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1 1 Linda S. Gottfredson, Professor 1 1 Linda S. Gottfredson, Professor

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1 1 Linda S. Gottfredson, Professor - PPT Presentation

University of Delaware August 7 2009 Presentation to accept 2008 George A Miller Award for outstanding article across specialty areas Division 1 APA Social Class Disparities in Health A Vexing Puzzle with a Surprising Answer ID: 557322

amp health disparities cognitive health amp cognitive disparities level education ses fact puzzle background load susceptibility stress explain men

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Slide1

1

1

Linda S. Gottfredson, Professor

University of DelawareAugust 7, 2009Presentation to accept 2008 George A. Miller Award for outstanding article across specialty areas, Division 1, APA

Social Class Disparities in Health: A Vexing Puzzle with a Surprising Answer?

American Psychological AssociationSlide2

2

2

What are “disparities”? What’s the vexing puzzle?

Is human cognitive diversity key to solving it?If yes, so what?AgendaAnswers: All surprisingSlide3

3

3

What are “disparities”?

Why such a vexing puzzle?Is human cognitive diversity the key to solving it?If yes, so what?Agenda ExamplesSlide4

4

4

“Disparity” =

group differences on health outcome X

“Explaining”

between

-group variation

Means, rates, etc.

16 yrs

12 yrs

8 yrs

Typical

indicators of socioeconomic status (SES)

Years education

Occupational status

Income

But not clear what they really represent or have in common

?Slide5

5

Typical health disparities by education; in all races & sexes:

% of non-ill 51-year-olds expected to have this chronic illness by age 63(Hayward et al, 2000)

HypertensionDiabetes whiteCOPD blackCancer

MenWomen

%

YearsSlide6

6

Typical health disparities by education; in all races & sexes:

% of non-ill 51-year-olds expected to have this chronic illness by age 63(Hayward et al, 2000)

HypertensionDiabetes whiteCOPD blackCancer

MenWomen

%

Fewer health problems in higher social classes (educ, occup, or $)

True for all races, sexes Exceptions are rare (e.g., cancer morbidity)

YearsSlide7

7

Disparities in health behavior by education; all races & sexes:

% who smoke, 2006 (age adjusted)(CDC, Health in the United States, 2008, Table 64)

%Slide8

8

Typical course of behavior disparities over time, by education: % who smoke, 1974-2006, ages 25+

(age-adjusted) (CDC, Health in the United States, 2008, Table 64)

16.520.6% better, gap bigger

%Slide9

9

9

Many families of health disparities

HEALTH HABITS

MORTALITY

KNOWLEDGE

CHRONIC ILLNESSES

INJURIES

INFECTIOUS DISEASES

ADHERENCESlide10

10

10

Many families of health disparities

HEALTH HABITS

MORTALITY

KNOWLEDGE

CHRONIC ILLNESSES

INJURIES

INFECTIOUS DISEASES

ADHERENCE

Outcomes for

populationsSlide11

11

11

a

bcdd

fg

This is not about individual differences in health outcomes

Not “explaining”

within-group variation

Within-group and between-group variance may arise from

different

mix of causes

Often misunderstood!Slide12

12

Study of populations aided by epidemiological approach

OutcomesMeans, rates, relative risk, odds ratios for groups

Predictors—classic trioExposure to hazards, help (probability)Host (susceptibility)Vector (virulence, burden)Slide13

13

Study of populations aided by epidemiological approach

OutcomesMeans, rates, relative risk, odds ratios for groups Predictors—classic trioExposure (probability)

Host (susceptibility)Vector (virulence, burden)

Missing 2/3

Current focus of SES disparities researchSlide14

14

14

What are “disparities”?

Why such a vexing puzzle? But first, what exactly are we trying to explain?StatisticallySubstantively Is human cognitive diversity the key to solving it?If yes, so what?Agenda

IllustrationSlide15

15

15

Illustration with 2 disparities

# 1

# 2Slide16

16

16

1

2345Social class groupings

Health(groupmeanor rate)

Each disparity is a gradient, with a slope (ß)

Statistically…

# 1

ß

1Slide17

17

17

1

2345

Social class groupings

Health(groupmeanor rate)

Each disparity is a gradient, with a slope

Statistically…

# 2

# 1

ß

1Slide18

18

18

1

2345Social class groupings

Health(groupmeanor rate)

Each disparity is a gradient, with a slope

Statistically…

# 2

# 1

ß

1

ß

2Slide19

19

19

1

2345Social class groupingsHealth

(groupmeanor rate)

Many families of health gradients (slopes):

Morbidity, mortality, knowledge, prevention, adherence, etc.

rare

ß

1

ß

8

ß

7

6

ß

5

ß

4

ß

3

ß

2Slide20

20

20

So, to explain SES disparities:

Explain the distribution of co-evolving gradients (ß, their standardized slopes)ß ß ßßßß3ß8

ßßßßß4

ß

ßß2

ß6

ß

ß

ß

ß

ß

ß

ß

ß

7

ß

ß

ß

ß

ß

2

ß

ß

1

ß

0

Slopes (steepness) of gradients

negative

positive

Common policy goal : All

β

= 0 Slide21

21

21

What are “disparities”?

Why such a vexing puzzle? But first, what exactly are we trying to explain?StatisticallySubstantively Is human cognitive diversity the key to solving it?If yes, so what?Agenda

ExamplesSlide22

22

22

General puzzle

: Health disparities are

too general for SES mechanisms to explain

They are pervasive, persistent and monotonic regardless of time, place, health system, disease, and behavior. Why??Slide23

23

23

Exposure hypothesis 1:

“Wealth = health” (can afford good care)health

wealth

No leveling off when resources are more than sufficient

REJECTED—Puzzle greater!Slide24

24

24

Experimental test of exposure hypothesis 1:

Equalize access to care equalize health Time 1: Unequal access Time 2: After equal access (free care)

Health disparities

grow

, not shrink

FAILED—Puzzle greater!

E.g., UK in 1950sSlide25

25

25

Experimental test of exposure hypothesis 2:

Unequal education unequal health Time 1: Unequal knowledge of signs and symptoms Time 2: After public health campaign

Knowledge disparities

grow

, not shrink

FAILED—Puzzle greater!Slide26

26

26

Or disparities even reverse direction with new screening tests

(e.g., death rates from breast cancer) Negative disparities for Outcome X at Time 1 Positive disparities for Outcome X at Time 2

ß

More educated women have

higher

death rates

Slide27

27

Access matters, but so does utilization

Even if equal access

Unequal use & misuse

Mammograms

Adherence to treatment

Seat belt use

Etc.

“Health literacy”Slide28

28

28

1

2345Social class groupingsHealth

(groupmeanor rate)

Summary of puzzle

rare

Exposure can’t explain why gradients:

Virtually never = zero

Virtually always positive

All monotonic (~linear)

For ~all health outcomes & behaviors

Steepen when resources equalized

What levers the gradients up or

down?

Can’t be material resources.Slide29

29

29

So, the field seeking more “fundamental cause” of SES disparities

This cause must: be pervasive & domain-general have linear (monotonic) effectsnot be materialMost popular suspect = inequality itselfrelative deprivation chronic psychological stress damaging physiological process: “allostatic load” Stress important, but can’t explain:why adding resources increases disparitiesdisparities in non-biological outcomes Slide30

30

2006

2003-2005

Biological mechanismsInvolved hereBut not here

First, physical illness is only one cause of injury & death: Causes of death, males by age(CDC, Health data interactive)

Common theme—all are preventableSlide31

31

Example: Unintentional (“accidental”) death Odds ratios by neighborhood income

(1980-86)

20 per 100,00021Reference group Odds = % affected Odds ratio = Odds for Group 1_______ % not Odds for reference group

Just differential exposure??Slide32

32

Selected causes of “motor vehicle traffic” death, by neighborhood income/capita

(1980-86)(Baker, O’Neill, Ginsburg, & Li, 1992)

203.215.20

.18

.26

elderly

adult men

young men

toddlers

young men

young men

Primarily:

Rate per 100,000Slide33

33

Selected “other” causes of unintentional death, by neighborhood income/capita

(1980-86) (Baker, O’Neill, Ginsburg, & Li, 1992)

20.02.60.04

2.30

.06

.38

.78

Odds ratio

Deaths per 100,000

.12

infants, elderly

rises with age

young men

toddlers, elderly

young men

young men

infants, elderly

Infants

Primarily:

Self-exposure

Differential biological

susceptivitySlide34

34

PreventionIt’s our job It’s daily, unrelenting, life-long (hazards are everywhere)It’s complex

It’s a highly cognitive, multi-step, active processSpot & avoid hazardsRecognize signs of system veering out of control

Take action to regain controlLimit progression of illness/accident or damage it doesAdhere to treatmentLearn from experience to adjust future behaviorThe common mechanism for illness and injury?

Passive-patient model is dead wrongSlide35

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35

What are “disparities”?

Why such a vexing puzzle?Is human cognitive diversity the key?If yes, so what?Agenda IQ/

gSlide36

36

36

Alternative hypothesis for disparities in health:

“Intelligence (g) differences are the “fundamental cause” Two g–based levers ratchet up gradients* Bigger IQ differences (people) Heavier cognitive load (tasks) susceptibility burden

*

Based on extensive research in education & employmentSlide37

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37

Gaps in IQ/

g (cognitive susceptibility-efficiency)Heaviercognitiveload (g loadingof tasks)

Heavier

cognitiveload (g loading

of tasks)

ßß

ß

ß

ß

ß

ß

ß

ß

ß

ß

ß

ß

Translated: A hypothesis about gradientsSlide38

38

38

Background fact #1

Great cognitive diversity is a biological fact about all populations70 75 80 85 90 95 100 105 110 115 120 125 130

IQSlide39

39

39

Background fact #2IQ

≈ g (general mental ability factor)g is no longer a black boxg is a domain-general facility for learning, reasoning, spotting & solving novel problemsHigher g reduces susceptibility to errorGives bigger edge as task complexity (cognitive load) increasesAllows one to exploit resources more fully & effectively (e.g., classroom instruction, medical treatments)Slide40

40

40

Background fact #2IQ

≈ g (general mental ability factor)g is no longer a black boxg is a domain-general facility for learning, reasoning, spotting & solving novel problemsHigher g reduces susceptibility to errorGives bigger edge as task complexity (cognitive load) increasesAllows one to exploit resources more fully & effectively (e.g., classroom instruction, medical treatments)Slide41

41

41

Gives an edge in planning; anticipating problems Slide42

42

42

Background fact #3

Mean IQs differ by occupation level and years education70 75 80 85 90 95 100 105 110 115 120 125 130

0-7

8

9-11

12

13-15

16+

Unskilled

Semiskilled

Skilled

Manager, Cler, Sales

Professional & Tech

Occupation:

Years education:

WAIS-R IQ (mean

+

1 SD), US adults ages 16-74

IQSlide43

43

43

Background fact #4:

Some SES indicators correlate more with IQ .8 Standardized academic achievement .6 Years education.5 Occupation level.3-.4 Income

(prior)

IQ

All

moderately

heritable,

& overlap

genetically

with IQSlide44

44

44

.8

Literacy.8 Standardized academic achievement .6 Years education.5 Occupation level

.3-.4 Income

(prior)

IQ

Excellent

Good

Weak

Background fact #4:

Conversely, some are better surrogates for IQ

Better surrogates for

g

s

how larger

health disparities Slide45

Better surrogates for

g

show larger

health disparities(steeper gradients)45income

occupation

education

“literacy”Slide46

46

46

.8

Literacy.8 Standardized academic achievement .6 Years education.5 Occupation level.3-.4 Income

Excellent

Good

Weak

Background fact #4:

Conversely, some are better surrogates for IQ

(prior)

IQ

Cannot “control” for SES without

controlling away much

of (genetic)

g

itselfSlide47

47

47

Gaps small when learning & reasoning demands are light Gaps large when learning & reasoning demands are heavy

Common in schools & jobs

Background fact #5:

Task complexity increases gaps in performanceSlide48

48

48

Gaps small when learning & reasoning demands are light Gaps large when learning & reasoning demands are heavy

Common in schools & jobs

Background fact #5:

Task complexity increases gaps in performance

Cognitive load brings out differences in cognitive susceptibilitySlide49

49

49

New technologies make life increasingly

complex, which puts yet higher premium on gPreventive & curative care becoming increasing complexSlide50

50

50

Background fact #6:

People differ more than often assumed U.S. Dept of Education 1993 survey of adult functional literacy (nationally representative sample, ages 16+, N=26,091)NALS Level

% pop.

Simulated Everyday Tasks

5

3%

Use calculator to determine cost of carpet for a room

Use table of information to compare 2 credit cards

4

17%

Use eligibility pamphlet to calculate SSI benefits

Explain difference between 2 types of employee benefits

3

31%

Calculate miles per gallon from mileage record chart

Write brief letter explaining error on credit card bill

2

27%

Determine difference in price between 2 show tickets

Locate intersection on street map

1

22%

Total bank deposit entry

Locate expiration date on driver’s license

Routinely able to perform tasks only up to this level of difficultySlide51

51

51

NALS Level

% pop.

Simulated Everyday Tasks

5

3%

Use calculator to determine cost of carpet for a room

Use table of information to compare 2 credit cards

4

17%

Use eligibility pamphlet to calculate SSI benefits

Explain difference between 2 types of employee benefits

3

31%

Calculate miles per gallon from mileage record chart

Write brief letter explaining error on credit card bill

2

27%

Determine difference in price between 2 show tickets

Locate intersection on street map

1

22%

Total bank deposit entry

Locate expiration date on driver’s license

Difficulty based on “process complexity”

level of inference

abstractness of info

distracting information

Not reading per se, but “problem solving”

Background fact #6:

People differ more than often assumed

U.S. Dept of Education 1993 survey of adult functional literacy

(nationally representative sample, ages 16+, N=26,091)

Cognitive load brings out cognitive susceptibilitiesSlide52

52

52

Item at NALS Level 1

Literal match One item Little distracting info 22% of US adults

78% of adults do better

80% probability of correctly answering items of this difficulty level

*

*Slide53

53

53

Item at NALS Level 2

X Simple inference Little distracting information

27% of US adults

51%

22%Slide54

54

54

Another item at NALS Level 2

27% of US adults

Match two pieces of info

51%

22%Slide55

55

55

Item at NALS Level 3

31% of US adults Cycle through complex table Irrelevant info

20%

49%Slide56

56

56

Item at NALS Level 4

More elements to match

More inferences

More distracting information

3%

80%

17% of US adults

Solved

Or,

Slide57

57

57

Item at NALS Level 5

97% Search through complex displays Multiple distractors Make high-level text-based inferences Use specialized knowledge

3% of US adultsSlide58

58

58

NALS Level

% pop.

Simulated Everyday Tasks

5

3%

Use calculator to determine cost of carpet for a room

Use table of information to compare 2 credit cards

4

17%

Use eligibility pamphlet to calculate SSI benefits

Explain difference between 2 types of employee benefits

3

31%

Calculate miles per gallon from mileage record chart

Write brief letter explaining error on credit card bill

2

27%

Determine difference in price between 2 show tickets

Locate intersection on street map

1

22%

Total bank deposit entry

Locate expiration date on driver’s license

US Dept of Education: People at levels 1-2 are below literacy level required to enjoy rights & fulfill responsibilities of citizenship

Could teach these individual

items, but not all such tasks

in daily life

Background fact #6:

People differ more than often assumed

U.S. Dept of Education 1993 survey of adult functional literacy

(nationally representative sample, ages 16+, N=26,091)Slide59

59

59

What are “disparities”?

Why such a vexing puzzle?Is human cognitive diversity the key to solving it?If yes, so what?Mine the other 2/3 (cognitive susceptibility & cognitive load)AgendaSlide60

Passive exposure matters

SES

differences predicted

Current SES stress modelAlternative g stress modelPredictorsTime 1Time 2Time 1

Time 2ExposurePassiveEp

+

+

ActiveEa

S

usceptibility

Biological

Sb

0

+

Cognitive

Sc

B

urden

Biological

Bb

Cognitive

Bc

Health

outcomes

Physiological

Yp

0

+

Behavioral

Yb

mechanism

Y = ∑

Ep

60Slide61

But so does

g-based self-exposure, susceptibility, & cognitive load

SES

differences predictedCurrent SES stress modelAlternative g stress modelPredictors

Time 1Time 2Time 1

Time 2Exposure

PassiveEp

+++

+

Active

Ea

+

+

S

usceptibility

Biological

Sb

0

+

?

+

Cognitive

Sc

+

+

B

urden

Biological

Bb

?

?

Cognitive

Bc

?

+

Health

outcomes

Physiological

Yp

0

+

?

++

Behavioral

Yb

+

+

mechanism

Y = ∑

Ep

Y

= ∑E(S)(B)

61Slide62

SES

differences predicted

Current SES stress model

Alternative g stress modelPredictorsTime 1Time 2Time 1Time 2

ExposurePassiveEp

Active

Ea

S

usceptibility

Biological

Sb

Cognitive

Sc

B

urden

Biological

Bb

Cognitive

Bc

Health

outcomes

Physiological

Yp

Behavioral

Yb

mechanism

Y = ∑

Ep

Y

= ∑E(S)(B)

62

Internal

External

External

Some are multiplicative

6 (not 1) generators of health disparities,

and multiplicative besidesSlide63

2 new points of leverage

SES

differences predicted

Current SES stress modelAlternative g stress modelPredictorsTime 1Time 2Time 1

Time 2ExposurePassiveEp

Active

Ea

S

usceptibility

Biological

Sb

Cognitive

Sc

B

urden

Biological

Bb

Cognitive

Bc

Health

outcomes

Physiological

Yp

Behavioral

Yb

mechanism

Y = ∑

Ep

Y

= ∑E(S)(B)

63

Internal

External

External

#1

#2

Respect diversity of needs

Lighten the loadSlide64

64

64

Need appreciate differential cognitive

needs#1Slide65

65

65

Need appreciate size of cognitive

burdensExample: Do job analysis of chronic diseases

Diabetes?

#2Slide66

66

66

Guidance for providers?

E.g., Matrices of cognitive riskIQIQLo

Hi

Lo

Hi

Lo

Hi

Some errors more dangerous

But all cumulate

Triage

Task complexity

Error rates to expect by

patient susceptibility

task cognitive load

#1

#2

#1

#2Slide67

67

67

Conclusions

Key mechanisms unrecognizedMechanisms highly exploitableHuge opportunity costsFor national policyFor clinic practiceFor vulnerable populationsAmerican Psychological AssociationSlide68

68

68

Thank You

Linda S. Gottfredson, ProfessorUniversity of Delawarehttp://www.udel.edu/educ/gottfredsongottfred@udel.edu(302) 831-1650

American Psychological Association