Child Tax Credit, Spending Patterns, and Mental Health: Mediation Analyses of Data from the U.S. Census Bureau's Household Pulse Survey during COVID-19.

This study examined the relationship between the receipt of COVID-19 child tax credit and adult mental health problems in the United States, and we explored whether and the extent to which a wide range of spending patterns of the credit-15 patterns regarding basic necessities, child education, and household expenditure-mediated the relationship. We used COVID-19-specialized data from the U.S. Census Bureau's Household Pulse Survey, a representative population sample (N = 98,026) of adult respondents (18 and older) who participated between 21 July 2021 and 11 July 2022. By conducting mediation analyses with logistic regression, we found relationships between the credit and lower levels of anxiety (odds ratio [OR] = 0.914; 95% confidence interval [CI] = 0.879, 0.952). The OR was substantially mediated by spending on basic necessities such as food and housing costs (proportion mediated = 46% and 44%, respectively). The mediating role was relatively moderate in the case of spending on child education and household expenditure. We also found that spending the credit on savings or investments reduces the effect of the child tax credit on anxiety (-40%) while donations or giving to family were not a significant mediator. Findings on depression were consistent with anxiety. The child tax credit-depression relationships were substantially mediated by spending on food and housing (proportion mediated = 53% and 70%). These mediation analyses suggested that different patterns of credit spending are important mediators of the relationship between the receipt of the child tax credit and mental illnesses. Public health approaches to improve adult mental health during and after the COVID-19 pandemic need to consider the notable mediating role of spending patterns.

Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset.

Major depressive disorder (MDD) is a global healthcare issue and one of the leading causes of disability. Machine learning combined with non-invasive electroencephalography (EEG) has recently been shown to have the potential to diagnose MDD. However, most of these studies analyzed small samples of participants recruited from a single source, raising serious concerns about the generalizability of these results in clinical practice. Thus, it has become critical to re-evaluate the efficacy of various common EEG features for MDD detection across large and diverse datasets. To address this issue, we collected resting-state EEG data from 400 participants across four medical centers and tested classification performance of four common EEG features: band power (BP), coherence, Higuchi's fractal dimension, and Katz's fractal dimension. Then, a sequential backward selection (SBS) method was used to determine the optimal subset. To overcome the large data variability due to an increased data size and multi-site EEG recordings, we introduced the conformal kernel (CK) transformation to further improve the MDD as compared with the healthy control (HC) classification performance of support vector machine (SVM). The results show that (1) coherence features account for 98% of the optimal feature subset; (2) the CK-SVM outperforms other classifiers such as K-nearest neighbors (K-NN), linear discriminant analysis (LDA), and SVM; (3) the combination of the optimal feature subset and CK-SVM achieves a high five-fold cross-validation accuracy of 91.07% on the training set (140 MDD and 140 HC) and 84.16% on the independent test set (60 MDD and 60 HC). The current results suggest that the coherence-based connectivity is a more reliable feature for achieving high and generalizable MDD detection performance in real-life clinical practice.

Using Remotely Supervised At-Home TES for Enhancing Mental Resilience.

We are in the midst of a mental health crisis with major depressive disorder being the most prevalent among mental health disorders and up to 30% of patients not responding to first-line treatments. Noninvasive Brain Stimulation (NIBS) techniques have proven to be effective in treating depression. However, there is a fundamental problem of scale. Currently, any type of NIBS treatment requires patients to repeatedly visit a clinic to receive brain stimulation by trained personnel. This is an often-insurmountable barrier to both patients and healthcare providers in terms of time and cost. In this perspective, we assess to what extent Transcranial Electrical Stimulation (TES) might be administered with remote supervision in order to address this scaling problem and enable neuroenhancement of mental resilience at home. Social, ethical, and technical challenges relating to hardware- and software-based solutions are discussed alongside the risks of stimulation under- or over-use. Solutions to provide users with a safe and transparent ongoing assessment of aptitude, tolerability, compliance, and/or misuse are proposed, including standardized training, eligibility screening, as well as compliance and side effects monitoring. Looking into the future, such neuroenhancement could be linked to prevention systems which combine home-use TES with digital sensor and mental monitoring technology to index decline in mental wellbeing and avoid relapse. Despite the described social, ethical legal, and technical challenges, the combination of remotely supervised, at-home TES setups with dedicated artificial intelligence systems could be a powerful weapon to combat the mental health crisis by bringing personalized medicine into people's homes.

Significantly Increased Public Interest in Major Depressive Disorder During the COVID-19 Pandemic: Insights From a Google Trends Analysis.

Background As early as before the coronavirus disease 2019 (COVID-19) pandemic, nearly one billion people worldwide suffered from mental health problems. Of all the mental health conditions, major depressive disorder (MDD) is the leading cause of global health-related burden. During the COVID-19 pandemic, many uncertain factors affecting mental health accumulated, such as virus transmission, blockade and ban, public transport restrictions, closure of schools and enterprises, and reduction of social interaction, which led to an increase in the potential risk of MDD, further increasing the global health-related burden. Methodology To better clarify the public interest in major depressive disorder during the COVID-19 pandemic, a Google Trends analysis was employed with data from December 2019 to December 2021, taking the cumulative diagnosis rate and cumulative mortality rate of COVID-19 as the reference standard, The changes in public interest and behavior in online searching for major depressive disorder in the three countries most affected by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus (i.e. the United States, Brazil, and India) were evaluated. Results We observed that during the COVID-19 pandemic, public interest in major depressive disorder increased significantly on the Internet. At the same time, compared with the United States, this upward trend is more prominent in India and Brazil. The study found that the major depressive disorder search index of the United States reached the maximum at the end of September 2021, the major depressive disorder search index of Brazil reached the maximum at the beginning of July 2021, and the major depressive disorder search index of India reached the maximum at the beginning of June 2021. The above time nodes are the first turning point of decline after the continuous surge of COVID-19 confirmed cases in the United States, Brazil, and India, indicating that there is an important time correlation between the surge of COVID-19 cases and the public online search term major depressive disorder. Conclusion The Google Trends analysis shows that public interest in major depressive disorder is on the rise under the COVID-19 pandemic and that COVID-19 may be associated with MDD. These findings deserve further exploration, especially as a growing body of research reports suggests that the COVID-19 pandemic has led to a surge in the prevalence of MDD. The epidemic alerts the vast majority of countries to urgently strengthen mental health systems and provide patients with the necessary interventions based on the determinants of poor mental health.