Purpose: The rural-urban gap in sociodemographic, lifestyle, and disease-related characteristics among COPD patients is prevalent. These differences may influence the prevalence of mild cognitive impairment (MCI). This study aimed to compare the prevalence and determinants of MCI between rural and urban areas among COPD patients.
Patients And Methods: The cross-sectional study sample comprised 372 COPD patients from China. We evaluated the cognitive function and lung function, collected sociodemographic, lifestyle, and disease-related information, to compare the prevalence of MCI in rural and urban areas. Using multivariate regression analysis to examine the effects of variables to MCI.
Results: The prevalence of MCI in rural areas was higher than that in urban areas (65.4% vs 47.9%, P=0.001). The prevalence in farm laborers was almost twice as high as that of non-farm laborers in urban areas (82.6% vs 43.1%), but no significant difference in rural areas (P=0.066). However, the data were lower in subjects who insisted on long-term home oxygen therapy (39.7%, CI:27.8-51.6, P<0.001), and who with higher monthly household income (49.6%, CI:40.2-58.9) in rural areas, but no significant difference in urban areas (P=0.985 and 0.502). Multivariate logistic regression analysis indicated that participants aged 71 years and above, former smokers were at a high risk of MCI in both urban and rural areas. However, participants who slept for 6-8 hours a day, shopping frequently, or exercised for more than 2 hours a day had a lower risk of MCI in urban areas. But rural participants who insisted on long-term home oxygen therapy had a lower probability of developing MCI.
Conclusion: This study revealed that there were significant differences in MCI among COPD patients in rural and urban areas, especially in exercise, sleeping, shopping, and long-term home oxygen therapy. Medical staff should give health guidance according to the actual situation of patients with COPD.
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http://dx.doi.org/10.2147/COPD.S434743 | DOI Listing |
J Ethn Subst Abuse
January 2025
Arizona State University, Tempe, Arizona.
Unlabelled: The large majority (over 70%) of American Indian adolescents who reside in cities rather than tribal lands or rural areas report relatively earlier onset of substance use and more harmful associated health effects, compared to their non-Native peers.
Objective: This study investigated multilevel ecodevelopmental influences on empirically derived patterns of substance use among urban American Indian adolescents.
Method: Data came from 8th, 10th, and 12th grade American Indian adolescents ( = 2,407) in metropolitan areas of Arizona.
Sci Rep
January 2025
Department of Forest Engineering, Faculty of Forestry, Kastamonu University, Kastamonu, Türkiye, Turkey.
Rapid urban growth is a subject of worldwide interest due to environmental problems. Population growth, especially migration from rural to urban areas, leads to land use and land cover (LULCC) changes in urban centres. Therefore, LULCC and urban growth analyses are among the studies that will help decision-makers achieve better sustainable management and planning.
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January 2025
College of Landscape Architecture and Art, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.
The scientific establishment of the Ecological Security Pattern (ESP) is crucial for fostering the synergistic development of ecological and recreational functions, thereby enhancing urban ecological protection, recreational development, and sustainable growth. This study aimed to propose a novel method of constructing ESP considering both ecological and recreational functions, and to reconstruct ESP by weighing the relationship between ecological protection and recreational development. Utilizing Fuzhou City as a case study, a comprehensive application of methodologies including Morphological Spatial Pattern Analysis (MSPA), landscape connectivity analysis, ArcGIS spatial analysis, social network analysis (SNA), and circuit theory is employed to develop both the ESP and the Recreational Spatial Pattern (RSP).
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January 2025
Civil and Environmental Engineering Department, Khalifa University, Abu Dhabi, UAE.
Estimating spatiotemporal maps of greenhouse gases (GHGs) is important for understanding climate change and developing mitigation strategies. However, current methods face challenges, including the coarse resolution of numerical models, and gaps in satellite data, making it essential to improve the spatiotemporal estimation of GHGs. This study aims to develop an advanced technique to produce high-fidelity (1 km) maps of CO and CH over the Arabian Peninsula, a highly vulnerable region to climate change.
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January 2025
School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, UK.
With the continuous development of intelligent transportation systems, traffic safety has become a major societal concern, and vehicle trajectory anomaly detection technology has emerged as a crucial method to ensure safety. However, current technologies face significant challenges in handling spatiotemporal data and multi-feature fusion, including difficulties in big data processing, and have room for improvement in these areas. To address these issues, this paper proposes a novel method that combines autoencoders, Mahalanobis distance, and dynamic Bayesian networks for anomaly detection.
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