Achieving Mental Health Equity: Addictions

Jordan, Ayana; Mathis, Myra; Isom, Jessica

doi:10.1016/j.psc.2020.05.007

Cited by 27 publications

(9 citation statements)

References 23 publications

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“…Interventions could involve policy and system reform of OTPs (e.g., methadone liberalization through the distribution of methadone through the pharmacy system or primary care) 49 , 50 or further deregulation of buprenorphine by completely eliminating federal enrollment requirements and patient panel limitations 51 . In addition to MOUD-related policy changes, Jordan and colleagues 52 suggest developing new models of clinical care (e.g., integrated services to address whole-person health) and implementing culturally sensitive treatment services, such as the delivery of addiction-related treatment or interventions in churches within Black communities 53 . Decisionmakers should also consider broader health equity policies that may impact access to MOUD, such as increasing health insurance coverage through Medicaid expansion, which is associated with increases in buprenorphine prescriptions 54 .…”

Section: Discussionmentioning

confidence: 99%

Differences in the Delivery of Medications for Opioid use Disorder during Hospitalization by Racial Categories: A Retrospective Cohort Analysis;

et al. 2022

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Background: As the drug-related overdose crisis and COVID-19 pandemic continue, communities need increased access to medications for opioid use disorder (MOUD) (i.e., buprenorphine and methadone). Disparities in the type of MOUD prescribed or administered by racial and ethnic categories are well described in the outpatient clinical environment. It is unknown, however, if these disparities persist when MOUD is provided in acute care hospitals. Methods: This study assessed differences in the delivery of buprenorphine versus methadone during acute medical or surgical hospitalizations for veterans with opioid use disorder (OUD) by racial categories (Black Non-Hispanic or Latino vs. White Non-Hispanic or Latino). Data were obtained retrospectively from the Veterans Health Administration (VHA) for federal fiscal year 2017. We built logistic regression models, adjusted for individual and hospital-related covariates, and calculated the predicted probabilities of MOUD delivery by racial categories. Results: The study cohort (n = 1,313 unique patients; N = 107 VHA hospitals) had a mean age of 57 (range 23 to 87 years), was predominantly male (96%), and composed entirely of Black (29%) or White (71%) patients. White patients were 11% more likely than Black patients to receive buprenorphine than methadone during hospitalization (p = 0.010; 95% CI: 2.7%, 20.0%). Among patients on MOUD prior to hospitalization, White patients were 21% more likely than Black patients to receive buprenorphine (p = 0.000; 95% CI: 9.8%, 31.5%). Among patients newly initiated on MOUD during hospitalization, there were no differences by racial categories. Conclusion: We observed disparities in the delivery of buprenorphine versus methadone during hospitalization by racial categories. The observed differences in hospital-based MOUD delivery may be influenced by MOUD received prior to hospitalization within the racialized outpatient addiction treatment system. The VHA and health systems more broadly must address all aspects of racism that contribute to inequitable MOUD access throughout all clinical contexts.

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Section: Discussionmentioning

confidence: 99%

Differences in the Delivery of Medications for Opioid use Disorder during Hospitalization by Racial Categories: A Retrospective Cohort Analysis;

et al. 2022

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“…Moreover, diagnostic literalism by means of close adherence to DSM-defined criteria obscures the fact that SUD criteria may contribute to mis-or overdiagnosis due to societal assumptions regarding alcohol and other drug use, which have historically manifested in racist and moralistic drug policies (Jordan et al, 2020). This (mis)classification concern is more practical than abstract, too: The legal problems criterion for SUD was removed from DSM-5 just ten years ago because it contributed to overdiagnosis of SUD among people with marginalized racial and ethnic identities, and underdiagnosis in women (e.g., Saha et al, 2006).…”

Section: Hitop and Nida Phab: Moving Forwardmentioning

confidence: 99%

New approaches to deep phenotyping in addictions

Watts¹,

Latzman²,

Boness³

et al. 2022

Preprint

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Objective: The causes of substance use disorders (SUDs) are largely unknown and the effectiveness of their treatments is limited. One crucial impediment to research and treatment progress surrounds how SUDs are classified and diagnosed. Given the substantial heterogeneity among individuals diagnosed with a given SUD (e.g., alcohol use disorder), identifying novel research and treatment targets and developing new study designs is daunting. Method: In this paper, we review and integrate two recently developed frameworks, the NIDA Phenotyping Assessment Battery (NIDA PhAB) and the Hierarchical Taxonomy of Psychopathology (HiTOP), that hope to accelerate progress in understanding the causes and consequences of psychopathology by means of deep phenotyping, or finer-grained analysis of phenotypes. Results and Conclusions: NIDA PhAB focuses on addiction-related processes across multiple units of analysis, whereas HiTOP focuses on clinical phenotypes and covers a broader range of psychopathology. We highlight that NIDA PhAB and HiTOP together provide deep and broad characterizations of people diagnosed with SUDs and complement each other in their efforts to address widely known limitations of traditional classification systems and their diagnostic categories. Next, we show how NIDA PhAB and HiTOP can be integrated to facilitate optimal rich phenotyping of addiction-related phenomena. Finally, we argue that such deep phenotyping promises to advance our understanding of the neurobiology of SUD and addiction, which will guide the development of personalized medicine and interventions.

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“…Therefore, it is difficult to describe it with traditional mathematical methods [12]. For perceptron model, its weight can be adjusted by learning, and the learning algorithm adopts supervised learning [13]. When it is used to classify two types of patterns, it is equivalent to separating two types of samples with a hyperplane in a highdimensional sample space.…”

Section: Introductionmentioning

confidence: 99%

Psychological Health Assessment Model of Enterprise Employees Based on DNN Technology

2022

Wireless Communications and Mobile Computing

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All enterprises gradually recognise the importance of employees’ healthy psychology to business activities in order to improve their own economic level and occupy a certain leading position in the economic market. The main factors affecting employees’ psychological health are used as input samples in this paper, and a network model of enterprise employees’ psychological health prediction based on DNN is developed. To form a specific set, the psychological health indicators are separated from the complex test items. The key influencing factors in psychological health assessment are chosen as input vectors, and the DNN algorithm’s output results are obtained, analysed, and compared. Following sample training, the artificial NN’s error between predicted and measured values is only 3.55 percent, achieving the desired effect. The DNN principle is used in this paper to create a mathematical prediction network model based on an analysis of psychological factors affecting employees in businesses. The calculation of the final result of the prediction system is simple and flexible when the parameters of the NN are changed, and the network model’s prediction efficiency and accuracy are greatly improved.

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Achieving Mental Health Equity: Addictions

Cited by 27 publications

References 23 publications

Differences in the Delivery of Medications for Opioid use Disorder during Hospitalization by Racial Categories: A Retrospective Cohort Analysis;

Differences in the Delivery of Medications for Opioid use Disorder during Hospitalization by Racial Categories: A Retrospective Cohort Analysis;

New approaches to deep phenotyping in addictions

Psychological Health Assessment Model of Enterprise Employees Based on DNN Technology

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