The Influence of Patient Race and Activation on Pain Management in Advanced Lung Cancer: a Randomized Field Experiment

Abstract

BACKGROUND: Pain management racial disparities exist, yet it is unclear whether disparities exist in pain management in advanced cancer. OBJECTIVE: To examine the effect of race on physicians' pain assessment and treatment in advanced lung cancer and the moderating effect of patient activation. DESIGN: Randomized field experiment. Physicians consented to see two unannounced standardized patients (SPs) over 18 months. SPs portrayed 4 identical roles-a 62-year-old man with advanced lung cancer and uncontrolled pain-differing by race (black or white) and role (activated or typical). Activated SPs asked questions, interrupted when necessary, made requests, and expressed opinions. PARTICIPANTS: Ninety-six primary care physicians (PCPs) and oncologists from small cities, and suburban and rural areas of New York, Indiana, and Michigan. Physicians' mean age was 52 years (SD = 27.17), 59% male, and 64% white. MAIN MEASURES: Opioids prescribed (or not), total daily opioid doses (in oral morphine equivalents), guidelineconcordant pain management, and pain assessment. KEY RESULTS: SPs completed 181 covertly audiorecorded visits that had complete data for the model covariates. Physicians detected SPs in 15% of visits. Physicians prescribed opioids in 71% of visits; 38% received guideline-concordant doses. Neither race nor activation was associated with total opioid dose or guidelineconcordant pain management, and there were no interaction effects (p > 0.05). Activation, but not race, was associated with improved pain assessment (ẞ, 0.46, 95% CI 0.18, 0.74). In post hoc analyses, oncologists (but not PCPs) were less likely to prescribe opioids to black SPs (OR 0.24, 95% CI 0.07, 0.81). CONCLUSIONS: Neither race nor activation was associated with opioid prescribing; activation was associated with better pain assessment. In post hoc analyses, oncologists were less likely to prescribe opioids to black male SPs than white male SPs; PCPs had no racial disparities. In general, physicians may be under-prescribing opioids for cancer pain.

“I did not know it was a medical condition”: Predictors, severity and help seeking behaviors of women with female sexual dysfunction in the Volta region of Ghana

“…The estimated 1:8,481 patient to provider ratio [75] has traditionally tipped in favor of male doctors [76][77][78][79], including Obstetric/Gynecologists (ObGyns) [80], even though there is ample literature to support the notion that women, particularly those in conservative cultures, prefer female OB-GYNs, given the intimacy of the services rendered by these providers [81][82][83]. Research shows that when patients are treated by physicians "who look like them" or have life experiences that closely match that of their patients', patients perceive their care to be of higher quality, are more satisfied with their care, have greater adherence to medical advice, and overall, have better health outcomes [84][85][86][87][88]. Providers also show a preference for same-gender consultations for sexual health matters [89].The global trend towards feminization of the healthcare workforce promises to bring changes to the patient-physician relationship, the local delivery of care, the global delivery of care to the society as a whole, as well as to the medical profession itself [72,74].…”

Is physician implicit bias associated with differences in care by patient race for metastatic cancer-related pain?

“…However, as previously reported, this hypothesis was not confirmed, i.e. activation did not attenuate SP race effects on opioid prescribing [ 16 ].…”

Patient activation reduces effects of implicit bias on doctor–patient interactions

“…We tested for interactions between SP race, SP activation, and physician IAT. We used the same control variables as prior research using this dataset ( 23 ): physician specialty (oncologist or PCP), physician race, physician sex, physician age, and research site (i.e., sites in Michigan vs. sites in Indiana or New York). We standardized continuous predictors and outcome measures and coded the binary categorical predictors using simple coding (e.g., −0.5 vs. 0.5 for binary variables).…”