Diagnostic variability for schizophrenia and major depression in a large public mental health care system dataset

Racial Differences in Self-Report of Mental Illness and Mental Illness Treatment in the Community: An Analysis of Jail Intake Data

Jails and prisons in the United States house people with elevated rates of mental health and substance use disorders. The goal of this cross-sectional study was to evaluate the frequency of racial/ethnic differences in the self-report of mental illness and psychiatric medication use at jail entry. Our sample included individuals who had been incarcerated between 2016 and 2020 at the Middlesex Jail & House of Correction, located in Billerica, MA. We used data from the "Offender Management System," the administrative database used by the jail containing data on people who are incarcerated, and COREMR, the electronic medical record (EMR) used in the Middlesex Jail & House of Correction. We evaluated two primary outcomes (1) self-reported mental illness history and (2) self-reported use of psychiatric medication, with the primary indicator of interest as race/ethnicity. At intake, over half (57%) of the sample self-reported history of mental illness and 20% reported the use of psychiatric medications. Among people who self-reported a history of mental illness, Hispanic (AOR: 0.73, 95% CI: 0.60-0.90), Black (AOR: 0.52, 95% CI: 0.43-0.64), Asian/Pacific Islander (Non-Hispanic) people (AOR: 0.31, 95% CI: 0.13-0.74), and people from other racial/ethnic groups (AOR: 0.33, 95% CI: 0.11-0.93) all had decreased odds of reporting psychiatric medications. Mental illness was reported in about one-half of people who entered jail, but only 20% reported receiving medications in the community prior to incarceration. Our findings build on the existing literature on jail-based mental illness and show racial disparities in self-report of psychiatric medications in people who self-reported mental illness. The timing, frequency, and equity of mental health services in both the community and the jail setting deserves further research, investment, and improvement.

AI in patient flow: applications of artificial intelligence to improve patient flow in NHS acute mental health inpatient units

Introduction

Growing demand for mental health services, coupled with funding and resource limitations, creates an opportunity for novel technological solutions including artificial intelligence (AI). This study aims to identify issues in patient flow on mental health units and align them with potential AI solutions, ultimately devising a model for their integration at service level.

Method

Following a narrative literature review and pilot interview, 20 semi-structured interviews were conducted with AI and mental health experts. Thematic analysis was then used to analyse and synthesise gathered data and construct an enhanced model.

Results

Predictive variables for length-of-stay and readmission rate are not consistent in the literature. There are, however, common themes in patient flow issues. An analysis identified several potential areas for AI-enhanced patient flow. Firstly, AI could improve patient flow by streamlining administrative tasks and optimising allocation of resources. Secondly, real-time data analytics systems could support clinician decision-making in triage, discharge, diagnosis and treatment stages. Finally, longer-term, development of solutions such as digital phenotyping could help transform mental health care to a more preventative, personalised model.

Conclusions

Recommendations were formulated for NHS trusts open to adopting AI patient flow enhancements. Although AI offers many promising use-cases, greater collaborative investment and infrastructure are needed to deliver clinically validated improvements. Concerns around data-use, regulation and transparency remain, and hospitals must continue to balance guidelines with stakeholder priorities. Further research is needed to connect existing case studies and develop a framework for their evaluation.

Artificial Intelligence: An Interprofessional Perspective on Implications for Geriatric Mental Health Research and Care

Artificial intelligence (AI) in healthcare aims to learn patterns in large multimodal datasets within and across individuals. These patterns may either improve understanding of current clinical status or predict a future outcome. AI holds the potential to revolutionize geriatric mental health care and research by supporting diagnosis, treatment, and clinical decision-making. However, much of this momentum is driven by data and computer scientists and engineers and runs the risk of being disconnected from pragmatic issues in clinical practice. This interprofessional perspective bridges the experiences of clinical scientists and data science. We provide a brief overview of AI with the main focus on possible applications and challenges of using AI-based approaches for research and clinical care in geriatric mental health. We suggest future AI applications in geriatric mental health consider pragmatic considerations of clinical practice, methodological differences between data and clinical science, and address issues of ethics, privacy, and trust.

Stigma experienced by people using mental health services in San Diego County

OBJECTIVE

This paper describes how individuals struggling with severe mental illness experience stigma along multiple dimensions including their experiences of discrimination by others, their unwillingness to disclose information about their mental health, and their internalization or rejection of the negative and positive aspects of having mental health problems.

METHODS

This cross-sectional study employs descriptive analyses and linear regression to assess the relationship between demographics, mental health diagnoses and self-reported stigma among people receiving mental health services in a large and ethnically diverse county public mental health system (n = 1,237) in 2009. We used the King Stigma Scale to measure three factors related to stigma: discrimination, disclosure, and positive aspects of mental illness.

RESULTS

Most people (89.7 %) reported experiencing some discrimination from having mental health problems. Regression analyses revealed that younger people in treatment experienced more stigma related to mental health problems. Women reported experiencing more stigma than men, but men were less likely to endorse the potentially positive aspects of facing mental health challenges than women. Although people with mood disorders reported more discomfort with disclosing mental illness than people with schizophrenia, they did not report experiencing more discrimination than people with schizophrenia.

CONCLUSIONS

Study findings suggest that the multidimensional experiences of stigma differ as a function of age, gender, and diagnosis. Importantly, these findings should inform anti-stigma efforts by describing different potential treatment barriers due to experiences of stigma among people using mental health services, especially among younger people and women who may be more susceptible to stigma.

Development and validation of a mental health subscale from the Quality of Well-Being Self-Administered

PURPOSE

The purpose of this study was to create and validate a mental health subscale for the Quality of Well-Being Self-Administered (QWB-SA).

METHODS

The QWB-SA and other measures such as the Profile of Mood States (POMS), Medical Outcomes Study 36 Item Short Form (SF-36), EuroQOL 5D (EQ-5D), and Health Utilities Index Mark 2 (HUI) were administered to three samples: a general population (N = 3,844), a non-psychiatric medical population (N = 535), and a psychiatric population (N = 915). Independent expert ratings of which items represented the construct of mental health were used along with psychometric methods to develop and validate a 10-item QWB-SA mental health scale.

RESULTS

The mental health scale demonstrated high internal consistency (Cronbach's alpha = 0.827-0.842) and strong correlations with other measures of mental health, such as the POMS (r = -0.77), mental health scale from the SF-36 (r = 0.72), EQ-5D mood item (r = 0.61), and HUI Emotion Scale (r = 0.59). It was not highly correlated with measures of physical health. Among the psychiatric population, the new mental health scale was moderately correlated with indicators of psychiatric problem severity.

CONCLUSIONS

It is now possible to report outcomes and relationships with mental health in studies that use the QWB-SA. This new mental health subscale can also be used with the large volume of previously collected data using the QWB-SA to examine the impact of illnesses and interventions on mental health-related quality of life.

Predisposing, enabling, and need factors associated with high service use in a public mental health system

The purpose of this study was twofold: (1) To investigate the individual- and system-level characteristics associated with high utilization of acute mental health services according to a widely-used theory of service use-Andersen's Behavioral Model of Health Service Use -in individuals enrolled in a large, public-funded mental health system; and (2) To document service utilization by high use consumers prior to a transformation of the service delivery system. We analyzed data from 10,128 individuals receiving care in a large public mental health system from fiscal years 2000-2004. Subjects with information in the database for the index year (fiscal year 2000-2001) and all of the following 3 years were included in this study. Using logistic regression, we identified predisposing, enabling, and need characteristics associated with being categorized as a single-year high use consumer (HU: >3 acute care episodes in a single year) or multiple-year HU (>3 acute care episodes in more than 1 year). Thirteen percent of the sample met the criteria for being a single-year HU and an additional 8% met the definition for multiple-year HU. Although some predisposing factors were significantly associated with an increased likelihood of being classified as a HU (younger age and female gender) relative to non-HUs, the characteristics with the strongest associations with the HU definition, when controlling for all other factors, were enabling and need factors. Homelessness was associated with 115% increase in the odds of ever being classified as a HU compared to those living independently or with family and others. Having insurance was associated with increased odds of being classified as a HU by about 19% relative to non-HUs. Attending four or more outpatient visits was an enabling factor that decreased the chances of being defined as a HU. Need factors, such as having a diagnosis of schizophrenia, bipolar disorder or other psychotic disorder or having a substance use disorder increased the likelihood of being categorized as a HU. Characteristics with the strongest association with heavy use of a public mental health system were enabling and need factors. Therefore, optimal use of public mental services may be achieved by developing and implementing interventions that address the issues of homelessness, insurance coverage, and substance use. This may be best achieved by the integration of mental health, intensive case management, and supportive housing, as well as other social services.

The elderly in the psychiatric emergency service (PES); a descriptive study

BACKGROUND

The impact of an aging population on the psychiatric emergency service (PES) has not been fully ascertained. Cognitive dysfunctions aside, many DSM-IV disorders may have a lower prevalence in the elderly, who appear to be underrepresented in the PES. We therefore attempted to more precisely assess their patterns of PES use and their clinical and demographic characteristics.

METHODS

Close to 30,000 visits to a general hospital PES (Montreal, Quebec, Canada) were acquired between 1990 and 2004 and pooled with over 17,000 visits acquired using the same methodology at three other services in Quebec between 2002 and 2004.

RESULTS

The median age of PES patients increased over time. However, the proportion of yearly visits attributable to the elderly (compared to those under 65) showed no consistent increase during the observation period. The pattern of return visits (two to three, four to ten, eleven or more) did not differ from that of patients under 65, although the latter made a greater number of total return visits per patient. The elderly were more often women (62%), widowed (28%), came to the PES accompanied (42%) and reported « illness » as an important stressor (29%). About 39% were referred for depression or anxiety. They were less violent (10%) upon their arrival. Affective disorders predominated in the diagnostic profile, they were less co-morbid and more likely admitted than patients under 65.

CONCLUSION

Although no proportional increase in PES use over time was found the elderly do possess distinct characteristics potentially useful in PES resource planning so as to better serve this increasingly important segment of the general population.

Using electronic medical records to determine the diagnosis of clinical depression

OBJECTIVE

To investigate the validity of using electronic medical records (EMR) database in a large health organization for identifying patients with clinical depression.

METHOD

The Massachusetts General Hospital EMR system was used to generate a sample of primary care patients seen in the primary care clinic in 2007. Using this sample, the validity of using certain fields in the EMR database (i.e., billing diagnosis, problem list, and medication list) to identify patients with clinical depression was compared to primary care physician (PCP) assessment by a written questionnaire. Based on this standard, the sensitivity, specificity, positive predictive value, negative predictive value, and the areas under receiver operating characteristic curve (AUC) of three specific EMR fields - individually and in combination - were calculated to identify which EMR field best predicted PCP classification.

RESULTS

The EMR fields "billing diagnosis", "problem list" and antidepressant in "medication list", were all able to identify patients' diagnosis of depression by their PCPs reasonably well. Having one or more "billing diagnosis" of depression had the highest sensitivity and highest AUC (77% sensitivity, 76% specificity, AUC 0.77) among any of the fields used alone.

CONCLUSION

The AUC for "billing diagnosis" of depression performed the best of the three single fields tested, with an AUC of 0.77, corresponding to a test with moderate accuracy. This analysis demonstrates that specific EMR fields can be used as a proxy for PCP assessment of depression for this EMR system. Limitations to our analysis include the physician response rate to our survey as well as the quality of the data, which is collected primarily for administrative and clinical purposes. When using administrative and clinical data in mental health studies, researchers must first assess the accuracy of choosing specific fields within their EMR system in order to determine the level of accuracy for them to be used as proxies for clinical diagnoses.

Aggressive behaviors in the psychiatric emergency service

Introduction:

Studies of aggressive behaviors in a nonforensic mental health setting have focused primarily on the inpatient ward and, on event prediction, using behavior-based clinical rating scales. Few studies have specifically targeted aggressive behaviors in the psychiatric emergency service or determined whether assessing the demographic and clinical characteristics of such patients might prove useful for their more rapid identification.

Methods:

We used a prospectively acquired database of over 20,900 visits to four services in the province of Quebec, Canada, over a two-year period from September 2002 onwards. A maximum of 72 variables could be acquired per visit. Visits with aggression (any verbally or physically intimidating behavior), both present and past, were tagged. Binary logistic regressions and cross-tabulations were used to determine whether the profile of a variable differed in visits with aggression from those without aggression.

Results:

About 7% of visits were marked by current aggression (verbal 49%, physical 12%, verbal and physical 39%). Including visits with a “past only” history of aggression increased this number to 20%. Variables associated with aggression were gender (male), marital status (single/separated), education (high school or less), employment (none), judicial history (any type), substance abuse (prior or active), medication compliance (poor), type of arrival to psychiatric emergency services (involuntary, police, judiciary, landlord), reason for referral (behavioral dyscontrol), diagnosis (less frequent in anxiety disorders), and outcome (more frequently placed under observation or admitted).

Conclusion:

Our results suggest that many state-independent variables are associated with aggressive behaviors in the psychiatric emergency service. Although their sum may not add up to a specific patient profile, they can nevertheless be useful in service planning, being easily integrated alongside state-dependent rating scales in a triage and/or observation instrument for daily use in the psychiatric emergency service.

Does traumatic brain injury predispose individuals to develop schizophrenia?

PURPOSE OF REVIEW

Psychosis following traumatic brain injury has been studied in both prospective and retrospective studies that have been frequently limited by unclear criteria for classifying psychiatric syndromes or case ascertainment bias regarding traumatic brain injury. Schizophrenia is a psychotic disorder commonly attributed to the interaction of genetic vulnerability and environmental events, yet its relationship with traumatic brain injury has not been systematically reviewed.

RECENT FINDINGS

The literature is not conclusive and highlights the challenges to observational research in conditions in which case ascertainment is difficult. The evidence supports a risk-modifying effect of traumatic brain injury in individuals who are genetically at risk for schizophrenia, but is less supportive of traumatic brain injury as an independent risk factor for schizophrenia in individuals without such risk.

SUMMARY

Research in psychotic disorders following traumatic brain injury must distinguish schizophrenia from other psychotic syndromes, and take familial risk factors into consideration.