Compliance with oral nutritional supplements and its influencing factors in postoperative patients with digestive tract tumors: a cross-sectional study.

BACKGROUND: Oral nutritional supplements are one of the preferred methods of nutritional support for postoperative patients. This study aims to investigate the current status of oral nutritional supplements compliance in postoperative patients with digestive tract tumors and its influencing factors. METHODS: Convenience sampling was employed to select 242 patients who underwent surgery for digestive tract tumors at a tertiary hospital in Shanghai from October 2022 to July 2023 as the study subjects. Data following a normal distribution were analyzed using independent sample t-tests, ANOVA single-factor analysis, Pearson correlation analysis, and multiple linear regression analysis to determine the factors influencing compliance with oral nutritional supplements. RESULTS: A total of 252 questionnaires were distributed, with 10 invalid questionnaires excluded, resulting in an effective questionnaire rate of 96.03%. The compliance score for oral nutritional supplements in postoperative patients with digestive tract tumors was (2.40 ± 1.45), General Self-efficacy Scale (GSES) score was (24.72 ± 4.86), Multidimensional Scale of Perceived Social Support Scale (MSPSS) score was (58.67 ± 11.09), and Belief about Medicines Questionnaire Scale (BMQ) score was (0.17 ± 2.78). Multiple linear regression analysis revealed that age, adverse reactions, educational level, self-efficacy, medication beliefs, and social support were factors influencing compliance with oral nutritional supplements in postoperative patients with digestive tract tumors (P < 0.05). CONCLUSION: Our study revealed that the compliance to oral nutritional supplements among postoperative patients with digestive tract tumors was at a moderate level and was closely associated with age, educational level, adverse reactions to oral nutritional supplements, medication beliefs, social support, and self-efficacy. Nursing staff should conduct nursing assessments based on the specific circumstances of patients and their families, provide personalized health education management plans based on the patients' educational level, enhance patients' nutrition knowledge, improve patient self-efficacy, and enhance social support for patients, while further improving patient nutrition management.

Development and validation of a predictive model for acute-on-chronic liver failure after transjugular intrahepatic portosystemic shunt.

BACKGROUND: Transjugular intrahepatic portosystemic shunt (TIPS) is a cause of acute-on-chronic liver failure (ACLF). AIM: To investigate the risk factors of ACLF within 1 year after TIPS in patients with cirrhosis and construct a prediction model. METHODS: In total, 379 patients with decompensated cirrhosis treated with TIPS at Nanjing Drum Tower Hospital from 2017 to 2020 were selected as the training cohort, and 123 patients from Nanfang Hospital were included in the external validation cohort. Univariate and multivariate logistic regression analyses were performed to identify independent predictors. The prediction model was established based on the Akaike information criterion. Internal and external validation were conducted to assess the performance of the model. RESULTS: Age and total bilirubin (TBil) were independent risk factors for the incidence of ACLF within 1 year after TIPS. We developed a prediction model comprising age, TBil, and serum sodium, which demonstrated good discrimination and calibration in both the training cohort and the external validation cohort. CONCLUSION: Age and TBil are independent risk factors for the incidence of ACLF within 1 year after TIPS in patients with decompensated cirrhosis. Our model showed satisfying predictive value.

Spatiotemporal characteristics and influencing factors for joint events of air pollution wave and cold wave in China.

Climate change triggered more environmental extremes. The joint events of air pollution wave and cold wave showed higher health risks than independent events, but little evidence is available for the spatiotemporal features of their co-occurrence. To better understand and forecast the joint events, a method framework was developed in this study. The temporal trend and spatial distribution of count and duration for joint events were measured at each grid cell (0.5°×0.5°) by integrating the PM2.5 air pollution wave and cold wave. The generalized linear mixed model was used to screen influencing variables that took into account socioeconomic characteristics, meteorological variables, and annual PM2.5 levels. During 2000 and 2018, the average annual count of joint events was 4.1 ± 6.8 days and the average duration ranged from 1.0 to 9.7 days. High spatial heterogeneity was observed throughout China, with a significant increase in joint events observed in Xinjiang area (the largest province in China). The most average count of joint events was observed in Henan province (one of the most populous provinces), while the longest duration was in Chongqing (a municipality, one of the megacities). Areas with higher PM2.5 levels, prolonged air pollution wave, and cold wave durations would experience more joint events. These findings can assist China in locating vulnerable areas and establishing effective local early warning systems. The method framework offers broader perspectives on mitigating health risks associated with extreme events in other countries and regions.

Analysis of the spatial correlation pattern of logistics carbon emission efficiency and its influencing factors: the case of China.

As logistics carbon emission efficiency is an essential industry linking regions, investigating this issue from a spatial correlation perspective is practically significant. Utilizing data from 282 prefecture-level cities spanning 2006 to 2019, we used a super slacks-based measure model, a modified gravity model, motif analysis, the Infomap algorithm, and an exponential random graph model to analyze the spatial correlation patterns and influencing factors of logistics carbon emission efficiency. The following conclusions were drawn. (1) The spatial correlation of logistics carbon emission efficiency during the study period exhibited a core-edge pattern, with the central region emerging as a high-correlation hub. (2) The scale of the spatial association network community of carbon emission efficiency in the logistics industry changed constantly, and the stability of the network community structure gradually increased. From a microstructural perspective, the dispersed-mode structure was a pivotal element in the formation of the spatial correlation network of logistics carbon emission efficiency. (3) Node interaction tendencies were a critical force driving network formation. Financial investment, government concern, international openness, population density, and innovation ability were conducive to the formation of spatial correlations of logistics carbon emission efficiency.

Study on the social support level of family doctor team members of primary medical and health care institutions in Tai′an city / 中华医院管理杂志

Objective:To compare the social support levels as understood by family doctor team members in township hospitals/community health centers, and village clinics/community clinics, and their influencing factors, in order to provide reference for improving the work status of family doctor team members and enhancing the quality of family doctors′ contracted services.Methods:A multi-stage random cluster sampling method was used to sample medical workers from contracted family doctor teams in township hospitals/community health centers and their subordinate village clinics/community clinics in 6 counties (cities, districts) of Tai′an city, Shandong province. In August 2020, a questionnaire survey was conducted on the perceived level of social support among family doctor team members using the perceived social support scale. Descriptive analysis was conducted on the data, and independent sample t-tests and one-way ANOVA were used to conduct univariate analysis on the influencing factors of perceived social support scores of family doctor team members at different levels, while multiple linear regression analysis was used to conduct multivariate analysis. Results:A total of 765 valid questionnaires were collected, with 203 and 562 from township hospitals/community health centers and village clinics/community clinics, respectively. The total perceived social support scores of family doctor team members in township hospitals/community health centers and village clinics/community clinics were (65.56±10.29) and (67.31±10.14), respectively, featuring statistically significant differences ( t=-2.11, P<0.05). In-mirage marital status ( β=0.18, P=0.008), good/very good self-rated health status ( β= 0.25, P=0.048), participation of work-related training within one year ( β=0.17, P=0.010), relatively satisfied/very satisfied for job promotion ( β= 0.17, P=0.046), as well as above/far above average self-rated economic status as ( β=-0.15, P=0.027), were the influencing factor on the perceived social support scores of family doctor team members in township hospitals/community health centers. In-marriage marital status ( β= 0.12, P=0.002), good/very good self-rated health status ( β=0.14, P=0.026), junior or intermediate level or above professional title ( β=-0.11, P=0.003; β=-0.10, P=0.006), participation of work-related training within one year ( β= 0.14, P<0.001), and relatively satisfied/very satisfied for job promotion ( β= 0.16, P<0.001) were the influencing factors on the perceived social support scores of family doctor team members in village clinics/community clinics. Conclusions:Members of the family doctor teams in primary medical institutions in Tai′an city had a higher level of understanding of social support. There were differences in the social support levels of family doctor team members between the two levels of primary medical and health care institutions, and the influencing factors were not completely consistent. Targeted measures should be taken based on specific circumstances to enable them to better receive and perceive support from family and friends, and to improve the quality of family doctors′ contracted services.

Current status and influencing factors of chronic disease self-management behavior of middle-aged and elderly deaf people aged 50 years and over in Shanghai / 上海预防医学

ObjectiveTo investigate the current status and influencing factors of chronic disease self-management behavior of middle-aged and elderly deaf people aged 50 years and over in Shanghai， and to provide reference for improving their chronic disease self-management behavior. MethodsDuring September 2021 to February 2022， 271 middle-aged and elderly deaf people aged 50 years and over in Shanghai were investigated face-to-face by general questionnaire， chronic disease self-management study measures， self-efficacy scale， social capital questionnaire， hospital anxiety and depression scale， health disturbance scale. ResultsThe mean score of chronic disease self-management behavior was 32.107±7.527. Gender， frequency of searching health knowledge by mobile phone， whether to eat fruit， self-efficacy and social capital were influencing factors of chronic disease self-management behaviors， which could explain 37.04% of the variance. ConclusionThe chronic disease self-management behavior of the middle-aged and elderly deaf people aged 50 years and over is low and needs to be improved. We can promote the middle-aged and elderly deaf people to form the good self-management behavior by improving their self-efficacy， improving their level of social capital and forming good living habits.

Spatial distribution and influencing factors of litter in urban areas based on machine learning - A case study of Beijing.

Littering in urban areas negatively affects their appearance, is harmful to the environment and increases pollution. It is a typical urban problem looming large upon Beijing and other megacities striving for liveability and harmony in economy, society and environment. This study analyzed the amount and spatial distribution of urban litter generation in Beijing based on the Kernel Density Estimation method and Anselin's Local Moran I method. We analyzed multiple factors affecting littering in urban areas based on the random forest machine learning method. The results show that the density distribution of litter presents a typical core edge diffusion spatial distribution pattern. High clusters of litter were found in most regions of Dongcheng District and central regions of Haidian District. We have verified that littering in urban areas is mostly affected by population, POIs (interest points), road networks, and the management of the city environment. Among these, permanent population, level of road cleaning, the presence of branch roads and commercial places are the four most important influencing factors. This study is of great significance to the prevention and treatment of littering in urban areas and can help city managers better address this problem.

Spatial and temporal differentiation and influencing factors of environmental governance performance in the Yangtze River Delta, China.

As the development of Yangtze River Delta urban agglomeration has been upgraded to a national level development strategy in China, relevant regions need to pay more attention to the environmental governance issues. Based on the 2013-2018 development data of 41 cities and the established evaluation indicator system with "press-state-response" (PSR) model, we mainly use a combination of global principal component analysis (GPCA) and entropy method to comprehensively measure regional environmental governance performance (EGP) in the Yangtze River Delta urban agglomeration. Next, the spatial relationship is explored and discussed with spatial autocorrelation analysis. Finally, the panel Tobit model is used to perform a regression analysis on the factors affecting the performance of environmental governance in the Yangtze River Delta region. The results are summarized as follows. (1) From 2013 to 2018, the overall environmental governance performance of the Yangtze River Delta region maintained a steady growth trend, of which the environmental governance performance of Jiangsu Province and Shanghai maintained a steady increase, and the environmental governance performance of Anhui and Zhejiang Province fluctuated greatly. (2) These cities with better EGP are mainly located in the central and southern regions of the Yangtze River Delta, and those cities with poor EGP are mainly located in the northern part of the Yangtze River Delta. The environmental governance performance gap between provinces and cities is obviously large, and there are significant spatial positive correlations, spatial spillover effects and a trend of agglomeration with increased volatility. (3) The regression analysis shows that the economic development level has a significant positive impact on the EGP of the Yangtze River Delta region. Meanwhile, the negative impact of industrial structure and foreign investment is significant, but the positive impact of R&D investment intensity is insignificant, on the performance of environmental governance in the Yangtze River Delta region.

Social Network Analysis of Factors Influencing Green Building Development in China.

Green buildings have been viewed as one of the most effective solutions to the negative environmental impacts of construction activities. For the sustainable development of the economy and the environment, many governments in the world have launched a variety of policies to encourage the development of green buildings. However, green targets achieved during the operational stage of green buildings are far below the expectations from the design stage. In addition, the development of green buildings is unevenly distributed in different cities. To help resolve these issues, this paper identifies 28 green building influencing factors from two perspectives, the life cycle and stakeholders. Then, a social network analysis is used to analyse their interactions and identify the critical factors. Our results show that government supervision, incremental cost, property management experience, and the awareness of environmental protection in green buildings are the critical influencing factors in promoting green building development. However, some factors related to contractors, designers and suppliers are not as important as perceived. Finally, some policy recommendations are proposed to promote green buildings in China.

Source Apportionment and Influencing Factor Analysis of Residential Indoor PM2.5 in Beijing.

In order to identify the sources of indoor PM2.5 and to check which factors influence the concentration of indoor PM2.5 and chemical elements, indoor concentrations of PM2.5 and its related elements in residential houses in Beijing were explored. Indoor and outdoor PM2.5 samples that were monitored continuously for one week were collected. Indoor and outdoor concentrations of PM2.5 and 15 elements (Al, As, Ca, Cd, Cu, Fe, K, Mg, Mn, Na, Pb, Se, Tl, V, Zn) were calculated and compared. The median indoor concentration of PM2.5 was 57.64 µg/m³. For elements in indoor PM2.5, Cd and As may be sensitive to indoor smoking, Zn, Ca and Al may be related to indoor sources other than smoking, Pb, V and Se may mainly come from outdoor. Five factors were extracted for indoor PM2.5 by factor analysis, explained 76.8% of total variance, outdoor sources contributed more than indoor sources. Multiple linear regression analysis for indoor PM2.5, Cd and Pb was performed. Indoor PM2.5 was influenced by factors including outdoor PM2.5, smoking during sampling, outdoor temperature and time of air conditioner use. Indoor Cd was affected by factors including smoking during sampling, outdoor Cd and building age. Indoor Pb concentration was associated with factors including outdoor Pb and time of window open per day, building age and RH. In conclusion, indoor PM2.5 mainly comes from outdoor sources, and the contributions of indoor sources also cannot be ignored. Factors associated indoor and outdoor air exchange can influence the concentrations of indoor PM2.5 and its constituents.