The COVID-19 pandemic has had a significant impact on mental health. Identifying risk factors and susceptible subgroups will guide efforts to address mental health concerns during the pandemic and long-term management and monitoring after the pandemic. We aimed to examine associations of insecurity (concerns about food, health insurance, and/or money), social support, and change in family relationships with poor mental health and to explore disparities in these associations. An online survey was collected from 3952 US adults between May and August 2020. Symptoms of anxiety, depression, stress, and trauma-related disorders were assessed by the Generalized Anxiety Disorder 7-item scale, the Patient Health Questionnaire-9, the Perceived Stress Scale-4, and the Primary Care Post-Traumatic Stress Disorder Screen, respectively. Social support was measured by the Oslo Social Support Scale. Logistic regression was used and stratified analyses by age, race/ethnicity, and sex were performed. We found a higher prevalence of poor mental health among those who were younger, female, with lower socioeconomic status, and racial/ethnic minorities. Participants who were worried about money, health insurance, or food had higher odds of symptoms of anxiety (OR = 3.74, 95% CI: 3.06-4.56), depression (OR = 3.20, 95% CI: 2.67-3.84), stress (OR = 3.08, 95% CI: 2.67-3.57), and trauma-related disorders (OR = 2.93, 95% CI: 2.42-3.55) compared to those who were not. Compared to poor social support, moderate and strong social support was associated with lower odds of all four symptoms. Participants who had changes in relationships with parents, children, or significant others had worse mental health. Our findings identified groups at higher risk for poor mental health, which offers insights for implementing targeted interventions.
This study explores the utilization and perception of Artificial Intelligence (AI) tools among students in higher education. With the growing accessibility of AI technologies, their integration into educational settings presents a new frontier for enhancing learning experiences. This research adopts a mixed-methods approach, including surveys and interviews, to delve into how students employ AI tools and their perceived benefits and drawbacks of AI usage in the context of entrepreneurship education in a business school. The findings reveal a diverse range of AI applications, highlighting benefits such as increased productivity, personalized learning, and enhanced linguistic capability. However, concerns regarding academic integrity, over-reliance on AI, and the need for clear usage guidelines are also identified. This study contributes to the understanding of AI's role in higher education and provides much-needed empirical evidence of AI usage from students’ perspectives. Our findings underscore the importance of balanced, informed, and ethical use of AI tools in higher education.
The ICH E14 guidance (ICH, 2005) recommend that a concurrent positive control should be included in a thorough QTc clinical trial to validate the study. The ICH E14 guidance (ICH, 2005) state that "The positive control should have an effect on the mean QTc interval of about 5 ms (i.e., an effect that is close to the QTc effect that represents the threshold of regulatory concern, around 5 ms)". This task may be carried out through some statistical tests. The current practice is to test at each time point where QT measurements are collected. This method is usually not efficient. In this article, I discuss two types of statistical procedures. The first one is a local statistical test to make a time-point-specific claim, i.e., to claim a mild QTc effect due to the positive control at some specific time points. A different approach, named as a global test, is also proposed, to make a general claim that the mean difference of the positive control and placebo after baseline adjustment will be about 5 ms without specifying at which time points. An example will be used to illustrate how to apply the two procedures. How to best allocate sample size in a parallel QTc study is also discussed in this paper.
In order to validate the results of a thorough QT/QTc clinical trial, ICH E14 recommended that a concurrent positive control treatment be included in the trial. Zhang (2008) recommended that the study results are validated if the positive control establishes assay sensitivity, i.e., has an effect on the mean QT/QTc interval of 5 ms or more. Zhang (2008) and Tsong et al. (2008) discussed the intersection-union test approach and an alternative global average test approach for testing assay sensitivity during the validation process. In this article, we further discuss the multiple comparison issues of the repeatedly measured QT difference between positive control treatment and placebo in the validation test. We describe and discuss several approaches for type I error rate adjustment that are applicable to the situation.
Platelet-rich plasma (PRP) has emerged as a popular biologic treatment for musculoskeletal injuries and conditions. Despite numerous investigations on the efficacy of PRP therapy, current utilization of this treatment within the United States is not widely known.To investigate the national utilization of PRP, including the incidence and conditions for which it is used in the clinical setting, and to determine the current charges associated with this treatment.Descriptive epidemiology study.Using a national database (PearlDiver) of private insurance billing records, we conducted a comprehensive search using Current Procedural Terminology (CPT) codes to identify patients who received PRP injections over a 2-year period (2010-2011). Associated International Classification of Diseases, 9th Revision (ICD-9) codes were identified to determine the specific conditions the injection was used to treat. The aggregate patient data were analyzed by yearly quarter, practice setting, geographic region, and demographics. PRP therapy charges were calculated and reported as per-patient average charges (PPACs).A total of 2571 patients who received PRP injections were identified; 51% were male and 75% were older than 35 years. The overall incidence ranged from 5.9 to 7.9 per 1000 patients over the study period. PRP was most commonly administered in hospitals (39%) and ambulatory surgical centers (37%) compared with in private offices (26%). The most common conditions treated were knee meniscus/plica disorders, followed by unspecified shoulder conditions, rotator cuff injuries, epicondylitis, and plantar fasciitis. Further evaluation revealed that 25% of all patients received injections for cartilage-related conditions, 25% meniscus, 25% unspecified, 12% tendon, 8% glenoid labrum, and 5% ligament. The PPAC for PRP treatment was US$1755 per injection.Despite a lack of consensus regarding PRP indications and efficacy, we observed widespread application of this treatment for a myriad of musculoskeletal injuries. Most treated patients were older than 35 years, and the most commonly treated conditions included cartilage and meniscus disorders. Given the current controversy surrounding this treatment, further studies are necessary to guide clinicians on the value of this therapy for each clinical diagnosis.
In 2008, we edited a special issue on thorough QT (TQT) clinical trials with the objective of introducing the topic to readers with interest in quantitative methods. The first special issue has pro...
