Racial disparities in pain: A social-cognitive perspective Diana Burgess, PhD Core Investigator Center for Chronic Disease Outcomes Research (CCDOR) Assistant Professor University of Minnesota, Department of Medicine

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Racial disparities in pain A social-cognitive
perspective Diana Burgess, PhDCore
InvestigatorCenter for Chronic Disease Outcomes
Research (CCDOR)Assistant ProfessorUniversity
of Minnesota, Department of Medicine
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Overview of talk

• Background
• Social-cognitive model How site of care may
  contribute to racial disparities in pain
  management
• Current Research Understanding presence and
  correlates of racial disparities in pain
  treatment using administrative data
• Opportunities/Future Directions
• Discussion

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Evidence of racial disparities in pain pain
treatment

• Greater prevalence of pain and greater
  impairment/severity of symptoms among nonwhites
• Contributors include
• Greater exposure to discrimination other
  stressors (Burgess, in press Edwards, 2008)
• Poorer pain treatment
• Racial/ethnic disparities in pain treatment
  (acute, chronic, bodily injury, postoperative,
  and cancer)
• E.g., Analysis of National Ambulatory Medical
  Care Survey from 1992 to 2001 - lower odds of
  receiving an opioid from a primary care physician
  for non-whites (Olsen, 2006)
• Systematic review by Green et al (2003)

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Evidence of racial disparities in VA

• Black veterans experience more pain, seek more
  treatment for pain report greater severity
  disability (2001 National Survey of Veterans
  Golightly, 2005, Dobscha, in press)
• Compared to whites, black veterans w/ chronic
  pain
• less likely to rate effectiveness of treatment as
  very good or excellent (Dobscha, in press)
• less likely than to be prescribed Schedule 2
  opioids (more potent) and were more likely to be
  prescribed Schedule 3 opioids (Burgess, 2009)
• Black veterans were less likely to have pain
  assessed than whites (Burgess, 2009)
• exploratory studies

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Racial disparities in pain management consistent
with disparities in other domains

• Over 500 peer-reviewed studies have found racial
  disparities in medical care (e.g., IOM report,
  Unequal Treatment)
• Systematic review (Saha, 2008) - evidence of
  disparities in the VA

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1. Patient preference2. Site of care 3.
Provider contribution
Potential sources of healthcare disparities
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1. Patient preferences (e.g., Non-whites more
likely to refuse treatment)

• Does not have strong support
• IOM report concluded that patient preferences are
  unlikely to be major sources of healthcare
  disparities
• Studies that have examined the role of patient
  preferences find that racial differences in
  refusal rates are small and that disparities
  persist controlling for patient preferences (e.g.
  Ayanian, 1999, Conigliaro, 2002 Hannan, 1999,
  Kressin, 2002, Petersen van Ryn, 2000 2006
  Whittle, 1997)

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2. Site of care (minorities treated in settings
w/ lower quality care)

• Growing evidence for this(e.g., Bach, 2004,
  2005 Skinner, 2005 Epstein, 2004 Clarke, 2007
  Lucas, 2006 Konety, 2005 Barnato 2005)
• Although disparities have been documented
  independent of treatment site
• Some evidence that racial disparities are more
  likely in healthcare settings with higher
  concentration of minority patients (Silber, 2007
  Groeneveld, 2007)

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3. Provider contribution to disparities

• Some evidence that providers diagnostic
  treatment decisions are influenced by patients
  race ethnicity
• likely to be unintentional, unconscious due to
  normal cognitive processes
• More research is needed to understand the
  underlying mechanisms
• Evidence of poorer quality of communication for
  non-white vs non-white patients

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How site of care may contribute to healthcare
disparities A social-cognitive model

• Model posits that characteristics of healthcare
  settings that lead providers to experience
  excessive levels of cognitive load will increase
  the likelihood that providers will
  unintentionally contribute to racial/ethnic
  disparities
• Cognitive load the amount of mental activity
  imposed on working memory
• can come from the task itself
• also from fatigue, stress, multi-tasking, time
  pressure, etc.
• Burgess, in press, Medical Decision Making

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This model is grounded in dual process models of
social cognition

• Automatic vs controlled processes
• Controlled processes relatively intentional,
  conscious, effortful, controllable
• Automatic processes relatively unintentional,
  unconscious, effortless, uncontrollable
• Cognitive load can interrupt, impair, or prevent
  execution of controlled processes
• This leads to a greater reliance on automatic
  processes, which are not disrupted under high
  levels of cognitive load
• Use of racial stereotypes is one of these
  automatic processes

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Primary Hypotheses of Model

• Hypothesis 1 Providers who experience excessive
  levels of cognitive load will make poorer
  clinical decisions and provide poorer care
• Hypothesis 2 Providers who experience excessive
  levels of cognitive load will be more likely to
  be influenced by racial stereotypes, which will
  lead to poorer processes outcomes of care
• Hypothesis 3 Racial minorities are more likely
  to be treated in settings in which providers
  experience excessively high levels of cognitive
  load (i.e., levels that harm performance)
• Hence, racial minorities will be more likely to
  receive poorer care

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Hypothesis 1 Providers who experience excessive
levels of cognitive load will make poorer medical
decisions and provide poorer care (for all
patients)

• Experienced clinicians rely on automatic
  processes (e.g., generating diagnosing, use of
  heuristics) but, ideally are able to
  strategically shift to controlled processes when
  needed
• Under cognitive load, clinicians ability to
  switch from automatic to controlled processing
  may become compromised.
• Evidence from aviation, human factors,
  educational research shows decreased performance
  when cognitive load is too high

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Hypothesis 2 Providers who experience excessive
levels of cognitive load will be more likely to
be influenced by racial stereotypes... This will
lead to poorer processes outcomes of care
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Hypothesis 2 Providers who experience excessive
levels of cognitive load will be more likely to
be influenced by racial stereotypes... This will
lead to poorer processes outcomes of care

• Stereotypes concepts that contain our knowledge,
  beliefs, expectations, and feelings about a
  social group
• Salient patient characteristics (e.g., race) may
  activate stereotypes , which may influence
  providers
• interpretation of behaviors and symptoms,
• expectations about patient behaviors,
• behaviors toward patients ...which can influence
  patients behaviors
• This can occur automatically (or implicitly),
  without conscious intent

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Everyone engages in stereotypingnot just
providers
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Stereotypes are more likely to be activated and
applied under high levels of cognitive load

• Under cognitive load it is
• More likely to rely on automatic processes such
  as stereotyping
• Less likely that we will
• override or correct for stereotypes that are
  activated, via controlled processes
• engage in individuation (focus on the unique
  features of the person), which may differ from
  the stereotype that was automatically activated

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Indirect evidence that cognitive load may
increases healthcare disparities via provider
stereotyping (Muroff, 2007)

• Hypothesis Gender stereotypes will be more
  likely to influence mental health diagnoses under
  high cognitive load
• Methods Retrospective chart review of patients
  treated in Psychiatric Emergency Services (N
  1236)
• Cognitive load was operationalized as high versus
  low levels of patient load (experienced by each
  provider)
• Results Under conditions of high cognitive load,
  being female increased the odds of receiving a
  diagnosis of depressive disorder (a disorder
  that has been shown to be over-diagnosed among
  women)

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Hypothesis 3 Racial minorities are more likely
to be treated in settings with excessive levels
of cognitive load

• Study by Varkey et al, 2009 Archives of Internal
  Medicine
• Physicians in clinics w/ at least 30 minority
  patients
• (N 27) were more likely than physicians in
  other clinics (N 69) to
• Lack access to referral specialists
• Have more difficult/complex patients
• Report lower levels of job satisfaction work
  control
• Report a chaotic workplace (4 X more likely)
• These are all sources of cognitive load that may
  contribute to lower performance and increase the
  likelihood of bias

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III. Current research Presence Correlates of
Disparities in Pain Management

• Will assess
• 1) the extent to which racial disparities in
  pain assessment, treatment, and outcomes exist
  across VHA facilities
• 2) whether racial disparities are smaller or less
  likely in organizations with greater structures
  processes that support high quality pain
  management.
• Such structures/processes free up cognitive
  resources for providers, improving the quality
  of decision-making/care overall and reducing the
  likelihood that racial stereotypes will influence
  decisions
• VA HSRD Co-investigators Bair, Farmer, Kerns,
  Nelson, Partin, van Ryn

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Secondary data analysis

• 2007 Survey of Healthcare Experiences of Patients
  (SHEP), ambulatory care model
• Sampling frame (AA vs. white, w/ visit in primary
  care)
• Pain outcomes (perceived effectiveness of pain
  treatment functional interference due to pain)
• Corporate data warehouse (CDW)
• Pain assessment (presence of a pain score)
• Pharmacy Benefits Management (PBM) database
• Pain treatment (pain medication)
• Clinical Practice Organizational Survey Primary
  Care Directors Module (CPOS-PC)
• Structures/processes that support pain management
  general measures of cognitive load
• OQP (Office of Quality Performance)
• Cognitive load (primary care access measures)

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Examine the presence correlates of racial
disparities among the following cohorts

• Pain Assessment Cohort Was pain assessed at
  SHEP sampling visit?
• Base sample SHEP responders non-responders
  whose index visit was in primary care
• Pain Treatment Cohort Pain medication issued at
  patient encounters one year prior to index visit
• Chronic pain sample Patients in base sample
  with chronic pain Dx in past year
• Pain Outcomes Cohort Perceived effectiveness of
  pain among treatment/ functional interference due
  to pain
• Outcomes sample Patients in chronic pain sample
  who responded to the SHEP

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IV. Opportunities/future research - using this
dataset administrative VA data

• Examine variation in pain management among other
  vulnerable/stigmatized groups
• Obese versus non-obese (Pilot study to be
  submitted, P.I. Diana Higgins)
• Women (gender stereotypes)
• Age/cohort (elderly, OEF/OIF)
• Mental health comorbidities
• Examine association between treatment outcomes

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Other research questions...

• In OEF/OIF population
• Are there racial differences in presence of pain
  or in the relationship among pain, PTSD
  post-concussive syndrome?
• What is the role of early cumulative exposure
  to stress adversity as a mediator/contributor
  (e.g., Shonoff, 2009 JAMA)?
• How might stereotypes/subtypes based on OEF/OIF
  status race/ethnicity affect pain treatment?

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