Ethnic differences in the diagnosis of schizophrenia and mood disorders during admission to an academic safety-net psychiatric hospital

Impact of Demographics and Insurance Coverage on Schizophrenia Treatment and Healthcare Resource Utilization Within an Integrated Healthcare System

Purpose

Little is known about the impact of health disparities on antipsychotic treatment and healthcare resource utilization (HRU) among patients with schizophrenia. The objective of this analysis is to examine treatment patterns and HRU by age, race/ethnicity, and insurance coverage among patients with schizophrenia in an integrated delivery network (IDN).

Patients and Methods

This cross-sectional study used electronic health record data from MedStar Health, an IDN in the Baltimore-Washington, DC, area. Patients were aged ≥18 years and had ≥2 outpatient encounters or ≥1 hospitalization with a diagnosis of schizophrenia between January 1, 2017 and March 31, 2021. Outcomes assessed included oral antipsychotic prescriptions, long-acting injectable antipsychotic (LAI) utilization, hospitalizations, emergency department (ED) visits, and outpatient visits. Analyses compared subgroups based on age, race/ethnicity (non-Hispanic Black, non-Hispanic White, and other), and type of insurance coverage at index (Medicare, Medicaid, and other) during 12 months of follow-up.

Results

A total of 78.1% of patients had ≥1 prescription for an antipsychotic and 69.1% received ≥1 second-generation antipsychotic. Second-generation long-acting injectables (SGA LAI) were utilized by 9.0% of patients, with the elderly and Medicaid beneficiaries having the lowest SGA LAI utilization. Overall, 61.7% of patients had ≥1 hospitalization, 56.4% had ≥1 outpatient visit, and 50.5% had ≥1 ED visit. Hospitalizations and ED visits were most common in those 18 to 24 years of age and in Medicaid beneficiaries, whereas outpatient visits were more common for the elderly and Medicare beneficiaries.

Conclusion

At the population level, the results indicate widespread underprescription/underutilization of antipsychotics that have been shown to improve clinical and economic outcomes in patients with schizophrenia, particularly SGA LAI. Within specific subpopulations, disparities in treatment selection and HRU were observed, suggesting the need for increased attention to at-risk groups to ensure consistent quality of care regardless of age, race/ethnicity, or insurance coverage.

Exploring Potential Ethnic Bias Among MMPI-3 Scales in Assessing Personality Psychopathology

This study examined statistical bias in the measurement of personality psychopathology in the Latinx population using the Minnesota Multiphasic Personality Inventory-3 (MMPI-3). Data were extracted from two studies that yielded a composite data set of 103 White individuals and 250 Latinx individuals. All participants were administered the MMPI-2-Restructured Form-Extended Battery (MMPI-2-RF-EX) or MMPI-3 and the Personality Inventory for the DSM-5 Short Form (PID-5-SF). First, we conducted correlation analyses between theoretically overlapping scales of the PID-5-SF and the MMPI-3 among White and Latinx individuals. The majority of theoretically associated scales were found to be at least moderately associated in the total sample. In addition, Steiger's z-tests indicated that correlations were similar in magnitude across the White and Latinx ethnic groups. Hierarchical regression subsequently determined the presence of slope and/or intercept bias. Only one analysis (the MMPI-3 Anger Proneness prediction of PID-5-SF Negative Affectivity) indicated statistically significant intercept bias. No evidence of slope bias was found. In other words, these analyses indicated that the vast majority of the relationships between MMPI-3 scales and associated personality psychopathology constructs (as measured by the PID-5-SF) remained consistent across both ethnic groups. Overall, the results supported the appropriate cross-cultural use of the MMPI-3 to assess personality psychopathology.

Ethnoracial Risk Variation Across the Psychosis Continuum in the US: A Systematic Review and Meta-Analysis

Importance

Studies suggest a higher risk of schizophrenia diagnoses in Black vs White Americans, yet a systematic investigation of disparities that include other ethnoracial groups and multiple outcomes on the psychosis continuum is lacking.

Objective

To identify ethnoracial risk variation in the US across 3 psychosis continuum outcomes (ie, schizophrenia and other psychotic disorders, clinical high risk for psychosis [CHR-P], and psychotic symptoms [PSs] and psychotic experiences [PEs]).

Data Sources

PubMed, PsycINFO and Embase were searched up to December 2022.

Study Selection

Observational studies on ethnoracial differences in risk of 3 psychosis outcomes.

Data Extraction and Synthesis

Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines were followed. Using a random-effects model, estimates for ethnoracial differences in schizophrenia and PSs/PEs were pooled and moderation by sampling and setting was determined, along with the assessment of heterogeneity and risk of bias.

Main Outcomes and Measures

Risk of schizophrenia and other psychotic disorder, CHR-P, and conversion to psychosis among CHR-P and PSs/PEs.

Results

Of 64 studies in the systematic review, 47 were included in the meta-analysis comprising 54 929 people with schizophrenia and 223 097 with data on PSs/PEs. Compared with White individuals, Black individuals had increased risk of schizophrenia (pooled odds ratio [OR], 2.07; 95% CI, 1.64-2.61) and PSs/PEs (pooled standardized mean difference [SMD], 0.10; 95% CI, 0.03-0.16), Latinx individuals had higher risk of PSs/PEs (pooled SMD, 0.15; 95% CI, 0.08-0.22), and individuals classified as other ethnoracial group were at significantly higher risk of schizophrenia than White individuals (pooled OR, 1.81; 95% CI, 1.31-2.50). The results regarding CHR-P studies were mixed and inconsistent. Sensitivity analyses showed elevated odds of schizophrenia in Asian individuals in inpatient settings (pooled OR, 1.84; 95% CI, 1.19-2.84) and increased risk of PEs among Asian compared with White individuals, specifically in college samples (pooled SMD, 0.16; 95% CI, 0.02-0.29). Heterogeneity across studies was high, and there was substantial risk of bias in most studies.

Conclusions and Relevance

Findings of this systematic review and meta-analysis revealed widespread ethnoracial risk variation across multiple psychosis outcomes. In addition to diagnostic, measurement, and hospital bias, systemic influences such as structural racism should be considered as drivers of ethnoracial disparities in outcomes across the psychosis continuum in the US.

Disparities by Race and Ethnicity in Inpatient Hospitalizations Among Autistic Adults

This study examined hospitalizations in a large, all-payer, nationally representative sample of inpatient hospitalizations in the US and identified differences in rates of hospitalization for conditions by race and ethnicity in autistic adults. Conditions examined included mood disorders, epilepsy, schizophrenia, and ambulatory care sensitive conditions (ACSCs). Compared to white, non-Hispanic autistic adults, Black, Hispanic, Asian or Pacific Islander (API), and autistic adults of another race had lower prevalence of admission for a principal diagnosis of a mood disorder. Conversely, Black, Hispanic, API, and autistic adults of another race had higher odds of admission for epilepsy than white autistic adults. Black and Hispanic autistic adults were more likely to have schizophrenia as a principal diagnosis compared to white autistic adults, but only Black autistic adults had increased odds for admission for an ACSCs compared to white autistic adults. Differences in diagnosis prevalence among hospitalized autistic adults may suggest differential access to comprehensive outpatient care that could prevent such hospitalizations, while also pointing to concerns of differential validity of diagnostic tools and treatment approaches. Insurance policy and programs should prioritize optimizing outpatient care to ensure access to care and emphasize the need for equitable treatment.

Diagnostic deep learning algorithms that use resting EEG to distinguish major depressive disorder, bipolar disorder, and schizophrenia from each other and from healthy volunteers

BACKGROUND

Mood disorders and schizophrenia affect millions worldwide. Currently, diagnosis is primarily determined by reported symptomatology. As symptoms may overlap, misdiagnosis is common, potentially leading to ineffective or destabilizing treatment. Diagnostic biomarkers could significantly improve clinical care by reducing dependence on symptomatic presentation.

METHODS

We used deep learning analysis (DLA) of resting electroencephalograph (EEG) to differentiate healthy control (HC) subjects (N = 239), from those with major depressive disorder (MDD) (N = 105), MDD-atypical (MDD-A) (N = 27), MDD-psychotic (MDD-P) (N = 35), bipolar disorder-depressed episode (BD-DE) (N = 71), BD-manic episode (BD-ME) (N = 49), and schizophrenia (SCZ) (N = 122) and also differentiate subjects with mental disorders on a pair-wise basis. DSM-III-R diagnoses were determined and supplemented by computerized Quick Diagnostic Interview Schedule. After EEG preprocessing, robust exact low-resolution electromagnetic tomography (ReLORETA) computed EEG sources for 82 brain regions. 20 % of all subjects were then set aside for independent testing. Feature selection methods were then used for the remaining subjects to identify brain source regions that are discriminating between diagnostic categories.

RESULTS

Pair-wise classification accuracies between 90 % and 100 % were obtained using independent test subjects whose data were not used for training purposes. The most frequently selected features across various pairs are in the postcentral, supramarginal, and fusiform gyri, the hypothalamus, and the left cuneus. Brain sites discriminating SCZ from HC were mainly in the left hemisphere while those separating BD-ME from HC were on the right.

LIMITATIONS

The use of superseded DSM-III-R diagnostic system and relatively small sample size in some disorder categories that may increase the risk of overestimation.

CONCLUSIONS

DLA of EEG could be trained to autonomously classify psychiatric disorders with over 90 % accuracy compared to an expert clinical team using standardized operational methods.

Public Stigma Toward Schizophrenia Within Latino Communities in the United States

Public stigma toward those experiencing symptoms of schizophrenia in the general population is high; yet research into such stigma within the diverse Latino communities remains under-investigated. This study employed a randomized experimental vignette methodology to assess various domains of public stigma toward individuals experiencing psychosis and/or diabetes within Latino communities. A communitybased sample of 243 Latino adults participated. Contrary to our expectations, respondents who were more sympathetic toward those with mental health problems tended to score higher on public stigma. The belief that a person was responsible for their own mental health problems was associated with higher levels of stigma. Results indicate that perceptions of dangerousness toward someone experiencing psychosis were common, and the perception that a person was responsible for their mental health problems was associated with higher levels of stigma Results emphasize the complex nature of stigma within the diverse Latino communities and the need for ongoing research.

Psychotic Misdiagnosis of Racially Minoritized Patients: A Case-Based Ethics, Equity, and Educational Exploration

The overdiagnosis and misdiagnosis of racially minoritized groups as having a primary psychotic disorder is one of psychiatry's longest-standing inequities born of real-time clinician racial bias. Evidence suggests that providers assign a diagnosis of schizophrenia and/or schizoaffective disorder according to race more than any other demographic variable, and this inequity persists even in the absence of differences in clinician symptom ratings. This case report describes the journey of one young Black woman through her racialized misdiagnosis of schizophrenia and the process by which interdisciplinary, health equity-minded providers across the spectrum of medical education and practice joined together to provide a culturally informed, systematic rediagnosis of major depressive disorder and post-traumatic stress disorder. Expert discussion is provided by three Black academic psychiatrists with expertise in social justice and health equity. We provide an evidence-based exploration of mechanisms of clinician racial bias and detail how the psychosis misdiagnosis of racially minoritized groups fails medical ethics and perpetuates iatrogenic harm to patients who truly need help with primary mood, trauma, and substance use disorders.

A Black and White History of Psychiatry in the United States

Histories of psychiatry in the United States can shed light on current areas of need in mental health research and treatment. Often, however, these histories fail to represent accurately the distinct trajectories of psychiatric care among black and white populations, not only homogenizing the historical narrative but failing to account for current disparities in mental health care among these populations. The current paper explores two parallel histories of psychiatry in the United States and the way that these have come to influence current mental health practices. Juxtaposing the development of psychiatric care and understanding as it was provided for, and applied to, black and white populations, a picture of the theoretic foundations of mental health emerges, revealing the separate history that led to the current uneven state of psychiatric care.

Clinicians' Racial Bias Contributing to Disparities in Electroconvulsive Therapy for Patients From Racial-Ethnic Minority Groups

Patients from racial-ethnic minority groups undergo disparate electroconvulsive therapy (ECT) treatment compared with Caucasian peers. One leading hypothesis is that clinicians may unknowingly display racial bias when considering ECT for patients of color. Studies have consistently shown that patients of color face numerous racially driven, provider-level interpersonal and perceptual biases that contribute to clinicians incorrectly overdiagnosing them as having a psychotic-spectrum illness rather than correctly diagnosing a severe affective disorder. A patient's diagnosis marks the entry to evidence-based service delivery, and ECT is best indicated for severe affective disorders rather than for psychotic disorders. As a consequence of racially influenced clinician misdiagnosis, patients from racial-ethnic minority groups are underrepresented among those given severe affective diagnoses, which are most indicated for ECT referral. Evidence also suggests that clinicians may use racially biased treatment rationales when considering ECT after they have given a diagnosis of a severe affective or psychotic disorder, thereby producing secondary inequities in ECT referral. Increasing the use of gold-standard treatment algorithms when considering ECT for patients of color is contingent on clinicians transcending the limitations posed by aversive racism to develop culturally unbiased, clinically indicated diagnostic and treatment rationales.

Schizophrenia hospitalization in the US 2005-2014: Examination of trends in demographics, length of stay, and cost

Primarily we aimed to examine the crude and standardized schizophrenia hospitalization trend from 2005 to 2014. We hypothesized that there will be a statistically significant linear trend in hospitalization rates for schizophrenia from 2005 to 2014. Secondarily we also examined trends in hospitalization by race/ethnicity, age, gender, as well as trends in hospitalization Length of Stay (LOS) and inflation adjusted cost.In this observational study, we used Nationwide Inpatient Sample data and International Classification of Diseases, Eleventh Revisions codes for Schizophrenia, which revealed 6,122,284 cases for this study. Outcomes included crude and standardized hospitalization rates, race/ethnicity, age, cost, and LOS. The analysis included descriptive statistics, indirect standardization, Rao-Scott Chi-Square test, t-test, and adjusted linear regression trend.Hospitalizations were most prevalent for individuals ages 45-64 (38.8%), African Americans were overrepresented (25.8% of hospitalizations), and the gender distribution was nearly equivalent. Mean LOS was 9.08 days (95% confidence interval 8.71-9.45). Medicare was the primary payer for most hospitalizations (55.4%), with most of the costs ranging from $10,000-$49,999 (57.1%). The crude hospitalization rates ranged from 790-1142/100,000 admissions, while the US 2010 census standardized rates were 380-552/100,000 from 2005-2014. Linear regression trend analysis showed no significant difference in trend for race/ethnicity, age, nor gender (P > .001). The hospitalizations' overall rates increased while LOS significantly decreased, while hospitalization costs and Charlson's co-morbidity index increased (P < .001).From 2005-2014, the overall US hospitalization rates significantly increased. Over this period, observed disparities in hospitalizations for middle-aged and African Americans were unchanged, and LOS has gone down while costs have gone up. Further studies addressing the important disparities in race/ethnicity and age and reducing costs of acute hospitalization are needed.