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Most studies of coronavirus disease 2019 (COVID-19) progression have focused on the transfer of patients within secondary or tertiary care hospitals from regular wards to intensive care units. Little is known about the risk factors predicting the progression to severe COVID-19 among patients in community isolation, who are either asymptomatic or suffer from only mild to moderate symptoms. Using a multivariable competing risk survival analysis, we identify several important predictors of progression to severe COVID-19—rather than to recovery—among patients in the largest community isolation center in Wuhan, China from 6 February 2020 (when the center opened) to 9 March 2020 (when it closed). All patients in community isolation in Wuhan were either asymptomatic or suffered from mild to moderate COVID-19 symptoms. We performed competing risk survival analysis on time-to-event data from a cohort study of all COVID-19 patients (n = 1753) in the isolation center. The potential predictors we investigated were the routine patient data collected upon admission to the isolation center: age, sex, respiratory symptoms, gastrointestinal symptoms, general symptoms, and computed tomography (CT) scan signs. The main outcomes were time to severe COVID-19 or recovery. The factors predicting progression to severe COVID-19 were: male sex (hazard ratio (HR) = 1.29, 95% confidence interval (CI) 1.04–1.58, p = 0.018), young and old age, dyspnea (HR = 1.58, 95% CI 1.24–2.01, p

Objective: This study aims to investigate the impact of social isolation on the utilization of primary health services among older adults in China., Methods: Data from the China Longitudinal Aging Social Survey (CLASS) conducted in 2018 were utilized. A binary logistic regression model was established, and propensity score matching (PSM) was employed for analysis., Results: The results of the binary logistic regression showed that family isolation within social isolation had a significant negative impact on the utilization of primary health services for older adults. In contrast, there was no significant association between friend isolation, community isolation, and the utilization of primary health services. Furthermore, the PSM results, using three matching methods (nearest neighbor matching, radius matching, and kernel matching), confirmed that family isolation significantly reduced older adults' utilization of primary health services, consistent with the baseline regression findings., Conclusion: Reducing the occurrence of family isolation among older adults may be a cost-effective intervention measure. Efforts should be directed toward improving family support for older adults, promoting the utilization of primary health services, and strengthening disease prevention., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Xie, Lyu, Li, Zhuang and Xu.)

Understanding how local species assembly depends on the regional biogeographic and environmental context is a challenging task in community ecology. In spatially implicit neutral models, a single immigration parameter, I(k), represents the flux of immigrants from a regional pool that compete with local offspring for establishment in communities. This flux counterbalances the effect of local stochastic extinctions to maintain local species diversity. If some species within the regional pool are not adapted to the local environment (habitat filtering), the migrant flux is reduced beyond that of the neutral model, such that habitat filtering influences the value of I(k) in non-neutral situations. Here, we propose a novel model in which immigrants from the regional pool are filtered according to their habitat preferences and the local environment, while taxa potentially retain habitat preferences from their ancestors (niche conservatism). Using both analytical reasoning and simulations, we demonstrate that I(k) is expected to be constant when estimated based on the community composition at several taxonomic levels, not only under neutral assumptions, but also when habitat filtering occurs, unless there is substantial niche conservatism. In the latter case, I(k) is expected to decrease when estimated based on the composition at species to genus and family levels, thus allowing a signature of niche conservatism to be detected by simply comparing I(k) estimates across taxonomic levels. We applied this approach to three rain forest data sets from South India and Central America and found no significant signature of niche conservatism when I(k) was compared across taxonomic levels, except at the family level in South India. We further observed more limited immigration in South Indian forests, supporting the hypothesis of a greater impact of habitat filtering and heterogeneity there than in Central America. Our results highlight the relevance of studying variations of I(k) in space and across taxonomic levels to test hypotheses about the ecological and evolutionary drivers of biodiversity patterns. [ABSTRACT FROM AUTHOR]

Most studies of coronavirus disease 2019 (COVID-19) progression have focused on the transfer of patients within secondary or tertiary care hospitals from regular wards to intensive care units. Little is known about the risk factors predicting the progression to severe COVID-19 among patients in community isolation, who are either asymptomatic or suffer from only mild to moderate symptoms. Using a multivariable competing risk survival analysis, we identify several important predictors of progression to severe COVID-19-rather than to recovery-among patients in the largest community isolation center in Wuhan, China from 6 February 2020 (when the center opened) to 9 March 2020 (when it closed). All patients in community isolation in Wuhan were either asymptomatic or suffered from mild to moderate COVID-19 symptoms. We performed competing risk survival analysis on time-to-event data from a cohort study of all COVID-19 patients ( n  = 1753) in the isolation center. The potential predictors we investigated were the routine patient data collected upon admission to the isolation center: age, sex, respiratory symptoms, gastrointestinal symptoms, general symptoms, and computed tomography (CT) scan signs. The main outcomes were time to severe COVID-19 or recovery. The factors predicting progression to severe COVID-19 were: male sex (hazard ratio (HR) = 1.29, 95% confidence interval (CI) 1.04-1.58, p  = 0.018), young and old age, dyspnea (HR = 1.58, 95% CI 1.24-2.01, p  < 0.001), and CT signs of ground-glass opacity (HR = 1.39, 95% CI 1.04-1.86, p  = 0.024) and infiltrating shadows (HR = 1.84, 95% CI 1.22-2.78, p  = 0.004). The risk of progression was found to be lower among patients with nausea or vomiting (HR = 0.53, 95% CI 0.30-0.96, p  = 0.036) and headaches (HR = 0.54, 95% CI 0.29-0.99, p  = 0.046). Our results suggest that several factors that can be easily measured even in resource-poor settings (dyspnea, sex, and age) can be used to identify mild COVID-19 patients who are at increased risk of disease progression. Looking for CT signs of ground-glass opacity and infiltrating shadows may be an affordable option to support triage decisions in resource-rich settings. Common and unspecific symptoms (headaches, nausea, and vomiting) are likely to have led to the identification and subsequent community isolation of COVID-19 patients who were relatively unlikely to deteriorate. Future public health and clinical guidelines should build on this evidence to improve the screening, triage, and monitoring of COVID-19 patients who are asymtomatic or suffer from mild to moderate symptoms., (© 2022 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company.)