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PURPOSE Identifying how people have been coping with stress during the COVID-19 pandemic allows us to anticipate how the population may react to similar stressors over time. In this study, we assessed patterns of coping styles among veterans and nonveterans, and stability and change in these strategies at 3 time points during the pandemic. METHODS Using an online survey platform, we circulated a questionnaire at 3 time points during the period when COVID-19 vaccines became widely available (December 2-27, 2020; January 21-February 6, 2021; and March 8-23, 2021). The questionnaire asked participants about their extent of use of 11 coping strategies, and symptoms of anxiety and depression. RESULTS A total of 2,085 participants (50.8% veterans) completed the questionnaire at 1 or more time points and 930 participants (62.8% veterans) completed it at all 3 time points. Cluster analysis identified 3 distinct coping styles: adaptive, distressed, and disengaged. Compared with nonveterans, veterans more commonly had adaptive and disengaged coping styles, and less commonly had a distressed coping style. The majority of the cohort (71.3%) changed coping style at least once during the study period. Participants who used the same coping style across all 3 time points reported lower levels of anxiety and depression. CONCLUSIONS Our data demonstrate a need to better understand the dynamic nature of coping with pandemic-level stressors across time. We did not find patterns of change in coping styles, but our findings point to potential advantages of stability in coping style. It is possible that less adaptive styles that are more stable may be advantageous for mental health. This research has implications for supporting patients dealing with stress in family medicine. Key words: veteran; coping styles; mental health; stress and coping; psychological distress; resilience, psychological; anxiety; depression; COVID-19; pandemic, INTRODUCTION Stress related to the COVID-19 pandemic may be contributing to mental health risk on a grand scale. Emerging evidence supports a widespread mental health impact. (1) Determining how people [...]

Background: To systematically analyze differences in atherosclerotic cardiovascular disease (ASCVD) burden between young and older adults. Methods: We estimated the prevalence, mortality, and disability-adjusted life years (DALYs) of ASCVD, including ischemic heart disease (IHD), ischemic stroke (IS), and peripheral artery disease (PAD), in individuals aged 20–54 and > 55 years from 1990–2019, utilizing data from the 2019 Global Burden of Disease Study. The annual percentage changes (EAPCs) for age-specific prevalence, mortality, or DALY rates were calculated to quantify the temporal trends of ASCVD burden. We also analyzed population attribution fractions (PAF) of premature ASCVD mortality and DALYs for different risk factors and compared the burden of extremely premature, premature, and non-premature ASCVD cases based on clinical classifications. Results: From 1990–2019, the global prevalence rates of IHD, IS, and PAD in the 20–54 years age group increased by 20.55% (from 694.74 to 837.49 per 100,000 population), 11.50% (from 439.48 to 490.03 per 100,000 population), and 7.38% (from 384.24 to 412.59 per 100,000 population), respectively. Conversely, the ASCVD prevalence in > 55years age group decreased. Adverse outcome burdens, including mortality and DALYs, varied among ASCVD subtypes. The decrease in the mortality/DALY burden of IHD and IS was lower in the 20–54 years group than in the > 55 years group. For PAD, DALYs among those aged 20–54 increased but decreased among those aged > 55 years. When grouped according to socio-demographic index (SDI) values, lower SDI regions exhibited a higher proportion of young ASCVD burden. The prevalence of young IHD, IS, and PAD in low SDI regions reached 20.70%, 40.05%, and 19.31% in 2019, respectively, compared with 12.14%, 16.32%, and 9.54%, respectively, in high SDI regions. Metabolic risks were the primary contributors to the ASCVD burden in both age groups. Increased susceptibility to ambient particulate matter pollution and inadequate control of high body-mass index and high fasting plasma glucose in young individuals may partially explain the differing temporal trends between young and older individuals. Conclusions: The ASCVD burden in young individuals may become a growing global health concern, especially in areas with lower socioeconomic development levels that require more effective primary prevention strategies. [ABSTRACT FROM AUTHOR]

Background: While spontaneous pneumothorax has been documented in COVID-19 patients, reports on recurrent spontaneous pneumothorax due to cystic lesions in convalescent COVID-19 patients are scarce. The progression of these lung cystic lesions remains inadequately explored. Case presentation and literature review: An 81-year-old male, a non-smoker with a history of rheumatoid arthritis, presented with fever, cough, and expectoration for 14 days. Initially diagnosed with moderate COVID-19, he deteriorated to severe COVID-19 despite adherence to local treatment guidelines. Successive identification of three cystic lesions termed "bulla" or "pneumatocele", and one cystic lesion with air-fluid level, referred to as "pneumo-hamatocele" (PHC), occurred in his lungs. Gradual improvement followed anti-inflammatory therapy and optimal supportive care. However, on day 42, sudden worsening dyspnea prompted a computed tomography (CT) scan, confirming a right spontaneous pneumothorax and subcutaneous emphysema, likely due to PHC rupture. Discharge followed chest tube implementation for pneumothorax resolution. On day 116, he returned to the hospital with mild exertional dyspnea. Chest CT revealed recurrent right pneumothorax from a remaining cyst in the right lung. Apart from our patient, literature retrieval identified 22 COVID-19 patients with spontaneous pneumothorax due to cystic lesions, with a male predominance (95.6%; 22/23). Diagnosis of pneumothorax and lung cystic lesions occurred around day 29.5 (range: 18–35) and day 26.4 (± 9.8) since symptom onset, respectively. Except for one patient whose pneumothorax occurred on day seven of illness, all patients eventually recovered. Conclusions: Recurrent spontaneous pneumothorax secondary to lung cystic lesions may manifest in convalescent COVID-19 patients, particularly males with COVID-19 pneumonia. Chest CT around 2 to 3 weeks post-symptom onset may be prudent to detect cystic lesion development and anticipate spontaneous pneumothorax. [ABSTRACT FROM AUTHOR]

The development of geospatial artificial intelligence (GeoAI) systems depends on the ability to learn effective representations of places. To learn accurate place representations from spatial interactions, it is important to extract features that capture both the spatial and non-spatial driving factors. However, existing methods lack a robust interpretation and the explanatory power of the learned representations on spatial factors remains unexplored. Here, we propose an approach to learning place representations from spatial interactions. Our method is inspired by flow allocation, which is the main focus of single-constrained gravity models. We first validate the method on synthetic flows with known driving factors and then apply it to multi-scale real-world flows. Results show that the learned representations can effectively capture features that explain place characteristics, along with the impact of spatial impedance. Our study not only contributes an efficient method to learn place representations from spatial interactions but also offers insights into pre-training procedures in GeoAI. [ABSTRACT FROM AUTHOR]

Various big geo-data provide a social sensing approach to measure spatial interactions. Existing studies often aggregate individual-level movement trajectories or social ties to obtain the interaction intensity between places, neglecting the detailed meanings (i.e. the semantics) behind spatial interactions. However, such meanings help to understand the relationship between two places, and consequently, the characteristics of both places. We argue that semantics can be extracted from spatial interactions through features of space, time, symmetry, and individual-based statistics. Whereafter the calculation and applications of the features are given. Furthermore, we discuss the construction of spatial interaction networks with semantics, as well as approaches to representing places according to spatial interactions. Finally, we illustrate the potential value of spatial interaction semantics in facilitating decision-making through an example in the context of tourism planning. [ABSTRACT FROM AUTHOR]

Observational data (e.g. electronic health records) has become increasingly important in evidence-based research on dynamic treatment regimes, which tailor treatments over time to patients based on their characteristics and evolving clinical history. It is of great interest for clinicians and statisticians to identify an optimal dynamic treatment regime that can produce the best expected clinical outcome for each individual and thus maximize the treatment benefit over the population. Observational data impose various challenges for using statistical tools to estimate optimal dynamic treatment regimes. Notably, the task becomes more sophisticated when the clinical outcome of primary interest is time-to-event. Here, we propose a matching-based machine learning method to identify the optimal dynamic treatment regime with time-to-event outcomes subject to right-censoring using electronic health record data. In contrast to the established inverse probability weighting-based dynamic treatment regime methods, our proposed approach provides better protection against model misspecification and extreme weights in the context of treatment sequences, effectively addressing a prevalent challenge in the longitudinal analysis of electronic health record data. In simulations, the proposed method demonstrates robust performance across a range of scenarios. In addition, we illustrate the method with an application to estimate optimal dynamic treatment regimes for patients with advanced non-small cell lung cancer using a real-world, nationwide electronic health record database from Flatiron Health. [ABSTRACT FROM AUTHOR]

Combination treatments have been of increasing importance in drug development across therapeutic areas to improve treatment response, minimize the development of resistance, and/or minimize adverse events. Pre‐clinical in‐vitro combination experiments aim to explore the potential of such drug combinations during drug discovery by comparing the observed effect of the combination with the expected treatment effect under the assumption of no interaction (i.e., null model). This tutorial will address important design aspects of such experiments to allow proper statistical evaluation. Additionally, it will highlight the Biochemically Intuitive Generalized Loewe methodology (BIGL R package available on CRAN) to statistically detect deviations from the expectation under different null models. A clear advantage of the methodology is the quantification of the effect sizes, together with confidence interval while controlling the directional false coverage rate. Finally, a case study will showcase the workflow in analyzing combination experiments. [ABSTRACT FROM AUTHOR]

Skin cancer has become increasingly common among young adults; however, this population does not consistently adhere to recommended methods for preventing the disease. Interventions in college settings have relied on appearance-focused appeals and have not been able to examine the cumulative effect of multiple behavior change and skin cancer risk communication strategies. The goal of the current study was to examine the unique and combined impacts of personalized ultraviolet (UV) radiation photographs, genetic testing for skin cancer risk, and general skin cancer prevention education. Participants were randomly assigned to one of four conditions: (1) skin cancer prevention education, (2) education + UV photo, (3) education + genetic testing, and (4) education + UV photo + genetic testing. Self-reported sun protection, tanning, and sunburn were assessed at baseline, immediately post-intervention, and 1 month post-intervention. The findings indicated benefits of the interventions to skin cancer prevention behaviors in the overall sample; however, the combined (UV photo + genetic testing) intervention had the most consistent positive effects on behaviors. Intervention effects were distinct across seasons. These results suggest that interventions containing multiple skin cancer risk communication strategies hold promise in benefitting health-promoting behavior changes in an at-risk, young adult population. Trial Registration Number: NCT03979872; Registered 6/5/2019. [ABSTRACT FROM AUTHOR]

When various urban functions are integrated into one location, they form a mixture of functions. The emerging big data promote an alternative way to identify mixed functions. However, current methods are largely unable to extract deep features in these data, resulting in low accuracy. In this study, we focused on recognizing mixed urban functions from the perspective of human activities, which are essential indicators of functional areas in a city. We proposed a framework to comprehensively extract deep features of human activities in big data, including activity dynamics, mobility interactions, and activity semantics, through representation learning methods. Then, integrating these features, we employed fuzzy clustering to identify the mixture of urban functions. We conducted a case study using taxi flow and social media data in Beijing, China, in which five urban functions and their correlations with land use were recognized. The mixture degree of urban functions in each location was revealed, which had a negative correlation with taxi trip distance. The results confirmed the advantages of our method in understanding mixed urban functions by employing various representation learning methods to comprehensively depict human activities. This study has important implications for urban planners in understanding urban systems and developing better strategies. [ABSTRACT FROM AUTHOR]