Characteristics of 12-Month Readmission for Hospitalized Patients with COPD: A Propensity Score Matched Analysis of Prospective Multicenter Study.

Purpose: Hospitalization for acute exacerbations of chronic obstructive pulmonary disease (AECOPD) is considered as severe exacerbations. Readmission for severe exacerbations is a crucial event for COPD patients. However, factors associated with readmission for severe exacerbations are incomplete. The study aimed to investigate different characteristics between the severe and non-severe exacerbation groups. Patients and Methods: Patients hospitalized for severe AECOPD were included in multi-centers, and their exacerbations in next 12 months after discharge were recorded. According to exacerbations, patients were separated into the severe-exacerbation group and the non-severe exacerbation group. Propensity-score matching (PSM) and multivariable analyses were performed to compare the baseline characteristics of two groups. The Hosmer-Lemeshow test and receiver operating characteristic curve were applied to evaluate how well the model could identify clusters. Results: The cohort included 550 patients with severe AECOPD across 27 study centers in China, and 465 patients were finally analyzed. A total of 41.5% of patients underwent readmission for AECOPD within 1 year. There were no significant differences in baseline characteristics between groups after PSM. Severe exacerbations in the 12 months were related to some factors, eg, the duration of COPD (13 vs 8 years, P<0.001), the COPD Assessment Test (CAT) score (20 vs 17, P<0.001), the blood eosinophil percentage (1.5 vs 2.0, P<0.05), and their inhaler therapies. Patients readmitted with AECOPD had a longer time of diagnosis (≥9 years), more symptoms (CAT ≥10), and lower blood eosinophils (Eos <2%). A clinical model was derived to help identify patients at risk of readmission with severe exacerbations. Conclusion: These analyses confirmed the relevance of COPD at admission with future severe exacerbations. A lower blood eosinophils percentage appears to be related to readmission when combined with clinical history. Further studies are needed to evaluate whether this study can predict the risk of exacerbations.

Systematic review and meta-analysis of the clinical outcomes of ACEI/ARB in East-Asian patients with COVID-19.

BACKGROUND: SARS-CoV-2 invades human cells and leads to COVID-19 by direct associating with angiotensin converting enzyme 2 (ACE2) receptors, the level of which may be increased by treatment with angiotensin-converting enzyme inhibitors (ACEIs) and/or angiotensin receptor blockers (ARBs). This meta-analysis aimed to explore the impact of ACEI/ARB treatment on the clinical outcomes of patients with COVID-19 infections among population in the East-Asia region. METHODS: We collected clinical data published from January 2000 to May 2022 in the English databases including PubMed, Embase, and the Cochrane Library. Two reviewers independently screened and identified studies that met the prespecified criteria. Review Manager 5.3 software was used to perform the meta-analysis. RESULTS: A total of 28 articles were included in this analysis. The results showed that patients who were prescribed with ACEI/ARB had a shorter duration of hospital stay [MD = -2.37, 95%CI (-3.59, -1.14), P = 0.000 2] and a lower mortality rate [OR = 0.61, 95% CI (0.52, 0.70), P<0.000 01] than patients who were not on ACEI/ARB. Furthermore, there was no statistically significant difference in disease severity [OR = 0.99, 95% CI (0.83, 1.17), P = 0.90] between individuals receiving ACEI/ARB or not. CONCLUSIONS: This meta-analysis suggested that the use of ACEI/ARB was not associated with adverse clinical outcomes in East-Asian Covid-19 patients and a reduced mortality and shorter duration of hospital stay among East-Asian population (especially for female subjects) was found. Thus, ACEI/ARB should be continued in patients infected by Covid-19.

Development and Implementation of an Intergenerational Bonding Program in a Co-Located Model: A Case Study in Singapore

There is a well-established body of evidence that intergenerational bonding programs (IGPs) can improve the overall well-being of older adults and strengthen relationships and understanding between generations. There is limited literature on the experience of IGPs in an Asian context, despite many of these countries facing faster rates of population ageing than other Western countries. In Singapore, intergenerational bonding is a priority in national efforts to encourage successful ageing. This paper presents a case study of the development and implementation of a co-located (shared site) model IGP in Singapore. Drawing on interviews with key stakeholders, the aim of this case study is to present the realities of the evolution of an IGP from conceptualisation through to implementation, and used the nursing home's COVID-19 experience to illustrate issues of sustainability affecting IGPs with vulnerable populations. The findings will inform the development and implementation of similar future programs.

Chemistry of Atmospheric Fine Particles During the COVID-19 Pandemic in a Megacity of Eastern China.

Air pollution in megacities represents one of the greatest environmental challenges. Our observed results show that the dramatic NOx decrease (77%) led to significant O3 increases (a factor of 2) during the COVID-19 lockdown in megacity Hangzhou, China. Model simulations further demonstrate large increases of daytime OH and HO2 radicals and nighttime NO3 radical, which can promote the gas-phase reaction and nocturnal multiphase chemistry. Therefore, enhanced NO3 - and SO4 2- formation was observed during the COVID-19 lockdown because of the enhanced oxidizing capacity. The PM2.5 decrease was only partially offset by enhanced aerosol formation with its reduction reaching 50%. In particular, NO3 - decreased largely by 68%. PM2.5 chemical analysis reveals that vehicular emissions mainly contributed to PM2.5 under normal conditions in Hangzhou. Whereas, stationary sources dominated the residual PM2.5 during the COVID-19 lockdown. This study provides evidence that large reductions in vehicular emissions can effectively mitigate air pollution in megacities.

Variation in Concentration and Sources of Black Carbon in a Megacity of China During the COVID-19 Pandemic.

Black carbon (BC) not only warms the atmosphere but also affects human health. The nationwide lockdown due to the Coronavirus Disease 2019 (COVID-19) pandemic led to a major reduction in human activity during the past 30 years. Here, the concentration of BC in the urban, urban-industry, suburb, and rural areas of a megacity Hangzhou were monitored using a multiwavelength Aethalometer to estimate the impact of the COVID-19 lockdown on BC emissions. The citywide BC decreased by 44% from 2.30 to 1.29 µg/m3 following the COVID-19 lockdown period. The source apportionment based on the Aethalometer model shows that vehicle emission reduction responded to BC decline in the urban area and biomass burning in rural areas around the megacity had a regional contribution of BC. We highlight that the emission controls of vehicles in urban areas and biomass burning in rural areas should be more efficient in reducing BC in the megacity Hangzhou.

Spatiotemporal variations and reduction of air pollutants during the COVID-19 pandemic in a megacity of Yangtze River Delta in China.

In recent decades, air pollution has become an important environmental problem in the megacities of eastern China. How to control air pollution in megacities is still a challenging issue because of the complex pollutant sources, atmospheric chemistry, and meteorology. There is substantial uncertainty in accurately identifying the contributions of transport and local emissions to the air quality in megacities. The COVID-19 outbreak has prompted a nationwide public lockdown period and provides a valuable opportunity for understanding the sources and factors of air pollutants. The three-month period of continuous field observations for aerosol particles and gaseous pollutants, which extended from January 2020 to March 2020, covered urban, urban-industry, and suburban areas in the typical megacity of Hangzhou in the Yangtze River Delta in eastern China. In general, the concentrations of PM2.5-10, PM2.5, NOx, SO2, and CO reduced 58%, 47%, 83%, 11% and 30%, respectively, in the megacity during the COVID-Lock period. The reduction proportions of PM2.5 and CO were generally higher in urban and urban-industry areas than those in suburban areas. NOx exhibited the greatest reduction (>80%) among all the air pollutants, and the reduction was similar in the urban, urban-industry, and suburban areas. O3 increased 102%-125% during the COVID-Lock period. The daytime elevation of the planetary boundary layer height can reduce 30% of the PM10, PM2.5, NOx and CO concentrations on the ground in Hangzhou. During the long-range transport events, air pollutants on the regional scale likely contribute 40%-90% of the fine particles in the Hangzhou urban area. The findings highlight the future control and model forecasting of air pollutants in Hangzhou and similar megacities in eastern China.

Predicting COVID-19 in China Using Hybrid AI Model.

The coronavirus disease 2019 (COVID-19) breaking out in late December 2019 is gradually being controlled in China, but it is still spreading rapidly in many other countries and regions worldwide. It is urgent to conduct prediction research on the development and spread of the epidemic. In this article, a hybrid artificial-intelligence (AI) model is proposed for COVID-19 prediction. First, as traditional epidemic models treat all individuals with coronavirus as having the same infection rate, an improved susceptible-infected (ISI) model is proposed to estimate the variety of the infection rates for analyzing the transmission laws and development trend. Second, considering the effects of prevention and control measures and the increase of the public's prevention awareness, the natural language processing (NLP) module and the long short-term memory (LSTM) network are embedded into the ISI model to build the hybrid AI model for COVID-19 prediction. The experimental results on the epidemic data of several typical provinces and cities in China show that individuals with coronavirus have a higher infection rate within the third to eighth days after they were infected, which is more in line with the actual transmission laws of the epidemic. Moreover, compared with the traditional epidemic models, the proposed hybrid AI model can significantly reduce the errors of the prediction results and obtain the mean absolute percentage errors (MAPEs) with 0.52%, 0.38%, 0.05%, and 0.86% for the next six days in Wuhan, Beijing, Shanghai, and countrywide, respectively.