Enduring Understandings 7-9 Explaining associations and judging causation

Wood County Schools1210 13th Street

Parkersburg, WV 26101June 22-26, 2009

Teach EpidemiologyProfessional Development Workshop

Enduring Understandings 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 Because studies are complicated by factors not controlled by the by factors not controlled by the observer, other explanations also observer, other explanations also must be considered, including must be considered, including confounding, chance, and bias.confounding, chance, and bias.

The “Not everything that glitters is The “Not everything that glitters is gold” Principlegold” Principle

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

The “Just because your friend sleeps in The “Just because your friend sleeps in class and never fails her courses does class and never fails her courses does not mean that sleeping in class does not not mean that sleeping in class does not cause F grades” Principlecause F grades” Principle

Reasons for associations ConfoundingConfounding

E is associated with C and C causes DE is associated with C and C causes D BiasBias

F causes D, but we thought F was an EF causes D, but we thought F was an E Reverse causalityReverse causality

““D” causes “E”D” causes “E” Sampling error (chance)Sampling error (chance) CausationCausation

E1 E1 DD E1 + E2 E1 + E2 D D E1 or E2 E1 or E2 D D E1 + E2 E1 + E2 OROR E3+E4 E3+E4DD

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

Death rates are low in AK and high Death rates are low in AK and high in FL.in FL.

African American women have African American women have higher infant mortality than others higher infant mortality than others in the US.in the US.

Confounding

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

persons in the home

Osteoporosis

Age

Confounding

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

Confounder

Confounding

YES confounding module YES confounding module example:example:Hypothetical cohort studyHypothetical cohort study20,000 men followed for 10 yrs20,000 men followed for 10 yrsRQ: Are bedsores related to RQ: Are bedsores related to mortality among elderly mortality among elderly patients with hip fractures?patients with hip fractures?

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

Bedsores and mortality

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

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


DiedDied Did not Did not diedie

BedsoresBedsores 55 severe55 severe

24 not24 not51 severe51 severe

694 not694 not824824

No No bedsoresbedsores

5 severe5 severe

281 not281 not5 severe5 severe

8285 not8285 not85768576

365365 90359035 94009400

Bedsores and Mortality (Severe)


BedsoresBedsores 5555 5151 106106


55 55 1010

6060 5656 116116

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

Bedsores and Mortality (Not severe)


BedsoresBedsores 2424 694694 718718


281281 82858285 85668566

305305 89798979 92849284

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

Bedsores and Mortality stratified by Medical SeveritySEVERESEVERE++

DiedDied Didn’t Didn’t diedie

BedsoresBedsores aa bb

No soresNo sores cc dd

RR = RR = 1.01.0

SEVERSEVERE-E-




RR = RR = 1.01.0

SEVERESEVERE++




RR = RR = 2.92.9

SEVERSEVERE-E-




RR = RR = 2.92.9

Bedsores

So….So…. Bedsores are unrelated to mortality Bedsores are unrelated to mortality

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

among those with problems of less among those with problems of less severity.severity.

…….. the adjusted RR = 1, and the unadjusted the adjusted RR = 1, and the unadjusted

RR = 2.9RR = 2.9

Confounding

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

Severity of medical problems






Bias

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 bias

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

subjects causes mistakessubjects causes mistakes Information biasInformation bias

The process for collecting The process for collecting informationinformation from the subjects from the subjects causes mistakescauses mistakes

Selection bias Healthy worker effectHealthy worker effect

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

Non-responseNon-response People who participate in a study may be People who participate in a study may be

different from people who do notdifferent from people who do not AttritionAttrition

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

Berkson’sBerkson’s Hospital controls in a case-control studyHospital controls in a case-control study

Information bias

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

Recall biasRecall bias Cases are more likely than controls Cases are more likely than controls

to recall past exposuresto recall past exposures Interviewer biasInterviewer bias

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

Birth defects and diet

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

Pesticides and cancer mortality In a study of the relationship In a study of the relationship

between home pesticide use and between home pesticide use and cancer mortality, controls are cancer mortality, controls are asked about pesticide use and asked about pesticide use and family members are asked about family members are asked about their loved ones’ usage patterns.their loved ones’ usage patterns.

Induced abortion & breast CA Positive association found in 5 Positive association found in 5

studiesstudies No association found in 6 studiesNo association found in 6 studies Negative association found in 1 Negative association found in 1

studystudy

Minimize bias

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

Standardized processes for data Standardized processes for data collectioncollection

MaskingMasking Clear, comprehensive case definitionsClear, comprehensive case definitions Incentives for participation/retentionIncentives for participation/retention






Reverse causality

Suspected disease actually precedes Suspected disease actually precedes suspected causesuspected cause

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

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

For example: Sexual For example: Sexual activity/Marijuanaactivity/Marijuana

Minimize effect of reverse causality Done in the planning and Done in the planning and

implementation phase of a studyimplementation phase of a study Pick study designs in which Pick study designs in which

exposure is measured before exposure is measured before disease onsetdisease onset

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






Sampling error/chance

E and D are associated in a E and D are associated in a sample, but not in the population sample, but not in the population from which the sample was drawn.from which the sample was drawn.

RR in the population

D+D+ D-D-

E+E+ 5050 5050 100100

E-E- 5050 5050 100100

100100 100100 200200

RR in sample1

D+D+ D-D-

E+E+ 2525 2525 5050

E-E- 2525 2525 5050

5050 5050 100100

RR in sample2

D+D+ D-D-

E+E+ 2020 3030 5050

E-E- 3030 2020 5050

5050 5050 100100

RR in sample3

D+D+ D-D-

E+E+ 3030 2020 5050

E-E- 1515 3535 5050

4545 5555 100100






Causal pathways

Necessary, sufficient—rare, if at allNecessary, sufficient—rare, if at all Not necessary, sufficient—also rareNot necessary, sufficient—also rare Necessary, not sufficient—TBNecessary, not sufficient—TB Not necessary, not sufficient--Most Not necessary, not sufficient--Most

causes fall into this category--heart causes fall into this category--heart disease, obesitydisease, obesity






The process of assessing causality Observe patternsObserve patterns Generate hypothesisGenerate hypothesis Design study to test hypothesisDesign study to test hypothesis Conduct studyConduct study Interpret the results…the big question is did Interpret the results…the big question is did

the exposure cause the disease?the exposure cause the disease? Are there alternate non-causal Are there alternate non-causal

explanations for the results we explanations for the results we found?found?

If not, then is this the whole story?If not, then is this the whole story?

So, what should we do?

Goal is to understand causalityGoal is to understand causality Use guidelines to help us make Use guidelines to help us make

sense of the evidencesense of the evidence

Key Guidelines

Temporality: a necessary conditionTemporality: a necessary condition ConsistencyConsistency Dose-responseDose-response Consideration of alternate Consideration of alternate

explanationsexplanations CoherenceCoherence

Enduring Understandings

7, 8, and 97, 8, and 9

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 Because studies are complicated by factors not controlled by the by factors not controlled by the observer, other explanations also observer, other explanations also must be considered, including must be considered, including confounding, chance, and bias.confounding, chance, and bias.

The “Not everything that glitters is The “Not everything that glitters is gold” Principlegold” Principle

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

The “Just because your friend sleeps in The “Just because your friend sleeps in class and never fails her courses does class and never fails her courses does not mean that sleeping in class does not not mean that sleeping in class does not cause F grades” Principlecause F grades” Principle

49

1. Cause

2. Confounding

3. Reverse Time Order

4. Chance

5. Bias

Possible Explanations for Finding an Association

50

Cause

A factor that produces a change in another factor.

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


51

Sample of 100

52

Sample of 100, 25 are Sick

53

Diagram

2x2 Table

DZ DZ

X

X

a bc d

Types of Causal Relationships

54

DZ DZ

X

X

a bc d

Diagram

2x2 Table

Types of Causal Relationships

55

Handout

56

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

57

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

58

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

59

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

60

a b

c d

Heart Attack

NoHeart Attack

Lack of Fitness

No Lack of Fitness

Lack of fitness and physical activity causes heart attacks.

61

a b

c d

Lead Poisoning

NoLead

Poisoning

Lack of Supervision

No Lack of

Supervision

Lack of supervision of small children causes lead poisoning.

62

Is the association causal?

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

1. Cause

2. Confounding


4. Chance

5. Bias

Snacks Key to Kids’ TV- Linked Obesity: China

Study

Depressed Teens More

Likely to Smoke

64

65

1. Cause

2. Confounding


4. Chance

5. Bias


66

All the people in a particular group.

Population


67

A selection of people from a population.

Sample


68

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.


69

Sample

Population

Process of predicting from what is observed

to what is not observed.

Observed

Not Observed

Inference

70

Deck of

100 cards

Population

71

a

25 cards

b

25 cards

c

25 cards

25 cards

d

Population

72

=

Population

a

25 cards

b c d

25 cards25 cards25 cards

=a b

c d

Odd #

Even #

No Marijuana

No Marijuana

Population

Total

73

=

Population

a

25 cards

b c d


= 2525

25 25

50

50

Total

Odd #

Even #

No Marijuana

No Marijuana

Population

74

=

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

75

=

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

76

=

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

77

25 cards

25 cards

25 cards

25 cards

Population

78

To occur accidentally.

To occur without design.

Chance

A coincidence.


79

Chance

80

Chance

81

Population

Sample

b

Sample

of

20 cards25 cards25 cards25 cards25 cards

Sample

82

Population

Sample

b

Sample

of


10

10

Total

55

5 5Odd #

Even #

No Marijuana

No Marijuana

Sample

83

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

84

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 %

____

85

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

86

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

87

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


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

88

An Association: TV and Aggressive ActsAn Association: TV and Aggressive Acts

Worksheet

“… the study of the distribution and determinants of health-related states or events …”

A Study Finds More Links Between TV and Violence

March 29, 2002

By GINA KOLATA

The New York Times ON THE WEB

Study Designs

Experimental Studies

Observational Studies

Randomized Controlled Trials

Other Experimental Studies

Cohort Studies

Case-Control Studies

Cross-Sectional Studies

Ecologic Studies

Cohort Studies

• A study in which a group of people is followed over time

• The group is made up of people who have the exposure of interest and people who do not have the exposure of interest

• Exposed and unexposed people are followed over time to determine whether they experience the outcome

Cohort Study

When epidemiologists ask a question, it is often of the form:

Does ______________ cause ______________?

Exposure - Outcome

(exposure) (outcome)

Do diesel exhaust fumes from school buses cause asthma?

Does eating chocolate cause acne?

Are males at higher risk of automobile accidents?

Does immunization with the measles vaccine prevent measles?

Does acupuncture result in pain relief?

Exposure - Outcome

For example:

When epidemiologists ask a question, it is often of the form:

Does ______________ cause ______________?(exposure) (outcome)

A Study Finds More Links Between TV and

Violence

March 29, 2002

By GINA KOLATA

Cohort Study Flow Diagram

A designated group of persons who are followed or traced over a period of time

Exposed

Not Exposed

Time

-

Cohort

Outcome

No Outcome

Outcome

No Outcome

By age 22

A Study Finds More Links Between TV and

Violence

March 29, 2002

By GINA KOLATA

Cohort Study Flow Diagram

A designated group of persons who are followed or traced over a period of time

Watching TV for > 1

hrs per day

Watching TV for < 1 hr per day

At age 14

-

Adolescents & Young Adults

Aggressive Acts

No Aggressive

Acts

Aggressive Acts

No Aggressive

Acts

Watched TV >

1 hour per day

At age 14

154 reported

aggressive acts

465 did not report

aggressive acts

Express it in Numbers

By age 22


Exposed

Outcome TotalNo

Outcome

Watched TV >

1 hour per day

At age 14

154 reported

aggressive acts

465 did not report

aggressive acts

By age 22

Exposed

Outcome TotalNo

Outcome

At age 14 By age 22


Watched TV

> 1 hour

per day

Aggressive Acts

No

Aggressive Acts

154 465 619

Total

Watched TV >

1 hour per day

154 reported

aggressive acts

465 did not report

aggressive acts

Exposed

Outcome TotalNo

Outcome

Risk

Aggressive Acts

No

Aggressive Acts

154 465 619

Total

154(154 + 465)

=154

619 24.9%=

Watched TV

> 1 hour

per day

An unproven idea, based on observation or reasoning, that can be

proven or disproven through investigation

An educated guess

Hypothesis

Watching TV causes aggressive acts.

Exposed

Outcome TotalNo

OutcomeAggressiv

e Acts

No

Aggressive Acts

154 465 619

Total

Does watching TV cause aggressive acts?

Risk

24.9%Watched TV

> 1 hour

per day

154(154 + 465)

= 24.9%154

619 =

By 22 years

Watching TV for > 1

hrs per day


At 14 years

-

Aggressive Acts

No Aggressive

Acts

Aggressive Acts

No Aggressive

Acts

24.9% risk of

committing an

aggressive act

? risk of

committing an

aggressive act



By 22 years

Watching TV for > 1

hrs per day


At 14 years

-

Aggressive Acts

No Aggressive

Acts

Aggressive Acts

No Aggressive

Acts

24.9% risk of

committing an

aggressive act

? risk of

committing an

aggressive actComparison

Group



Exposed

Outcome TotalNo

OutcomeAggressiv

e Acts

No

Aggressive Acts

154 465 619

Total

Comparison Group

Risk

24.9%Watched TV > 1 hour per dayWatched TV < 1 hour per day

5 reported aggressive acts

83 did not report aggressive

acts

Watched TV < 1 hour

per day

At age 14 By age 22

Exposed

Outcome TotalAggressiv

e Acts

No

Aggressive Acts

154 465 619

Total

Comparison Group

Risk

24.9%Watched TV > 1 hour per dayWatched TV < 1 hour per day

5 reported aggressive acts

83 did not report aggressive

acts

Watched TV < 1 hour

per day

At age 14 By age 22

5 83 88 5.7%

Exposed

Outcome TotalNo

OutcomeAggressiv

e Acts

No

Aggressive Acts

154 465 619

Total Risk

24.9%Watched TV > 1 hour per day

Watched TV < 1 hour per day

5 83 88 5.7%Exp

osu

re

Outcome

Contingency Table

Exposed

Outcome TotalNo

OutcomeAggressiv

e Acts

No

Aggressive Acts

154 465 619

Total Risk



5 83 88 5.7%


Exposed

Outcome TotalNo

OutcomeAggressiv

e Acts

No

Aggressive Acts

154 465 619

Total Risk



5 83 88 5.7%

Compared to those who watched TV for < 1 hour per day, those who watched TV for > 1 hours per day were ____

times as likely to commit aggressive acts.

4.4


Times as

Likely

A way of quantifying the relationship between two risks

Tells us the number of times one risk is larger or smaller than another

Relative Risk

Cartoon from Larry Gotnick’s The Cartoon Guide to Statistics, HarperPerennial, 1993

“… the control of health problems”

What should be done?

Exposed

Outcome TotalNo

OutcomeAggressiv

e Acts

No

Aggressive Acts

154 465 619

Total Risk



5 83 88 5.7%

4.4

Relative Risk

When things turn up together

Association

Pretzels Auto Accidents

Confounding

Another Exposure

Association Cause

When an observed association between an exposure and an outcome is distorted because the exposure of interest is associated with

some other exposure that causes the outcome

Drinking Alcoholic

Beverages

Association of Interest

• Confounding is the distortion of an exposure-outcome association brought about by the association of another factor with both outcome and exposure.

• A confounder confuses our conclusions about the relationship between an exposure and an outcome.

Confounding

Pretzels Auto Accidents

Another Exposure

Association Cause


X

Drinking Alcoholic

BeveragesX


Association

When things turn up together

Aggressive Acts

No

Aggressive Acts

Total


Watched TV > 1 hour per day

Relative Risk

619154 465

Total Risk

24.9%

5 83 88 5.7%

4.4

Confounding

Association Cause

?

Watching TV

Aggressive Acts




Watching TV

Aggressive Acts

Confounding

Association Cause

Living in a Violent

Neighborhood




Watching TV

Aggressive Acts

Confounding

Association Cause

Lack of Adequate

Supervision




Watching TV

Aggressive Acts

Association Cause

Lack of Adequate

Supervision

X

X





Assessment

In a study of the hypothesis that drinking orange juice prevents the flu, 3,000 students at Wright High School, who did not have the flu on December 31, 2000, were followed from January 1 through March 31, 2001. By the end of the study, among the 1000 students who drank orange juice, 123 students had developed the flu. Among the 2000 students who did not drink orange juice, 342 students had developed the flu. Display the above data on a 2x2 table, calculate risks of flu, calculate the relative risk, and explain whether or not the results support the hypothesis that drinking orange juice prevents the flu.

123

124

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

125

Sir Austin Bradford Hill “The Environment and Disease:

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

January 14, 1965

Teach Epidemiology


126

“In what circumstances can we pass from this observed association

to a verdict of causation?”

Teach Epidemiology


127

“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 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


129

Stress causes ulcers.

Helicobacter pylori causes ulcers.

Teach Epidemiology


130

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*

*

**

*

*

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Teach Epidemiology


131Teach Epidemiology


132

In the News

• Assemble into three-person teams• Select an article• Use the article to create a lesson plan to teach

one or more of the Enduring Understandings to a specified class for 30 minutes

• Teach the lesson– Specify the student population and course– Engage us as though we were the students

• Help us to understand what you did to generate the lesson plan

Teach Epidemiology

Article Choices

• Early childhood behavior and substance use• Huffing and suicide• Soft drinks and diabetes• Circumcision and AIDS• Prenatal smoking and attention deficit• ADHD among girls• Traffic and childhood asthma• Breast-feeding and childhood obesity• Depression and sexual risk-taking• Family stress and childhood illness• ADHD medications and mortality

Teach Epidemiology

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1. Teach epidemiology.

2. As a group, create a 30-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 30-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. Teach epidemiology.

8. Metacognition--After the lesson, reflect on your preparation for and teaching of the lesson.

Teach Epidemiology

Teaching Epidemiology Rules

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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

Teaching Epidemiology

Metacognition

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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


139Teach Epidemiology


Group

Assignments

Births: Class 1, p. 6-12

War: Qs 11-21

Case-control: Class 1, p. 16-21

Confounding: p. 32-36

Bias: p. 25-29 and 30-32

Alpine Fizz: Procs 2, 4, 5

Documents

Enduring Understandings 7-9 Explaining associations and judging causation