards

25 cards

25 cards

25 cards

Population

104

To occur accidentally.

To occur without design.

Chance

A coincidence.


105

Chance

106

Chance

107

Population

Sample

b

Sample

of

20 cards25 cards25 cards25 cards25 cards

Sample

108

Population

Sample

b

Sample

of


10

10

Total

55

5 5Odd #

Even #

No Marijuana

No Marijuana

Sample

109

Population

Sample

b

Sample

of


10

10

Total

55

5 5

Risk

5 / 10 or 50 %

5 / 10 or 50 %Odd #

Even #

No Marijuana

No Marijuana

Sample

110

Population

Sample

b

Sample

of


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 %

____

111

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

112

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

113

Relative Risks

Greater than 1 Less than 1

Chance

114

Study Links Having an Odd Address to Marijuana Use


115

Relative Risks



116

Study Links Having an Even Address to Marijuana Use


117

Relative Risks


1

By ChanceBy Chance

25 cards25 cards25 cards25 cards

Chance

118

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

119

Relative Risks


1

By ChanceBy Chance


Chance

50 cards

120

b

Sample

of

20 cards

TotalRisk

5 / 10 = 50 %

5 / 10 = 50 %

50

Relative Risk

75

%___

%

=Odd #

Even #

No Marijuana

No Marijuana


121

Relative Risks


1

By ChanceBy Chance


Chance

75 cards

122

b

Sample

of

20 cards

TotalRisk

5 / 10 = 50 %

5 / 10 = 50 %

50 1

Relative Risk

99

%___

%

=Odd #

Even #

No Marijuana

No Marijuana


123

Relative Risks


1

By ChanceBy Chance


Chance

99 cards

124




Rate

Study Links

Spanking to

Aggression


Youth Smoking

Study Links Iron

Deficiency to Math

Scores


1. Cause

2. Confounding


4. Chance

5. Bias


Study


Likely to Smoke

Association is not necessarily causation.


126

Where are we?

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

127

Teach Epidemiology

Explaining Associations and Judging Causation

1. Cause

2. Confounding


4. Chance

5. Bias

Teach Epidemiology


Coffee and Cancer of the Pancreas

130

131

Guilt or Innocence?Causal or Not Causal?

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

Teach Epidemiology


132

Sir Austin Bradford Hill “The Environment and Disease:

Association or Causation?” Proceedings of the Royal Society of Medicine

January 14, 1965

Teach Epidemiology


Handout

133

“In what circumstances can we pass from this observed association

to a verdict of causation?”

Teach Epidemiology


134

“Here then are nine different viewpoints from all of which we should study association

before we cry causation.”

Teach Epidemiology


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


Handout

Teach Epidemiology


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


Teach Epidemiology


Handout

139

Stress causes ulcers.

Helicobacter pylori causes ulcers.

Teach Epidemiology


140

*

*

*

**

*

*

*

*

Teach Epidemiology




142

143

“Does Playing Video Games Cause Asthma?”

Teach Epidemiology


Handout

144

145

Time Check

3:30 PM

146


Teach Epidemiology

148

Rules

1. Teach epidemiology

2. As a group, create a 20-minute lesson during which we will develop a deeper understanding of an enduring epidemiological understanding.

3. 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.

4. When teaching assume the foundational epidemiological knowledge from the preceding days of the workshop.

5. Try to get us to uncover the enduring epidemiological understanding. Try to only tell us something when absolutely necessary.

6. End each lesson by placing it in the context of the appropriate enduring epidemiological understanding.

7. Be certain that the lesson is taught in 20 minutes or less.

8. Teach epidemiology.

Teach Epidemiology


149

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


Metacognition

150

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




Group 2

Pages 32-36



Group 3

Procedures 2, 4, and

5

Documents

Teach Epidemiology