Perspectives on Teaching

Transmission:Explain concepts.

Transmission:Explain biological

concepts related to PH clearly to achieve mastery of content

Developmental:Application to PH problems

Developmental: Structured thinking.Moving from simple to complex thinking & problem solving.Assessing validity.

Introduction to Epidemiology

The Biology of Public Health

Perspectives on TeachingPerspectives on Teaching

Transmission: …have mastery of content and represent it accurately and efficiently. … provide clear objectives, adjust pace, answer questions, clarify, summarize, etc.

Apprenticeship: …reveal inner workings of skilled performance & translate it into accessible language and an order set of tasks. Progression from simple to complex. Must know what learners can do on their own….

Developmental: Primary goal is to help learners develop increasingly sophisticated cognitive structures for comprehending content. Key is two skills: a) effective questioning that challenges learners to move to more complex forms of thinking, and b) “bridging knowledge”: questions, problems, cases, examples.

Nurturing: via knowledge that they can succeed in learning if they try; achievement is a product of their own efforts and ability. Good teachers create a climate of caring and trust and setting challenging, but achievable goals and providing encouragement and support.

My Rules of Engagement

1. Think of yourself as a more experienced learner.

2. Be a leader, not a boss. Have a plan. Listen.

3. Be yourself; don’t try to be funny.

4. You must earn their trust. Never humiliate a student.

5. Don’t cram content. Leave time to discuss.

6. Use stories to engage and show relevance.

7. Use problems to engage and challenge.

8. Change pace & methods.

9. Visualize concepts; reduce use of text on slides.

10. Reflect on your teaching before and after class.

Comments on ‘Engaging’

Use of PowerPoint

Comments on ‘Engaging’

Use of PowerPoint

ODDS ODDS RATIORATIO

““THE RATIO OF THE ODDS OF HAVING THE THE RATIO OF THE ODDS OF HAVING THE TARGET DISORDER IN THE EXERIMENTAL TARGET DISORDER IN THE EXERIMENTAL GROUP RELATIVE TO THE ODDS IN FAVOUR GROUP RELATIVE TO THE ODDS IN FAVOUR OF HAVING THE TARGET DISORDER IN THE OF HAVING THE TARGET DISORDER IN THE CONTROL GROUP (IN COHORT STUDIES OR CONTROL GROUP (IN COHORT STUDIES OR SYSTEMATIC REVIEWS) OR THE ODDS IN SYSTEMATIC REVIEWS) OR THE ODDS IN FAVOUR OF BEING EXPOSED IN SUBJECTS FAVOUR OF BEING EXPOSED IN SUBJECTS WITH THE TARGET DISORDER DIVIDED BY WITH THE TARGET DISORDER DIVIDED BY THE ODDS IN FAVOUR OF BEING EXPOSED THE ODDS IN FAVOUR OF BEING EXPOSED IN CONTROL SUBJECTS (WITHOUT THE IN CONTROL SUBJECTS (WITHOUT THE TARGET DISORDER).”TARGET DISORDER).”

Yes No

Hepatitis

1 29

18 7Yes

No

19 36

Ate at

Deli?

A case-control study comparing odds of exposure.

18/1 7/29

Odds Ratio = 18/17/29

= 75

Odds of exposure

Hepatitis cases were 75 times more likely to have eaten at the Deli.

Cases Controls

The Odds Ratio

Beginning With a Pair-Share Discussion of a Problem

-

An Embedded Flash Animation-

Checking Understanding With an Audience Response System

Measuring Disease Frequency

Measuring Disease Frequency

The mayor of your town was startled to learn that there are 3 people who were recently diagnosed with hepatitis A in his neighborhood. He is concerned that this may just be the tip of the iceberg, and he is wondering if this signals an epidemic. He wants your help in assessing the magnitude of the problem.

What information do you need in order to assess:

How big the problem is in town, Whether there is an epidemic starting. How the problem in your town compares to that of neighboring towns.

PopulationPopulation

Examples: Residents of Boston Members of Blue Cross/Blue Shield Postmenopausal women in Massachusetts Coal miners in Pennsylvania Adolescents in U.S.

A group of people with some common characteristic (age, race, gender, place of residence).

Residents of Marshfield, MA

Residents of Marshfield, MA

Sample:19 who got hepatitis

38 who did not

Fixed population: membership is permanent and defined by some event.

Example: Survivors of the atomic bomb blasts in Japan

Dynamic Population:membership can be transient.

Example: Residents of Boston

Basic ConceptsBasic Concepts

Ratio Proportion Rate

A number obtained by dividing one number by another.

RatioRatio

Example: the ratio of women to men in a class

# women 120 = 8

# men 15 1

A ratio doesn’t have any dimensions or units.

It just indicates the relative magnitude of the two entities.

Women=

Men

=

Women

Men

A type of ratio that relates a part to a whole; often expressed as a percentage (%) .

ProportionProportion

Example: proportion of women in a class

# women = 120 = 88.9%

total # students 135Men

Women

Women

A type of ratio that relates a part to a whole; often expressed as a percentage (%) .

ProportionProportion

Example: The proportion of students who developed a respiratory infection during the semester.

# with colds 45 33.3%

total # students 135 = =

RateRate

A type of ratio in which the denominator also takes into account the dimension of time.

Example:

120 miles in 2 hours

120 miles = 60 miles per hr.

2 hours

Example:

60 gallons in 3 hours

60 gal. = 20 gal. per hr.

3 hours

RateRate

A type of ratio in which the denominator also takes into account the dimension of time.

Example: the rate of myocardial infarctions (heart attacks) in a study population taking low dose aspirin.

254.8 per 100,000 person-years

Counts of DiseaseCounts of Disease

If events aren’t recorded, there is no way to detect trends.

The simple count of HIV+ people provides the basis for significant discussions among city officials and health care providers.

HIV+ people in our town

2001 5 2002 7 2003 10 2004 3 2005 5 2006 19

Counts of DiseaseCounts of Disease

Simple counts are essential to public health planners and policy makers by providing a direct measure of the need for resources for specific problems.

HIV+HIV+

Is HIV more of a problem in our town?

But Count Data Alone Are Insufficient for Making Comparisons

But Count Data Alone Are Insufficient for Making Comparisons

Obviously, you need to take into account the time frame and size of each population.

Our Town 75

Next Town 35

Measures of Disease FrequencyMeasures of Disease Frequency

Prevalence (a proportion)

Incidence

• Cumulative incidence (a proportion)

• Incidence rate (a rate)

The focus is on existing disease at a specific time, not the development of new cases.

The proportion of a population that has disease at a given time.

PrevalencePrevalence

The focus is on existing disease at a specific point in time.

Imagine you took a snapshot of a class and labeled those suffering from hay fever or other allergies with a red “A” .

The proportion of a population or group that has disease at a specific “point” in time.

Point PrevalencePoint Prevalence

A

AA

A

A

The proportion of a population that has disease during a given period of time.

1980 19811979

Prevalence

= 310 (cataracts) 2,477 (total)

= .125

= 12.5%

Period PrevalencePeriod Prevalence

310 had cataracts

310 had cataracts

Eye exam survey of 2,477 people

x x xx x x x xxx x x

Prevalence of HIV in MA in 2003

Prevalence of HIV in MA in 2003

8,263 HIV+8,263 HIV+

Total MA population = 5.7 million in 2003

= 0.00145

= 0.145%

= 14.5 per 10,000

Express it this way.

Numerator: # new cases during a span of time.

Denominator: includes only people “at risk”.

The focus is on measuring the probability of developing disease during a span of time.

Frequency of new cases during a span of time in people “at risk”.

IncidenceIncidence

X X X X X XX XX X

2003 2004 2005

PrevalenceIn 2003 = 0.00145%

Prevalence versus IncidencePrevalence versus Incidence

2006 2007 2008 2009 2010

Prevalence is the probability of having disease at a point in time.

X X XX X XX X X X XX

Incidence: Frequency of new cases during a span of time in people at risk.

Incidence is the probability of developing disease during a span of time.

IncidenceIncidence

• Both focus on # new cases of disease (numerator) during a period of observation.

• The difference is the way they handle time.

Cumulative incidence

(a proportion)

Incidence rate

(a true rate)

Cumulative IncidenceCumulative Incidence

A proportion A fixed block of observation time Assumes complete follow-up for all subjects. You don’t know the precise “time at risk” for each

person. The time period is described in words

(“… during spring semester.”)

x xxx x xx x xx xx x x x xx x x x x x x

Jan. 2007(50 students)

May 2007(45 students)

CI = 25/50 = 50% during spring semester

Example: 25 colds in a class of 50 during spring semester.

In reality, people are moving in and out of Boston, and some will die (& no longer be members of the population).

But there is no way to know thedetails of this. The best we can do is assume that the number of people in the population staysthe same and they are always at risk.

TB Incidence in Boston During 2005?TB Incidence in Boston During 2005?

We need to assume the population is fixed, i.e. all people were followed for the entire block of time.

CI = # new cases 2005 est. pop. size

Cumulative incidence

(a proportion)

TB Incidence in Boston During 2005?TB Incidence in Boston During 2005?

Cumulative Incidence of AIDS in MA During 2004

Cumulative Incidence of AIDS in MA During 2004

CI = 523 new AIDS cases = 9.2/100,000 Population at risk: about 5.7 million from 1/1/04 to 1/31/04

Which has greater rate of relief? Which has greater proportion of relief?

New drug

Old drug

X

XX X

X

X

X

XX X

X

X

1 2 3 4 5 6 7 8 9 10

oooo

oooo

Hours

Here, the outcome of interest is relief of pain.

Incidence Rate of HIV Seropositivity in ProstitutesIncidence Rate of HIV Seropositivity in Prostitutes

******************* Follow-up ********************

Subject 1989 1990 1991 1992 1993 1994 Disease-free Yrs

1 --------- +------- --------- --------- --------- -------- 1

2 --------- ? 1

3 --------- --------- +------- --------- --------- -------- 2

4 --------- ? 1

5 --------- --------- --------- ? 3

6 --------- --------- --------- --------- --------- ? 5

7 --------- -------- --------- --------- --------- -------- 6

8 --------- --------- --------- --------- --------- ? 5

9 --------- +------- --------- --------- --------- -------- 1

10 --------- +------- --------- --------- --------- ? 1

IR = 4 new AIDS cases = 0.15 = 15/100 P-Yrs 26 person-yrs

Sum = 26 yrs

Incidence RateIncidence Rate

Total # new cases

Total amount of disease-free observation time for a group

Incidence RateIncidence Rate

SubjectA-B-C-D-E-F-G-H-I-J-K-L-

x

x

x81 82 83 84 85 86 87 88 89 90 91 92 93 94 95

IR = 3 = 28 107.7 p-ys 1000 p-yrs

Total =107.7

person-yrs

Timeat Risk

8.311.0

14.014.0

10.2 3.0

7.0

10.0

3.0

9.06.2

12.0

X = when theygot disease

CI = 3 12 over 14 yrs

CI versus IR?CI versus IR?

Which has greater rate of relief? Which has greater proportion of relief?

New drug

Old drug

X

XX X

X

X

X

XX X

X

X

1 2 3 4 5 6 7 8 9 10

oooo

oooo

CI = 6/10 = 60% over 10 years

IR = 6/49 p-yrs = 12.2/100 P-yrs

CI = 6/10 = 60% over 10 years

IR = 6/85 p-yrs = 7/100 P-yrs

Obesity Risk of Non-fatal Myocardial Infarction

Association?

<21

21-23

23-25

25-29

>29

BMI:

wgt kghgt m2

# MIs(non-fatal)

41

57

56

67

85

person-yearsof observation

177,356

194,243

155,717

148,541

99,573

rate of MI per100,000 P-Yrs

(incidence)

23.1

29.3

36.0

45.1

85.4126 lb @ 5’6” = 21175 lb @ 5’6” = 29

126 lb @ 5’6” = 21175 lb @ 5’6” = 29

The Nurse’s Health StudyThe Nurse’s Health Study

2003 2004 2005 2006 2007 2008 2009 2010

X X XX X XX X X X XX

Incidence: Frequency of new cases during a span of time in people at risk.

Incidence is the probability of developing disease during a span of time.

Incidence provides a way of measuring the risk of becoming diseased.

Summary –Measures of Disease FrequencySummary –Measures of Disease Frequency

Prevalence (a proportion) = People # People with disease at a point in

time Total People # People in the study population

Cumulative Incidence (a proportion) = People # new cases in a specified period Total People # People (at risk) in the study population

Incidence Rate (a rate) = People # new cases of disease People-Time Total observation time in a group at risk

The proportion of exposed people who develop disease. (Not really a rate; it’s a special type of cumulative incidence.)

Attack Rate

TBexposure

(a cumulative incidence)

Passengers on Honolulu to Baltimore flight within

2 rows of index casePositive TB tests

Case Fatality RateCase Fatality Rate

The proportion of diseased people who die - in this case 2/6 = 33%. (Again, not a rate, but a special type of cumulative incidence.)

A measure of the severity or risk of dying from the disease if you have it.

Example:, 33% of people who got SARS died.

SARS cases

If the prevalence, incidence, and average duration of disease have been relatively constant, this relationship can be used to predict the effects of changing incidence or average duration.

Prevalence Depends on Incidence & Duration of Disease

Prevalence = Incidence x Average Duration of Disease

P = I x D

Not surprisingly, brief, acute illnesses such as viral gastroenteritis do not have high prevalence, because they don’t last long.

In contrast, diseases like diabetes have greater prevalence because they aren’t rapidly fatal, but there is not real cure; they are just controlled.

Prevalence = Incidence x Average Duration of Disease

P = I x D

Average Duration of Disease Affects PrevalenceAverage Duration of Disease Affects Prevalence

Since D = P/IR then D = 23/100,000 persons = 0.5 years 46/100,000 person-years

Conclusion: People with lung cancer survive an average of 6 months from diagnosis to death.

Calculating the Mean Duration of DiseaseIf Prevalence = Incidence x Avg. Duration,

then Avg. Duration = Prevalence Incidence

Example: Lung cancer: If incidence = 46 new cancers per 100,000 P-Yrs (i.e., in a population of 100,000 you expect 46 cases per year), and prevalence = 23 per 100,000 population.

What is the average duration of lung cancer?

Use of an Audience Response System to:

Assess understanding,

Reinforce concepts,

Identify & clarify misconceptions, and

Build confidence.

(And have fun.)

PH officials used surveillance data to determine the number of new cases of tuberculosis in Boston during 2008, and they computed the frequency of new TB cases using census data as an estimate of the population size in 2008. What measure of disease frequency did they calculate?

0%

0%

0%

0%

0%1. Prevalence2. Cumulative incidence3. Incidence rate4. Attack rate5. Case fatality rate

Which measure of disease frequency best describes the percentage of men found to have previously undiagnosed prostate cancer at autopsy?

0%

0%

0%

0%


Investigators reported that 136 deaths occurred among 272 persons who had been infected with avian flu. What measure of disease frequency does this represent?

0%

0%

0%

0%1. Person-yrs2. Incidence rate3. Case-fatality rate4. Prevalence

Which measure of disease frequency best describes the percentage of malaria patients who are found to have chloroquine-resistant malaria?

0%

0%

0%

0%


Which measure of disease frequency best describes the rate at which myocardial infarctions occurred among smokers, expressed as the number per 100,000 person-years of observation?

0%

0%

0%

0%


An “In-class Quiz.”

Study population of 1,000. Dashed line = disease present (Lung Cancer)Patients 1, 2, 3, & 4) had the disease before the study began. During the year of the study, 6 new cases occur (start of dashed lines). Among the total of 10 cases, there were 6 deaths during the year. The 990 other individuals in the study did not become ill or die.

1994 1995Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

1<---------------------------------------------------------------------------------------------------------Alive2<---------------------------------------Dead3<--------------------------------------------------------------Dead4<-----Dead5 -----------------------------------------------------------------------------------------------------Alive6 ----------------------------Dead7 -------------------------------------------------------------------------Alive8 ------------Dead9 ------------------------------------------------------------Alive10 ---------------------------------------Dead

Prevalence of disease on:Jan. 1, 1994?July 1, 1994?Dec. 31, 1994?

XX

XX

XX

What was the cumulative incidence during 1994?

What was the case-fatality rate during 1994?

Study population of 1,000. Dashed line = disease present (Lung Cancer)Patients 1, 2, 3, & 4) had the disease before the study began. During the year of the study, 6 new cases occur (start of dashed lines). Among the total of 10 cases, there were 6 deaths during the year

The 990 other individuals in the study did not become ill or die.



4/1,0005/9964/994

XX

XX

XXPrevalence of disease on:

Jan. 1, 1994?July 1, 1994?Dec. 31, 1994?

Study population of 1,000. Dashed line = disease present (Lung Cancer)Patients 1, 2, 3, & 4) had the disease before the study began. During the year of the study, 6 new cases occur (start of dashed lines). Among the total of 10 cases, there were 6 deaths during the year The 990 other individuals in the study did not become ill or die.



XX

XX

XX

6/996What was the cumulative incidence during 1994?

Study population of 1,000. Dashed line = disease present (Lung Cancer)Patients 1, 2, 3, & 4) had the disease before the study began. During the year of the study, 6 new cases occur (start of dashed lines). Among the total of 10 cases, there were 6 deaths during the year. The 990 other individuals in the study did not become ill or die.



XX

XX

XX

6/10 or 60%

What was the case-fatality rate during 1994?

Descriptive Epidemiology-

Using Interactive PowerPoint

Descriptive Epidemiology-

Using Interactive PowerPoint

Evolution of Medical InformationEvolution of Medical Information

1. Description & hypothesis generation

2. Hypothesis testing to establish valid associations

3. Evaluation of efficacy of treatment or prevention

Differences: If the frequency of disease differs in two circumstances, it may be due to a factor that differs in the two circumstances. Example: stomach cancer in Japan & US

Similarities: If a high frequency of disease is found in several different circumstances & one can identify a common factor, then the common factor may be responsible. Example: AIDS in IV drug users, or recipients of transfusions, & hemophiliacs.

Correlations: If the frequency of disease varies in relation to some factor, then that factor may be a cause of the disease. Example: differences in colon cancer vary with per capita meat consumption.

Hypotheses arise from observation of …Hypotheses arise from observation of …

Descriptive information provides clues.

Descriptive information provides clues.

What factors might be associated with disease?Are there similarities among the diseased?

Are there differences between diseased & well people?

What correlates with disease?

Person: characteristics? Place: specific locations or settings? Time: does it vary over time?

X

XX

X

X

X

X

X

X

X

Hepatitis OutbreakHepatitis Outbreak

Marshfield, MA had an outbreak of hepatitis A.

How did they identify the source?

What Might Provide Clues (hypotheses)?What Might Provide Clues (hypotheses)?

Door 1 Door 2

Door 4Door 3

Door 5

Done

LL

IN EC

B

SM

Interview Some CasesInterview Some Cases

Back

Epidemic CurveEpidemic Curve

Back

Spot Map –Residence of Hepatitis Cases

Spot Map –Residence of Hepatitis Cases

Back

Rick’s DeliMcDonald’sJaime’s PubPapa Gino’sFriendly’s

They hypothesized that the source was probably an infected food handler at:

Based on these clues:

• Knowledge of biology of hepatitis A (transmission, incubation)• Time course: epidemic curve of “point source”• Diverse age, occupation, location• Interview with a series of cases & similarities in restaurant use

Measures of Association

-

An In-Class Quiz

Measures of Association

-

An In-Class Quiz

Is There An Association?Is There An

Association?

Exposure(Risk Factor) Outcome

2) Calculate the difference in incidence between the two groups. (Subtract incidence in control group from the incidence in the exposed group).

Options For Comparing IncidenceOptions For Comparing Incidence

1) Calculate the ratio of the incidences for the two groups. (Divide incidence in exposed group by the incidence in the control group).

Or

Ie

I0

Ie- I0

For Cohort Type Studies

Yes No

Wound Infection

1 78 79

7 124 131 Yes

No

8 202 210 Subjects

RR = 7/131 = 5.3 = 4.2 1/79 1.3

RR = 7/131 = 5.3 = 4.2 1/79 1.3

CumulativeIncidence

5.3%

1.3%

(7/131)

(1/79)

Had IncidentalAppendectomy

Measuring Association with Relative RiskMeasuring Association with Relative Risk

RR = =5.3%

1.3%= 4.2

Interpretation: “In this study the risk of wound infection was 4.2 times greater in patients who had incidental appendectomy compared to those who did not have appendectomy.”

Interpretation: “In this study the risk of wound infection was 4.2 times greater in patients who had incidental appendectomy compared to those who did not have appendectomy.”

5.3%

1.3%

Also had appendectomy

No appendectomy

Relative Risk in Appendectomy StudyRelative Risk in Appendectomy Study

A ratio;

no dimensions.

RR = =5.3%

= 1.05.3%

What If Relative Risk = 1.0 ?What If Relative Risk = 1.0 ?

5.3%

5.3%

Exposed group

Unexposed group

Yes NoMyocardial Infarction

Yes

No

378 21,693 22,071 subjects

139 10,898 11,037 exposed

Aspirin Use

239 10,795 11,034 not exposed

RR = .0126 = 0.55 .0221

RR = .0126 = 0.55 .0221

Iexposed = 139/11,037 = .0126

Iunexposed = 239/11034 = .0221

Iexposed = 139/11,037 = .0126

Iunexposed = 239/11034 = .0221

What If Relative Risk < 1.0 ?What If Relative Risk < 1.0 ?

Yes NoMyocardial Infarction

Yes

No

378 21,693 22,071

139 10,898 11,037

Aspirin Use

239 10,795 11,034

RR = .0126 = 0.55 .0221

RR = .0126 = 0.55 .0221

Subjects who used aspirin had 0.55 times the risk of myocardial infarction compared to those who did not use aspirin.

Subjects who used aspirin had 0.55 times the risk of myocardial infarction compared to those who did not use aspirin.

Interpretation of Relative Risk < 1.0Interpretation of Relative Risk < 1.0

Relative Risk = 55.2 /100,000 P-Yr. = 55.2 = 0.47 116.6 /100,000 P-Yr. 116.6

1. Women using hormone replacement therapy had 0.47 times the risk of coronary disease compared to women who did not use HRT.

2. Women using hormone replacement therapy had 0.47 times more risk of coronary disease compared to women who did not use HRT.

3. Women using hormone replacement therapy had 0.47 times less risk of coronary disease compared to women who did not use HRT.

Which is the correct interpretation of the relative risk = 0.47?

Which is the best interpretation of the risk ratio (relative risk)?

0%

0%

0%

1. Women using hormone replacement therapy had 0.47 times the risk of coronary disease compared to women who did not use HRT.

2. Women using hormone replacement therapy had 0.47 times more risk of coronary disease compared to women who did not use HRT.

3. Women using hormone replacement therapy had 0.47 times less risk of coronary disease compared to women who did not use HRT.

The Nurse’s Health Study

ObesityNon-fatal MyocardialInfarction

?

# MIs(non-fatal)

41

57

56

67

85

Person-yearsof observation

177,356

194,243

155,717

148,541

99,573

Rate of MI per100,000 P-Yrs

(incidence)23.1

29.3

36.0

45.1

85.4

Relative Risk1.0

1.3

1.6

2.0

3.7

An “r x c” (row/column) Table –Multiple Rows & Columns

An “r x c” (row/column) Table –Multiple Rows & Columns

<21

21-23

23-25

25-29

>29

BMI:

wgt kghgt m2

How would you interpret the RR= 3.7 in the heaviest group?

# MIs(non-fatal)

41

57

56

67

85


177,356

194,243

155,717

148,541

99,573


(incidence)23.1

29.3

36.0

45.1

85.4

Relative Risk

1.0

1.3

1.6

2.0

3.7

<21

21-23

23-25

25-29

>29

BMI:

wgt kghgt m2

0%

0%1. The heaviest women had 3.7 times the risk compared to all the other women.

2. The heaviest women had 3.7 times the risk compared to the leanest women.

RD = Incidence in exposed - Incidence in unexposed

Risk Difference = Ie - I0

The Risk Difference (Attributable Risk)

The Risk Difference (Attributable Risk)

Yes No

Wound Infection

1 78 79

7 124 131 Yes

No

8 202 210 subjects


CumulativeIncidence

5.3%

1.3%

RD = 0.053 – 0.013 = 0.04 = 4 per 100RD = 0.053 – 0.013 = 0.04 = 4 per 100

Risk Difference in Appendectomy StudyRisk Difference in Appendectomy Study

Even if appendectomy is not done, there is a risk of wound infection (1.3 per 100).

… the RD is the excess risk in those who have the factor, i.e., the risk of wound infection that can be attributed to having an appendectomy, assuming there is a cause-effect relationship.

Risk Difference Gives a Different Perspective on the Same Information

Risk Difference Gives a Different Perspective on the Same Information

Adding an appendectomy appears to increase the risk by (4 per 100 appendectomies), so ...

1.3/100

5.3/100

Exposed

85.4

23.1

ObesityNon-fatal

MyocardialInfarction

?

# MIs(non-fatal)

41

57

56

67

85


177,356

194,243

155,717

148,541

99,573


(incidence)

29.3

36.0

45.1

Relative Risk

1.0

1.3

1.6

2.0

3.7

Risk Difference= 85.4/100,000 - 23.1/100,000 = 62.3 excess cases / 100,000 P-Y in the heaviest group

Risk Difference= 85.4/100,000 - 23.1/100,000 = 62.3 excess cases / 100,000 P-Y in the heaviest group

Risk Difference in The Nurse’s Health StudyRisk Difference in The Nurse’s Health Study

<21

21-23

23-25

25-29

>29

BMI:

wgt kghgt m2

Among the heaviest women there were 62 excess cases of heart disease per 100,000 person-years of follow up that could be attributed to their excess weight.

InterpretationInterpretation

This suggests that if we followed 50,000 women with BMI > 29 for 2 years we might expect 62 excess myocardial infarctions due to their weight. (Or one could prevent 62 deaths by getting them to reduce their weight.)

The proportion (%) of disease in the exposed group that can be attributed to the exposure, i.e., the proportion of disease in the exposed group that could be prevented by eliminating the risk factor.

AR% = AR x 100

Ie

.04 x 100 = 75%

.053What % of infections in the exposed group can be attributed to having the exposure?

ExposedNot

Exposed

Attributable Risk % - The Attributable Proportion

Attributable Risk % - The Attributable Proportion

.013

.053.04

Interpretation: 75% of infections in the exposed group could be attributed to doing an incidental appendectomy.

A Short In-class Quiz to

• Assess Understanding• Reinforce concepts• Build skill and confidence• Clarify

A prospective cohort study was used to compare lung cancer mortality in smokers and non-smokers.

Among 20,000 non smokers there were 20 deaths from lung cancer during 5 years of study. Among 5,000 smokers there were 100 deaths from lung cancer during the 5 year study period.

1) Organize this information in a 2x2 table.2) Calculate the cumulative incidence of death (per 1,000)

due to lung cancer in smokers and non-smokers.3) Calculate the relative risk; interpret it in words.4) Calculate the risk difference; interpret it in words.5) Calculate the attributable proportion; interpret it in words.

A prospective cohort study was used to compare lung cancer mortality in smokers and non-smokers.

Among 20,000 non smokers there were 20 deaths from lung cancer during 5 years of study. Among 5,000 smokers there were 100 deaths from lung cancer during the 5 year study period.

1) Organize this information in a 2x2 table.2) Calculate the cumulative incidence of death (per 1,000) due to lung

cancer in smokers and non-smokers.3) Calculate the relative risk; interpret it in words.4) Calculate the risk difference; interpret it in words.5) Calculate the attributable proportion; interpret it in words.

100 4900

20 19980

5000

20000

100/5,000=0.02=20/1,000 over 5 yrs

20/20,000=0.001=1/1,000 over 5 yrs

RR-20/1 RD=19/1,000 over 5 yrs

AR% = 19/20 x 100 = 95%

Measuring Association in a Case-Control Study

Measuring Association in a Case-Control Study

Cohort Type Studies

X

XX X

Time passes

Case-Control Studies

XXXXX X

XX

Assess prior exposures

Is disease more likely in exposed persons?

Are diseased persons more likely to have been exposed?

Yes No

Hepatitis

1 29

18 7Yes

No

19 36

Ate at Deli

A case-control study comparing odds of exposure.

The Odds Ratio

18/1 7/29

Odds Ratio = 18/17/29

= 75

Odds of exposure:

Hepatitis cases were 75 times more likely to have eaten at the Deli.

Case Control

To calculate incidence, you need to take a group of disease-free people and measure the occurrence of disease over time.

Odds of having the risk factor prior to disease?

controls

cases

(Already have disease)

(No disease)

But in a case-control study we find diseased and non-diseased people and we measure and compare the prevalence of prior exposures.

Yes No

Wound Infection

1 78 79

7 124 131


CumulativeIncidence

5.3%

1.3%

How many exposed people did it take to generate the 7 cases in the 1st cell?

Yes

No

A retrospective cohort study….

Yes No

Hepatitis

1 29

18 7Yes

No

Ate at Deli

18/1 7/29Odds of exposure

Case Control

How many exposed people did it take to generate the 18 cases in the 1st cell?

Yes No

Hepatitis

1 29

18 7Yes

No

Ate at Deli

Case Control

In cases =18/1In controls =7/29

= 75

Odds of Exposure

Deli =18/7No Deli =1/29

= 75

Odds of Disease

Odds Ratios Are Interpreted Like Risk Ratios

Odds Ratios Are Interpreted Like Risk Ratios

Example:“Individuals who ate at the Deli had 75 times

the risk of hepatitis A compared to those who did not eat at the Deli.”

An odds ratio is a good estimate of the risk ratio when the outcome is relatively uncommon.

BUT

The odds ratio exaggerates relative risk when the outcome is more common.

You can always calculate an odds ratio, but…

In cohort studies and clinical trials you can calculate incidence, so you can calculate either a relative risk or an odds ratio.

In a case-control study, you can only calculate an odds ratio.

Yes No

Got Giardiasis

14 341 355

16 108 124 Yes

No

Exposed to Kiddy Pool

Cohort Design: Calculate RR or OR

Cohort Design: Calculate RR or OR

I can compute either RR or OR here. Why?

Compare frequencyof Giardia

Kid pool

Kid pool

Not

vs.

Yes No Giardia

16 108

14 341

RelativeRisk = 3.3

RelativeRisk = 3.3

16 / (16+108) = 12.9%

14 / (14+341) = 3.9%

Incidence

Viewed as a Retrospective Cohort StudyBy Comparing:

Viewed as a Retrospective Cohort StudyBy Comparing:

Those were in kiddy pool vs. Those who were not.

Not

Compare frequencyof kiddy pool use.

Giardia

No Giardia

Those who got Giardia to those who didn’t.

Giardia No Giardia

16 108

14 341

Kid pool

Not

Odds of having been in kiddy pool

16 / 14 108 / 341

Odds = 16/14Ratio 108/341

= 3.6

Odds = 16/14Ratio 108/341

= 3.6

Viewed as a Case-Control StudyBy Comparing:

Viewed as a Case-Control StudyBy Comparing:

vs.

Compare frequencyof kiddy pool use.

Giardia

No Giardia

Those who got Giardia to those who didn’t.

Giardia No Giardia

16 108

14 341

Kid pool

Not

Odds = 16/14Ratio 108/341

= 3.6

Odds = 16/14Ratio 108/341

= 3.6

Cross Product: Another Way to Calculate the Odds Ratio

Cross Product: Another Way to Calculate the Odds Ratio

vs.

a/cb/d

a b

c da x db x c=

Odds = 16x341Ratio 108x14

= 3.6

Odds = 16x341Ratio 108x14

= 3.6

With a Common Outcome OR Exaggerates RR

With a Common Outcome OR Exaggerates RR

Yes NoOutcome

45 341 386 unexposed

60 108 168 exposed

Yes

No

Risk Factor

Ie = 60 168

I0 = 45 386

60 / (60+108)

45 / (45+341)

60 / (60+108)

45 / (45+341)RR =

RR= 3.06 OR = 4.21

60 / 45 108 / 341

60 / 45 108 / 341OR =

Let’s see if you’ve been paying attention.

What does one measure and compare in a case-control study?

0%

0%

0%

0%

0%1. Cumulative incidence

2. Incidence rate3. Risk of disease4. Frequency of past

exposures5. Risk difference

A study of smoking and lung cancer was conducted in a small island population. There were a total of 1,000 people in the study, and at the beginning of the study none had lung cancer. Four hundred were smokers and 600 were not. Subjects were followed for 10 years. Of the smokers, fifty developed lung cancer. Of the non-smokers, 10 developed lung cancer. What kind of study was this?

0%

0%

0%

0%1. A case series

2. A case-control study

3. A retrospective cohort study

4. A prospective cohort study

In the previous study examining the association between smoking and lung cancer, suppose the RELATIVE RISK = 17. How would you interpret this relative risk in words?

0%

0%

0%

0%1. There were 17 more cases of lung cancer in the smokers.

2. Smokers had 17 times the risk of lung cancer compared to non-smokers.

3. Smokers had 17% more lung cancers compared to non-smokers.

4. 17% of the lung cancers in smokers were due to smoking.

In a cohort study one may measure the degree of association between an exposure and an outcome by calculating either a relative risk or an odds ratio?

0%

0%

0%1. True

2. False

3. I’m not sure

In a case-control study one may measure the degree of association between an exposure and an outcome by calculating either a relative risk or an odds ratio.

0%

0%1. True

2. False

When is an odds ratio a legitimate estimate of relative risk?

0%

0%

0%

0%1. Whenever one is conducting a

case-control study.

2. When the exposure is relatively uncommon.

3. When the outcome is relatively uncommon.

4. When the sample size is large.

Disparities in Health Care Based on Race, Ethnicity, or Gender

-

Audience Response for Opinion

& Shifts in Opinion

Do you believe there are significant disparities in health care based on race or ethnicity?

0%

0%

0%1. Yes

2. No

3. I don’t have an opinion

Do you believe there are significant disparities in health care based on gender?

0%

0%

0%1. Yes

2. No

3. I don’t have an opinion.

Do you believe that physicians are an important source of race or gender-based disparities in health care?

0%

0%

0%1. Yes

2. No


Nightline ReportNightline Report

Many studies have described disparities in health care based on race and gender, but the etiology is not always clear. Socioeconomic differences contribute to disparities between blacks and whites, but are other factors also responsible? Do conscious or subconscious attitudes of physicians contribute to these disparities?

Shulman et al. sought to address this question in a study that was reported in the N. Engl. J. Med. (Feb. 1999). The study sparked much discussion.

Racial Disparities in Health Care

(Go to 5:21)

N. Engl. J. Med.Abstract

N. Engl. J. Med.Abstract

?

Racial Disparities in Health Care


0%

0%

0%1. Yes

2. No



0%

0%

0%1. Yes

2. No



0%

0%

0%1. Yes

2. No


Yes No

Referral for Cardiac Cath

326 34 360

305 55 360 Black

White

Incidence

• What was the probability that a black patient would be referred for catheterization?

• What was the probability that a white patient would be referred for catheterization? (Calculate and compare your answer with your neighbor’s.)

Yes No

326 34 360 326/360=90.5%

305 55 360 305/360=84.7% Black

White

Incidence

• What was the probability that a black patient would be referred for catheterization?

• What was the probability that a white patient would be referred for catheterization? (Calculate and compare your answer with your neighbor’s.)

Referral for Cardiac Cath.

Yes No

326 34 360 90.5%

305 55 360 84.7% Black

White

Incidence

• What was the “relative risk” of referral?(Calculate and compare your answer with your neighbors.)


Yes No

Black

White

Incidence

= 0.94 305/326 55/34OR = = 0.58 RR =

326 34 360 90.5%

305 55 360 84.7%

84.7%90.5%

The OR suggests a larger difference than the RR. Why?


Yes No

Black

163 17 180 90.5%

163 17 180 90.5%

Referral for Cath.

White

IncidenceYes No

Black

163 17 180 90.5%

142 38 180 78.9%

Referral for Cath.

White

Incidence

What does the stratified analysis suggest?

Males Females

Stratified by GenderStratified by Gender


0%

0%

0%1. Yes

2. No



0%

0%

0%1. Yes2. No3. I don’t have an opinion.


0%

0%

0%1. Yes2. No3. I don’t have an opinion.

:10

The conclusion that was widely circulated in the press was that blacks and women were 40% less likely than white men to be referred for cardiac catheterization.

?

If you were writing the abstract for the Shulman study, how would you report the major findings?

(Small group and open discussion)

How would you rate the presidency of George W. Bush?

0%

0%

0%

0%

0%

0%1. Excellent2. Very good3. Good4. Fair5. Poor6. Worst U.S. President ever

:10

How would you rate the presidency of Barack Obama?

0%

0%

0%

0%

0%

0%1. Excellent2. Very good3. Good4. Fair5. Poor6. Worst U.S. President ever

:10

Active Learning Outside the Classroom

• Homework

• “Stat Tools”

• On-line quizzes

• Data sets for analysis (Framingham Heart Study)

• Team projects (semester long prospective cohort study)

• Online, interactive case-based modules (hepatitis outbreak)

Documents

Perspectives on Teaching