Bereavement, Memorial Attendance, and Mental Health During the COVID-19 Pandemic: Longitudinal Results from the Nurses' Health Study.

BACKGROUND: Mortality increased during the COVID-19 pandemic. Many bereaved individuals were not able to gather to memorialize their loved ones, yet it is unknown if this contributed to worsening mental health. OBJECTIVE: Examine the association of bereavement in the early part of the COVID-19 pandemic with subsequent psychological distress and the role of memorial attendance in reducing psychological distress among the bereaved. DESIGN, SETTINGS, SUBJECTS: In May 2020, 39,564 older females from the Nurses' Health Study II enrolled in a longitudinal COVID-19 substudy (meanage = 65.2 years, SD = 4.5). METHODS: Linear regression analyses estimated associations of bereavement reported between March and October, 2020 with subsequent psychological distress between January and October 2021, adjusting for sociodemographic and prepandemic depression symptoms. Secondary models examined associations between memorial attendance and psychological distress. RESULTS: Bereavement during the early part of the COVID-19 pandemic was associated with higher psychological distress (adjusted ß = 0.21, 95% CI: 0.15, 0.26) assessed over the next year. Among the bereaved, memorial attendance was associated with lower psychological distress (in-person: adjusted ß = -0.41, 95% CI: -0.53, -0.29; online: adjusted ß = -0.24, 95% CI: -0.46, --0.02). CONCLUSION: Attending memorials was associated with lower subsequent psychological distress among bereaved older females.

Disparities in Adult Fast-Food Consumption in the U.S. by Race and Ethnicity, National Health and Nutrition Examination Survey 2017-2018.

INTRODUCTION: This study provides the most recent estimates for fast-food consumption in the U.S., overall and by race/ethnicity and age. METHODS: Data from adults (aged ≥20 years, N=3,560) in the National Health and Nutrition Examination Survey, 2017-2018, were used to identify the (1) percentage of adults consuming fast food, (2) estimated mean percentage of calories consumed from fast food, and (3) estimated mean total calories consumed from fast food on a typical day. Intake was measured by in-person, 24-hour dietary recall. Analysis was conducted in 2020. RESULTS: During 2017-2018, fast food was consumed by 36.5% of adults on a typical day, accounting for 13.8% of daily calories, an average of 309 kcal/day. More non-Hispanic Black adults consumed fast food (42.6%), consumed the largest percentage of daily calories from fast food (17.4%), and consumed the greatest number of daily calories from fast food (381 kcal/day) than adults of other racial/ethnic groups. Young non-Hispanic Black adults had the highest level of fast-food consumption, and this was significantly higher than that among Mexican Americans: percentage consuming fast food (53.5% vs 42.5%, p=0.02) and percentage of calories from fast food (24.1% vs 16.8%, p=0.03). Young non-Hispanic Black adults consumed the highest total fast-food calories, which were significantly higher than that among non-Hispanic Asian young adults (526 kcal vs 371 kcal, p=0.04). No significant differences in the study outcomes were observed by race/ethnicity and age compared with non-Hispanic White adults of the same group. CONCLUSIONS: Fast-food consumption among adults in the U.S. is high, particularly among young non-Hispanic Black adults.

Cross-cancer Prediction: A Novel Machine Learning Approach to Discover Molecular Targets for Development of Treatments for Multiple Cancers.

Conventional cancer drug development has long been limited to organ- or tissue-specific cancer types. However, it has become increasingly known that specific genetic abnormalities are responsible for the carcinogenesis of multiple cancers. The recent US Food and Drug Administration (FDA) approval of the first multi-cancer drug, Keytruda, has demonstrated the feasibility of developing new drugs that target multiple cancers. Despite a promising future, methodological development for identifying multi-cancer molecular targets remains encumbered. This study developed a novel machine learning approach to identify such genes responsible for multiple cancers by synthesizing salient genomic information from cancer-specific classification models. This approach centered on the cross-cancer prediction method for identifying groups of cancers with high cross-cancer predictability. Furthermore, a robust hybrid classifier, comprising Prediction Analysis for Microarrays and Random Forest, was developed to integrate predictive models for gene inference. This approach has successfully identified key genes shared by endometrial cancer, mammary gland ductal carcinoma, and small cell lung cancer. The results are supported by published experimental evidence. This framework holds potential to transform the current methods of discovering multi-cancer molecular targets for clinical oncology.