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Linda S. Gottfredson, Professor University of Delaware August 7, 2009

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

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Linda S. Gottfredson, Professor University of Delaware August 7, 2009

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  1. Social Class Disparities in Health: A Vexing Puzzle with a Surprising Answer? 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 American Psychological Association 1

  2. Agenda • What are “disparities”? • What’s the vexing puzzle? • Is human cognitive diversity key to solving it? • If yes, so what? Answers: All surprising 2

  3. Agenda • What are “disparities”? • Why such a vexing puzzle? • Is human cognitive diversity the key to solving it? • If yes,so what? Examples 3

  4. “Disparity” = group differences on health outcome X “Explaining” between-group variation Means, rates, etc. 8 yrs 12 yrs 16 yrs • Typical indicators of socioeconomic status (SES) • Years education • Occupational status • Income • But not clear what they really represent or have in common ? 4

  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) Men Women % Years Hypertension Diabetes white COPD black Cancer

  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) Men Women % Years • Fewer health problems in higher social classes (educ, occup, or $) • True for all races, sexes • Exceptions are rare (e.g., cancer morbidity) Hypertension Diabetes white COPD black Cancer

  7. Disparities in health behavior by education; all races & sexes: % who smoke, 2006 (age adjusted)(CDC, Health in the United States, 2008, Table 64) %

  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) 16.5 % 20.6 % better, gap bigger

  9. Many families of health disparities CHRONIC ILLNESSES KNOWLEDGE ADHERENCE HEALTH HABITS INJURIES MORTALITY INFECTIOUS DISEASES 9

  10. Many families of health disparities CHRONIC ILLNESSES KNOWLEDGE Outcomes for populations ADHERENCE HEALTH HABITS INJURIES MORTALITY INFECTIOUS DISEASES 10

  11. This is not about individual differences in health outcomes Not “explaining” within-group variation c a d b d g f Within-group and between-group variance may arise from different mix of causes Often misunderstood! 11

  12. Study of populations aided by epidemiological approach • Outcomes • Means, rates, relative risk, odds ratios for groups • Predictors—classic trio • Exposure to hazards, help (probability) • Host (susceptibility) • Vector (virulence, burden)

  13. Study of populations aided by epidemiological approach • Outcomes • Means, rates, relative risk, odds ratios for groups • Predictors—classic trio • Exposure (probability) • Host (susceptibility) • Vector (virulence, burden) Current focus of SES disparities research Missing 2/3

  14. Agenda • What are “disparities”? • Why such a vexing puzzle? But first, what exactly are we trying to explain? • Statistically • Substantively • Is human cognitive diversity the key to solving it? • If yes,so what? Illustration 14

  15. Illustration with 2 disparities # 1 # 2 15

  16. Statistically… Each disparity is a gradient, with a slope (ß) ß1 # 1 Health (group mean or rate) 1 2 3 4 5 Social class groupings 16

  17. Each disparity is a gradient, with a slope Statistically… ß1 # 1 Health (group mean or rate) # 2 1 2 3 4 5 Social class groupings 17

  18. Each disparity is a gradient, with a slope Statistically… ß1 # 1 Health (group mean or rate) ß2 # 2 1 2 3 4 5 Social class groupings 18

  19. Many families of health gradients (slopes):Morbidity, mortality, knowledge, prevention, adherence, etc. ß1 ß3 ß4 ß5 Health (group mean or rate) -ß6 rare ß2 ß7 ß8 1 2 3 4 5 Social class groupings 19

  20. So, to explain SES disparities: Explain the distribution of co-evolving gradients (ß, their standardized slopes) Common policy goal : All β = 0 ß ß ß ß ß ß3 ß8 ß ß ß ß ß2 ß ß ß ß ß4 ß ß ß2 ß ß ß ß ß7 ß ß ß ß1 ß ß6 ß negative 0 positive Slopes (steepness) of gradients 20

  21. Agenda • What are “disparities”? • Why such a vexing puzzle? But first, what exactly are we trying to explain? • Statistically • Substantively • Is human cognitive diversity the key to solving it? • If yes,so what? Examples 21

  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?? 22

  23. Exposure hypothesis 1: “Wealth = health” (can afford good care) REJECTED—Puzzle greater! health No leveling off when resources are more than sufficient wealth 23

  24. Experimental test of exposure hypothesis 1:Equalize access to care equalize health • Time 1: Unequal access FAILED—Puzzle greater! • Time 2: After equal access (free care) E.g., UK in 1950s Health disparities grow, not shrink 24

  25. Experimental test of exposure hypothesis 2:Unequal education unequal health • Time 1: Unequal knowledge of signs and symptoms FAILED—Puzzle greater! • Time 2: After public health campaign Knowledge disparities grow, not shrink 25

  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 -ß More educated women have higher death rates • Positive disparities for Outcome X at Time 2 ß 26

  27. Access matters, but so does utilization • Mammograms • Adherence to treatment • Seat belt use • Etc. Even if equal access Unequal use & misuse “Health literacy”

  28. Summary of puzzle • Exposure can’t explain why gradients: • Virtually never = zero • Virtually always positive • All monotonic (~linear) • For ~all health outcomes & behaviors • Steepen when resources equalized Health (group mean or rate) rare What levers the gradients up or down? Can’t be material resources. 1 2 3 4 5 Social class groupings 28

  29. So, the field seeking more “fundamental cause” of SES disparities • This cause must: • be pervasive & domain-general • have linear (monotonic) effects • not be material • Most popular suspect = inequality itself • relative deprivation chronic psychological stress damaging physiological process: “allostatic load” • Stress important, but can’t explain: • why adding resources increases disparities • disparities in non-biological outcomes 29

  30. First, physical illness is only one cause of injury & death: Causes of death, males by age(CDC, Health data interactive) But not here Biological mechanisms Involved here 2003-2005 2006 Common theme—all are preventable

  31. Example: Unintentional (“accidental”) death Odds ratios by neighborhood income(1980-86) 20 per 100,000 21 Reference group Just differential exposure?? Odds = % affected Odds ratio = Odds for Group 1_______ % not Odds for reference group

  32. Selected causes of “motor vehicle traffic” death, by neighborhood income/capita(1980-86)(Baker, O’Neill, Ginsburg, & Li, 1992) Primarily: Rate per 100,000 elderly adult men .26 toddlers .20 young men young men 15 young men 20 .18 3.2

  33. Selected “other” causes of unintentional death, by neighborhood income/capita (1980-86)(Baker, O’Neill, Ginsburg, & Li, 1992) Primarily: Deaths per 100,000 infants, elderly .12 rises with age toddlers, elderly young men .06 young men 2.30 young men Odds ratio .04 infants, elderly 20.0 Infants 2.60 .78 .38 Self-exposure Differential biological susceptivity

  34. The common mechanism for illness and injury? • Prevention • It’s our job • It’s daily, unrelenting, life-long (hazards are everywhere) • It’s complex • It’s a highly cognitive, multi-step, active process • Spot & avoid hazards • Recognize signs of system veering out of control • Take action to regain control • Limit progression of illness/accident or damage it does • Adhere to treatment • Learn from experience to adjust future behavior Passive-patient model is dead wrong

  35. Agenda • What are “disparities”? • Why such a vexing puzzle? • Is human cognitive diversity the key? • If yes,so what? IQ/g 35

  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 & employment 36

  37. Translated: A hypothesis about gradients ß ß ß Heavier cognitive load (g loading of tasks) Heavier cognitive load (g loading of tasks) ß ß ß ß ß ß ß ß ß ß Gaps in IQ/g(cognitive susceptibility-efficiency) 37

  38. 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 38

  39. Background fact #2IQ ≈ 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 • Higher g reduces susceptibility to error • Gives bigger edge as task complexity (cognitive load) increases • Allows one to exploit resources more fully & effectively (e.g., classroom instruction, medical treatments) 39

  40. Background fact #2IQ ≈ 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 • Higher g reduces susceptibility to error • Gives bigger edge as task complexity (cognitive load) increases • Allows one to exploit resources more fully & effectively (e.g., classroom instruction, medical treatments) 40

  41. Gives an edge in planning; anticipating problems 41

  42. Background fact #3 Mean IQs differ by occupation level and years education WAIS-R IQ (mean + 1 SD), US adults ages 16-74 Occupation: Professional & Tech Manager, Cler, Sales Skilled Semiskilled Unskilled Years education: 16+ 13-15 12 9-11 8 0-7 70 75 80 85 90 95 100 105 110 115 120 125 130 IQ 42

  43. Background fact #4: Some SES indicators correlate more with IQ .8 Standardized academic achievement .6 Years education .5 Occupation level .3-.4 Income All moderately heritable, & overlap genetically with IQ (prior) IQ 43

  44. .8 Literacy .8 Standardized academic achievement .6 Years education .5 Occupation level .3-.4 Income Background fact #4: Conversely, some are better surrogates for IQ Better surrogates for g show larger health disparities Excellent Good Weak (prior) IQ 44

  45. Better surrogates for g show largerhealth disparities(steeper gradients) income occupation education “literacy”

  46. .8 Literacy .8 Standardized academic achievement .6 Years education .5 Occupation level .3-.4 Income Background fact #4: Conversely, some are better surrogates for IQ Cannot “control” for SES without controlling away much of (genetic) g itself Excellent Good Weak (prior) IQ 46

  47. Background fact #5:Task complexity increases gaps in performance • Gaps small when learning & reasoning demands are light • Gaps large when learning & reasoning demands are heavy Common in schools & jobs 47

  48. Background fact #5:Task complexity increases gaps in performance • Gaps small when learning & reasoning demands are light • Gaps large when learning & reasoning demands are heavy Cognitive load brings out differences in cognitive susceptibility Common in schools & jobs 48

  49. New technologies make life increasingly complex, which puts yet higher premium on g Preventive & curative care becoming increasing complex 49

  50. Background fact #6:People differ more than often assumedU.S. Dept of Education 1993 survey of adult functional literacy (nationally representative sample, ages 16+, N=26,091) Routinely able to perform tasks only up to this level of difficulty 50

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