Racial disparities in pain: A social-cognitive perspective

Diana Burgess, PhD

Core InvestigatorCenter for Chronic Disease Outcomes Research (CCDOR)

Assistant ProfessorUniversity of Minnesota, Department of Medicine

Overview of talk

I. BackgroundII. Social-cognitive model: How site of care may contribute to

racial disparities in pain managementIII. Current Research: Understanding presence and correlates of

racial disparities in pain treatment using administrative dataIV. Opportunities/Future DirectionsV. Discussion

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)

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

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

1. Patient preference2. Site of care

3. Provider contribution

Potential sources of healthcare disparities

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)

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

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

• 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

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

• 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

This model is grounded in dual process models of social cognition

• 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

Primary Hypotheses of Model

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

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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• 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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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

Everyone engages in stereotyping—not just providers

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• 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

*Stereotypes are more likely to be activated and applied under high levels of cognitive load

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)

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

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 HSR&D; Co-investigators: Bair, Farmer, Kerns, Nelson, Partin, van Ryn

III. Current research: Presence & Correlates of Disparities in Pain Management*

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)

Examine the presence & correlates of racial disparities among the following cohorts:

1. Pain Assessment Cohort: Was pain assessed at SHEP sampling

visit?– Base sample: SHEP responders & non-responders whose index visit was in

primary care

2. 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

3. 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

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

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?