Does COVID-19 affect mental health and substance use in young adults?

BACKGROUND: Mental health has been impacted by COVID-19 throughout the United States and beyond. The mental health and well-being were further affected with excessive substance use during the pandemic. The aim of this research was to explore how the COVID-19 affects the mental health of the young adults (18-24 years) in the South Jersey area. We also examined the association between mental health symptoms in young adults and substance use during the first and second year of the pandemic. METHODS AND MATERIAL: A cross-sectional survey was conducted with (n = 711) 527 participants that included young adults (18-24 years old) across university campus in south jersey and in the community cohorts. Multinomial regression analysis and Chi-squared test were used to explore the association between mental symptoms and substance use. Data were analyzed using Microsoft Excel Spreadsheet for descriptive statistics and Python 3.0 scikit-learn package. RESULTS: The study showed that "Lonely" and "Hopeless" were the top two mental health symptoms. It was observed that the symptoms of "Lonely" and "Hopeless" increased for both males and females. In general, males seemed to be affected more than females in this study for mental health symptoms. For substance use, "Nervous" and "Smoking" showed positive correlation in 2020 and "Hopeless" and "Alcohol" were positively correlated in 2021. CONCLUSIONS: Young adults' mental health symptoms and substance use has been proven to be affected through the pandemic and this research results even though localized will assist the community and educational institutions to plan better support to assist young adults with better health and wellness initiatives.

Assessing perceived effectiveness of career development efforts led by the women in American Medical Informatics Association Initiative.

OBJECTIVE: We sought to ascertain perceived factors affecting women's career development efforts in the American Medical Informatics Association (AMIA) and to provide recommendations for improvements. MATERIALS AND METHODS: Data were collected using a 27-item survey administered via the AMIA newsletter and other social channels. Survey questions comprised 3 demographics, 15 Likert-scale, and 9 open-ended items. Likert-scale responses were summarized across respondent ages, career stages, and career domains, and open-ended responses were thematically analyzed. RESULTS: We received survey responses from 109 AMIA women members. Our findings demonstrate that AMIA had made strides in promoting career development, and the most effective AMIA efforts included social events (83%), panel discussions (80%), and scientific sessions (79%). However, despite these efforts, women members perceived that gender-specific challenges persisted within AMIA, and recognized the need for increased networking opportunities (96%), raising awareness of gender-specific challenges (95%), and encouraging gender proportional representation in leadership (92%). DISCUSSION: International and national biomedical informatics professional communities have put forth efforts to address gender-specific issues in career development. Yet, our study identified that some of these, including the deep-rooted gender power hierarchy and bias, are still perceived as profound in AMIA. CONCLUSION: Even though existing career development efforts for women are highly effective, important perceived gender-specific career development issues require further attention and investigation to improve existing AMIA activities.

Construction of Disease Similarity Networks Using Concept Embedding and Ontology.

Discovering disease similarities are beneficial for the diagnosis and treatment of mental diseases. In this research, we proposed a data driven method, that is, integrating a variety of publicly available data resources including Unified Medical Language System (UMLS) Metathesaurus, Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) and cui2vec concept embedding to construct a mental disease similarity network. The resulting mental disease similarity network offered a new view for navigating and investigating disease relations; it also revealed popular mental disease in the literature in terms of the number of connections and similarities with other diseases. It shows that depressive disorder is directly connected with nine other popular diseases and connects 52 other diseases in the network. The top three popular mental diseases are depressive disorder, dysthymia (now known as persistent depressive disorder), and neurosis. Future research will focus on studying the clusters generated from the similarity network.

Using SNOMED Distance to Measure Semantic Similarity of Clinical Trials.

The objective of this research is to propose to develop an innovative distance function, called SNOMED distance, which captures the nature of the semantic distance within the topological structure of SNOMED, to identify semantic similarity between clinical trials.