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Day 4. Teach Epidemiology. Professional Development Workshop. Centers for Disease Control and Prevention Global Health Odyssey Museum Tom Harkin Global Communications Center June 6-10, 2011. Teach Epidemiology. Teach Epidemiology.

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slide1

Day

4

Teach Epidemiology

Professional Development Workshop

Centers for Disease Control and PreventionGlobal Health Odyssey MuseumTom Harkin Global Communications Center June 6-10, 2011

slide3

Teach Epidemiology

Teach Epidemiology

slide4

Teach Epidemiology

Teach Epidemiology

slide5

MMWR

http://www.cdc.gov/

slide22

Time Check

8:15 AM

slide24

Teach Epidemiology

Teach Epidemiology

slide26

Enduring Epidemiological Understandings

Knowledge that “… is connected and organized, and … ‘conditionalized’ to specify the context in which it is applicable.”

National Research Council , Learning and Understanding

Teach Epidemiology

slide27

Making Group Comparisons and Identifying Associations

The goal of every epidemiological study is to harvest valid and precise information about the relationship between an exposure and a disease in a population.

The various study designs merely represent different ways of harvesting this information.

Essentials in Epidemiology in Public Health

Ann Aschengrau and George R. Seage III

Teach Epidemiology

slide28

Time Check

9:00 AM

slide30

Teach Epidemiology

Teach Epidemiology

vocab review
Vocab Review
  • Genotype – combination of alleles
  • Alleles – variations of a gene
  • Homozygous – both alleles are the same
  • Heterozygous – both alleles are different
slide32

P – First people living near ports, then further inland.

P – Europe

T –1340’s-1350’s

huron2.aaps.k12.mi.us

slide33

*Statistics in 1300’s?

*Activity!

*Hypotheses???

1. Bacon fat?? What kind of study?

2. Genotypes?? What kind of study?

natural selection
Natural Selection
  • Overpopulation
  • Genetic Variation
  • Struggle to survive – Selective Pressure
  • Differential Survival and Reproduction
anecdote
Anecdote

“A previously healthy 36 year-old man with clinically diagnosed CMV infection in September 1980 was seen in April of 1981 because of a 4-month history of fever, dyspnea and cough. On admission, he was found to have P. carinii pneumonia, oral candidiasis, and CMV retinitis. A complement-fixation CMV titer in April 1981 was 1928. The patient has been treated with 2 short courses of TMP/SMX that have been limited because of a sulfa-induced neutropenia. He is being treated for candidiasis with topical nystatin” –MMWR 1981

population
Population

P – healthy homosexual males, intravenous drug users

P – World wide (including U.S.)

T – Early 1980’s

exception steve crohn
Exception – Steve Crohn
  • Fits perfectly into the HIV high-risk category
  • Male partner died of HIV
  • He never got sick.
  • WHY???
ccr5 and delta 32 mutation
CCR5 and Delta 32 Mutation

*Maybe related to black plague resistance.

*Maybe related to HIV resistance

*What kind of study?

slide39

Enduring Epidemiological Understandings

Knowledge that “… is connected and organized, and … ‘conditionalized’ to specify the context in which it is applicable.”

National Research Council , Learning and Understanding

Teach Epidemiology

slide40

Making Group Comparisons and Identifying Associations

The goal of every epidemiological study is to harvest valid and precise information about the relationship between an exposure and a disease in a population.

The various study designs merely represent different ways of harvesting this information.

Essentials in Epidemiology in Public Health

Ann Aschengrau and George R. Seage III

Teach Epidemiology

slide41

Time Check

9:45 AM

slide43

Teach Epidemiology

Teach Epidemiology

slide44

Teach Epidemiology

EPI-501

Marian R Passannante, PhD

Associate Professor

University of Medicine and Dentistry of New Jersey

New Jersey Medical School

School of Public Health

chance

Enduring Epidemiological Understandings

Chance

First , choose a statistical method to test the association between an exposure and an outcome.

47

Teach Epidemiology

chance1

Enduring Epidemiological Understandings

Chance

First , choose a statistical method to test the association between an exposure and an outcome.

Exposure: Statin use

status

Outcome: Colorectal

Cancer

status

49

Teach Epidemiology

chance2

Enduring Epidemiological Understandings

Chance

First , choose a statistical method to test the association between an exposure and an outcome.

“background

Statins are… effective lipid-lowering agents. Statins inhibit the growth of colon-cancer cell lines, and secondary analyses of some, but not all, clinical trials suggest that they reduce the risk of colorectal cancer.”

50

Teach Epidemiology

chance3

Enduring Epidemiological Understandings

Chance

First , choose a statistical method to test the association between an exposure and an outcome.

“methods

The Molecular Epidemiology of Colorectal Cancer study is a population-based case–control study of patients who received a diagnosis of colorectal cancer in northern Israel between 1998 and 2004 and controls matched according to age, sex, clinic, and ethnic group. We used a structured interview to determine the use of statins in the two groups and verified self-reported statin use by examining prescription records in a subgroup of patients for whom prescription records were available."

51

Teach Epidemiology

chance4

Enduring Epidemiological Understandings

Chance

First , choose a statistical method to test the association between an exposure and an outcome.

Exposure: Statin use status

Outcome: Colorectal

Cancer status

Statistical method: Chi-square

To test whether the proportion of cases who took statins differs from the proportion of controls who took statins.

52

Teach Epidemiology

chance5

Enduring Epidemiological Understandings

Chance

Second, choose a level of risk (called a or the alpha level) that we are willing to take when conducting that test.

Most common alpha levels: 0.05 or 0.01

Third, conduct the statistical test and get a p value.

53

Teach Epidemiology

chi square c 2 test

c2 = S [(0-E)2/E]

O = observed # of events in a cell

E = expected # of events in a cell if there were

no association between the independent and dependent variables

Expected Value = (Row Total x Column Total) Grand Total

Enduring Epidemiological Understandings

Chi-square (c2) test

54

Teach Epidemiology

chance6

Enduring Epidemiological Understandings

Chance

If the percentage of cases who took statins is the same as the percentage of controls who took statins, what % would

you expect to find among cases and controls?

Exposure: Statin use status

Outcome: Colorectal

Cancer status

Statistical method: Chi-square

55

Teach Epidemiology

chance7

Enduring Epidemiological Understandings

Chance

If the percentage of cases who took statins is the same as the percentage of controls who took statins, what % would

you expect to find among cases and controls?

Exposure: Statin use status

Outcome: Colorectal

Cancer status

Statistical method: Chi-square

56

Teach Epidemiology

chance8

Enduring Epidemiological Understandings

Chance

If the percentage of cases who took statins is the same as the percentage of controls who took statins, what % would

you expect to find among cases and controls?

Exposure: Statin use status

Outcome: Colorectal

Cancer status

Statistical method: Chi-square

57

Teach Epidemiology

chance9

Enduring Epidemiological Understandings

Chance

If the percentage of cases who took statins is the same as the percentage of controls who took statins, what % would

you expect to find among cases and controls?

Exposure: Statin use status

Outcome: Colorectal

Cancer status

Statistical method: Chi-square

58

Teach Epidemiology

chance10

Enduring Epidemiological Understandings

Chance

Expected Values appear in red below:

Exposure: Statin use status

Outcome: Colorectal

Cancer status

Statistical method: Chi-square

c2 = S [(0-E)2/E]

59

Teach Epidemiology

chance11

Enduring Epidemiological Understandings

Chance

Expected Values appear in red below:

Exposure: Statin use status

Outcome: Colorectal

Cancer status

Statistical method: Chi-square

To test whether the proportion of cases who took statins differs from the proportion of controls who took statins, chi-square compares what was observed and what one would expect to find if there were no association. The chi-square test result will provide a p value. We will compare our p value to our preset level of risk or alpha level.

60

Teach Epidemiology

chance12

Enduring Epidemiological Understandings

Chance

What does a p value tell us?

61

Teach Epidemiology

chance13

Enduring Epidemiological Understandings

Chance

What does a p value tell us?

If you conducted a statistical test and got a p value of .04 it would mean that 4 times out of 100 you might find a significant association like the one observed in your study by chance alone.

Significance testing provides a p value and this value tell you how likely the result you found is due to chance alone.

Let’s look at the analysis results for the statin study….

62

Teach Epidemiology

chance14

Enduring Epidemiological Understandings

Chance

Third, we conduct the statistical test and get a p value.

63

Teach Epidemiology

chance15

Enduring Epidemiological Understandings

Chance

Third, we conduct the statistical test and get a p value.

If the p value is less than alpha

(say .05) we say that our

exposure and outcome are

significantly associated.

With p <.0001, we can

say that there is a strong association between statin

use and disease outcome. How would you describe the direction of the relationship?

64

Teach Epidemiology

chance16

Enduring Epidemiological Understandings

Chance

How would you measure the strength of the association between statin use and colorectal cancer in this

case-control study?

65

Teach Epidemiology

chance17

Enduring Epidemiological Understandings

Chance

How would you describe the relationship?

Which measure of risk would you calculate?

The odds ratio:

120 x 1781 = .50

234 x 1833

“In analyses including 1953 patients with colorectal cancer and 2015 controls, the use of statins for at least five years (vs. the nonuse of statins) was associated with a significantly reduced … risk of colorectal cancer (odds ratio, 0.50; 95 percent confidence interval, 0.40 to 0.63).”

66

Teach Epidemiology

chance18

Enduring Epidemiological Understandings

Chance

Although you may have found a statistically significant association, it is always possible that your sample is not really a good representation of the total population and the result you got was by chance alone.

How to avoid this problem?

67

Teach Epidemiology

chance19

Enduring Epidemiological Understandings

Chance

Although you may have found a statistically significant association, it is always possible that your sample is not really a good representation of the total population and the result you got was by chance alone.

How to avoid this problem?

Increase your sample size

68

Teach Epidemiology

confounding

Enduring Epidemiological Understandings

Confounding

Is alcohol consumption associated with lung cancer?

Exposure Outcome

Alcohol consumption lung cancer

Confounding occurs when a third factor is associated with both an exposure and an outcome. This third factor may create the appearance of a causal association between the exposure and outcome even if it isn’t really there.

71

Teach Epidemiology

confounding1

Enduring Epidemiological Understandings

Confounding

Confounding occurs when a third factor is associated with both an exposure and an outcome.

Exposure Outcome

Alcohol consumption lung cancer

Cigarette Smoking

Confounder

Smoking is associated with alcohol use and lung cancer.

72

Teach Epidemiology

confounding2

Enduring Epidemiological Understandings

Confounding

Can you think of another relationship between an exposure and an outcome where there might be a confounder?

Exposure Outcome

Potential Confounder

73

Teach Epidemiology

confounding3

Enduring Epidemiological Understandings

Confounding

Is there an association between OC use and MI?

OC use MI (heart attack)

Exposure Outcome

Cigarette Smoking

Potential Confounder

74

Teach Epidemiology

confounding4

Enduring Epidemiological Understandings

Confounding

Can you think of a way to take a confounder into consideration when looking at the relationship between an exposure and an outcome?

Exposure Outcome

Potential Confounder

75

Teach Epidemiology

control confounding through study design

Enduring Epidemiological Understandings

Control Confounding through Study Design

Randomization (in experimental studies, randomly assign participants so that an equal proportion of those with the possible confounding factor will be in the exposure groups)

Drug A Survival

Drug B

Age (Possible Confounder)

76

Teach Epidemiology

control confounding through study design1

Enduring Epidemiological Understandings

Control Confounding through Study Design

Alcohol use lung cancer

cigarette smoking

Restriction (limit the study to those who do not have the confounding factor- e.g. never smokers)

Matching(in a case-control study match cases with controls who have or do not have the potential confounder- e.g. lung cancer cases matched to controls who have the same smoking status)

77

Teach Epidemiology

control confounding through analysis

Enduring Epidemiological Understandings

Control Confounding through Analysis

Alcohol use lung cancer

cigarette smoking

Stratification (Conduct the analysis separately for different levels of the possible confounder- smoking status)

Statistical methods (A number of statistical methods allow for the assessment of the relationship between an exposure and an outcome, while controlling for possible confounders.)

78

Teach Epidemiology

slide80

Enduring Epidemiological Understandings

Bias*

a systematic deviation of results or inferences from the truth or processes leading to such systematic deviation; any systematic tendency in the collection, analysis, interpretation, publication, or review of data that can lead to conclusions that are systematically different from the truth. In epidemiology, does not imply intentional deviation.

*Definition Source: Principles of Epidemiology in Public Health Practice Third Edition , U.S. DHHS, CDC

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Teach Epidemiology

slide81

Enduring Epidemiological Understandings

Bias

Bias can appear in all types of epidemiologic studies

Many types of bias have been identified by epidemiologists

Two main forms of bias in epidemiologic studies

Information Bias

Selection Bias

81

Teach Epidemiology

slide82

Enduring Epidemiological Understandings

Bias

Information Bias*

systematic difference in the collection of data regarding the participants in a study (e.g., about exposures in a case-control study, or about health outcomes in a cohort study) that leads to an incorrect result (e.g., risk ratio or odds ratio) or inference.

Can you think of some situations that might result in information bias?

*Definition Source: Principles of Epidemiology in Public Health Practice Third Edition , U.S. DHHS, CDC

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Teach Epidemiology

slide83

Enduring Epidemiological Understandings

Bias

Information Bias

Misclassification Bias

Non-differential (inaccuracy of the data collection unrelated to exposure or disease status)

Differential (rate of misclassification is different in different study groups)

Recall Bias

Observer Bias

83

Teach Epidemiology

slide84

Enduring Epidemiological Understandings

Bias

Misclassification Bias

Non-differential (inaccuracy of the data collection- unrelated to exposure or disease status)

some controls might be called cases

some exposed people might be identified

as non-exposed

How might this impact ORs and RRs if there is a true relationship between exposure and outcome?

84

Teach Epidemiology

slide85

Enduring Epidemiological Understandings

Bias

Differential Misclassification (rate of misclassification is different in different study groups)

Recall Bias (Case-Control Study)

(differential recollection of exposure among cases and controls)

Observer Bias (Cohort study/Trial)

(if those assessing the disease outcome look more closely at the exposed group compared to the unexposed group)

85

Teach Epidemiology

how can we minimize these

Enduring Epidemiological Understandings

How can we minimize these?

Differential Misclassification (rate of misclassification is different in different study groups)

Recall Bias (Case-Control Study)

(differential recollection of exposure among cases and controls)

Observer Bias (Cohort study/Trial)

(if those assessing the disease outcome look more closely at the exposed group compared to the unexposed group)

86

Teach Epidemiology

how to minimize bias

Enduring Epidemiological Understandings

How to minimize bias

Differential (rate of misclassification is different in different study groups)

Recall Bias (Case-Control Study)

may be minimized with careful interview/survey item construction

Observer Bias (Cohort study/Trial)

can be controlled by blinding those who are assessing the outcome regarding the exposure status

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Teach Epidemiology

blinding

Enduring Epidemiological Understandings

Blinding*

Blinding (masking)- intervention assignment is hidden from participants, trial investigators, or assessors.

Single-blind- one of the three categories of individuals (normally participant) remains unaware of intervention assignment

Double-blind- participants, investigators, and assessors usually all remain unaware of the intervention assignments

Triple-blind- usually means a double-blind trial that also maintains a blind data analysis.

* F Schulz, David A Grimes, Blinding in randomised trials: hiding who got what THE LANCET • Vol 359 • February 23, 2002 • www.thelancet.com

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Teach Epidemiology

double vs single blinding
Double vs Single Blinding

F Schulz, David A Grimes, Blinding in randomised trials: hiding who got what THE LANCET • Vol 359 • February 23, 2002 • www.thelancet.com

slide90

Potential Benefits accruing dependent on those individuals successfully blindedF Schulz, David A Grimes, Blinding in randomised trials: hiding who got what THE LANCET • Vol 359 • February 23, 2002 • www.thelancet.com

slide91

Enduring Epidemiological Understandings

Bias

Selection Bias*

systematic difference in the enrollment of participants in a study that leads to an incorrect result (e.g., risk ratio or odds ratio) or inference.

*Definition Source: Principles of Epidemiology in Public Health Practice Third Edition , U.S. DHHS, CDC

91

Teach Epidemiology

slide92

Enduring Epidemiological Understandings

Selection Biassystematic difference in the enrollment of participants in a study that leads to an incorrect result (e.g., risk ratio or odds ratio) or inference.

Cross-sectional study

Prevalence study of asthma

Non-respondents

Higher proportion of smokers

Higher prevalence of asthma symptoms

How might this impact prevalence estimates?

92

Teach Epidemiology

slide93

Enduring Epidemiological Understandings

Selection Biassystematic difference in the enrollment of participants in a study that leads to an incorrect result (e.g., risk ratio or odds ratio) or inference.

Case-Control Study:

Study of smoking Lung Cancer

Cases: Lung CA in hospital

Controls: Heart attack (MI) patients in hospital

How might this impact the OR?

93

Teach Epidemiology

slide94

Enduring Epidemiological Understandings

Selection Biassystematic difference in the enrollment of participants in a study that leads to an incorrect result (e.g., risk ratio or odds ratio) or inference.

Cohort Study:

Study of H20 Exercise Heart Attack

Participants in H20 Class at Y

Neighborhood non-participants

How might this impact the Relative Risk?

94

Teach Epidemiology

necessary and sufficient

Enduring Epidemiological Understandings

Necessary and Sufficient
  • Necessary- without the factor, the disease never develops
  • Sufficient- in the presence of the factor the disease always develops

Rare to have a factor be both necessary and sufficient.

E.g. When a group of people are exposed to someone with active TB disease, not all of them get infected.

96

Teach Epidemiology

necessary but not sufficient

Enduring Epidemiological Understandings

Necessary but not sufficient

compromised Exposure to TB Bacillus

immune system (necessary but not sufficient)

Malnutrition

lack compromised

Crowded medical care immune system

Living conditions

Source: modified from Figure 5.1 Bonita, Ruth. Basic epidemiology 2nd edition

97

necessary and sufficient1

Enduring Epidemiological Understandings

Necessary and Sufficient
  • Necessary- without the factor, the disease never develops
  • Sufficient- in the presence of the factor the disease always develops

Can you think of a factor that is both necessary and sufficient to cause disease?

98

Teach Epidemiology

necessary and sufficient2

Enduring Epidemiological Understandings

Necessary and Sufficient

If the title of this article is correct….

Nature Medicine11, 740 - 747 (2005) Published online: 12 June 2005; | doi:10.1038/nm1261 Smallpox vaccine–induced antibodies are necessary and sufficient for protection against monkeypox virus

Necessary: Without the antibodies you won’t be protected

Sufficient: With the antibodies you will be protected

99

Teach Epidemiology

assessing a causal relationship

Enduring Epidemiological Understandings

Assessing a Causal Relationship

Source: Principles of Epidemiology in Public Health Practice Third Edition , U.S. DHHS, CDC

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Teach Epidemiology

odds ratio of lung cancer by 75 by country

Enduring Epidemiological Understandings

Odds Ratio of lung cancer by 75 by country

Strength of Association:

relationship must be clear

Look for large Odds Ratios and Relative Risks

Source: British Journal of Cancer (2004) 91, 1280–1286. doi:10.1038/sj.bjc.6602078 www.bjcancer.com Table 2

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Teach Epidemiology

slide104

Enduring Epidemiological Understandings

Consistency: observation of the association must be repeatable in different populations at different times

Ecological Studies

The rising death rate from lung cancer in many counties along with a rise in cigarette consumption

Case Control Studies*

By 1959, 21 independent groups of investigators in 8 different countries. More studies followed with similar results.

Cohort Studies*

By 1959, large cohort studies in two countries by three independent groups.

*Source: Cornfeld et al J. Nat. Cancer Inst. 22:173–203, 1959

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Teach Epidemiology

slide105

Enduring Epidemiological Understandings

Temporality:

the cause (exposure) must precede the effect (the disease).

Source: The American Cancer Society . Cancer Statistics 2010.http://www.cancer.org/research/cancerfactsfigures/cancerfactsfigures/cancer-facts-and-figures-2010

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Teach Epidemiology

slide106

Enduring Epidemiological Understandings

Odds ratios of lung cancer by age 75, for current cigarette smokers stratified by amount smoked per day

Biological Gradient:

There must be a dose response.

Source: British Journal of Cancer (2004) 91, 1280–1286. doi:10.1038/sj.bjc.6602078 www.bjcancer.com Table 4

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Teach Epidemiology

assessing a causal relationship1

Enduring Epidemiological Understandings

Assessing a Causal Relationship

http://www.txtwriter.com/onscience/articles/smokingcancer2.html

Biological Plausibility: the explanation must make sense biologically.

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assessing a causal relationship2

Enduring Epidemiological Understandings

Assessing a Causal Relationship

Source: Principles of Epidemiology in Public Health Practice Third Edition , U.S. DHHS, CDC

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Teach Epidemiology

slide110

Time Check

10:15 AM

slide112

Teach Epidemiology

Teach Epidemiology

slide113

Time Check

10:30 AM

slide115

Teach Epidemiology

Teach Epidemiology

slide116

Time Check

11:30 AM

slide118

Teach Epidemiology

Teach Epidemiology

slide119

Time Check

12:30 PM

slide121

Teach Epidemiology

Teach Epidemiology

slide122

Time Check

1:00 PM

slide124

Teach Epidemiology

Teach Epidemiology

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

Teach Epidemiology

slide131

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

slide132

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

slide133

Possible Explanations for Finding an Association

1.

Cause

2.

Confounding

3.

Reverse Time Order

Chance

4.

5.

Bias

slide134

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.

slide135

Possible Explanations for Finding an Association

1.

Cause

2.

Confounding

3.

Reverse Time Order

Chance

4.

5.

Bias

slide136

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.

slide139

Types of Causal Relationships

Diagram

2x2 Table

DZ

DZ

X

a

b

c

d

X

slide140

Types of Causal Relationships

Diagram

2x2 Table

DZ

DZ

X

a

b

c

d

X

slide143

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

slide144

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

slide145

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

slide146

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

slide147

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

slide148

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

slide149

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

slide150

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

slide151

NoHeart Attack

Heart Attack

Lack of Fitness

No Lack of Fitness

Lack of fitness and physical activity causes heart attacks.

a bc d

slide152

NoLead Poisoning

Lead Poisoning

Lack of Supervision

No Lack of Supervision

Lack of supervision of small children causes lead poisoning.

a bc d

slide154

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

slide157

Possible Explanations for Finding an Association

1.

Cause

2.

Confounding

3.

Reverse Time Order

Chance

4.

5.

Bias

slide158

Possible Explanations for Finding an Association

Population

All the people in a particular group.

slide159

Possible Explanations for Finding an Association

Sample

A selection of people from a population.

slide160

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.

slide161

Observed

Not Observed

Inference

Population

Sample

Process of predicting from what is observed

to what is not observed.

slide162

Population

Deck of 100 cards

slide163

a

b

c

d

25 cards

25 cards

25 cards

25 cards

Population

slide164

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

slide165

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

=

=

slide166

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

slide167

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%

slide168

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 %

slide169

25 cards

25 cards

25 cards

25 cards

Population

slide170

Possible Explanations for Finding an Association

Chance

To occur accidentally.

To occur without design.

A coincidence.

slide173

Population

b

Sample

25 cards

25 cards

25 cards

25 cards

Sample

Sample of 20 cards

slide174

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

slide175

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 %

slide176

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 %

slide177

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

%

___

%

=

slide178

= 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

slide179

Chance

Relative Risks

Greater than 1

Less than 1

slide180

Ties, Links, Relationships, and Associations

Study Links Having an Odd Address to Marijuana Use

slide181

Possible Explanations for Finding an Association

Relative Risks

Greater than 1

Less than 1

slide182

Ties, Links, Relationships, and Associations

Study Links Having an Even Address to Marijuana Use

slide183

1

By Chance

By Chance

25 cards

25 cards

25 cards

25 cards

Chance

Relative Risks

Greater than 1

Less than 1

slide184

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

%

___

%

=

slide185

1

By Chance

By Chance

25 cards

25 cards

25 cards

25 cards

Chance

50 cards

Relative Risks

Greater than 1

Less than 1

slide186

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

%

___

%

=

slide187

1

By Chance

By Chance

25 cards

25 cards

25 cards

25 cards

Chance

75 cards

Relative Risks

Greater than 1

Less than 1

slide188

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

%

___

%

=

slide189

1

By Chance

By Chance

25 cards

25 cards

25 cards

25 cards

Chance

99 cards

Relative Risks

Greater than 1

Less than 1

slide190

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

slide195

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

slide197

Explaining Associations and Judging Causation

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

Causal or Not Causal?

Guilt or Innocence?

197

Teach Epidemiology

slide198

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

slide199

Explaining Associations and Judging Causation

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

199

Teach Epidemiology

slide200

Explaining Associations and Judging Causation

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

200

Teach Epidemiology

slide201

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

slide203

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

slide205

Explaining Associations and Judging Causation

Stress causes ulcers.

Helicobacter pylori causes ulcers.

Teach Epidemiology

slide206

*

*

*

*

*

*

*

*

*

Explaining Associations and Judging Causation

Teach Epidemiology

slide209

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.

209

slide210

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

210

slide211

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

211

slide212

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

212

slide213

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

213

slide214

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

214

slide215

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.

215

slide216

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

216

slide217

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

217

slide219

Time Check

2:45 PM

slide221

Teach Epidemiology

Teach Epidemiology

slide222

Time Check

3:00 AM

slide224

Teach Epidemiology

Teach Epidemiology

slide225

Time Check

3:30 PM

slide227

Teach Epidemiology

Teach Epidemiology

slide228

Time Check

4:00 PM

slide231

What do you mean - Teach Epidemiology?

Leverage the Science Olympiad Competition

http://soinc.org/

Teach Epidemiology

slide232

Think Like an Epidemiologist Challenge

New Jersey Science Olympiad

High School Finals

March 17, 2009

slide233

Finish

Handout

Test the hypothesis:

People who watch more TV eat more junk food.

slide241

Reporting Out

4

Finish

Handouts

Finish

slide243

Enduring Epidemiological Understandings

Knowledge that “… is connected and organized, and … ‘conditionalized’ to specify the context in which it is applicable.”

National Research Council , Learning and Understanding

Teach Epidemiology

slide244

Epi – Grades 6-12

Authentic Assessment

  • Are realistic; simulate the way a person’s understanding is tested in the real world
  • Require judgment and innovation to address an unstructured problem, rather than following a set routine
  • Ask students to “do” the subject rather than simply recall what was taught
  • Replicate the context in which a person would be tested at work, in the community, or at home
  • Are messy and murky
  • Require a repertoire of knowledge and skill to be used efficiently and effectively
  • Allow opportunities for rehearsal, practice, consultation, feedback, and refinement

Teach Epidemiology

slide246

Epi – Grades 6-12

Finish

http://www.njscienceolympiad.org/content/events/c/websites/epidemiology/index.html

Teach Epidemiology

slide247

Think Like an Epidemiologist Challenge

New Jersey Science Olympiad, March 16, 2010

Finish

Name

School

Thank you for stepping up, being a pioneer, and competing in the first Think Like an Epidemiologist Challenge trial event.

You worked with others, developed epidemiologic knowledge and skills, and used judgment and innovation to actually "do" epidemiology under pressure.

We hope you enjoyed the challenge.

Detectives in the Classroom

Teach Epidemiology

Robert Wood Johnson Foundation

Special thanks to the Epidemiology Section of the American Public Health Association for allowing us to distribute their Section pins to the student participants in the 2010 Think Like an Epidemiologist Challenge.