BACKGROUND: In people affected by chronic obstructive pulmonary disease (COPD), self-care is crucial for improving quality of life, decreasing symptom burden, and reducing health care-related costs. Unlike other chronic conditions, little is known about the factors that influence different self-care styles in COPD patients.
OBJECTIVE: To explore the factors that could influence the self-care styles of patients with COPD.
METHODS: A mixed methods case study design was used. Quantitative and qualitative data were collected at the same stage in a purposive sample of patients with COPD through questionnaires, interviews, and focus groups. Data were analyzed separately and then integrated to compare the cases.
RESULTS: Thirty-seven patients with COPD were recruited from an outpatient clinic, pulmonary rehabilitation unit and online in a patient support group. On average, participants scored below the level of adequacy in all self-care dimensions. Self-care maintenance was influenced by patient age, education level, and economic status. Most participants reported performing self-care behaviors, while some did not because they found it difficult or because they did not recognize their importance. When the quantitative and qualitative data of patients with higher and lower levels of self-care were integrated, four different styles of self-care were identified according to COPD severity, psychological distress and level of self-efficacy: proactive, inactive, reactive, and hypoactive.
CONCLUSIONS: Personal, clinical, psychological, and social factors not only influence the level of self-care performed by COPD patients but also contribute to the understanding of different self-care styles. This knowledge could support health care professionals in tailoring educational interventions.

UNASSIGNED: With the rapid development of artificial intelligence and Internet-of-Things technology, internal support systems among families are gradually weakening, which can no longer satisfy the current demands of older adults. In this context, smart senior care has become a new development direction. However, existing studies on the demand for smart senior care are primarily concentrated in economically developed provinces and mega-cities in eastern China; their research results or conclusions may not apply to underdeveloped areas in the Western region. Therefore, our study selects Lanzhou as a representative city in an underdeveloped western region to investigate the demand of older adults for smart senior care and analyze the influencing factors.
UNASSIGNED: This cross-sectional study included 4,815 older adults from Lanzhou, China. A structured questionnaire was designed to investigate the demands of the older adults for smart senior care and analyze thie influencing factors. The Chi-square test was used for single factor analysis of each variable. The logistic regression model included the statistically significant variables to analyze factors influencing older adults\' demand for smart senior care. A significance level of p < 0.05 was considered statistically significant.
UNASSIGNED: Among the surveyed older adults, 1,625 (33.75%) expressed a demand for smart senior care. The finding indicated that participants\' age, level of education, marital status, monthly income, number of children, type of endowment insurance, and knowledge of smart senior care were significantly associated with their demands for smart senior care (p < 0.05). Notably, medical care emerged as the smart senior care service with the highest demand rate (79.45%).
UNASSIGNED: In Lanzhou, older adults show a low level of knowledge but a high demand for smart senior care. Their demand is influenced by personal, family, health conditions, senior care security, and other factors. To advance smart senior care, government departments should accelerate the improvement of the laws and regulations on smart senior care while vigorously enhancing the service\'s publicity to raise knowledge about it. Additionally, the service contents for smart senior care should be expanded to meet the diversified demands of older adults.

Abnormal uterine bleeding (AUB) during the menopausal transition results in reproductive endocrine disorders and both physiological and pathological changes, substantially impacting women\'s health. This study aimed to investigate the factors influencing AUB in perimenopausal women. Between April 2021 and June 2022, 120 perimenopausal women with AUB in the menopausal transition, diagnosed and treated at the Gynaecology Department of Kunming Tongren Hospital, were included in the case group. Concurrently, women undergoing routine health examinations at the same hospital were randomly selected as the control group. Univariate and multivariate logistic regression analyses identified factors related to AUB. The univariate analysis revealed significant associations (P < 0.05) between AUB and several factors, including age, body mass index (BMI), age at menarche, gravidity, and intrauterine device (IUD) placement in perimenopausal women. The multivariate regression analysis indicated that the independent risk factors for AUB include benign endometrial lesions (odds ratio [OR] 5.243, 95% confidence interval [CI] 3.082-9.458, P < 0.001), endometrial thickness ≥ 10 mm (OR 1.573, 95% CI 0.984-3.287, P < 0.001), age ≥ 50 years (OR 2.045, 95% CI 1.035-4.762, P = 0.001), BMI ≥ 25 kg/m2 (OR 2.436, 95% CI 1.43-4.86, P = 0.002), and IUD placement (OR 2.458, 95% CI 1.253-4.406, P < 0.001). Abnormal uterine bleeding during the menopausal transition is associated with several factors, including age, BMI, and IUD placement, highlighting the importance of early screening for these risk factors in the diagnosis and treatment of AUB.

Cities, where human energy activities and greenhouse gas emissions are concentrated, contribute significantly to alleviating the impacts of global climate change. Utilizing the China Carbon Emissions Accounting Database (CEADs) to provide carbon dioxide emission inventories for urban areas in China at the prefecture level, this study closely examines the historical evolution trajectories of carbon emissions across 247 urban units from 2005 to 2019. The logarithmic cubic function model was employed to simulate these trajectories, evaluating urban emission peaks and classifying the different carbon emission trajectories. Further, the Geographical and Temporal Weighted Regression model was employed to explore spatiotemporal traits and essential variables that impact the variations in carbon emissions among four identified trajectory types. Our results showed that Chinese urban carbon emission trajectories can be classified into four categories: a) peaking emissions, b) fluctuating growth, c) continuous growth, and d) passive decline. Specifically, 43 cities, primarily in North China, proactively attained their emission peak post-2010, driven by the reduction in secondary industry and energy intensity. 90 cities, largely industrial hubs in the southeast coast and inland, reached an emission plateau around 2015, exhibiting fluctuating growth due to dependencies on secondary industries. 101 cities, predominantly located in western and central regions, demonstrated a clear upward trend in carbon emissions, propelled by rapid urbanization and heavy industry-oriented economic development. Lastly, 13 cities, typically in the northeastern and southwestern regions, experienced a passive decline in carbon emissions, attributable to resource depletion or economic downturns. It is evident that China\'s city-level carbon peaking has demonstrated some effectiveness, yet considerable progress is still required.

Rural tourism is a powerful way to revitalize the countryside, and its spatial pattern is crucial for sustainable development. This paper analyzes the spatial distribution of rural tourism characteristic villages in Henan Province by taking 723 villages as the research object and using the nearest neighbor index, kernel density analysis, and spatial autocorrelation. It investigates the influencing factors utilizing the optimal parameters-based geographical detector (OPGD) model. The results show that, firstly, the overall spatial distribution of the rural tourism characteristic villages in Henan Province is characterized by aggregation and unbalanced distribution, and the overall spatial distribution density demonstrates the aggregation characteristics of \"four cores and one belt\". Secondly, the rural tourism characteristic villages can be divided into four primary categories, agricultural industry, rural culture, and featured villages and towns. The spatial distributions of the four main categories are all clustered. Thirdly, the primary factors affecting the differences in the spatial distribution of the rural tourism characteristic villages are the topographic features, economic development level, tourism market potential, traffic capacity, and relevant policies, among which the critical factor is the number of A-class scenic spots in the tourism market potential. To promote the optimisation of the spatial pattern of rural tourism, it is necessary to strengthen resource integration. Furthermore, it is important to conduct in-depth exploration of more factors in order to provide comprehensive guidance for the sustainable development of rural tourism.

The outbreak of coronavirus disease 2019 (COVID-19) has not only posed significant challenges to public health but has also impacted every aspect of society and the environment. In this study, we propose an index of notifiable disease outbreaks (NDOI) to assess the impact of COVID-19 on other notifiable diseases in Shanghai, China. Additionally, we identify the critical factors influencing these diseases using multivariate statistical analysis. We collected monthly data on 34 notifiable infectious diseases (NIDs) and corresponding environmental and socioeconomic factors (17 indicators) from January 2017 to December 2020. The results revealed that the total number of cases and NDOI of all notifiable diseases decreased by 47.1% and 52.6%, respectively, compared to the period before the COVID-19 pandemic. Moreover, the COVID-19 pandemic has led to improved air quality as well as impacted the social economy and human life. Redundancy analysis (RDA) showed that population mobility, particulate matter (PM2.5), atmospheric pressure, and temperature were the primary factors influencing the spread of notifiable diseases. The NDOI is beneficial in establishing an early warning system for infectious disease epidemics at different scales. Furthermore, our findings also provide insight into the response mechanisms of notifiable diseases influenced by social and environmental factors.

High levels of urban green infrastructure (UGI) development can help mitigate the climate, biodiversity, and habitat crises faced by cities and support the achievement of sustainable urban development. Based on the relevant data of 41 cities in the Yangtze River Delta region obtained from 2011 to 2020, this study measured the development level of natural and geographic conditions, economic development, urban construction, social and cultural development, and eco-environment quality and urban green infrastructure (UGI); evaluated the development trend of UGI in the region during the 12th Five-Year Plan and 13th Five-Year Plan by using entropy TOPSIS; and used fs/QCA to explain the high-level development path of each city toward the achievement of a green infrastructure. The results showed that (1) the development level of UGI in the Yangtze River Delta region decreases from southeast to northwest, and gradually decreases from Shanghai, Hangzhou, and other central cities. (2) There were several different configurations of high levels and non-high levels of UGI development drivers across regions, confirming the existence of multiple causality and asymmetry indices in the drivers of UGI. (3) During the \"12th Five-Year Plan\" and the \"13th Five-Year Plan\" period, the conditions needed to achieve a high level of UGI gradually became stricter, expanding from nature-social culture and urban construction-eco-environmental drivers to nature-urban construction, nature-social culture-eco-environmental, urban construction-economy-social culture-eco-environmental drivers. Research findings can provide greater guidance and implications for future sustainable urban development.

Amid global industrialization and urbanization, mountainous rural settlements, especially those in metropolitan fringe area, are experiencing significant spatial changes in location and scale. This study takes Pingnan County, Fujian Province, China, as an example. Utilizing land use data and employing methods including standard deviation ellipse, average nearest neighbor index, kernel density estimation, spatial hotspot detection, binary logistic regression model, and Geodetector, this study aims to scientifically identify the spatial pattern characteristics and influencing factors of its settlements. The results show that: (1) The spatial distribution of settlements in Pingnan County is biased toward the southern part of the county; the center of settlement\'s spatial distribution is located south of the junction of Gufeng Town and Pingcheng Town; the spatial distribution trend of settlements is north-east-southwest. Settlements are generally aggregated, and the aggregation degree of Gufeng Town is obviously lower than that of other towns. (2) The density distribution of settlements presents a \"core-periphery\" structure and a \"north-south linear\" structure in space; the spatial pattern characteristics show high-density, large patches in Gufeng Town, high-density, small patches in Changqiao Town, Tangkou Town and Gantang Town, and medium-density or low-density, small patches in other towns. (3) Settlement location is mainly affected by the elevation, distance to cultivated land, and distance to main roads, while settlement scale is mainly affected by slope, relief degree of land surface, and distance to urban centers. The interaction between these factors exhibits enhancement effects, with natural terrain and location conditions exerting the most prominent influence. These findings underscore the strong constraints posed by natural topography on mountainous rural settlements in metropolitan fringe areas, coupled with a more pronounced influence from socio-economic factors. The study\'s results hold significant implications for optimizing the layout of such settlements, guiding land spatial planning, and promoting rural revitalization.

UNASSIGNED: The aim of this retrospective study was to investigate the short-term and long-term efficacy of high-intensity focused ultrasound (HIFU) therapy for abdominal wall endometriosis (AWE) and explore its potential influencing factors.
UNASSIGNED: A total of 80 patients with AWE who underwent HIFU therapy were retrospectively analyzed. Follow-ups were also conducted to evaluate the changes in lesion size and pain relief. Multivariate logistic regression analysis was applied to investigate factors influencing HIFU therapy for AWE.
UNASSIGNED: Among the 80 patients with AWE who received HIFU therapy, the effective rates were 76.3%, 80.5%, and 90.5% after 3, 12 and 24 months of follow-up, respectively. Multivariate logistic regression analysis revealed that the AWE lesion diameter and sonication intensity had statistically significant effects on the 3-month and 12-month efficacy of HIFU therapy for AWE, while age, BMI, disease duration, average sonication power and grey-scale changes did not have statistically significant effects. Four patients with AWE experienced recurrence after HIFU therapy, for a three-year cumulative recurrence rate of 6.3%. Furthermore, ten patients required reintervention after treatment, for a five-year cumulative reintervention rate of 13.9%.
UNASSIGNED: This study further confirmed the safety and effectiveness of HIFU therapy for AWE. Factors such as AWE lesion diameter and sonication intensity have been identified as key influencers affecting the short-term and long-term efficacy of HIFU therapy for AWE. The first two years following HIFU therapy constitute crucial periods for observation, and judiciously extending follow-up intervals during this timeframe is advised.

Economic data on congenital heart disease are scarce in economically underdeveloped areas of China. Therefore, this study aimed to shed light on the level and changing trend of congenital heart disease inpatients\' economic burden in underdeveloped areas.
This study used a multi-stage stratified cluster sampling method to select 11,055 inpatients with congenital heart disease from 197 medical and health institutions in Gansu Province. Their medical records and expenses were obtained from the Hospital Information System. Univariate analysis was conducted using the rank sum test and Spearman rank correlation. Quantile regression and random forest were used to analyze the influencing factors.
From 2015 to 2020, the average length of stay for congenital heart disease patients in Gansu Province was 10.09 days, with an average inpatient cost of USD 3,274.57. During this period, the average inpatient costs per time increased from USD 3,214.85 to USD 3,403.41, while the average daily inpatient costs increased from USD 330.05 to USD 376.56. The average out-of-pocket costs per time decreased from USD 2,305.96 to USD 754.77. The main factors that affected the inpatient costs included length of stay, cardiac procedure, proportion of medications, age, and hospital level.
Congenital heart disease causes a significant economic burden on both families and society. Therefore, to further reduce the patient\'s financial burden, the length of stay should be reasonably reduced, and the rational distribution of medical resources should be continuously promoted to ensure equitable access to healthcare services.