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Research examining mental health outcomes following economic downturns finds both pro-cyclic and counter-cyclic associations. Pro-cyclic findings (i.e. economic downturns correspond with decline in illnesses) invoke increase in leisure time and risk-averse behavior as underlying drivers of reduction in harmful consumption during economic recessions. By contrast, counter-cyclic evidence (i.e. economic downturns correspond with increase in illnesses) suggests increase in mental illness with economic decline owing to heightened stress and loss of resources, particularly among certain age and socioeconomic groups.

To examine the relation between monthly aggregate employment decline and psychiatric emergency department visits across 96 counties within 49 Metropolitan Statistical Areas in the United States.

For this study, data on all psychiatric outpatient Emergency Department (ED) visits for 4 US states (Arizona, California, New Jersey and New York) were retrieved from the State Emergency Department Databasnomic recessions, these aggregate trends may mask countervailing trends among vulnerable groups. Limitations of this study include the absence of sex-specific analyses and lack of information on emergent or non-emergent nature of psychiatric ED visits.

Psychiatric ED visits during recessions may vary by age and income groups.

Findings from this study may serve to develop targeted policies for low-income groups during macroeconomic downturns.

Future research may examine trends in emergent versus non-emergent psychiatric ED visits following economic recessions.

Future research may examine trends in emergent versus non-emergent psychiatric ED visits following economic recessions.

SSRIs and SNRIs are antidepressants that have largely substituted old antidepressants like Monoamine Oxidase Inhibitors (MAOIs) and Tricyclic Antidepressants (TCAs). They have been widely used since 1987 when the FDA approved the first SSRI Fluoxetine and the first SNRI Venlafaxine in 1993. Since then, several new SSRIs and SNRIs have been approved and entered the market. Utilization, pricing, and spending trends of SSRIs and SNRIs have not been analyzed yet in Medicaid.

To assess the trends of drug expenditure, utilization, and price of SSRI and SNRI antidepressants in the US Medicaid program, and to highlight the market share of SSRIs and SNRIs and the effect of generic drug entry on Medicaid drug expenditure.

A retrospective descriptive data analysis was conducted for this study. National pharmacy summary data for study brand and generic drugs were retrieved from the Medicaid State Outpatient Drug Utilization Data. These data were collected by the US Centers for Medicare and Medicaid Services (CMS). ver time.

An increase in utilization and spending for both SSRI and SNRI drugs was observed. After each generic drug entered the market, utilization shifted from the brand name to the respective generic due to their lower price. These generic substitutions demonstrate a meaningful cost-containment policy for Medicaid programs.

Our findings show the overall view of Medicaid expenditure on one of the most commonly prescribed drug classes in the US. They also provide an important insight toward the antidepressant market and the importance of monitoring different drugs and their alternatives.

Our findings show the overall view of Medicaid expenditure on one of the most commonly prescribed drug classes in the US. They also provide an important insight toward the antidepressant market and the importance of monitoring different drugs and their alternatives.

The COVID-19 health crisis has disproportionately impacted populations who have been historically marginalized in health care and public health, including low-income and racial and ethnic minority groups. Members of marginalized communities experience undue barriers to accessing health care through virtual care technologies, which have become the primary mode of ambulatory health care delivery during the COVID-19 pandemic. Insights generated during the COVID-19 pandemic can inform strategies to promote health equity in virtual care now and in the future.

The aim of this study is to generate insights arising from literature that was published in direct response to the widespread use of virtual care during the COVID-19 pandemic, and had a primary focus on providing recommendations for promoting health equity in the delivery of virtual care.

We conducted a narrative review of literature on health equity and virtual care during the COVID-19 pandemic published in 2020, describing strategies that have been proposed in the literature at three levels (1) policy and government, (2) organizations and health systems, and (3) communities and patients.

We highlight three strategies for promoting health equity through virtual care that have been underaddressed in this literature (1) simplifying complex interfaces and workflows, (2) using supportive intermediaries, and (3) creating mechanisms through which marginalized community members can provide immediate input into the planning and delivery of virtual care.

We conclude by outlining three areas of work that are required to ensure that virtual care is employed in ways that are equity enhancing in a post-COVID-19 reality.

We conclude by outlining three areas of work that are required to ensure that virtual care is employed in ways that are equity enhancing in a post-COVID-19 reality.

During the COVID-19 pandemic, health professionals have been directly confronted with the suffering of patients and their families. selleck kinase inhibitor By making them main actors in the management of this health crisis, they have been exposed to various psychosocial risks (stress, trauma, fatigue, etc). Paradoxically, stress-related symptoms are often underreported in this vulnerable population but are potentially detectable through passive monitoring of changes in speech behavior.

This study aims to investigate the use of rapid and remote measures of stress levels in health professionals working during the COVID-19 outbreak. This was done through the analysis of participants' speech behavior during a short phone call conversation and, in particular, via positive, negative, and neutral storytelling tasks.

Speech samples from 89 health care professionals were collected over the phone during positive, negative, and neutral storytelling tasks; various voice features were extracted and compared with classical stress measures v technology with timely intervention strategies, it could contribute to the prevention of burnout and the development of comorbidities, such as depression or anxiety.

Technology use has become the most critical approach to maintaining social connectedness during the COVID-19 pandemic. Older adults (aged >65 years) are perceived as the most physiologically susceptible population to developing COVID-19 and are at risk of secondary mental health challenges related to the social isolation that has been imposed by virus containment strategies. To mitigate concerns regarding sampling bias, we analyzed a random sample of older adults to understand the uptake and acceptance of technologies that support socialization during the pandemic.

We aimed to conduct a population-based assessment of the barriers and facilitators to engaging in the use of technology for web-based socialization among older adults in the Canadian province of British Columbia during the COVID-19 pandemic.

We conducted a cross-sectional, population-based, regionally representative survey by using the random-digit dialing method to reach participants aged >65 years who live in British Columbia. Data weuch data on older adults' use of technology are limited by sampling biases, but this study, which used a random sampling method, demonstrated that older adults used technology to mitigate social isolation during the pandemic. Web-based socialization is the most promising method for mitigating potential mental health effects that are related to virus containment strategies. Providing telephone training; creating task lists; and implementing the facilitators described by participants, such as facilitated socialization activities, are important strategies for addressing barriers, and these strategies can be implemented during and beyond the pandemic to bolster the mental health needs of older adults.This article is concerned with a distributed filtering problem for Markov jump systems subject to the measurement loss with unknown probabilities. A centralized robust Kalman filter is designed by using variational Bayesian methods and a modified interacting multiple model method based on information theory (IT-IMM). Then, a distributed robust Kalman filter based on the centralized filter and a hybrid consensus method called hybrid consensus on measurement and information (HCMCI) is designed. Moreover, boundedness of the estimation errors and the estimation error covariances are studied for the distributed robust Kalman filter.The principal component analysis network (PCANet) is an unsupervised deep network, utilizing principal components as convolution filters in its layers. Albeit powerful, the PCANet suffers from two fundamental problems responsible for its performance degradation. First, the principal components transform the data as column vectors (which we call the amalgamated view) and incur a loss of spatial information present in the data. Second, the generalized pooling in the PCANet is unable to incorporate spatial statistics of the natural images, and it also induces redundancy among the features. In this research, we first propose a tensor-factorization-based deep network called the tensor factorization network (TFNet). The TFNet extracts features by preserving the spatial view of the data (which we call the minutiae view). We then proposed HybridNet, which simultaneously extracts information with the two views of the data since their integration can improve the performance of classification systems. Finally, to alleviate the feature redundancy among hybrid features, we propose Attn-HybridNet to perform attention-based feature selection and fusion to improve their discriminability. Classification results on multiple real-world datasets using features extracted by our proposed Attn-HybridNet achieves significantly better performance over other popular baseline methods, demonstrating the effectiveness of the proposed techniques.Chest computed tomography (CT) image data is necessary for early diagnosis, treatment, and prognosis of Coronavirus Disease 2019 (COVID-19). Artificial intelligence has been tried to help clinicians in improving the diagnostic accuracy and working efficiency of CT. Whereas, existing supervised approaches on CT image of COVID-19 pneumonia require voxel-based annotations for training, which take a lot of time and effort. This paper proposed a weakly-supervised method for COVID-19 lesion localization based on generative adversarial network (GAN) with image-level labels only. We first introduced a GAN-based framework to generate normal-looking CT slices from CT slices with COVID-19 lesions. We then developed a novel feature match strategy to improve the reality of generated images by guiding the generator to capture the complex texture of chest CT images. Finally, the localization map of lesions can be easily obtained by subtracting the output image from its corresponding input image. By adding a classifier branch to the GAN-based framework to classify localization maps, we can further develop a diagnosis system with improved classification accuracy.