Examining Racial/Ethnic Differences in Patterns of Opioid Prescribing: Results from an Urban Safety-Net Healthcare System.

Prescription opioids still account for a large proportion of overdose deaths and contribute to opioid use dependence (OUD). Studies earlier in the epidemic suggest clinicians were less likely to prescribe opioids to racial/ethnic minorities. As OUD-related deaths have increased disproportionately amongst minority populations, it is essential to understand racial/ethnic differences in opioid prescribing patterns to inform culturally sensitive mitigation efforts. The purpose of this study is to estimate racial/ethnic differences in opioid medication use among patients prescribed opioids. Using electronic health records and a retrospective cohort study design, we estimated multivariable hazard models and generalized linear models, assessing racial/ethnic differences in OUD diagnosis, number of opioid prescriptions, receiving only one opioid prescription, and receiving ≥18 opioid prescriptions. Study population (N=22,201) consisted of adult patients (≥18years), with ≥3 primary care visits (ensuring healthcare system linkage), ≥1 opioid prescription, who did not have an OUD diagnoses prior to the first opioid prescription during the 32-month study period. Relative to racial/ethnic minority patients, White patients, in both unadjusted and adjusted analyses, had a greater number of opioid prescriptions filled, a higher proportion received ≥18 opioid prescriptions, and a greater hazard of having an OUD diagnosis subsequent to receiving an opioid prescription (all groups p<0.001). Although opioid prescribing rates have declined nationally, our findings suggest White patients still experience a high volume of opioid prescriptions and greater risk of OUD diagnosis. Racial/ethnic minorities are less likely to receive follow-up pain medications, which may signal low care quality. Identifying provider bias in pain management of racial/ethnic minorities could inform interventions seeking balance between adequate pain treatment and risk of opioid misuse/abuse.

Racial/ethnic disparities in emergency department wait times in the United States, 2013-2017.

BACKGROUND: Previous research shows that Black and Hispanic patients have longer ED wait times than White patients, but these data do not reflect recent changes such as the Affordable Care Act. In addition, previous research does not account for the non-normal distribution of wait times, wherein a sizable subgroup of patients seen promptly and those not seen promptly experience long wait times. METHODS: We utilized National Hospital Ambulatory Medical Care Survey (NHAMCS) datasets (2013-2017) to examine mean ED wait time comparing visits by Black, Hispanic, and Asian patients to White patients. Using a two-part regression model, we adjusted for patient, hospital, and health system factors, and estimated differences, for each of five triage levels, in (a) likelihood of waiting at least 5 min and (b) difference in wait time among those not seen promptly. RESULTS: Our cohort included 38,800 White, 14,838 Black, 10,619 Hispanic, and 1257 Asian patient visits. Black (triage level 3) and Hispanic (triage levels 3 and 4) patients had longer mean wait times than White patients. Adjusted likelihood of not being seen promptly was lower among Blacks (triage levels 3, 4 and 5), Hispanics (triage level 5) and Asians (triage level 5) compared to Whites. Among those waiting at least 5 min, adjusted wait time was longer among Blacks in triage level 3 (5.2 min, 95% CI, 1.3 to 9.0) and level 4 (2.5 min, 95% CI, 0.2 to 4.9), Hispanics in triage level 4 (4.7 min, 95% CI, 1.7 to 7.7) and Asians in triage level 5 (16.3 min, 95% CI, 0.6 to 31.9) compared to Whites. CONCLUSIONS: Minority patients were less likely to wait to be seen, but waited longer if not seen promptly. These data exhibit that ED wait time disparities persist for African American and Hispanic patients and extend this observation to Asian patients.

Patient and provider perception of appropriateness, acceptability, and feasibility of behavioral health home (BHH) core components based on program implementation in an urban, safety-net health system.

Background: This manuscript evaluates patient and provider perspectives on the core components of a Behavioral Health Home (BHH) implemented in an urban, safety-net health system. The BHH integrated primary care and wellness services (e.g., on-site Nurse Practitioner and Care Manager, wellness groups and tools, population health management) into an existing outpatient clinic for people with serious mental illness (SMI). Methods: As the qualitative component of a Hybrid Type I effectiveness-implementation study, semi-structured interviews were conducted with providers and patients 6 months after program implementation, and responses were analyzed using thematic analysis. Valence coding (i.e., positive vs. negative acceptability) was also used to rate interviewees' transcriptions with respect to their feedback of the appropriateness, acceptability, and feasibility/sustainability of 9 well-described and desirable Integrated Behavioral Health Core components (seven from prior literature and two additional components developed for this intervention). Themes from the thematic analysis were then mapped and organized by each of the 9 components and the degree to which these themes explain valence ratings by component. Results: Responses about the team-based approach and universal screening for health conditions had the most positive valence across appropriateness, acceptability, and feasibility/sustainability by both providers and patients. Areas of especially high mismatch between perceived provider appropriateness and measures of acceptability and feasibility/sustainability included population health management and use of evidence-based clinical models to improve physical wellness where patient engagement in specific activities and tools varied. Social and peer support was highly valued by patients while incorporating patient voice was also found to be challenging. Conclusions: Findings reveal component-specific challenges regarding the acceptability, feasibility, and sustainability of specific components. These findings may partly explain mixed results from BHH models studied thus far in the peer-reviewed literature and may help provide concrete data for providers to improve BHH program implementation in clinical settings. Plain language abstract: Many people with serious mental illness also have medical problems, which are made worse by lack of access to primary care. The Behavioral Health Home (BHH) model seeks to address this by adding primary care access into existing interdisciplinary mental health clinics. As these models are implemented with increasing frequency nationwide and a growing body of research continues to assess their health impacts, it is crucial to examine patient and provider experiences of BHH implementation to understand how implementation factors may contribute to clinical effectiveness. This study examines provider and patient perspectives of acceptability, appropriateness, and feasibility/sustainability of BHH model components at 6-7 months after program implementation at an urban, safety-net health system. The team-based approach of the BHH was perceived to be highly acceptable and appropriate. Although providers found certain BHH components to be highly appropriate in theory (e.g., population-level health management), their acceptability of these approaches as implemented in practice was not as high, and their feedback provides suggestions for model improvements at this and other health systems. Similarly, social and peer support was found to be highly appropriate by both providers and patients, but in practice, at months 6-7, the BHH studied had not yet developed a process of engaging patients in ongoing program operations that was highly acceptable by providers and patients alike. We provide these data on each specific BHH model component, which will be useful to improving implementation in clinical settings of BHH programs that share some or all of these program components.

Understanding the Role of Past Health Care Discrimination in Help-Seeking and Shared Decision-Making for Depression Treatment Preferences.

As a part of a larger, mixed-methods research study, we conducted semi-structured interviews with 21 adults with depressive symptoms to understand the role that past health care discrimination plays in shaping help-seeking for depression treatment and receiving preferred treatment modalities. We recruited to achieve heterogeneity of racial/ethnic backgrounds and history of health care discrimination in our participant sample. Participants were Hispanic/Latino (n = 4), non-Hispanic/Latino Black (n = 8), or non-Hispanic/Latino White (n = 9). Twelve reported health care discrimination due to race/ethnicity, language, perceived social class, and/or mental health diagnosis. Health care discrimination exacerbated barriers to initiating and continuing depression treatment among patients from diverse backgrounds or with stigmatized mental health conditions. Treatment preferences emerged as fluid and shaped by shared decisions made within a trustworthy patient-provider relationship. However, patients who had experienced health care discrimination faced greater challenges to forming trusting relationships with providers and thus engaging in shared decision-making processes.

Disparities in Suicidality by Gender Identity Among Medicare Beneficiaries.

INTRODUCTION: Suicidality is higher for gender minorities than the general population, yet little is known about suicidality in disabled or older adult gender minorities. METHODS: This study used 2009-2014 Medicare claims to identify people with gender identity-related diagnosis codes (disabled, n=6,678; older adult, n=2,018) and compared their prevalence of suicidality with a 5% random non-gender minority beneficiary sample (disabled, n=535,801; older adult, n=1,700,008). Correlates of suicidality were assessed (via chi-square) for each of the 4 participant groups separately, and then disparities within eligibility status (disabled or older adult) were assessed using logistic regression models, adjusting first for age and mental health chronic conditions and then additionally for Medicaid eligibility, race/ethnicity, or U.S. region (each separately). The primary hypotheses were that gender minority beneficiaries would have higher suicidality but that suicidality disparities would persist after adjusting for covariates. Data were analyzed between 2017 and 2019. RESULTS: Gender minority beneficiaries had higher unadjusted suicidality than non-gender minority beneficiaries in the disabled cohort (18.5% vs 7.1%, p<0.001). Significant suicidality predictors in all 4 groups included the following: age (except in older adult gender minorities), Medicaid eligibility, depression or behavioral health conditions, avoidable hospitalizations, and violence victimization. In age- and mental health-adjusted logistic regression models, gender minorities had higher odds of suicidality than non-gender minority beneficiaries (disabled, OR=1.95, p<0.0001; older adult, OR=2.10, p<0.0001). Disparities were not attenuated after adjusting for Medicaid eligibility, race/ethnicity, or region. CONCLUSIONS: Heightened suicidality among identified gender minority Medicare beneficiaries highlights a pressing need to identify and reduce barriers to wellness in this population.

Depression Treatment Expenditures for Adults in the USA: a Systematic Review.

PURPOSE OF REVIEW: We review 2016-2019 peer-reviewed literature which summarizes the factors contributing to high expense of treating depression among adults in the USA, and interventions that have been conducted to decrease depression treatment expenditures. RECENT FINDINGS: Treatment expenditures associated with depression are high and growing, driven in part by increased health care utilization and a shift toward increased insurance coverage of medications and therapies. The majority of identified articles describe the elevated financial burden associated with treating individuals with chronic medical conditions who also have a depression diagnosis. The few available studies documenting health care system-level interventions identify that multi-target treatment for comorbid illness, collaborative care management, and integration of psychiatric treatment into primary care show promise for reducing depression treatment expenditures. Additional research is needed to identify innovative, cost-effective state, and federal payer-initiated depression treatment models, and evaluation of collaborative care and integrated care models implemented to scale across multiple health care systems.

Identification of suicidal behavior among psychiatrically hospitalized adolescents using natural language processing and machine learning of electronic health records.

OBJECTIVE: The rapid proliferation of machine learning research using electronic health records to classify healthcare outcomes offers an opportunity to address the pressing public health problem of adolescent suicidal behavior. We describe the development and evaluation of a machine learning algorithm using natural language processing of electronic health records to identify suicidal behavior among psychiatrically hospitalized adolescents. METHODS: Adolescents hospitalized on a psychiatric inpatient unit in a community health system in the northeastern United States were surveyed for history of suicide attempt in the past 12 months. A total of 73 respondents had electronic health records available prior to the index psychiatric admission. Unstructured clinical notes were downloaded from the year preceding the index inpatient admission. Natural language processing identified phrases from the notes associated with the suicide attempt outcome. We enriched this group of phrases with a clinically focused list of terms representing known risk and protective factors for suicide attempt in adolescents. We then applied the random forest machine learning algorithm to develop a classification model. The model performance was evaluated using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. RESULTS: The final model had a sensitivity of 0.83, specificity of 0.22, AUC of 0.68, a PPV of 0.42, NPV of 0.67, and an accuracy of 0.47. The terms mostly highly associated with suicide attempt clustered around terms related to suicide, family members, psychiatric disorders, and psychotropic medications. CONCLUSION: This analysis demonstrates modest success of a natural language processing and machine learning approach to identifying suicide attempt among a small sample of hospitalized adolescents in a psychiatric setting.