Within the nationwide effort to combat the opioid epidemic and reduce opioid prescribing, researchers have described different prescribing patterns for non-White racial and ethnic groups, including Black and LatinX populations. This remains a largely unexplored area within hospital medicine. Earlier studies of racial disparities demonstrate how some patients are assessed less often for pain and prescribed fewer opioids from the emergency department, surgical settings, and outpatient primary care practices. Researchers also have documented racial and ethnic disparities in analgesia for cancer pain and chronic noncancer pain.1-11 Studies have demonstrated that White patients are more likely to receive opioid prescriptions compared with Black patients. Even with similar documented pain scores, there is evidence that Black patients receive fewer analgesics compared with White patients. For example, a recent study found that Black and Hispanic patients presenting to the emergency room for renal colic received less opioid medication compared with White patients.3 A study across 22 sites in Northern California found that racial minorities with long-bone fractures received fewer opioids at discharge than White patients.1
It is unknown whether differential prescribing patterns by race exist among patients hospitalized on general medicine services. The objective of our study was to assess whether race and ethnicity were associated with the likelihood of opioids being prescribed and the duration of opioids prescribed when these patients are discharged from the hospital. Quantifying and seeking to understand these differences are the first steps toward ensuring racial and ethnic health equity in patient care.
Study Population and Data Sources
We identified all adults (age ≥18 years) discharged from the acute care inpatient general medicine services between June 1, 2012, and November 30, 2018, at the University of California, San Francisco (UCSF) Helen Diller Medical Center at Parnassus Heights, a 785-bed urban academic teaching hospital. All data were obtained from the hospital’s Epic-based electronic medical record (Epic Systems Corporation). Data elements were extracted from Clarity, the relationship database that stores Epic inpatient data. Patients discharged from the inpatient cardiology or bone marrow transplant services were not included. We excluded patients who did not receive opioids in the last 24 hours of their hospitalization. Patients with cancer-related pain diagnoses or sickle cell disease pain crises and patients who were discharged to hospice or followed by palliative care were excluded from the study based on International Classification of Diseases, Tenth Revision (ICD-10) codes (available on request) or service codes, when available, or admitting provider electronic health record documentation (Appendix Figure 1). Palliative care and hospice patients have significantly different pain needs, with management often directed by specialists. Patients with sickle cell disease are disproportionately Black and have distinct opioid prescribing patterns.12,13 We also excluded discharge opioid prescriptions that were a resumption of the patient’s opioid prescription before admission based on medication documentation. Only new prescriptions signed by the discharging hospitalist, including different doses and formulations, were included in this study.
We performed a subgroup analysis of patients who were not prescribed opioids before their admission based on medication reconciliation but were started on opioids while hospitalized.
We examined two primary outcomes: whether a patient received an opioid prescription at discharge, and, for patients prescribed opioids, the number of days prescribed. Days of opioids at discharge were calculated as total morphine milligram equivalents (MMEs) prescribed divided by MMEs administered during the final 24 hours of hospitalization. This metric was used as a patient-specific approach to calculating how long an opioid prescription will last after discharge, standardized according to the actual opioid requirements from hospitalization.14 If a patient was discharged with prescriptions for several opioids, the longest single prescription duration was used.
The primary predictor was the patient’s primary self-reported race/ethnicity, categorized as White, Black, LatinX, Asian, Native Hawaiian or other Pacific Islander, American Indian or Alaska Native, and other/unknown. Other/unknown included patients who were listed as other, declined, or who were otherwise unspecified. Self-reported race/ethnicity is obtained through reporting to the registrar. These race/ethnicity groupings were done in concordance with US Census Bureau definitions. Researchers classified patients as LatinX if they had Hispanic documented as their ethnicity, no matter their racial identification. These categorizations were chosen to be consistent with the existing literature, recognizing the role of a combined race/ethnicity definition for Hispanic or LatinX populations.15 These definitions of race/ethnicity are self-reported and reflect socially—not genetically defined—groupings.16 This variable serves as a surrogate for the structural factors that contribute to racism, the determining factor for racially disparate outcomes.17
Covariate Data Collection
Additional data were obtained regarding patient demographics, hospitalization factors, and medical diagnoses. Demographic factors included age, sex, and limited English proficiency (LEP) status. LEP was defined as having a primary language other than English and requiring an interpreter. Hospitalization factors included length of stay, whether they required intensive care unit (ICU) management, average daily MMEs administered during their entire hospitalization, MMEs administered during the final 24 hours of their hospitalization, whether the patient was on a teaching service or direct-care hospitalist service, their disposition on discharge, and year. Medical diagnosis variables included the adjusted Elixhauser Comorbidity Index based on ICD-10 codes; whether the patient was taking opioids at admission; and specific diagnoses of cancer, posttraumatic stress disorder (PTSD), and mood, anxiety, or psychotic disorder based on ICD-10 documentation.18
All statistical analyses were performed using Stata software version 16 (StataCorp LP). Baseline demographic variables, hospitalization factors, and medical diagnosis variables were stratified by race/ethnicity. Within group comparisons were performed using chi-square or analysis of varianace (ANOVA) testing. For regression analyses, we fit two models. First, we fit a multivariable logistic regression model on all patients who received opioids during the last 24 hours of their hospitalization to examine the association between patient race/ethnicity and whether a patient received opioids at discharge, adjusting for additional patient, hospitalization, and medical covariates. Then we fit a negative binomial regression model on patients who were prescribed opioids at discharge to examine the association between patient race/ethnicity and the amount of opioids prescribed at discharge, adjusting for covariates. We used a negative binomial model because of the overdispersed distribution of discharge opioid prescriptions and only examined patients with an opioid prescription at discharge. We included the listed variables in our model because they were all found a priori to be associated with discharge opioid prescriptions.19 Instead of using days of opioids based on the last 24 hours, we performed a secondary analysis using the actual days of opioids supplied as the outcome. For example, a prescription of 12 tablets with every 6 hours dosing would be 3 days’ duration.
For both models, subgroup analyses were performed using the adjusted models restricted to patients newly prescribed opioids during their hospitalization and who were not previously taking opioids based on admission medication reconciliation. After testing for effect modification, this subgroup analysis was performed to reduce selection bias associated with earlier opioid use.
For all models, we reported predicted population opioid prescribing rates from the average marginal effects (AME).20 Marginal effects were used because ours was a population level study and the outcome of interest was relatively common, limiting the effective interpretation of odds ratios.21 Marginal effects allow us to observe the instantaneous effect a given independent variable has on a dependent variable, while holding all other variables constant. It is implemented using the margins command in Stata. Marginal effects enable us to present our results as differences in probabilities, which is a more accurate way to describe the differences found among patient groups. Further, marginal effects are less sensitive to changes in model specifications.22The UCSF Institutional Review Board for Human Subjects Research approved this study with a waiver of informed consent.
We identified 10,953 patients who received opioids during the last 24 hours of hospitalization (see Appendix Figure 1 for study consort diagram). The patient population was 52.2% White, 18.4% Black, 11.5% Latinx, 10.1% Asian, 6.2% other/unknown, 0.9% Native Hawaiian/Other Pacific Islander, and 0.8% American Indian/Alaska Native (Table 1, Appendix Table 1). Black patients had fewer cancer diagnoses and the highest rate of prescribed opioids on admission. Asian patients were older and more likely to be female, and had higher rates of cancer, the highest median comorbidity index, and the smallest median daily MME during both the last 24 hours and total duration of hospitalization. Representative of general medicine patients, the most common principal discharge diagnoses in our dataset were pneumonia, cellulitis, altered mental status, sepsis, and abdominal pain.
Overall, 5541 (50.6%) patients who received opioids in the last 24 hours of their hospitalization received an opioid prescription at discharge. There were significant differences among racial/ethnic groups receiving an opioid prescription at discharge. Black patients were less likely to be discharged with an opioid compared with White patients (47.7% vs 50.3%; P < .001) (Table 2). The median discharge prescription duration for all patients was 9.3 days (interquartile range [IQR], 3.8-20.0). Black patients received the fewest median days of opioids at 7.5 days (IQR, 3.2-16.7) compared with White patients at 8.8 days (IQR, 3.7-20.0; P < .001) (Table 2).
Adjusted Regression Results
Demographic, clinical, and diagnosis specific factors were significantly associated with opioid prescriptions, including previous opioid use, sex, and a concurrent cancer diagnosis. There were fewer opioid prescriptions over time (Figure).
Following multivariable logistic regression for the association between race/ethnicity and opioid on discharge and controlling for covariates, we found that Black patients were less likely to receive an opioid prescription on discharge compared with White patients (predicted population rate, 47.6% vs 50.7%; AME −3.1%; 95% CI, −5.5% to −0.8%). Asian patients were more likely to receive a prescription on discharge compared with White patients (predicted population rate, 55.6% vs 50.7%; AME +4.9; 95% CI, 1.5%-8.3%).
Following multivariable negative binomial regression for the association between race/ethnicity and the number of opioid days on discharge, we found that Black patients received a shorter duration of opioid days compared with White patients (predicted days of opioids on discharge, 15.7 days vs 17.8 days; AME −2.1 days; 95% CI, −3.3 to −0.87) (Table 3). There were no significant differences among patients and the other racial/ethnic groups.
Our secondary analysis from the negative binomial regression with the days of opioids supplied metric yielded similar results to our primary analysis showing that Black patients received statically significantly fewer days of opioid therapy compared with White patients (Appendix Table 2).
Subgroup Regression Results
After testing for effect modification, which was negative, we examined the relationships for patients started on opioids during their hospitalization (Appendix Table 3 and Appendix Table 4). There were 5101 patients with newly prescribed opioids during their hospitalization. Adjusting for covariates, we found that Black patients were less likely to receive opioids at discharge compared with White patients (predicted population rate, 34.9% vs 40.4%; AME −5.5%; 95% CI, −9.2% to −1.9%). American Indian or Alaska Native patients were more likely to receive opioids on discharge (predicted population rate, 58.3% vs 40.4%; AME +17.9%; 95% CI, 1.0%-34.8%). We also found that Asian patients received more days of opioids on discharge (predicted days of opioid on discharge, 16.7 vs 13.7 days; AME +3.0 days; 95% CI, 0.6-5.3 days) (Appendix Table 4, Appendix Figure 2).
We found that Black patients discharged from the general medicine service were less likely to receive opioids and received shorter courses on discharge compared with White patients, adjusting for demographic, hospitalization, and medical diagnosis variables. Asian patients were more likely to receive an opioid prescription at discharge—a finding not reported in the literature on opioid prescribing disparities in most other practice settings.1
Previous studies have shown racial disparities in pain management in emergency and surgical settings, but these relationships have not been characterized in an inpatient medicine population. Medicine patients comprise the majority of admitted patients in the United States and reflect a wide diversity of medical conditions, many requiring opioids for pain management. Determining the etiology of these differential prescribing patterns was not within the scope of our study, but earlier studies demonstrate a number of reasons why these patterns exist across racial and ethnic groups in other practice settings.23,24 These reports give us insight into potential mechanisms for our study population.
Differences in pain management likely represent the multiple structural mechanisms by which racism operates.17 Drawing from the existing literature and the socioecological model, we hypothesize the ways that individual, interpersonal relationships, organizations, communities, and public policy impact opioid prescribing.25,26 Using this model and considering the framework of Critical Race Theory (CRT), we can work towards understanding how race and ethnicity stand in as surrogates for racism and how this manifests in different outcomes and identify areas for intervention. CRT draws attention to race consciousness, contemporary orientation, centering in the margins, and praxis. In the context of this analysis, we recognize race consciousness and the interactions among factors such as race/ethnicity, language, and diagnoses such as PTSD.27 This approach is necessary because racism is a multilevel construct influenced by macrolevel factors.28
Individually and interpersonally, there is clinician-driven bias in pain assessment, which is activated under times of stress and diagnostic uncertainty and is amplified by a lack of clear guidelines for pain management prescriptions.23,29-32 Institutional and organizational culture contribute to disparities through ingrained culture, practice patterns, and resource allocation.29,33 Last, public policy and the larger sociopolitical environment worsen disparities through nondiverse workforces, state and federal guidelines, criminal justice policy, supply chain regulation, and access to care.
As individual clinicians, departments, and health systems leaders, we must identify areas for intervention. At the individual and interpersonal levels, there is evidence that taking implicit association tests could help clinicians become more aware of their negative associations, and empathy-inducing, perspective-taking interventions can reduce pain treatment bias.31,34 At the institutional level, we must report data on disparities, create guidelines for pain management, and reevaluate the educational curriculum and culture to assess how certain biases could be propagated. The lack of straightforward guidelines leads to unclear indications for opioid prescriptions, exacerbating provider-level differences in prescribing. At the policy level, legislation that promotes workplace diversity, increases training for and access to pain specialists, and incentivizes data collection and reporting could help reduce disparities.35 Equitable access to prescriptions and care is essential. Pharmacies often understock opioids in minority neighborhoods, meaning that even if a patient is prescribed an opioid on discharge, he or she might have difficulty filling the prescription.36
One could question whether fewer opioid prescriptions for Black patients protects against the harms of opioid overprescribing, and therefore is not reflective of harmful inequity.37 Ongoing national programs aim to reduce the harmful effects of opioids, which is reflected in the reduction in opioid prescribing over time in our institution. Our point is that differences in prescribing could reflect practices that do result in patient harm, such as less adequately controlled pain among Black patients.1,3 Undertreated pain has negative health and social consequences and further contributes to substance-use stigma within minority communities.38 Moreover, Black people who describe more discrimination in medical settings were more likely to report subsequent opioid misuse.39
Although the above mechanisms might partially explain our findings among Black patients, the higher rate of prescribing for Asian patients is more challenging to explain. Our models adjusted for clinical factors. Notably, our Asian patients had the highest baseline comorbidity index, oldest mean age, and highest cancer rates, and it is possible that we were unable to fully account for illness severity or related pain needs (Table 1). It also is possible—although speculative—that factors such as language, provider concordance, and the type of disease process all contribute.40 Some researchers have proposed a “stereotype content model” that seeks to establish a pathway among social structure (status of a patient) to clinician stereotypes (is this patient warm and/or competent) to emotional prejudices (envy, pride) and ultimately to discrimination (active/passive, help/harm).23Our study has limitations. Our model was limited by the available data collected on our patients. Covariates including primary care follow-up, pain scores, and overdose history were not available. Furthermore, our categorization of race/ethnicity was based on self-reported data. We had 676 patients with race/ethnicity specified as other/unknown. We recognize the heterogeneity within these racial/ethnic categorizations. For example, within the LatinX or Asian communities, there are large differences based on region, country, ethnic, or cultural groups. Our study only included patients presenting to a hospital in San Francisco, which is different from the racial/ethnic makeup of other cities across the nation. Our electronic health record capture of history of opioid use disorder and mood disorders is contingent on individual clinician documentation. We did not account for provider-level differences, which is an important part of variation in prescribing differences. We also did not examine differences at the diagnosis-specific level. Finally, we could not determine the indication or appropriateness of opioid prescriptions.
Future studies will be necessary to characterize this relationship at a diagnosis-specific level and to describe causal pathways. Within our own institution, these findings present an opportunity for positive change. We hope to continue to explore the etiology of these disparities and identify areas where differences could impact patient outcomes, such as pain control. It is essential to develop appropriate recommendations for inpatient and discharge opioid prescribing to help minimize disparities and to mitigate potential harms of overprescribing. All health systems should continue to collect data on their own disparities in opioid prescribing and educate clinicians on promoting more equitable practices.
The authors thank Sneha Daya, MD, Sachin Shah, MD, MPH, and the UCSF Division of Hospital Medicine Data Core.