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Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
Slide 1 - Welcome to the University of Delaware
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Slide 1 - Welcome to the University of Delaware

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  • Disparities a big concern of health policy, like it is in education policyThey both begin with much the same presumptions—that, but for social inequality, the disparities would not exist (or be so large). That disparities in external circumstances (“get under the skin” to) create internal disparities, not vice versa. That our health is in other people’s hands, especially those of health providers.
  • Points:Rates per 100,000 range widely—from .11 to 30Chance is major component in all “accidents,” but all accidental deaths are preventablePreventable includes preventing the incident, noticing its onset, limiting its progress, limiting its damage, getting appropriate care, adhering to care
  • Points:Rates per 100,000 range widely—from .11 to 30Chance is major component in all “accidents,” but all accidental deaths are preventablePreventable includes preventing the incident, noticing its onset, limiting its progress, limiting its damage, getting appropriate care, adhering to care
  • Transcript

    • 1. 11 Linda S. Gottfredson, Professor 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? American Psychological Association
    • 2. 22 1. What are “disparities”? 2. What’s the vexing puzzle? 3. Is human cognitive diversity key to solving it? 4. If yes, so what? Agenda Answers: All surprising
    • 3. 33 1. What are “disparities”? 2. Why such a vexing puzzle? 3. Is human cognitive diversity the key to solving it? 4. If yes, so what? Agenda Examples
    • 4. 44 “Disparity” = group differences on health outcome X “Explaining” between-group variation Means, rates, etc. 16 yrs12 yrs8 yrs Typical indicators of socioeconomic status (SES) • Years education • Occupational status • Income But not clear what they really represent or have in common ?
    • 5. 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) 0 10 20 30 40 50 60 70 8 12 16 0 10 20 30 40 50 60 70 8 12 16 Hypertension Diabetes white COPD black Cancer Men Women % Years
    • 6. 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) 0 10 20 30 40 50 60 70 8 12 16 0 10 20 30 40 50 60 70 8 12 16 Hypertension Diabetes white COPD black Cancer Men Women % • Fewer health problems in higher social classes (educ, occup, or $) •True for all races, sexes • Exceptions are rare (e.g., cancer morbidity) Years
    • 7. 7 Disparities in health behavior by education; all races & sexes: % who smoke, 2006 (age adjusted) (CDC, Health in the United States, 2008, Table 64) 0 5 10 15 20 25 30 35 40 <Diploma HS diploma Some college BA+ % White female Black female White males Black males %
    • 8. 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) 0 5 10 15 20 25 30 35 40 45 50 1974 1979 1983 1987 1991 1995 1999 2003 2006 <diploma HS diploma Some college BA+ 16.5 20.6 % better, gap bigger %
    • 9. 99 Many families of health disparities HEALTH HABITS MORTALITY KNOWLEDGE CHRONIC ILLNESSES INJURIES INFECTIOUS DISEASES ADHERENCE
    • 10. 1010 Many families of health disparities HEALTH HABITS MORTALITY KNOWLEDGE CHRONIC ILLNESSES INJURIES INFECTIOUS DISEASES ADHERENCE Outcomes for populations
    • 11. 1111 a b c d d f g 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!
    • 12. 12 Study of populations aided by epidemiological approach • Outcomes o Means, rates, relative risk, odds ratios for groups • Predictors—classic trio o Exposure to hazards, help (probability) o Host (susceptibility) o Vector (virulence, burden)
    • 13. 13 Study of populations aided by epidemiological approach • Outcomes o Means, rates, relative risk, odds ratios for groups • Predictors—classic trio o Exposure (probability) o Host (susceptibility) o Vector (virulence, burden) Missing 2/3 Current focus of SES disparities research
    • 14. 1414 1. What are “disparities”? 2. Why such a vexing puzzle? But first, what exactly are we trying to explain? Statistically • Substantively 3. Is human cognitive diversity the key to solving it? 4. If yes, so what? Agenda Illustration
    • 15. 1515 Illustration with 2 disparities # 1 # 2
    • 16. 1616 1 2 3 4 5 Social class groupings Health (group mean or rate) Each disparity is a gradient, with a slope (ß) Statistically… # 1 ß1
    • 17. 1717 1 2 3 4 5 Social class groupings Health (group mean or rate) Each disparity is a gradient, with a slope Statistically… # 2 # 1 ß1
    • 18. 1818 1 2 3 4 5 Social class groupings Health (group mean or rate) Each disparity is a gradient, with a slope Statistically… # 2 # 1 ß1 ß2
    • 19. 1919 1 2 3 4 5 Social class groupings Health (group mean or rate) Many families of health gradients (slopes): Morbidity, mortality, knowledge, prevention, adherence, etc. rare ß1 ß8 ß7 -ß6 ß5 ß4 ß3 ß2
    • 20. 2020 So, to explain SES disparities: Explain the distribution of co-evolving gradients (ß, their standardized slopes) ß ß ß ß ß ß3 ß8 ß ß ß ß ß4 ß ß ß2ß6 ß ß ß ß ß ß ß ß7 ß ß ß ß ß2 ß ß1 ß 0 Slopes (steepness) of gradients negative positive Common policy goal : All β = 0
    • 21. 2121 1. What are “disparities”? 2. Why such a vexing puzzle? But first, what exactly are we trying to explain? • Statistically Substantively 3. Is human cognitive diversity the key to solving it? 4. If yes, so what? Agenda Examples
    • 22. 2222 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??
    • 23. 2323 Exposure hypothesis 1: “Wealth = health” (can afford good care) health wealth No leveling off when resources are more than sufficient
    • 24. 2424 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 E.g., UK in 1950s
    • 25. 2525 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
    • 26. 2626 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 -ßMore educated women have higher death rates
    • 27. 27 Access matters, but so does utilization Even if equal access Unequal use & misuse • Mammograms • Adherence to treatment • Seat belt use • Etc. “Health literacy”
    • 28. 2828 1 2 3 4 5 Social class groupings Health (group mean or 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.
    • 29. 2929 So, the field seeking more “fundamental cause” of SES disparities • This cause must: o be pervasive & domain-general o have linear (monotonic) effects o not be material • Most popular suspect = inequality itself o relative deprivation chronic psychological stress damaging physiological process: “allostatic load” • Stress important, but can’t explain: o why adding resources increases disparities o disparities in non-biological outcomes
    • 30. 30 0% 20% 40% 60% 80% 100% 1-4 5-9 10- 17 18- 24 25- 44 45- 54 55- 64 65+ Illness Suicide Homicide "Accidents" 20062003-2005 Biological mechanisms Involved here But 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 preventable
    • 31. 31 Example: Unintentional (“accidental”) death Odds ratios by neighborhood income (1980-86) 0.0 0.5 1.0 1.5 2.0 2.5 Oddsratio Neighborhoodincome/capita Motor vehicle traffic Other unintentional 20 per 100,000 21 Reference group Odds = % affected Odds ratio = Odds for Group 1_______ % not Odds for reference group Just differential exposure??
    • 32. 32 Selected causes of “motor vehicle traffic” death, by neighborhood income/capita (1980-86) (Baker, O’Neill, Ginsburg, & Li, 1992) 0 0.5 1 1.5 2 2.5 3 3.5 <6,000 6-7,000 8-9,000 10-11,000 12-13,000 14,000+ Neighborhood income/capita Oddsratio Pedestrian, traffic Pedestrian, train Ped, off-road MV occupant MV train All MV 20 3.2 15 .20 .18 .26 elderly adult men young men toddlers young men young men Primarily: Rate per 100,000
    • 33. 33 Selected “other” causes of unintentional death, by neighborhood income/capita (1980-86) (Baker, O’Neill, Ginsburg, & Li, 1992) 0 1 2 3 4 5 6 7 8 <6,000 6-7,999 8-9,999 10-11,999 12-13,999 >14,000 Exposure/neglect Natural disasters Fires/burns Lightning Motor vehicle Drown Choke on food Suffocation20.0 2.60 .04 2.30 .06 .38 .78 Oddsratio 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 susceptivity
    • 34. 34 • Prevention o It’s our job o It’s daily, unrelenting, life-long (hazards are everywhere) o It’s complex • It’s a highly cognitive, multi-step, active process o Spot & avoid hazards o Recognize signs of system veering out of control o Take action to regain control o Limit progression of illness/accident or damage it does o Adhere to treatment o Learn from experience to adjust future behavior The common mechanism for illness and injury? Passive-patient model is dead wrong
    • 35. 3535 1. What are “disparities”? 2. Why such a vexing puzzle? 3. Is human cognitive diversity the key? 4. If yes, so what? Agenda IQ/g
    • 36. 3636 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 & employment
    • 37. 3737 Gaps in IQ/g (cognitive susceptibility-efficiency) Heavier cognitive load (g loading of tasks) Heavier cognitive load (g loading of tasks) ßß ß ß ß ß ß ß ß ß ß ß ß Translated: A hypothesis about gradients
    • 38. 3838 Background fact #1 Great cognitive diversity is a biological fact about all populations 70 75 80 85 90 95 100 105 110 115 120 125 130 IQ
    • 39. 3939 Background fact #2 IQ ≈ g (general mental ability factor) • g is no longer a black box • g is a domain-general facility for learning, reasoning, spotting & solving novel problems o Higher g reduces susceptibility to error o Gives bigger edge as task complexity (cognitive load) increases o Allows one to exploit resources more fully & effectively (e.g., classroom instruction, medical treatments)
    • 40. 4040 Background fact #2 IQ ≈ g (general mental ability factor) • g is no longer a black box • g is a domain-general facility for learning, reasoning, spotting & solving novel problems o Higher g reduces susceptibility to error o Gives bigger edge as task complexity (cognitive load) increases o Allows one to exploit resources more fully & effectively (e.g., classroom instruction, medical treatments)
    • 41. 4141 Gives an edge in planning; anticipating problems
    • 42. 4242 Background fact #3 Mean IQs differ by occupation level and years education 70 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 & TechOccupation: Years education: WAIS-R IQ (mean + 1 SD), US adults ages 16-74 IQ
    • 43. 4343 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 IQ
    • 44. 4444 .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 show larger health disparities
    • 45. Better surrogates for g show larger health disparities (steeper gradients) 45 income occupation education “literacy”
    • 46. 4646 .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 itself
    • 47. 4747 • 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
    • 48. 4848 • 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 susceptibility
    • 49. 4949 New technologies make life increasingly complex, which puts yet higher premium on g Preventive & curative care becoming increasing complex
    • 50. 5050 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 difficulty
    • 51. 5151 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 susceptibilities
    • 52. 5252 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 * *
    • 53. 5353 Item at NALS Level 2 X • Simple inference • Little distracting information 27% of US adults 51%22%
    • 54. 5454 Another item at NALS Level 2 27% of US adults • Match two pieces of info 51%22%
    • 55. 5555 Item at NALS Level 3 31% of US adults • Cycle through complex table • Irrelevant info 20%49%
    • 56. 5656 Item at NALS Level 4 • More elements to match • More inferences • More distracting information 3%80% 17% of US adults Solved Or,
    • 57. 5757 Item at NALS Level 5 97% • Search through complex displays • Multiple distractors • Make high-level text-based inferences • Use specialized knowledge 3% of US adults
    • 58. 5858 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)
    • 59. 5959 1. What are “disparities”? 2. Why such a vexing puzzle? 3. Is human cognitive diversity the key to solving it? 4. If yes, so what?  Mine the other 2/3 (cognitive susceptibility & cognitive load) Agenda
    • 60. Passive exposure matters SES differences predicted Current SES stress model Alternative g stress model Predictors Time 1 Time 2 Time 1 Time 2 Exposure Passive Ep + + Active Ea Susceptibility Biological Sb 0 + Cognitive Sc Burden Biological Bb Cognitive Bc Health outcomes Physiological Yp 0 + Behavioral Yb mechanism Y = ∑Ep 60
    • 61. But so does g-based self-exposure, susceptibility, & cognitive load SES differences predicted Current SES stress model Alternative g stress model Predictors Time 1 Time 2 Time 1 Time 2 Exposure Passive Ep + + + + Active Ea + + Susceptibility Biological Sb 0 + ? + Cognitive Sc + + Burden Biological Bb ? ? Cognitive Bc ? + Health outcomes Physiological Yp 0 + ? ++ Behavioral Yb + + mechanism Y = ∑Ep Y = ∑E(S)(B) 61
    • 62. SES differences predicted Current SES stress model Alternative g stress model Predictors Time 1 Time 2 Time 1 Time 2 Exposure Passive Ep Active Ea Susceptibility Biological Sb Cognitive Sc Burden 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 besides
    • 63. 2 new points of leverage SES differences predicted Current SES stress model Alternative g stress model Predictors Time 1 Time 2 Time 1 Time 2 Exposure Passive Ep Active Ea Susceptibility Biological Sb Cognitive Sc Burden 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 load
    • 64. 6464 Need appreciate differential cognitive needs #1
    • 65. 6565 Need appreciate size of cognitive burdens Example: Do job analysis of chronic diseases Diabetes? #2
    • 66. 6666 Guidance for providers? E.g., Matrices of cognitive risk IQ IQ Lo 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 #2
    • 67. 6767 Conclusions • Key mechanisms unrecognized • Mechanisms highly exploitable • Huge opportunity costs oFor national policy oFor clinic practice oFor vulnerable populations American Psychological Association
    • 68. 6868 Thank You Linda S. Gottfredson, Professor University of Delaware http://www.udel.edu/educ/gottfredson gottfred@udel.edu (302) 831-1650 American Psychological Association

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