Racial Disparities In Health Care Research Articles

Racial disparities in patients with ALL according to a national dataset.

e13733 Background: Racial disparities in healthcare refer to differences in health outcomes, access to healthcare &quality of care among racial or ethnic groups. Aim of study is to investigate the characteristics and clinical outcomes of racial differences in patients admitted with ALL. Methods: We queried the National Inpatient Sample between 2017-2020 for adult patients of different races who were hospitalized with ALL. The primary outcome was inpatient mortality; secondary outcomes were cardiac arrest, invasive mechanical ventilation, length of stay & hospital cost. Multivariable logistic, linear & Poisson regression analyses were used to estimate clinical outcomes with p-value < 0.05 was significant. Results: Total 110999 hospitalizations with ALL. Mean age 46 years & 55.8% male. Among admitted, 59599 (53.7 %) were Caucasian, 9205 (8.3 %) were Black, 27565 (24.8%) were Asian, 4540 (4.1%) were Hispanic, and 10090 (9.1%) were others. Subgroup with Caucasian, Blacks, Asians, Hispanics, and other races had Medicare 34.9% vs 30.2% vs 13.5% vs 17.9% vs 12.5%, Medicaid 13.3% vs 29.7% vs 41.4% vs 19.2 vs 34.8%, Non-insured 2.1% vs 5.1% vs 10.6% vs 4.2% vs 7.7%; DM 19% vs 23.8% vs 24.6% vs 17.4% vs 20.7%; Protein calorie malnutrition 11 % vs 10.6% vs 10.1% vs 12.6% vs 10.2%, respectively. All-cause mortality was 3810, where subgroup with Caucasian, Black, Asian, Hispanic, and others were 2085 (3.5%) vs 410 (4.4%) vs 885 (3.2%) vs 145 (3.2%) vs 285 (2.8%). Conclusions: Among the racial categories, Caucasians comprised the largest portion followed by Asians, Blacks, Hispanics. Mortality and intubation proportion was highest among Blacks followed by Hispanics & Asians. Cardiac arrest proportion was highest among Blacks followed by Hispanics and Asians. LOS was highest among Hispanics followed by Asians & Blacks. Resource utilization was highest in Hispanics followed by Asians & Blacks. Understanding these disparities is essential for improving healthcare equity and ensuring that all patients receive appropriate & equitable care regardless of their racial/ethnic background. Interventions include improving access to healthcare, culturally competent care and addressing social determinants of health that affect racial/ethnic groups. [Table: see text]

Racial and Ethnic Characteristics and Outcomes of Patients Diagnosed with CLL/SLL in the USA.

This study was designed to compare outcomes among patients by race and ethnicity in the post-covalent Bruton tyrosine kinase inhibitor (cBTKi) treatment era. A nationwide electronic health record (EHR)-derived de-identified database was utilized that included patients diagnosed with CLL from 2013 to 2022 who received systemic therapy for their disease. Use of cBTKi therapy, time to next treatment or death (TTNT-D), and overall survival (OS) were compared by race in unadjusted (Kaplan-Meier method) and adjusted analyses (Cox proportional hazards regression). This study included 4,572 White (71.8%) and 558 Black (8.8%) patients with CLL; 270 were Hispanic or Latino (4.2%). Patients who were Black were significantly younger, more were female, had later stage disease, were of lower socioeconomic status (SES), and were more likely to have unmutated immunoglobulin heavy chain gene (IGHV) and to have received cBTKi therapy than White patients (all p ≤ 0.002). SES was also significantly different by ethnicity. TTNT-D and OS were not different by race in either unadjusted or adjusted analyses (all p > 0.05). In unadjusted and adjusted analyses, TTNT-D and OS were not different by race. These data did not identify racial healthcare disparities in the era following the introduction of cBTKi therapy despite differences in baseline characteristics.

Introduction to a special section: Racial disparities in health care.

Papers in the Special Section on Racial Disparities in Health Care stemmed from. the 60th Anniversary of Family Process Conference, The Heart of the Matter: Systemic Imperatives to Address Health Disparities and Racism in the Time of COVID, which took place in Washington, DC in September 2021. Of the 12 presenters at the conference, these four were asked to recreate their talks into articles. They address key issues that help to explain health disparities in people of color, particularly African Americans, in the United States, as well as suggest innovations to clinical interventions and health care delivery systems to better serve people who have suffered adversity from the racial inequities in the American system.

Intergroup time bias and aversive racism in the medical context.

Time is fundamental to organizing all aspects of human life. When invested in relationships, it has a psychological meaning as it indicates how much individuals value others and their interest in maintaining social relationships. Previous research has identified an intergroup time bias (ITB) in racialized social relations, defined as a discriminatory behavior in which White individuals invest more time in evaluating White than Black individuals. This research proposes an aversive racism explanation for the ITB effect and examines its consequences in the medical context. In four experimental studies (N = 434), we found that White medical trainees invested more time in forming impressions of White (vs. Black) male patients. Study 5 (N = 193) further revealed more time investment in diagnosing, assessing pain, and prescribing opioids for White than Black male patients. This biased time effect mediated the impact of patients' skin color on health care outcomes, leading to greater diagnostic accuracy and pain perception, and lower opioid prescriptions. A meta-analytical integration of the results (Study 6) confirmed the ITB effect reliability across experiments and that it is stronger in participants with an aversive racist profile (vs. consistently prejudiced or nonprejudiced). These findings provide the first evidence that bias in time investment favoring White (vs. Black) patients is associated with aversive racism and impacts medical health care outcomes. Furthermore, these results offer insights into the sociopsychological meaning of time investment in health care and provide a theoretical explanation for an understudied insidious form of discrimination that is critical to comprehending the persistency of racial health care disparities. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Identifying and improving the “ground truth” of race in disparities research through improved EMR data reporting. A systematic review

BackgroundStudies about racial disparities in healthcare are increasing in quantity; however, they are subject to vast differences in definition, classification, and utilization of race/ethnicity data. Improved standardization of this information can strengthen conclusions drawn from studies using such data. The objective of this study is to examine how data related to race/ethnicity are recorded in research through examining articles on race/ethnicity health disparities and examine problems and solutions in data reporting that may impact overall data quality. MethodsIn this systematic review, Business Source Complete, Embase.com, IEEE Xplore, PubMed, Scopus and Web of Science Core Collection were searched for relevant articles published from 2000 to 2020. Search terms related to the concepts of electronic medical records, race/ethnicity, and data entry related to race/ethnicity were used. Exclusion criteria included articles not in the English language and those describing pediatric populations. Data were extracted from published articles. This review was organized and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement for systematic reviews. FindingsIn this systematic review, 109 full text articles were reviewed. Weaknesses and possible solutions have been discussed in current literature, with the predominant problem and solution as follows: the electronic medical record (EMR) is vulnerable to inaccuracies and incompleteness in the methods that research staff collect this data; however, improved standardization of the collection and use of race data in patient care may help alleviate these inaccuracies. InterpretationConclusions drawn from large datasets concerning peoples of certain race/ethnic groups should be made cautiously, and a careful review of the methodology of each publication should be considered prior to implementation in patient care.

Racial and Socioeconomic Disparities Among Patients with Chronic Lymphocytic Leukemia (CLL) Treated in the US Community Oncology Setting

Background: Since the first targeted therapy for CLL was approved by the Food and Drug Administration in 2014, new therapies and clinical practices have improved the care for patients with CLL. There is increased attention to racial disparities in healthcare. Objectives: To describe characteristics and outcomes of patients with CLL and assess whether there is equitable benefit from contemporary CLL therapies across racial and socioeconomic groups by analyzing real-world data collected in a large community oncology setting in the US. Methods: Patients with CLL treated in the US Oncology Network (The Network) from January 2015 through June 2023 were included. Data were sourced from structured data fields in iKnowMed™ (iKM), an oncology-specific electronic health record (EHR) system that captures outpatient practice encounters, including data on demographic and relevant clinical information for nearly 40% of US community oncology practices. iKM records are updated daily, and deterministically linked to supplementary Datavant mortality data and social determinants of health (SDoH) EHR data. Kaplan-Meier methods, univariate and multivariable cox regression were used to analyze overall survival (OS) and to determine the adjusted hazard ratio (HR) of OS. Considering that estimating the OS from initial diagnosis date is subject to immortal time bias, we performed the following two analyses: (1) time from the first visit date at a Network practice on or after the diagnosis date to OS, and (2) time from the initial diagnosis date among those newly diagnosed patients. In the primary analysis of OS, patients without a death date were censored at their last visit at The Network. In a sensitivity analysis, all follow-up data were censored at July 1, 2023. Results: The study included 12,253 CLL patients across all 4 census regions in the US, 60% were male. Of the 10,521 (86%) patients with race reported, 91.0% were white, 5.1% Black, and 3.9% were Other race. The median (Q1, Q3) age was 69 (61, 76) with Black patients 1.2 years younger than White patients (median: 69.0 versus 67.8, P=0.004). The initial CLL diagnosis was available for 11,576 (94%) patients and the majority (6,874; 59%) either had their initial diagnosis in The Network or had their first visit in The Network within 30 days after their initial diagnosis elsewhere. The median time from initial diagnosis to the first visit in The Network was slightly shorter for Black patients (2.4 weeks) as compared to White (3.9 weeks, p=0.001). At the initial diagnosis, more Black patients had Rai Stage III or IV than White patients (54% versus 46%, P=0.002). Death dates were available for 2,231 (18%) of patients. The analysis of time from first visit to death showed that the 8-year death rate was significantly higher for Black patients (61%) than White patients (43%, P=0.002) with statistically significant HR (95% confidence interval [CI]) of 1.34 (1.11, 1.61), P=0.002 in the univariate analysis. The median (95% CI) OS was 7.5 (6.4, not estimable) years for Black patients. The median OS was not estimable for White patients for this study because the study was designed to use data in the last 8 years and the median survival for White patients was longer than 8 years. After adjusting for education level and income, the race effect was no longer significant 1.25 (0.76, 2.05), P=0.37. The wider CI was due to SDoH data being only available for 2,928 (24%) of patients. Even with reduced statistical power, income of <$30k was significantly associated with higher risk of a shorter time to death with HR (95% CI) of 1.78 (1.32, 2.41), P<0.001 as compared to >$75k in the adjusted model. The analysis of time from the initial diagnosis date to OS among those newly diagnosed patients and the sensitivity analyses showed similar trends. Conclusion: This study identifies racial disparities in this modern era of CLL therapy using real-world data from a large community setting in the US. Black race and low income are prognostic of less favorable OS in CLL. Further research is needed to determine whether racial and income disparities in CLL are due to differences in access to therapy, quality of care, disease biology, comorbidities or other factors. This research may contribute to developing interventions to ensure that progress in CLL therapy benefits all patients.

Sample Size Calculations for Stepped Wedge Designs with Treatment Effects that May Change with the Duration of Time under Intervention.

The stepped wedge design is often used to evaluate interventions as they are rolled out across schools, health clinics, communities, or other clusters. Most models used in the design and analysis of stepped wedge trials assume that the intervention effect is immediate and constant over time following implementation of the intervention (the "exposure time"). This is known as the IT (immediate treatment effect) assumption. However, recent research has shown that using methods based on the IT assumption when the treatment effect varies over exposure time can give extremely misleading results. In this manuscript, we discuss the need to carefully specify an appropriate measure of the treatment effect when the IT assumption is violated and we show how a stepped wedge trial can be powered when it is anticipated that the treatment effect will vary as a function of the exposure time. Specifically, we describe how to power a trial when the exposure time indicator (ETI) model of Kenny et al. (Statistics in Medicine, 41, 4311-4339, 2022) is used and the estimand of interest is a weighted average of the time-varying treatment effects. We apply these methods to the ADDRESS-BP trial, a type 3 hybrid implementation study designed to address racial disparities in health care by evaluating a practice-based implementation strategy to reduce hypertension in African American communities.

Editorial: A closer examination of the racial discrepancies in ED cardiac care.

The undercurrent of racial disparities in healthcare is undeniable and remains pervasive despite concerted efforts to mitigate disparities in outcomes. This is well documented in a number of areas including access to care, pain management, as well as the evaluation and management of chest pain. The latest findings from the STOP-CP cohort study call attention yet again to an apparent gap in care between White and non-White patients in the ED evaluation of chest pain. While this is concerning, it also highlights an important question for us as clinicians to reflect upon. How do we interpret and assess disparities in clinical evaluation NOT seemingly associated with downstream disparities in outcomes?

Race Differences in Cardiac Testing Rates for Patients with Chest Pain in a Multisite Cohort.

Identifying and eliminating racial healthcare disparities is a public health priority. However, data evaluating race differences in emergency department (ED) chest pain care is limited. We conducted a secondary analysis of the High-Sensitivity Cardiac Troponin T to Optimize Chest Pain Risk Stratification (STOP-CP) cohort, which prospectively enrolled adults with symptoms suggestive of acute coronary syndrome without ST-elevation from 8 EDs in the United States (US) from 2017-2018. Race was self-reported by patients and abstracted from health records. Rates of 30-day non-invasive testing, cardiac catheterization, revascularization, and adjudicated cardiac death or myocardial infarction (MI) were determined. Logistic regression was used to evaluate the association between race and 30-day outcomes with and without adjustment for potential confounders. Among 1454 participants, 42.3% (615/1454) were non-white. At 30-days NIT occurred in 31.4% (457/1454), cardiac catheterization in 13.5% (197/1454), revascularization in 6.0% (87/1454), and cardiac death or MI in 13.1% (190/1454). Among whites vs. non-whites, NIT occurred in 33.8% (284/839) vs 28.1% (173/615) (OR 0.76, 95%CI 0.61-0.96) and catheterization in 15.9% (133/839) vs. 10.4% (64/615) (OR 0.62, 95%CI 0.45-0.84). After adjusting for covariates, non-white race remained associated with decreased 30-day NIT (aOR 0.71, 95%CI 0.56-0.90) and cardiac catheterization (aOR 0.62, 95% CI 0.43-0.88). Revascularization occurred in 6.9% (58/839) of whites vs. 4.7% (29/615) of non-whites (OR 0.67, 95%CI 0.42-1.04)). Cardiac death or MI at 30-days occurred in 14.2% of whites (119/839) vs. 11.5% (71/615) of non-whites (OR 0.79 95%CI 0.57-1.08). After adjustment there was still no association between race and 30-day revascularization (aOR 0.74, 95%CI 0.45-1.20) or cardiac death or MI (aOR 0.74, 95%CI 0.50-1.09). In this US cohort, non-white patients were less likely to receive NIT and cardiac catheterization compared to whites, but had similar rates of revascularization and cardiac death or MI.

Association of race and in-hospital outcomes following acute pulmonary embolism: A retrospective cohort study.

Racial disparities in health care are well established, with Black patients frequently experiencing the most significant consequences of this inequality. Acute pulmonary embolism (PE) is increasing in incidence and an important cause of morbidity and mortality in the United States, but little is known about racial disparities in the inpatient setting. Black and White patients admitted with acute PE will have different in-hospital outcomes. All PE patients from January 1, 2016 to June 30, 2017 were retrospectively identified using ICD-10 codes. Data were abstracted by manual chart review for all image-confirmed PEs. A total of 782 patients with acute PE were identified, of which 319 (40.8%) were Black and 463 (59.2%) were White. Black patients had higher BMI (median [Q1-Q3]: 30.3 [25.4-36.6] vs. 29.3 [24.5-33.8] kg/m2 , p = .017), were younger (61 [48-74] vs. 67 [54-75] years, p = .001), and were more likely to have a history of heart failure (16.0 vs. 7.1%, p < .001), while White patients had higher rates of malignancy (46.9 vs. 34.5%, p = .001) and recent surgery (29.6 vs. 18.2%, p < .001). Black patients were more likely to receive systemic thrombolysis (3.1% vs. 1.1%, p = .040), while White patients had numerically higher rates of surgical embolectomy (0.3% vs. 1.1%, p = .41). No difference in inpatient mortality was observed; however, Black patients had longer hospital length of stay (5.0 [3-9] vs. 4.0 [2-9] days, p = .007) and were more likely to receive warfarin (23.5 vs. 12.1%, p < .001). Similar in-hospital mortality rates were observed in Black and White patients following acute PE. However, Black patients had longer hospital stays, higher warfarin prescription, and fewer traditional PE-related risk factors.

Military healthcare system mitigates racial disparities for severe maternal morbidity from preeclampsia

In the United States, Black women die at 2.5 times the rate of White women and 3.5 times the rate of Hispanic women. These racial health care disparities have been largely attributed to access to health care and other social determinants of health. We hypothesize that the Military healthcare system models universal health care access seen in other developed countries and should equalize these rates. Delivery data from 41 Military treatment facilities across the Department of Defense (Army, Air Force, and Navy) including over 36,000 deliveries from 2019 to 2020 were compiled in a convenience dataset through the National Perinatal Information Center. After aggregation, the parameters of percent of deliveries complicated by Severe Maternal Morbidity and percent of severe maternal morbidity secondary to pre-eclampsia with and without transfusion were calculated. Risk ratios were calculated by race for the resulting summary data. American Indian/Alaska Native were excluded because of limited total number deliveries preventing statistical analyses. Overall, the risk of severe maternal morbidity was increased among Black women compared to White women. The risk of severe maternal morbidity related to pre-eclampsia showed no significant difference among races with or without transfusion. When other races were set as reference group, there was a significant difference for White women, suggesting a protective effect. Although women of color still experience overall severe maternal morbidity at higher rates than their White counterparts, TRICARE may have equalized the risk of severe maternal morbidity for deliveries complicated by pre-eclampsia.

Racial discrepancies in presentation of hospitalized infantile hemangioma cases using the Kids'Inpatient Database.

Racial discrepancies in presentation of hospitalized infantile hemangioma cases using the Kids'Inpatient Database.

395. Impact of Racial Disparities on Clostridioides difficile Infection (CDI) Outcomes at a Southern California Academic Teaching Hospital

Abstract Background Infectious diseases are the second leading contributor to racial disparities in healthcare. Clostridioides difficile infection (CDI) is a common community/hospital acquired infection that can cause life-threatening colitis. Despite the influence that socioeconomic factors, insurance status, and access to care can have on CDI outcomes, there is a dearth of literature that evaluates the impact of racial differences. The objective of this study was to identify racial disparities in CDI and describe clinical outcomes among hospitalized adults. Methods This is an IRB approved, retrospective study of adult patients (≥ 18 years) hospitalized with initial episodes of CDI between January 2020 to June 2021. Initial CDI episodes were identified by the ICD-10 code A04.72. Medical records were reviewed for demographics, comorbid conditions, insurance, total length of stay (LOS), and need for intensive care unit (ICU) admission. Patients were dichotomized by race/ethnicity into either the racially and ethnically minoritized (REM) group or the non-Hispanic White (n-REM) group. P value of ≤ 0.05 was defined as significant. Results In total 220 patients (61.8% REM, 136/220; 38.2% n-REM, 84/220) with an initial CDI episode were included. Majority of the REM patients were Hispanic (38.2%) followed by Black (13.6%), and Asian (5%). Compared to the n-REM group, the REM patients were younger (60 vs 69 years, p =0.016) and were more likely to have underlying comorbidities (diabetes mellitus 33% vs 9.5%; p &amp;lt; 0.001 and chronic kidney disease 48.5% vs 21.3%; p &amp;lt; 0.001). The REM patients were more likely to present with fulminant CDI (21.3% vs 11.9%; p =0.1). The REM patients were also more likely to be admitted to the ICU during their stay (41.9% vs 26.2%; p =0.021) and have a longer hospital length of stay (LOS) (12 vs 7 days; p =0.02). Lastly, the REM patients were more likely to be under-insured with 91 patients (66.9%) having Medi-Cal insurance compared to 34 patients (40.5%) in the n-REM group (p &amp;lt; 0.001). Conclusion Despite being younger, REM patients were more likely to require an ICU admission, have underlying comorbidities, have a longer LOS, and be under-insured. There is a need for further studies to understand disparities between REM and n-REM patients regarding CDI. Disclosures All Authors: No reported disclosures.

Distributive justice criteria and social categorization processes predict healthcare allocation bias.

Drawing on theories of distributive justice and intergroup discrimination, we examined how much distributive justice criterion and racial group membership contribute to bias in healthcare allocation decisions, by testing a theoretical model that specifies perceived stereotypicality and individual responsibility as a serial mediation process in the relationship between disease's contraction controllability (controllable vs. non-controllable) and bias in medical decision-making. White Portuguese medical students (N=213) participated in an online experimental study conducted in two phases. In phase 1, we manipulated the cause of disease contagion and the salience of patient's racial categorization, and measured the stereotypicality of behaviour. In phase 2, we assessed perceived responsibility and likelihood of recommending medical treatment. Controllable (vs. non-controllable) contraction behaviours in phase 1 were perceived as more stereotypic. As a spillover effect, more stereotypical behaviours in phase 1 predicted more patient's responsibility for their disease in phase 2. Importantly, controllable behaviours of disease contraction in phase 1 negatively affected recommendations for medical treatment in phase 2; and this negative effect was serially mediated by the stereotypicality of behaviour and patient responsibility. Furthermore, patients' skin colour moderated this process, meaning that perceptions of controllable behaviour as more stereotypic were stronger for Black than for White patients. This research shows how stereotyping and social categorization bias allocation decisions through the patient's level of responsibility in decision-making processes. The findings are discussed in light of principles of distributive justice and the literature on intergroup relations with respect to racial disparities in health care.

Addressing algorithmic bias and the perpetuation of health inequities: An AI bias aware framework

The emergence and increasing use of artificial intelligence and machine learning (AI/ML) in healthcare practice and delivery is being greeted with both optimism and caution. We focus on the nexus of AI/ML and racial disparities in healthcare: an issue that must be addressed if the promise of AI to improve patient care and health outcomes is to be realized in an equitable manner for all populations. We unpack the challenge of algorithmic bias that may perpetuate health disparities. Synthesizing research from multiple disciplines, we describe a four-step analytical process used to build and deploy AI/ML algorithms and solutions, highlighting both the sources of bias as well as methods for bias mitigation. Finally, we offer recommendations for moving the pursuit of fairness further.

Is It All in Our Heads? An Investigation into American and Historical Legacies of Racism and Social Frameworks that Perpetuate Racial Inequalities in Twenty-First Century Healthcare Systems

Racial bias in the healthcare system, originating from the eminent founders of science and medicine, has numerous adverse effects on black populations and continues to have harmful consequences today (Byrd and Clayton, 2001). From poor clinical decision-making to preventing people of color from entering prominent fields in medicine, racism is ubiquitous in medicine and healthcare (Byrd and Clayton, 2001). The impact of racial bias on patient care is of great interest with many studies illustrating the detrimental impacts of bias on minority groups, specifically in black communities. However, there is additional research that concludes racial bias does not play a role in patient care or in medicine (Dehon et al., 2017). The lack of acknowledgment within academia concerning racial disparities in healthcare and science further oppresses black voices. With my research, I investigate the extent to which various biased social frameworks in healthcare, medicine, and science negatively impact black individuals. I also address major historical events during the creation of the modern-day healthcare system and how these events perpetuate racism today. Focusing on the twenty-first century, I demonstrate that systemic historical and social events during this period eternalize racism in the modern-day American healthcare system.

Health Care System Distrust, Race, and Surrogate Decision-Making Regarding Code Status.

Previous studies have shown that black patients are more likely to prefer life-sustaining treatments such as cardiopulmonary resuscitation at end-of-life (EOL) compared to non-Hispanic white patients. Given prior racial disparities in health care, distrust has been proposed to explain these preferences. As many hospitalized older adults require surrogates to make medical decisions, we explored surrogates' code status preferences and the role of trust in these decisions. We conducted secondary analyses of an observational study of patient/surrogate dyads admitted to three hospitals in a Midwest metropolitan area. Distrust was assessed using the Revised Health Care System Distrust Scale. A single item asked the surrogate which code status they thought was best for the patient, full code or do not resuscitate. We enrolled 350 patient/surrogate dyads (101 black; 249 white). In bivariate analysis, higher proportion of black surrogates preferred full code (62.4% vs. 38.3%, p=0.0001). After adjusting for trust and sociodemographic and psychological covariates, race was still significantly associated with preference for full code (adjusted odds ratio=2.13; 95% confidence interval: 1.16-3.92; p=0.0153). Surrogate race was not associated with distrust in bivariate or multivariable analysis, adjusting for sociodemographic and psychological covariates (p=0.3049). Although black race was associated with preferences for full code status, we observed no association between race and distrust. Differences in code status preference may be due to other factors related to race and culture. To ensure that patients are receiving EOL care that is consistent with their values, more work is needed to understand the cultural complexities behind EOL care preferences.

1.51 Persistent Racial Healthcare Disparities in Psychiatric Services

Black and Hispanic children are less likely to receive psychiatric services when compared to White children due to factors such as financial and cultural barriers, underdiagnosis, and poor cultural competence in healthcare workers. Our State University of New York (SUNY) Upstate Medical University outpatient clinic is working to reduce these disparities by: decreasing the intake packet completion time and process, working more closely with hospital teams to fast-track higher acuity patients, and increasing mental health providers who identify as people of color.

Medicaid can and should play an active role in advancing health equity.

Medicaid can and should play an active role in advancing health equity.

Hospital Allocation and Racial Disparities in Health Care

Abstract We develop a framework to measure the role of hospital allocation in racial disparities in health care and use it to study Black and white heart attack patients. Black patients receive care at lower-performing hospitals than white patients. However, over two decades, the performance gap between hospitals treating Black and white patients shrank by over two-thirds. This progress is due to more rapid performance improvement at hospitals that tended to treat Black patients, rather than reallocation of patients. Hospital improvement is correlated with adoption of a productivity-raising input, beta-blockers. Our work highlights reallocation and performance improvement as future disparity-reduction levers.