scholarly journals Learning the Mental Health Impact of COVID-19 in the United States With Explainable Artificial Intelligence: Observational Study

10.2196/25097 ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. e25097
Author(s):  
Indra Prakash Jha ◽  
Raghav Awasthi ◽  
Ajit Kumar ◽  
Vibhor Kumar ◽  
Tavpritesh Sethi
Keyword(s):  
Mental Health ◽  
United States ◽  
Machine Learning ◽  
Mental Illness ◽  
Social Security ◽  
Bayesian Network ◽  
Age Groups ◽  
The United States ◽  
Supervised Machine Learning ◽  
Key Factors

Background The COVID-19 pandemic has affected the health, economic, and social fabric of many nations worldwide. Identification of individual-level susceptibility factors may help people in identifying and managing their emotional, psychological, and social well-being. Objective This study is focused on learning a ranked list of factors that could indicate a predisposition to a mental disorder during the COVID-19 pandemic. Methods In this study, we have used a survey of 17,764 adults in the United States from different age groups, genders, and socioeconomic statuses. Through initial statistical analysis and Bayesian network inference, we have identified key factors affecting mental health during the COVID-19 pandemic. Integrating Bayesian networks with classical machine learning approaches led to effective modeling of the level of mental health prevalence. Results Overall, females were more stressed than males, and people in the age group 18-29 years were more vulnerable to anxiety than other age groups. Using the Bayesian network model, we found that people with a chronic mental illness were more prone to mental disorders during the COVID-19 pandemic. The new realities of working from home; homeschooling; and lack of communication with family, friends, and neighbors induces mental pressure. Financial assistance from social security helps in reducing mental stress during the COVID-19–generated economic crises. Finally, using supervised machine learning models, we predicted the most mentally vulnerable people with ~80% accuracy. Conclusions Multiple factors such as social isolation, digital communication, and working and schooling from home were identified as factors of mental illness during the COVID-19 pandemic. Regular in-person communication with friends and family, a healthy social life, and social security were key factors, and taking care of people with a history of mental disease appears to be even more important during this time.

scholarly journals Learning the Mental Health Impact of COVID-19 in the United States With Explainable Artificial Intelligence: Observational Study (Preprint)

2020 ◽  
Author(s):  
Indra Prakash Jha ◽  
Raghav Awasthi ◽  
Ajit Kumar ◽  
Vibhor Kumar ◽  
Tavpritesh Sethi
Keyword(s):  
Mental Health ◽  
United States ◽  
Machine Learning ◽  
Mental Illness ◽  
Social Security ◽  
Bayesian Network ◽  
Age Groups ◽  
The United States ◽  
Supervised Machine Learning ◽  
Key Factors

BACKGROUND The COVID-19 pandemic has affected the health, economic, and social fabric of many nations worldwide. Identification of individual-level susceptibility factors may help people in identifying and managing their emotional, psychological, and social well-being. OBJECTIVE This study is focused on learning a ranked list of factors that could indicate a predisposition to a mental disorder during the COVID-19 pandemic. METHODS In this study, we have used a survey of 17,764 adults in the United States from different age groups, genders, and socioeconomic statuses. Through initial statistical analysis and Bayesian network inference, we have identified key factors affecting mental health during the COVID-19 pandemic. Integrating Bayesian networks with classical machine learning approaches led to effective modeling of the level of mental health prevalence. RESULTS Overall, females were more stressed than males, and people in the age group 18-29 years were more vulnerable to anxiety than other age groups. Using the Bayesian network model, we found that people with a chronic mental illness were more prone to mental disorders during the COVID-19 pandemic. The new realities of working from home; homeschooling; and lack of communication with family, friends, and neighbors induces mental pressure. Financial assistance from social security helps in reducing mental stress during the COVID-19–generated economic crises. Finally, using supervised machine learning models, we predicted the most mentally vulnerable people with ~80% accuracy. CONCLUSIONS Multiple factors such as social isolation, digital communication, and working and schooling from home were identified as factors of mental illness during the COVID-19 pandemic. Regular in-person communication with friends and family, a healthy social life, and social security were key factors, and taking care of people with a history of mental disease appears to be even more important during this time.

scholarly journals Bibliotherapy for Mental Health

2017 ◽  
Vol 2 (2) ◽  
pp. 67
Author(s):  
Jennifer Yontz-Orlando
Keyword(s):  
Mental Health ◽  
United States ◽  
Mental Illness ◽  
Medical Condition ◽  
Action Plan ◽  
The United States ◽  
World Health ◽  
Group Setting ◽  
Health Crisis ◽  
The Uk

The United States is facing an epidemic of mental illness, affecting nearly 60 million Americans annually (http://www.nami.org/ ). The World Health Organization describes mental health as “a long neglected problem” and has established an action plan for 2013-2020 (http://www.who.int/mental_health/action_plan_2013/en/). One way to combat mental illness is through bibliotherapy, which is the use of written materials including fiction, nonfiction, and poetry to support emotional and psychiatric healing.Bibliotherapy has been in existence since ancient times, but began in earnest in the United States in the 1850’s during the “Great Awakening.” At that time, mental illness began to be seen as a medical condition rather than a supernatural phenomenon. Since then, due to the changing nature of our institutions, interest in bibliotherapy waned until the 1950’s when there was a slight resurgence in its practice. However, in the last 20 years, bibliotherapy has gained a stronghold in the United Kingdom. To relieve the stress of an overcrowded mental health system, public policy in the UK has supported the use of bibliotherapy in a variety of its institutions. There are many ways to conduct bibliotherapy, but studies show that when the process is interactive, such as in a support group setting, the results are better. Also, bibliotherapy can be conducted by many sorts of professionals, including doctors, therapists, social workers, teachers, and librarians. Studies also show that when the bibliotherapists are trained in the best practices of bibliotherapy, results improve. Bibliotherapy is an effective, low-cost alternative for people in need of therapeutic assistance. The UK model should be studied and implemented in the United States and in other nations to help solve the mental health crisis.

A brief history of the criminalization of mental illness

CNS Spectrums ◽  
2020 ◽  
Vol 25 (5) ◽  
pp. 638-650 ◽  
Author(s):  
Joel A. Dvoskin ◽  
James L. Knoll ◽  
Mollie Silva
Keyword(s):  
Mental Health ◽  
United States ◽  
Mental Illness ◽  
Community Mental Health ◽  
Mental Disorders ◽  
The United States ◽  
Mental Illnesses ◽  
The People ◽  
History Of ◽  
Serious Mental Illnesses

This article traces the history of the way in which mental disorders were viewed and treated, from before the birth of Christ to the present day. Special attention is paid to the process of deinstitutionalization in the United States and the failure to create an adequately robust community mental health system to care for the people who, in a previous era, might have experienced lifelong hospitalization. As a result, far too many people with serious mental illnesses are living in jails and prisons that are ill-suited and unprepared to meet their needs.

Social Security

2012 ◽  
Author(s):  
Larry DeWitt ◽  
Edward D. Berkowitz
Keyword(s):  
United States ◽  
Social Security ◽  
Older People ◽  
Age Groups ◽  
The United States ◽  
Critical Question ◽  
Term Survival ◽  
History Of ◽  
Government Benefits

This chapter considers the history of Social Security, arguing that the 1950 amendments represented the fundamental adjustment that allowed the program’s long-term survival. It analyzes current issues in Social Security related to gender, race, and the program’s long-term solvency. It concludes that Social Security has legitimized the receipt of government benefits among many Americans and changed the nature of old age in the United States by providing older people with a guaranteed means of support. A large and costly program, Social Security has evolved into the United States’ major antipoverty program. Nonetheless it faces the criticism of those who argue that it favors older people over other age groups and that it represents an inefficient form of government coercion. Whether the program will be sustained in the future or modified in a significant way remains a critical question.

Confronting 60 Minutes’ “Imminent Danger”: The Evidence on Schizophrenia and Psychotropic Medications, Violence, and Forced Orders to Treat

2014 ◽  
Vol 16 (1) ◽  
pp. 51-62 ◽  
Author(s):  
Toby T. Watson
Keyword(s):  
Mental Health ◽  
United States ◽  
Mental Illness ◽  
Severe Mental Illness ◽  
Mentally Ill ◽  
The United States ◽  
Psychotropic Medications ◽  
Television Show ◽  
Imminent Danger ◽  
The World

Recently, considerable attention has been given to individuals labeled “mentally ill,” with the possibility that they too often go untreated with psychotropic medications and in turn, commit disproportionally higher rates of violence. The world-known television show60 Minutesbroadcasted a special on this topic in the United States on September 29, 2013; however, they created a disturbingly inaccurate picture of those who suffer with what some label as “mental illness.” There are decades of peer-reviewed research demonstrating that individuals diagnosed with severe mental illness, labeledschizophrenia,and given psychotropic medications are in fact less likely to recover from their disorder and more likely to be rehospitalized. Additionally, although mental health commitments, often calledforced orders to treat,are quite common and now being supported more so due to such programming, the research on mental health commitments has not shown they are actually effective.

scholarly journals Employer costs of mental health claims and how psychologically healthy workplaces align with successful DM programs

2014 ◽  
Vol 9 ◽  
Author(s):  
Wendy Coduti
Keyword(s):  
Mental Health ◽  
United States ◽  
Mental Illness ◽  
Emotional Disorders ◽  
Psychological Health ◽  
Work Performance ◽  
The United States ◽  
Health Claims ◽  
Future Research ◽  
The Impact

Mental health (MH) and disability management (DM) businesses and DM professionals are proficient at addressing employee physical health, yet promoting employee MH is often ignored. Individuals claiming long-term disability (LTD), 85% identified MH conditions as their primary disability (Carls et al., 2012). Mental health LTD expenses are often higher due to longer recovery and challenges in return to work (Salkever, Goldman, Purushothaman, & Shinogle, 2000). Financial burdens of depression, anxiety, and emotional disorders are among the greatest of any disease condition in the workforce (Johnston et al., 2009). Globally, a fifth to a quarter of employees go to work everyday with a mental illness (Lorenzo-Romanella, 2011). Health care research has shown the impact of mental illness on work performance, however many employers and researchers are unaware of the value quality MH care has on employees and costs (Langlieb, & Kahn, 2005). The American Psychological Association (APA) identified five categories of workplace practices that promote psychological health in employees including: employee involvement; work-life balance; employee growth and development; health and safety; and employee recognition (APA, 2014). Organizational benefits of the five elements include: improved quality, performance and productivity; reduced absenteeism, presenteeism and turnover; fewer accidents and injuries; improved ability to attract and retain quality employees; improved customer service and satisfaction; and lower healthcare costs (APA, 2014). The presenters will discuss employer costs of MH claims and how psychologically healthy workplaces align with successful DM programs, decreasing MH claims and costs. Opportunities for future research include the United States Affordable Care Act (ACA) and its impact on MH (Mechanic, 2012) through provisions that encourage employers to adopt health promotion programs (Goetzel et al., 2012) and opportunities for research including comparisons of multinational employers regarding MH costs in countries with single payer systems, and in those without (United States), (Tanner, 2013).

scholarly journals Characteristics of Twitter Use by State Medicaid Programs in the United States: Machine Learning Approach

10.2196/18401 ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. e18401
Author(s):  
Jane M Zhu ◽  
Abeed Sarker ◽  
Sarah Gollust ◽  
Raina Merchant ◽  
David Grande
Keyword(s):  
Public Health ◽  
United States ◽  
Machine Learning ◽  
Public Health Education ◽  
The United States ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Care Organization ◽  
The Public ◽  
The Mean

Background Twitter is a potentially valuable tool for public health officials and state Medicaid programs in the United States, which provide public health insurance to 72 million Americans. Objective We aim to characterize how Medicaid agencies and managed care organization (MCO) health plans are using Twitter to communicate with the public. Methods Using Twitter’s public application programming interface, we collected 158,714 public posts (“tweets”) from active Twitter profiles of state Medicaid agencies and MCOs, spanning March 2014 through June 2019. Manual content analyses identified 5 broad categories of content, and these coded tweets were used to train supervised machine learning algorithms to classify all collected posts. Results We identified 15 state Medicaid agencies and 81 Medicaid MCOs on Twitter. The mean number of followers was 1784, the mean number of those followed was 542, and the mean number of posts was 2476. Approximately 39% of tweets came from just 10 accounts. Of all posts, 39.8% (63,168/158,714) were classified as general public health education and outreach; 23.5% (n=37,298) were about specific Medicaid policies, programs, services, or events; 18.4% (n=29,203) were organizational promotion of staff and activities; and 11.6% (n=18,411) contained general news and news links. Only 4.5% (n=7142) of posts were responses to specific questions, concerns, or complaints from the public. Conclusions Twitter has the potential to enhance community building, beneficiary engagement, and public health outreach, but appears to be underutilized by the Medicaid program.

scholarly journals A Machine Learning Analysis of COVID-19 Mental Health Data

2022 ◽  
Author(s):  
Mostafa Rezapour ◽  
Lucas Hansen
Keyword(s):  
Mental Health ◽  
United States ◽  
Machine Learning ◽  
Survey Data ◽  
Social Research ◽  
Multinomial Logistic Regression ◽  
The United States ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
The Individual

Abstract In late December 2019, the novel coronavirus (Sars-Cov-2) and the resulting disease COVID-19 were first identified in Wuhan China. The disease slipped through containment measures, with the first known case in the United States being identified on January 20th, 2020. In this paper, we utilize survey data from the Inter-university Consortium for Political and Social Research and apply several statistical and machine learning models and techniques such as Decision Trees, Multinomial Logistic Regression, Naive Bayes, k-Nearest Neighbors, Support Vector Machines, Neural Networks, Random Forests, Gradient Tree Boosting, XGBoost, CatBoost, LightGBM, Synthetic Minority Oversampling, and Chi-Squared Test to analyze the impacts the COVID-19 pandemic has had on the mental health of frontline workers in the United States. Through the interpretation of the many models applied to the mental health survey data, we have concluded that the most important factor in predicting the mental health decline of a frontline worker is the healthcare role the individual is in (Nurse, Emergency Room Staff, Surgeon, etc.), followed by the amount of sleep the individual has had in the last week, the amount of COVID-19 related news an individual has consumed on average in a day, the age of the worker, and the usage of alcohol and cannabis.

Mental Health Courts

2019 ◽  
pp. 83-112
Author(s):  
David DeMatteo ◽  
Kirk Heilbrun ◽  
Alice Thornewill ◽  
Shelby Arnold
Keyword(s):  
Mental Health ◽  
United States ◽  
Substance Abuse ◽  
Mental Illness ◽  
Problem Solving ◽  
The United States ◽  
Mental Health Court ◽  
Drug Courts ◽  
Mental Health Courts ◽  
Current State

This chapter focuses on mental health courts, a problem-solving court that developed in the wake of drug courts to address the needs of offenders with mental health diagnoses or co-occurring mental health and substance abuse concerns. In this chapter, the authors first review the overrepresentation of individuals with mental illness in the criminal justice system. They then describe the history and current state of mental health courts in the United States. The chapter then provides a detailed summary of the research on mental health courts. Although there is considerably less research on mental health courts than on drug courts, the available research provides reason to be cautiously optimistic. Within this discussion, the authors also note the limitations in mental health court research. Finally, the authors conclude the chapter with a discussion of innovative mental health court practices and the future of mental health courts.

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