Teach Epidemiology

transcript

Centers for Disease Control and PreventionMorgantown, West Virginia

June 20-24, 2011

Teach EpidemiologyProfessional Development Workshop

3Teach Epidemiology

Teach Epidemiology

http://www.cdc.gov/

Time Check

8:15 AM

8Teach Epidemiology

Teach Epidemiology

Teach EpidemiologyDay 4Morgantown, WVDiane Marie M St. George, PhDUniversity of MD School of MedicineDept of Epidemiology and Public Health

EU7: One possible explanation for EU7: One possible explanation for finding an association is that the finding an association is that the exposure causes the outcome. exposure causes the outcome. Because studies are complicated by Because studies are complicated by factors not controlled by the observer, factors not controlled by the observer, other explanations also must be other explanations also must be considered, including confounding, considered, including confounding, chance, and bias.chance, and bias.

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

EU9: While a given exposure may be EU9: While a given exposure may be necessary to cause an outcome, the necessary to cause an outcome, the presence of a single factor is seldom presence of a single factor is seldom sufficient. Most outcomes are caused sufficient. Most outcomes are caused by a combination of exposures that may by a combination of exposures that may include genetic make-up, behaviors, include genetic make-up, behaviors, social, economic, and cultural factors social, economic, and cultural factors and the environment. and the environment.

Reasons for associations

Confounding Bias Reverse causality Sampling error (chance) Causation

Confounding in our lives

Age-adjusted rates of… Rates of lung cancer adjusted for smoking

Osteoporosis risk is higher among women who live alone than among women who live with others.

Confounding

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

Number of persons in the

homeOsteoporosis

Confounding

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

Exposure Outcome

Confounder

Confounding

YES confounding module example:Cohort study9,400 elderly in the hospitalRQ: Are bedsores related to

mortality among elderly patients with hip fractures?

Bedsores and Mortality

E+ 79 745 824

E- 286 8290 8576

365 9035 9400

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

Avoid bedsores…Live forever!!

Could there be some other explanation for the observed association?

Bedsores and mortality

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

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

Died Did not die

Bedsores 55 severe

24 not

51 severe

694 not

No bedsores

5 severe

281 not

5 severe

8285 not

365 9035 9400

Bedsores and Mortality (Severe)

Died Did not die

Bedsores 55 51 106

No bedsores

5 5 10

60 56 116

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

Bedsores and Mortality (Not severe)

Died Did not die

Bedsores 24 694 718

No bedsores

281 8285 8566

305 8979 9284

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

Bedsores and Mortality stratified by Medical SeveritySEVERE+ Died Didn’t die

Bedsores a b

No sores c d

RR = 1.0

SEVERE- Died Didn’t die

Bedsores a b

No sores c d

RR = 1.0

Bedsores

Bedsores are unrelated to mortality among those with severe problems.

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

Adjusted RR = 1, and the unadjusted RR = 2.9

Controlling confounding

Study design phaseMatchingRestrictionRandom assignment

Study analysis phaseStratificationStatistical adjustment

Bias Case Studies

In groups, review the assigned case studies.

Pesticides and cancer mortality

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

Birth defects and diet

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

Types of bias

Selection biasThe process for selecting/keeping subjects

causes mistakes Information bias

The process for collecting information from the subjects causes mistakes

Selection bias People who agree to participate in a

study may be different from people who do not

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

Hospital controls may not represent the source population for the cases

Information bias

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

Cases are more likely than controls to recall past exposures

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

Minimize bias

Can only be done in the planning and implementation phase

Standardized processes for data collection Masking Clear, comprehensive case definitions Incentives for participation/retention

Reverse causality

Suspected disease actually precedes suspected cause

Pre-clinical disease Exposure DiseaseFor example: Memory deficits Reading

cessation Alzheimer’s Cross-sectional study

For example: Sexual activity/Marijuana

Minimize effect of reverse causality

Done in the planning and implementation phase of a study

Pick study designs in which exposure is measured before disease onset

Assess disease status with as much accuracy as possible

Sampling error/chance

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

RR in the populationRR in the population

D+D+ D-D-

E+E+ 5050 5050 100100

E-E- 5050 5050 100100

100100 100100 200200

RR in sample 1RR in sample 1

D+D+ D-D-

E+E+ 2525 2525 5050

E-E- 2525 2525 5050

5050 5050 100100

D+D+ D-D-

E+E+ 4545 55 5050

E-E- 1515 3535 5050

5050 5050 100100

D+D+ D-D-

E+E+ 2020 3030 5050

E-E- 3030 2020 5050

5050 5050 100100

Minimize sampling error (chance)

Random selection Adequate sample size

Time Check

9:45 AM

48Teach Epidemiology

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Time Check

10:00 AM

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Time Check

11:00 AM

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Time Check

11:30 AM

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Hypothesis

Total Risk Relative Risk

or %Exposure Outcome

?Turned Up Together

Healthy People

Teach Epidemiology

Where are we?

Enduring Epidemiological Understandings

Suicide Higher in Areas with Guns

Family Meals Are Good for Mental Health

Lack of High School Diploma Tied to US Death

Study Links

Spanking to

Aggression

Study Concludes: Movies Influence

Youth Smoking

Study Links Iron

Deficiency to Math

Scores

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

Pollution Linked with Birth Defects in US Study

Ties, Links, Relationships, and Associations

Snacks Key to Kids’ TV- Linked Obesity: China

Depressed Teens More

Likely to Smoke

Study Links

Spanking to

Aggression

Youth Smoking

Study Links Iron

Deficiency to Math

Scores

Likely to Smoke

1. Cause

2. Confounding

3. Reverse Time Order

4. Chance

5. Bias

Possible Explanations for Finding an Association

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.

1. Cause

2. Confounding

4. Chance

5. Bias

A factor that produces a change in another factor.

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

Sample of 100

Sample of 100, 25 are Sick

Diagram

2x2 Table

a bc d

Types of Causal Relationships

a bc d

Diagram

2x2 Table

Types of Causal Relationships

Handout

X1X1X1

a bc d

Diagram

2X12 Table

Necessary and Sufficient

a bc d

X1 DZX2 X3+ +X1

X1X1X1

Diagram

2X12 Table

Necessary but Not Sufficient

a bc d

Diagram

2X12 Table

Not Necessary but Sufficient

a bc d

X1X1X1

DZX5 X6+ +

X2 X3+ +

X8 X9+ +

Not Necessary and Not Sufficient

Diagram

2X12 Table

a bc d

Diagram

2x2 Table

Necessary and Sufficient

a bc d

X DZX X+ +

Diagram

2x2 Table

Necessary but Not Sufficient

a bc d

Diagram

2x2 Table

Not Necessary but Sufficient

a bc d

DZX X+ +

X X+ +

Not Necessary and Not Sufficient

Diagram

2x2 Table

Heart Attack

NoHeart Attack

Lack of Fitness

No Lack of Fitness

Lack of fitness and physical activity causes heart attacks.

Lead Poisoning

NoLead

Poisoning

Lack of Supervision

No Lack of

Supervision

Lack of supervision of small children causes lead poisoning.

Is the association causal?

Study Links

Spanking to

Aggression

Youth Smoking

Study Links Iron

Deficiency to Math

Scores

1. Cause

2. Confounding

4. Chance

5. Bias

Likely to Smoke

Time Check

Noon AM

Teach Epidemiology

Time Check

1:00 PM

Teach Epidemiology

1. Cause

2. Confounding

4. Chance

5. Bias

All the people in a particular group.

Population

A selection of people from a population.

Sample

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.

Sample

Population

Process of predicting from what is observed

to what is not observed.

Observed

Not Observed

Inference

Deck of

100 cards

Population

25 cards

Population

25 cards

25 cards25 cards25 cards

Even #

No Marijuana

Population

25 cards

= 2525

Even #

No Marijuana

Population

=M&M’s

No M&M’s

25 cards

Even #

No Marijuana

Population

25 cards

25 / 50 or 50%

Even #

No Marijuana

Population

25 cards

25 / 50 or 50 %

25 / 50 or 50 %50 % / 50% = = 1

____Odd #

Even #

No Marijuana

Population

25 cards

Population

To occur accidentally.

To occur without design.

Chance

A coincidence.

Chance

Population

Sample

20 cards25 cards25 cards25 cards25 cards

Sample

Population

Sample

5 5Odd #

Even #

No Marijuana

Sample

Population

Sample

5 / 10 or 50 %

5 / 10 or 50 %Odd #

Even #

No Marijuana

Sample

Population

Sample

5 / 10 or 50 %

5 / 10 or 50 %Odd #

Even #

No Marijuana

Sample

Relative Risk

50 % / 50% = = 150 %

Sample

20 cards

TotalRisk

5 / 10 = 50 %

Relative Risk

By Chance CDC

=Odd #

Even #

No Marijuana

Sample

5 / 10 or 50 %

Relative Risk

How many students picked a sample with 5 people in each cell?

= 150 %

Even #

No Marijuana

Chance

By Chance

Relative Risks

Greater than 1 Less than 1

Chance

Study Links Having an Odd Address to Marijuana Use

Relative Risks

Study Links Having an Even Address to Marijuana Use

Relative Risks

By ChanceBy Chance

25 cards25 cards25 cards25 cards

Chance

Sample

20 cards

TotalRisk

5 / 10 = 50 %

Relative Risk

=Odd #

Even #

No Marijuana

Different Sample Sizes

Relative Risks

By ChanceBy Chance

Chance

50 cards

Sample

20 cards

TotalRisk

5 / 10 = 50 %

Relative Risk

=Odd #

Even #

No Marijuana

Relative Risks

By ChanceBy Chance

Chance

75 cards

Sample

20 cards

TotalRisk

5 / 10 = 50 %

Relative Risk

=Odd #

Even #

No Marijuana

Relative Risks

By ChanceBy Chance

Chance

99 cards

Study Links

Spanking to

Aggression

Youth Smoking

Study Links Iron

Deficiency to Math

Scores

1. Cause

2. Confounding

4. Chance

5. Bias

Likely to Smoke

Association is not necessarily causation.

Teach Epidemiology

Explaining Associations and Judging Causation

Handout

Teach Epidemiology

1. Cause

2. Confounding

4. Chance

5. Bias

Teach Epidemiology

Coffee and Cancer of the Pancreas

Guilt or Innocence?Causal or Not Causal?

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

Teach Epidemiology

Sir Austin Bradford Hill “The Environment and Disease:

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

January 14, 1965

Teach Epidemiology

Handout

“In what circumstances can we pass from this observed association

to a verdict of causation?”

Teach Epidemiology

“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

Timeline

Cohort Study

Randomized Controlled Trial

Timeline

Case-Control Study

Timeline

Cross-Sectional Study

Timeline

Healthy PeopleHealthy People

Random Assignment

Healthy PeopleHealthy People

Teach Epidemiology

Handout

Stress causes ulcers.

Helicobacter pylori causes ulcers.

Teach Epidemiology

Epidemiology

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

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

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, ….

Outcome

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

Outcome

If an association was found due to chance, ….

found due to chance, ….

Outcome

If an association was found due to bias, ….

found due to bias, ….

Outcome

If an association was causal, ….

Hypothesized Exposure XX

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

causal, ….

1. Cause

2. Confounding

4. Chance

5. Bias

Study Links

Spanking to

Aggression

Youth Smoking

Study Links Iron

Deficiency to Math

Scores

1. Cause

2. Confounding

4. Chance

5. Bias

Likely to Smoke

To create “… a professional community that discusses new teacher materials and strategies and that supports the risk taking and struggle entailed in transforming

practice.”

Teach Epidemiology

Your Teach Epidemiology Stories

Welcome to

Teach Epidemiology

Your Teach Epidemiology Stories

To create “… a professional community that discusses new teacher materials and strategies and that supports the risk taking and struggle entailed in transforming

practice.”

June 20-24, 2011

Time Check

2:45 PM

Teach Epidemiology

Time Check

3:00 PM

Teach Epidemiology

Time Check

3:30 PM

Teach Epidemiology

Enduring Understandings

… the big ideas that reside at the heart of a discipline and have lasting value outside the classroom.

… the big ideas that reside at the heart of epidemiology and have lasting value outside the classroom.

“… to see past the surface features of any problem to

the deeper, more fundamental principles of

the discipline.”

National Research Council Learning and Understanding

Epidemiology

Hypothesis

?Turned Up Together

Healthy People

?Associated

Turned Up Together

Confounding

Reverse Time Order

Chance

Epidemiology

Give people fish, they have food for a day,

Teach people how to fish, they have food for a lifetime.

Teach Epidemiology

Explore Public Health Career Paths

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

Teach Epidemiology

What do you mean - Teach Epidemiology?

Explore Public Health Career Paths

http://pathwaystopublichealth.org/

Teach Epidemiology

Leverage the Science Olympiad Competition

http://soinc.org/

Teach Epidemiology

Create and Teach a New Epidemiology Lesson

Teach Epidemiology

Infuse Epidemiology into Existing Lesson about Something Else

Teach Epidemiology

Teaching Existing Epidemiology Lessons

http://ccnmtl.columbia.edu/projects/epiville/

Teach Epidemiology

http://www.diseasedetectives.org/

Teach Epidemiology

http://www.cdc.gov/excite/

Teach Epidemiology

http://www2a.cdc.gov/epicasestudies/

Teach Epidemiology

http://www.cdc.gov/excite/ScienceAmbassador/ScienceAmbassador.htm

Teach Epidemiology

http://www.buffetbusters.ca/

Teach Epidemiology

http://www.montclair.edu/Detectives/

Teach Epidemiology

http://www.montclair.edu/drugepi/

Teach Epidemiology

http://www.collegeboard.com/yes/ft/iu/units.html

View a News Item from an Epidemiologic Perspective

http://www.nationalacademies.org/headlines/

Teach Epidemiology

Empowers students to be scientifically literate participants in the democratic decision-making process concerning public health policy.

Empowers students to make more informed personal health-related decisions.

Increases students’ media literacy and their understanding of public health messages.

Increases students’ understanding of the basis for determining risk.

Improves students’ mathematical and scientific literacy.

Expands students’ understanding of scientific methods and develops their critical thinking skills.

Provides students with another mechanism for exploring important, real world questions about their health and the health of others.

Introduces students to an array of career paths related to the public’s health.

Top 8 Reasons to Teach / Learn about Epidemiology

Teach Epidemiology

Innovation

… an idea, practice or object that is perceived as new by an individual or other unit of adoption.

Everett M. Rogers, Diffusion of Innovations

Workshop Goal

Diffusion

The process by which an innovation is communicated through certain channels over time among the members of a social system

(with the aim being to maximize the exposure and reach of innovations, strategies, or programs.)

Everett M. Rogers, Diffusion of Innovations

Teach Epidemiology

Workshop Goal

Teach Epidemiology

To increase the frequency with which epidemiology is taught to students in grades 6-12

Post-Workshop Assessment

Teach Epidemiology

Handout

Workshop Evaluation

June 20-24, 2011

Thank You

Time Check

4:00 PM

Teach Epidemiology

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