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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. 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. 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. 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. 4
    “Disparity” = group differences on health outcome X
    “Explaining” between-group variation
    Means, rates, etc.
    8 yrs
    16 yrs
    12 yrs
    Typical indicators of socioeconomic status (SES)
    • Years education
    • 5. Occupational status
    • 6. Income
    But not clear what they really represent or have in common
    ?
    4
  • 7. 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
  • 8. 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 $)
    • 9. True for all races, sexes
    • 10. Exceptions are rare (e.g., cancer morbidity)
    Hypertension
    Diabetes white
    COPD black
    Cancer
  • 11. 7
    Disparities in health behavior by education; all races & sexes: % who smoke, 2006 (age adjusted)(CDC, Health in the United States, 2008, Table 64)
    %
  • 12. 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
  • 13. 9
    Many families of health disparities
    CHRONIC ILLNESSES
    KNOWLEDGE
    HEALTH HABITS
    ADHERENCE
    INJURIES
    MORTALITY
    INFECTIOUS DISEASES
    9
  • 14. 10
    Many families of health disparities
    CHRONIC ILLNESSES
    KNOWLEDGE
    Outcomes for populations
    HEALTH HABITS
    ADHERENCE
    INJURIES
    MORTALITY
    INFECTIOUS DISEASES
    10
  • 15. 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
  • 16. 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)
  • 17. 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
  • 18. 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
  • 19. 15
    Illustration with 2 disparities
    # 1
    # 2
    15
  • 20. 16
    Statistically…
    ß1
    # 1
    Health
    (group
    mean
    or rate)
    1
    2
    3
    4
    5
    Social class groupings
    16
    Each disparity is a gradient, with a slope (ß)
  • 21. 17
    Statistically…
    ß1
    # 1
    Each disparity is a gradient, with a slope
    Health
    (group
    mean
    or rate)
    # 2
    1
    2
    3
    4
    5
    Social class groupings
    17
  • 22. 18
    Statistically…
    ß1
    Each disparity is a gradient, with a slope
    # 1
    Health
    (group
    mean
    or rate)
    ß2
    # 2
    1
    2
    3
    4
    5
    Social class groupings
    18
  • 23. 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
  • 24. 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
    0
    negative
    positive
    Slopes (steepness) of gradients
    20
  • 25. 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
  • 26. 22
    They are pervasive, persistent and monotonic regardless of time, place,
    health system, disease, and behavior. Why??
    22
    General puzzle: Health disparities are too general for SES mechanisms to explain
  • 27. 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
  • 28. 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
  • 29. 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
  • 30. 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

    More educated women have higher death rates
    • Positive disparities for Outcome X at Time 2
    ß
    26
  • 31. 27
    Access matters, but so does utilization
    Even if equal access
    Unequal use & misuse
    “Health literacy”
  • 35. 28
    Summary of puzzle
    Exposure can’t explain why gradients:
    • Virtually never = zero
    • 36. Virtually always positive
    • 37. All monotonic (~linear)
    • 38. For ~all health outcomes & behaviors
    • 39. 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
  • 40. 29
    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
  • 41. 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
    2006
    2003-2005
    Common theme—all are preventable
  • 42. 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
  • 43. 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
  • 44. 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
  • 45. 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
  • 46. 35
    Agenda
    What are “disparities”?
    Why such a vexing puzzle?
    Is human cognitive diversity the key?
    If yes,so what?
    IQ/g
    35
  • 47. 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)
    • 48. Heavier cognitive load (tasks)
    susceptibility
    burden
    *Based on extensive research in education & employment
    36
  • 49. 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
  • 50. 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
  • 51. 39
    Background fact #2IQ ≈ g (general mental ability factor)
    • g is no longer a black box
    • 52. g is a domain-general facility for learning, reasoning, spotting & solving novel problems
    • 53. Higher g reduces susceptibility to error
    • 54. Gives bigger edge as task complexity (cognitive load) increases
    • 55. Allows one to exploit resources more fully & effectively (e.g., classroom instruction, medical treatments)
    39
  • 56. 40
    Background fact #2IQ ≈ g (general mental ability factor)
    • g is no longer a black box
    • 57. g is a domain-general facility for learning, reasoning, spotting & solving novel problems
    • 58. Higher g reduces susceptibility to error
    • 59. Gives bigger edge as task complexity (cognitive load) increases
    • 60. Allows one to exploit resources more fully & effectively (e.g., classroom instruction, medical treatments)
    40
  • 61. 41
    41
    Gives an edge in planning; anticipating problems
  • 62. 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
  • 63. 43
    Background fact #4: Some SES indicators correlate more with IQ
    All
    moderately
    heritable,
    & overlap
    genetically
    with IQ
    .8 Standardized academic achievement
    .6 Years education
    .5 Occupation level
    .3-.4 Income
    (prior)
    IQ
    43
  • 64. 44
    Background fact #4: Conversely, some are better surrogates for IQ
    .8 Literacy
    .8 Standardized academic achievement
    .6 Years education
    .5 Occupation level
    .3-.4 Income
    Better surrogates for g
    show larger
    health disparities
    Excellent
    Good
    Weak
    (prior)
    IQ
    44
  • 65. Better surrogates for g show largerhealth disparities(steeper gradients)
    45
    income
    occupation
    education
    “literacy”
  • 66. 46
    Background fact #4: Conversely, some are better surrogates for IQ
    Cannot “control” for SES without
    controlling away much of (genetic) g itself
    .8 Literacy
    .8 Standardized academic achievement
    .6 Years education
    .5 Occupation level
    .3-.4 Income
    Excellent
    Good
    Weak
    (prior)
    IQ
    46
  • 67. 47
    • Gaps small when learning & reasoning demands are light
    • 68. Gaps large when learning & reasoning demands are heavy
    Common in schools & jobs
    47
    Background fact #5:Task complexity increases gaps in performance
  • 69. 48
    • Gaps small when learning & reasoning demands are light
    • 70. Gaps large when learning & reasoning demands are heavy
    Cognitive load brings out differences in cognitive susceptibility
    Common in schools & jobs
    48
    Background fact #5:Task complexity increases gaps in performance
  • 71. 49
    New technologies make life increasingly
    complex, which puts yet higher premium on g
    Preventive & curative care becoming increasing complex
    49
  • 72. 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
  • 73. 51
    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)
    Difficulty based on “process complexity”
    Cognitive load brings out cognitive susceptibilities
    • level of inference
    • 74. abstractness of info
    • 75. distracting information
    Not reading per se, but “problem solving”
    51
  • 76. 52
    Item at NALS Level 1
    *
    22% of US adults
    78% of adults do better
    • Literal match
    • 77. One item
    • 78. Little distracting info
    80% probability of correctly answering items of this difficulty level
    *
    52
  • 79. 53
    Item at NALS Level 2
    27% of US adults
    51%
    22%
    X
    • Simple inference
    • 80. Little distracting information
    53
  • 81. 54
    Another item at NALS Level 2
    27% of US adults
    51%
    22%
    • Match two pieces of info
    54
  • 82. 55
    Item at NALS Level 3
    31% of US adults
    20%
    49%
    • Cycle through complex table
    • 83. Irrelevant info
    55
  • 84. 56
    Item at NALS Level 4
    17% of US adults
    3%
    80%
    Or,
    Solved
    • More elements to match
    • 85. More inferences
    • 86. More distracting information
    56
  • 87. 57
    57
    Item at NALS Level 5
    3% of US adults
    97%
    • Search through complex displays
    • 88. Multiple distractors
    • 89. Make high-level text-based inferences
    • 90. Use specialized knowledge
  • 58
    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)
    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
    58
  • 91. 59
    Agenda
    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)
    59
  • 92. Passive exposure matters
    60
  • 93. But so does g-basedself-exposure, susceptibility, & cognitive load
    61
  • 94. 62
    External
    Internal
    External
    Some are multiplicative
    6 (not 1) generators of health disparities, and multiplicative besides
  • 95. 2 new points of leverage
    63
    External
    Internal
    #1
    Respect diversity of needs
    External
    #2
    Lighten the load
  • 96. 64
    Need appreciate differential cognitive needs
    #1
    64
  • 97. 65
    Need appreciate size of cognitive burdens
    #2
    Diabetes?
    Example: Do job analysis of chronic diseases
    65
  • 98. 66
    Guidance for providers?E.g., Matrices of cognitive risk
    Error rates to expect by
    patient susceptibility
    task cognitive load
    Hi
    Hi
    #1
    #2
    • Some errors more dangerous
    • 99. But all cumulate
    IQ
    IQ
    #1
    Triage
    Lo
    Lo
    Lo
    Hi
    Task complexity
    #2
    66
  • 100. 67
    Conclusions
    • Key mechanisms unrecognized
    • 101. Mechanisms highly exploitable
    • 102. Huge opportunity costs
    • 103. For national policy
    • 104. For clinic practice
    • 105. For vulnerable populations
    American Psychological Association
    67
  • 106. 68
    68
    Thank You
    Linda S. Gottfredson, Professor
    University of Delaware
    http://www.udel.edu/educ/gottfredson
    gottfred@udel.edu
    (302) 831-1650
    American Psychological Association