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John is 28years old and has been mentally ill for as long as anyone can remember. His doctors say he has a schizophrenia disorder. Sometimes he hears God whispering in his ear. Other times it's Satan teasing and mocking him. John's been arrested for public intoxication, trespassing, disorderly conduct, aggressive loitering, burglary and theft. In and out of jail, he stays a few days at a half-way house, then is back on the streets, sleeping in some bushes, panhandling, gulping 40-ounce cans of beer, and hustling in exchange for drugs. Sometimes, he can't stop laughing. Other times, he cries so hard he chokes. Some days, even John agrees that he would be better off in a hospital somewhere. 'The problem is, ' he says, I don't know where to go.' Last year, John was sentenced to 10 years in prison after breaking into a woman's apartment and kidnapping her because he believed he was sent by God to protect her., The facts Because of 'deinstitutionalization' and failure to adequately fund community mental health programs, hundreds of thousands of individuals with serious mental illness have been 'trans-institutionalized' from America's public mental [...]

2024 NOV 2 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- New study results on depression have been published. According to news reporting [...]

Electroconvulsive therapy (ECT) is a method of stimulating the brain with electricity to treat some types of severe mental illness and brain disorders. The procedure sends small electric currents through [...]

Olivia has suffered with depression and struggled to form romantic attachments ever since she was sexually assaulted. Counselling and medication scarcely helped. So in September 2023 she flew to Mexico [...]

INTRODUCTION I. BACKGROUND A. The History of Psychedelics B. Psychedelics as Treatment for Mental Illness C. The Psychedelic Renaissance--Recent Push for Psychedelic Reform II. REGULATORY FRAMEWORK FOR PSYCHEDELIC REGULATION A. [...]

2024 FEB 24 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- Research findings on Pharmaceutical Research are discussed in a new report. According [...]

2023 DEC 30 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- Data detailed on antidepressants have been presented. According to news originating from [...]

1 | INTRODUCTION Medicare Advantage (MA) is Medicare's managed care alternative to traditional fee-for-service Medicare (TM), which provides Part A and B benefits through private health plans. MA enrollment has [...], Objective: To examine the effects of Medicare Advantage (MA) enrollment on health care use and dissatisfaction with care received among Medicare beneficiaries with mental illness. Data Sources: I identified traditional Medicare (TM) and MA beneficiaries with mental illness using the Medicare Current Beneficiary Survey for 2012-2016. Study Design: I included two types of outcomes: four measures of health care use and 10 measures of care dissatisfaction. My primary independent variable was enrollment in TM versus MA. To address selective enrollment into MA, I used an instrumental variable (IV) approach. Following prior research, I decomposed the MA benchmark into exogenous and endogenous components and then used the exogenous component as my instrument. Data Collection/Extraction Methods: Not Applicable. Principal Findings: IV analyses showed that compared with TM enrollment, MA enrollment significantly decreased outpatient hospital visits and medical provider visits by 6.73 (95% CI: -12.10 to -1.36) and 36.48 (95% CI: -52.67 to -20.28). However, there were no significant changes in inpatient hospital admissions and prescription drug purchases. Compared with TM enrollment, MA enrollment significantly increased dissatisfaction with out-of-pocket expenses by 25.51 percentage points (95% CI: 0.43 to 50.60). However, there were no significant changes in other measures of care dissatisfaction in terms of access to care, quality of care, and prescription medication. Conclusions: These findings suggest that MA enrollment may lead to low health care use among those with mental illness, indicating efficient care delivery. Also, MA enrollment may not preclude those with mental illness from accessing needed care. However, high dissatisfaction with out-of-pocket expenses among MA beneficiaries may imply the use of out-of-network providers. Further research is warranted to investigate whether high dissatisfaction with out-of-pocket expenses may be attributable to MA's narrow networks for mental services. KEYWORDS health care use, Medicare Advantage, mental illness, out-of-pocket cost, traditional Medicare What is known on this topic * Prior research has found that Medicare Advantage (MA) plans tend to have narrow networks overall, but the breadth of the networks is more restricted for mental health services than other types of services. * This may raise concerns about limited access to care among MA beneficiaries with mental illness. What this study adds * I examined the effects of MA enrollment on health care use and dissatisfaction with care received among Medicare beneficiaries with mental illness. * My instrumental variable analyses showed that MA enrollment significantly decreased outpatient hospital visits and medical provider visits, but it did not increase dissatisfaction with care access, suggesting high efficiency of care rather than under-provision of care. * MA enrollment significantly increased dissatisfaction with out-of-pocket expenses for medical care, suggesting there is use of out-of-network mental health providers due to MA's narrow networks for mental services.

A nursing governance approach INTRODUCTION Perinatal mental illness is common, and barriers to access and engagement with health services prevent many women from receiving effective treatment and support. (1) Nurse-led [...]

1 | INTRODUCTION With the accelerated aging population, nursing homes (NHs) are serving an increasing proportion of residents with cognitive issues (e.g., dementia) and mental health conditions. Previous studies reported [...], Objective: Nursing homes (NHs) are serving an increasing proportion of residents with cognitive issues (e.g., dementia) and mental health conditions. This study aims to: (1) implement unsupervised machine learning to cluster NHs based on residents' dementia and mental health conditions; (2) examine NH staffing related to the clusters; and (3) investigate the association of staffing and NH quality (measured by the number of deficiencies and deficiency scores) in each cluster. Data sources: 2009-2017 Certification and Survey Provider Enhanced Reporting (CASPER) were merged with LTCFocUS.org data on NHs in the United States. Study design: Unsupervised machine learning algorithm (K-means) clustered NHs based on percent residents with dementia, depression, and serious mental illness (SMI, e.g., schizophrenia, anxiety). Panel fixed-effects regressions on deficiency outcomes with staffing-cluster interactions were conducted to examine the effects of staffing on deficiency outcomes in each cluster. Data extraction methods: We identified 110,463 NH-year observations from 14,671 unique NHs using CASPER data. Principal findings: Three clusters were identified: low dementia and mental illnesses (Postacute Cluster); high dementia and depression, but low SMI (Long-stay Cluster); and high dementia and mental illnesses (Cognitive-mental Cluster). From 2009 to 2017, the number of Postacute Cluster NHs increased from 3074 to 5719, while the number of Long-stay Cluster NHs decreased from 6745 to 3058. NHs in Long-stay/Cognitive-mental Clusters reported slightly lower nursing staff hours in 2017. Regressions suggested the effect of increasing staffing on reducing deficiencies is statistically similar across NH clusters. For example, 1 hour increase in registered nurse hours per resident day was associated with -0.67 (standard error [SE] = 0.11), -0.88 (SE = 0.12), and -0.97 (SE = 0.15) deficiencies in Postacute Cluster, Long-stay Cluster, and Cognitive-mental Cluster, respectively. Conclusions: Unsupervised machine learning detected a changing landscape of NH serving residents with dementia and mental illnesses, which requires assuring staffing levels and trainings are suited to residents' needs. KEYWORDS deficiency score, dementia, mental illnesses, nursing homes, staffing, unsupervised machine learning What is known on this topic * Nursing homes are serving very high proportions of residents with dementia and mental illnesses. * Better staffing in nursing homes is generally associated with better quality of care, including a lower number of deficiencies. What this study adds * Over the last decade, significant changes in resident conditions were reflected by a different clustering of dementia and mental health conditions in nursing homes. * Nursing homes that served residents with more dementia and mental health issues had similar or lower staffing levels but were more likely to have a dementia special care unit. * The protective effects of staffing on nursing home quality measured by deficiency scores were similar regardless of nursing home clusters.