Closing the False Divide: Sustainable Approaches to Integrating Mental Health Services into Primary Care

Mental disorders account for 25% of all health-related disability worldwide. More patients receive treatment for mental disorders in the primary care sector than in the mental health specialty setting. However, brief visits, inadequate reimbursement, deficits in primary care provider (PCP) training, and competing demands often limit the capacity of the PCP to produce optimal outcomes in patients with common mental disorders. More than 80 randomized trials have shown the benefits of collaborative care (CC) models for improving outcomes of patients with depression and anxiety. Six key components of CC include a population-based approach, measurement-based care, treatment to target strategy, care management, supervision by a mental health professional (MHP), and brief psychological therapies. Multiple trials have also shown that CC for depression is equally or more cost-effective than many of the current treatments for medical disorders. Factors that may facilitate the implementation of CC include a more favorable alignment of medical and mental health services in accountable care organizations and patient-centered medical homes; greater use of telecare as well as automated outcome monitoring; identification of patients who might benefit most from CC; and systematic training of both PCPs and MHPs in integrated team-based care

Modeling eating disorders of cognitive impaired people

Millions of people all around the world suffer from eating disorders, known as anorexia nervosa, bulimia nervosa, pica, and others. When eating disorders coexist with other mental health disorders, eating disorders often go undiagnosed and untreated; a low number of sufferers obtain treatment for the eating disorder. Unfortunately, eating disorders have also the highest mortality rate of any mental illness, upwards of 20%. This paper focuses on monitoring eating disorders of cognitive impaired people as patients with the Alzheimer’s disease. The proposed approach relies on the application of Ambient Intelligence (AmI) technologies and a new method for the detection of abnormal human behaviors in a controlled environment

Global Real-World Evidence of Sofosbuvir/Velpatasvir as a Highly Effective Treatment and Elimination Tool in People with Hepatitis C Infection Experiencing Mental Health Disorders

HCV elimination; Mental health disorders; Sofosbuvir/velpatasvirEliminación del VHC; Trastornos de salud mental; Sofosbuvir/velpatasvirEliminació del VHC; Trastorns de salut mental; Sofosbuvir/velpatasvirHepatitis C virus (HCV) is prevalent in people with mental health disorders, a priority population to diagnose and cure in order to achieve HCV elimination. This integrated analysis pooled data from 20 cohorts in seven countries to evaluate the real-world effectiveness of the pangenotypic direct-acting antiviral (DAA) sofosbuvir/velpatasvir (SOF/VEL) in people with mental health disorders. HCV-infected patients diagnosed with mental health disorders who were treated with SOF/VEL for 12 weeks without ribavirin as part of routine clinical practice were included. The primary outcome was sustained virological response (SVR) in the effectiveness population (EP), defined as patients with an available SVR assessment. Secondary outcomes were reasons for not achieving SVR, characteristics of patients with non-virological failures, adherence, and time from HCV RNA diagnosis to SOF/VEL treatment initiation. A total of 1209 patients were included; 142 did not achieve an SVR for non-virological reasons (n = 112; 83 lost to follow-up, 20 early treatment discontinuations) or unknown reasons (n = 30). Of the 1067 patients in the EP, 97.4% achieved SVR. SVR rates in the EP were ≥95% when stratified by type of mental health disorder and other complicating baseline characteristics, including active injection drug use and antipsychotic drug use. Of 461 patients with data available in the EP, only 2% had an adherence level < 90% and 1% had an adherence level < 80%; all achieved SVR. Patients with mental health disorders can be cured of HCV using a well-tolerated, pangenotypic, protease inhibitor-free SOF/VEL regimen. This DAA allows the implementation of a simple treatment algorithm, with minimal monitoring requirements and fewer interactions with central nervous system drugs compared with protease-inhibitor DAA regimens.Article processing charges, statistical support and medical writing assistance were funded by Gilead Sciences Ltd

Smartphone-Based Tracking of Sleep in Depression, Anxiety, and Psychotic Disorders

Purpose of ReviewSleep is an important feature in mental illness. Smartphones can be used to assess and monitor sleep, yet there is little prior application of this approach in depressive, anxiety, or psychotic disorders. We review uses of smartphones and wearable devices for sleep research in patients with these conditions.Recent FindingsTo date, most studies consist of pilot evaluations demonstrating feasibility and acceptability of monitoring sleep using smartphones and wearable devices among individuals with psychiatric disorders. Promising findings show early associations between behaviors and sleep parameters and agreement between clinic-based assessments, active smartphone data capture, and passively collected data. Few studies report improvement in sleep or mental health outcomes.SummarySuccess of smartphone-based sleep assessments and interventions requires emphasis on promoting long-term adherence, exploring possibilities of adaptive and personalized systems to predict risk/relapse, and determining impact of sleep monitoring on improving patients' quality of life and clinically meaningful outcomes.Peer reviewe

Screening for and monitoring of cardio-metabolic risk factors in outpatients with severe mental illness in a primary care setting

Objective: Recent findings suggest that premature death in patients with severe mental illness (SMI) can be attributed to the high comorbidity of cardio-metabolic disorders. This study investigated the prevalence and monitoring of some risk factors for cardio-metabolic disease in a cohort with SMI, compared to the general medical population. Method: 101 participants with SMI and 100 controls were recruited from a primary care clinic. Assessments of risk factors with standard clinical measurements were done after healthcare workers and patient-participants had completed the structured questionnaires. Clinical files were reviewed to determine frequency of monitoring of risk factors. Results: We found no differences between the groups in demographic variables. A similar prevalence of abnormal blood pressure (BP), increased Body Mass Index (BMI) and increased waist circumference was noted in both groups. Females in both groups were more likely to have an abnormal waist circumference. Patients with SMI were significantly less likely to have recordings of their weight or BP in their clinic file. Healthcare workers and patients with SMI were largely unaware of the increased risk of cardio-metabolic illness. Conclusion: This study suggests that patients with SMI received poorer health monitoring than other patients attending a primary care clinic and that both healthcare workers and patients were poorly informed about the increased risk of cardio-metabolic disorders in patients with SMI.Key words: Mental illness; Comorbidity; Risk factors; Metabolic; Cardio-vascula

Progress in drug metabolism, volume 1 Edited by J. W. Bridges and L. F. Chasseaud John Wiley and Sons; London, New York, Sydney, Toronto, 1976 xiii + 286 pages. $ 19.75, £ 9.80

Objective: People with psychotic disorders have an increased metabolic risk and a shortened life expectancy compared to the general population. Two large studies showed that metabolic disorders were untreated in a majority of the patients. Since then, guidelines have urged monitoring of metabolic health. This study examined the course of metabolic disorders over time in people with psychotic disorders and investigated current treatment rates. Methods: A total of 1,259 patients with psychotic disorders, as defined by the DSM-IV, from 4 Dutch mental health institutions participated in 3 yearly assessments of the Pharmacotherapy Monitoring and Outcome Survey (PHAMOUS) between 2006 and 2014. Patients' metabolic parameters were measured, and the use of pharmacologic treatment for hypertension (systolic blood pressure >= 140 mm Hg and/or diastolic blood pressure >= 90 mm Hg), dyslipidemia (5% = 2.5 mmol/L or SCORE risk >= 10% and LDL cholesterol level >= 1.8 mmol/L and/or triglycerides >= 2.3 mmol/L), and hyperglycemia (hemoglobin A(1c) concentration > 7% and/or fasting glucose concentration >= 7.2 mmol/L) was recorded. Results: Prevalence of the metabolic syndrome, as defined by the National Cholesterol Education Program criteria, was > 50% at each assessment. On the basis of the European Society of Cardiology guidelines, pharmacotherapy for metabolic disorders was recommended for 52%-59% of the patients at each assessment. Treatment rates with antihypertensive (from 31% to 38%, P <.001) pharmacotherapy increased throughout the assessments. However, half of the patients were not treated for their metabolic risk factors while being monitored for 3 years or longer. Older patients were more likely to receive treatment, and patients who received treatment had lower blood pressure and lower cholesterol and triglyceride concentrations than patients not receiving the recommended treatment. Conclusions: Metabolic risk factors are still seriously undertreated in people with psychotic disorders. Better adherence to and better implementation of guidelines about monitoring and treating metabolic disorders in psychiatry are crucial. (C) Copyright 2017 Physicians Postgraduate Press, Inc

An emotion and cognitive based analysis of mental health disorders from social media data

[EN] Mental disorders can severely affect quality of life, constitute a major predictive factor of suicide, and are usually underdiagnosed and undertreated. Early detection of signs of mental health problems is particularly important, since unattended, they can be life-threatening. This is why a deep understanding of the complex manifestations of mental disorder development is important. We present a study of mental disorders in social media, from different perspectives. We are interested in understanding whether monitoring language in social media could help with early detection of mental disorders, using computational methods. We developed deep learning models to learn linguistic markers of disorders, at different levels of the language (content, style, emotions), and further try to interpret the behavior of our models for a deeper understanding of mental disorder signs. We complement our prediction models with computational analyses grounded in theories from psychology related to cognitive styles and emotions, in order to understand to what extent it is possible to connect cognitive styles with the communication of emotions over time. The final goal is to distinguish between users diagnosed with a mental disorder and healthy users, in order to assist clinicians in diagnosing patients. We consider three different mental disorders, which we analyze separately and comparatively: depression, anorexia, and self-harm tendencies.The authors thank the EU-FEDER Comunitat Valenciana 2014- 2020 grant IDIFEDER/2018/025. The work of Paolo Rosso was in the framework of the research project PROMETEO/2019/121 (DeepPattern) by the Generalitat Valenciana.Uban, A.; Chulvi-Ferriols, MA.; Rosso, P. (2021). An emotion and cognitive based analysis of mental health disorders from social media data. Future Generation Computer Systems. 124:480-494. https://doi.org/10.1016/j.future.2021.05.032S48049412

Crime, re-offence, and substance abuse of patients with severe mental disorder

Scientific research has shown that there is a causal association between crime, re-offence, and severe mental disorder. Numerous authors have found that psychosis, particularly schizophrenic psychosis, personality disorders, namely antisocial personality disorder, and substance abuse are disorders, which considerably increase the criminal risk. The patients’ decompensation and the associated comorbidity, in this regard, can never be neglected. Therefore, in this paper a review of literature was performed whose goals demonstrate: 1) the relationship between severe mental disorder and crime; 2) the relationship between severe mental disorder and re-offence; 3) the relationship between substance use and crime; 4) the relationship between substance use in individuals with severe mental disorder and crime and re-offence. Promoting the independence and well-being of these patients contributing to the maintenance of social peace, which requires timely monitoring and evaluation of the clinical condition and functionality of the individual, through articulation in a network, which would allow to assess and foster the skills of the individual as a social being.info:eu-repo/semantics/publishedVersio

Prescription drug monitoring program inquiry in psychiatric assessment: detection of high rates of opioid prescribing to a dual diagnosis population

OBJECTIVE: An epidemic of prescription drug abuse is disproportionately impacting the mentally ill. We examined the utility of a state prescription drug monitoring database for assessing recent controlled substance prescribing to patients presenting for dual diagnosis treatment. METHOD: In a community mental health center that provides integrated dual diagnosis care, we queried the Indiana Scheduled Prescription Electronic Collection and Tracking (INSPECT) system for all cases that were open as of August 2, 2011, and had been practitioner-diagnosed (per DSM-IV criteria) by January 2, 2012. INSPECT provided a record of controlled substance dispensations to each patient; diagnostic evaluation was conducted blind from prescription data compilation covering the prior 12 months. Demographic data, insurance status, and DSM-IV diagnoses were compiled from the clinic's electronic medical record. RESULTS: The sample (N = 201) was 51% female, 56% white, and two-thirds uninsured. Over 80% were dually diagnosed with substance use disorders and psychotic, mood, or anxiety disorders. Nicotine and alcohol disorders were identified in most, with about a third diagnosed with cannabis, cocaine, or opioid disorders. A majority of patients (n = 115) had been prescribed opioids in the prior year, with nearly 1 in 5 prescribed an opioid and benzodiazepine simultaneously. Patients were dispensed a mean of 4 opioid prescriptions and 213 opioid pills. More opioid prescriptions correlated with opioid dependence (OR = 1.08; 95% CI, 1.016-1.145), and more prescribers correlated with personality disorder diagnoses (OR = 1.112; 95% CI, 1.001-1.235). Higher rates and riskier patterns of controlled substance prescribing were identified in patients with Medicaid/Medicare insurance compared to uninsured patients. CONCLUSIONS: Prescription drug monitoring is a powerful tool for assessing addictions and high frequencies of patient exposures to prescribed opioids in a dual diagnosis clinic. Improved prevention and treatment strategies for addictions as facilitated by more research and clinical use of prescription drug monitoring in psychiatric care are warranted

Antibiotics Induced Seizures and Encephalopathy

Drug-induced seizures and delirium are common among patients with critical illnesses, especially those in an intensive care unit. With an increase in the use of potent, broad-spectrum antibiotics, the etiology for encephalopathy remains under-recognized. Antibiotics-induced nonconvulsive seizures should also be considered in patients with unexplained mental status, therefore continuous electroencephalography monitoring is often needed for its detection. Prompt discontinuation, substitution, or dose adjustment of the causative antibiotics might help improve prognosis. Also, antibiotics should be used with caution especially in patients with known epilepsy, central nervous system disorders, critical illnesses, or renal dysfunction.ope