Frontiers in Public Health最新文献

Introduction: COVID-19-related mortality varied substantially within and across countries, which may not be fully explained by demographic and clinical differences. Some studies suggest that local governors' ideology has a substantial effect on mortality because of their political stances for and against the stringency of public health interventions. However, recent research indicates that stringency is not consistently related to lower mortality. This study investigated whether regional differences in COVID-19 mortality are attributable to differences in local governance arrangements for collaborative policy responses to pandemic situations in Japan, where local infection control was managed via a decentralized system.

Methods: From December 2020 to November 2021, we conducted a city-level, cross-sectional, ecological study of 20 cities designated by the government ordinance of Japan. COVID-19-related mortality rates were obtained per 3-month period to account for viral variant change. The local deliberative decision to suspend the national health insurance policy that temporarily revokes the benefit eligibility of households because of unpaid premiums was considered an indicator of flexible and collaborative governance, as the decision required unanimous consensus from diverse stakeholders within and across local government institutes. Negative binomial regression was used adjusting for covariates, including population density and emergency care accessibility.

Results: During the study, 5,383 COVID-19-related deaths were reported. Of the 20 cities, five had implemented the suspension decision before or during the pandemic. Cities with the suspension policy showed an adjusted mortality relative risk of 0.75 (95% confidence interval: 0.60 to 0.94) compared with cities that retained the temporary revocation policy. Conclusions: The results suggest that local governments with flexible collaborative policymaking were more successful in mitigating the COVID-19 mortality impact in Japan. The findings highlight the potentially important effect of governance arrangements on population health disparities.

Background: This study investigated the level of knowledge and risk perception related to climate change and its health impacts among a university population in Southern Italy.

Methods: Data were collected through a paper-based questionnaire administered in classrooms and offices, covering sociodemographic characteristics, climate change knowledge, risk perception, environmental attitudes, and awareness of the "One Health" approach.

Results: Among 551 participants, 57.2% achieved high knowledge, which was associated with older age, a climate-related academic or professional background, and attendance at the University of Catanzaro. Most respondents (96.3%) recognized the impact of global warming on human health, though 11% believed climate change severity was overstated, a view more common among men and married or separated individuals. Awareness of the "One Health" concept was limited to 41.4%, yet those familiar with it acknowledged its importance in preventing climate-related diseases. Internet and social media were the primary information sources.

Conclusions: Findings reveal generally high awareness but notable variability across subgroups, highlighting the need for targeted educational interventions that combine scientific knowledge with environmental attitudes to promote effective mitigation and adaptation strategies.

Background: With the deep integration of information technology and education, online teaching has become an important form of nursing education. However, students may encounter problems such as low participation and difficulty in internalizing knowledge during online learning. Existing studies have shown that deep learning plays a key role in promoting students' learning outcomes and professional skills development, but its mediating role between online real-time teaching interaction and teaching effect among undergraduate nursing students has not been fully studied.

Objective: This study aims to explore the relationships among online real-time teaching interactions, deep learning, and online teaching effect for undergraduate nursing students, with a focus on analyzing the mediating role of deep learning in the relationship between online real-time teaching interactions and teaching effect.

Methods: A total of 587 nursing students from the universities in Hebei Province offering nursing programs were recruited via convenience sampling to complete the questionnaire survey. The research instruments included the online real-time teaching interaction scale, the deep learning scale, and the teaching effect scale. Data analysis was performed using SPSSAU, which involved descriptive statistics, correlation analysis, regression analysis, and a test of the mediating effect of deep learning.

Results: There is a significant positive correlation between deep learning and teaching effect among undergraduate nursing students (r = 0.407, p < 0.01), as well as between online real-time teaching interactions and teaching effect (r = 0.398, p < 0.01). Deep learning acts as a partial mediator in the relationship between online real-time teaching interactions and teaching effect, with a mediating effect value of 0.165, accounting for 36.344% of the total effect.

Conclusion: This study reveals that online real-time teaching interaction not only directly and positively predicts the teaching effect of undergraduate nursing students but also exerts an indirect influence by enhancing their deep learning levels. The results indicate that in online nursing education, the intentional design of interactive segments that promote deep learning is crucial. Educators should focus on constructing an interactive environment capable of stimulating higher-order thinking among nursing students to more effectively enhance the quality of online teaching.

Background: Maintaining a health-promoting lifestyle is essential for long-term cardiovascular risk management in patients undergoing percutaneous coronary intervention (PCI). However, how lifestyle behaviors change before and after first-time PCI, and which factors are associated with these changes, remain insufficiently understood.

Aims: This study aimed to identify patterns and short-term transitions in health-promoting lifestyle behaviors before and after first-time PCI in patients with coronary heart disease (CHD).

Methods: This was a prospective longitudinal study with a 6-month follow-up. A total of 603 patients with CHD from three tertiary hospitals in Zhengzhou were enrolled. Lifestyle profiles were identified at baseline (T1) and at 6 months after PCI (T2) using latent profile transition analysis (LPTA), and multinomial logistic regression was used to examine factors associated with profile transitions.

Results: Three different lifestyles were consistently identified at both time points: Unhealthy, Moderately Healthy, and Healthy. Although some transitions toward healthier profiles were observed between T1 and T2, most participants remained in their initial profile, indicating relative stability over the 6-month period. Higher levels of general self-efficacy and perceived social support significantly increased the likelihood of positive lifestyle changes (p < 0.001). In contrast, higher BMI, lower education, and lower income were associated with reduced odds of behavioral improvement (p < 0.05).

Conclusion: Lifestyle behaviors among patients undergoing first-time PCI are heterogeneous and show limited change over the first 6 months following the procedure. Psychological and social resources, particularly self-efficacy and social support, are important correlates of favorable lifestyle transitions and may represent useful targets for post-PCI care and rehabilitation.

Background: Neurological disorders have become a significant global public health challenge due to their high rates of disability and mortality. This study analyzed epidemiological trends of neurological disorders in G7 countries from 1990 to 2021 based on Global Burden of Disease (GBD) 2021 data and predicted the disease burden for the next 30 years.

Methods: Using the Joinpoint regression model and the Nordpred Age-Period-Cohort (APC) model, the study evaluated indicators such as incidence, mortality, and Disability-Adjusted Life Years (DALYs) of neurological disorders. It also analyzed the impact of gender, age, and social factors on the disease burden.

Results: The overall burden of neurological disorders in G7 countries is lower than the global level, but there are significant gender differences. Women have a higher prevalence rate, which may be related to migraine and hormonal fluctuations, while men have more prominent Years of Life Lost (YLL) due to premature death, except in Japan and Italy. Regarding age distribution, the risk of disease gradually increases for individuals over 10 years old, and the mortality rate rises sharply after 70 years old. The association between aging and neurodegenerative diseases (such as Alzheimer's disease) is particularly significant. Historical trend analysis from 1990 to 2021 shows that the global age-standardized incidence and mortality rates have remained stable overall. However, male mortality rates have increased significantly in the United States, Japan, and Germany. Predictions for the next 30 years indicate that despite stabilizing age-standardized rates, the number of neurological disease cases in G7 countries will continue to increase due to population growth and aging.

Conclusion: The study untangles the unique challenges faced by G7 countries in preventing and controlling neurological disorders. It emphasizes the need to develop precise intervention strategies that consider age, gender, and social factors, providing valuable insights for developing countries.

Introduction: Rural areas are typically characterized by poor traffic conditions, less road supervision, and more illegal driving behaviors. These factors lead to significantly higher mortality and increased rates of road traffic crashes in rural areas than in urban areas. Therefore, an in-depth study of traffic risk factors in the rural areas of developing countries with large rural populations and extensive rural road coverage is particularly urgent and important.

Method: Based on the report data of 38,458 traffic crash occurring in rural areas of Guangdong Province between 2006 and 2014 in the Road Traffic Crash Database of the Ministry of Public Security of China, the logit model was used to evaluate the impact of four factors-drivers, vehicles, roads, and environment-on at-fault crash behavior and serious casualties in rural areas. Furthermore, the risk factors for rural and urban areas were comparatively analyzed.

Results: In rural areas, male drivers, drivers without valid licenses, drivers of unsafe vehicles and drivers of trucks are more likely to cause at-fault crashes and cause serious casualties, as are drivers impacted by certain conditions, including sand and gravel roads, mountainous terrain, and nighttime without street lighting. In urban areas, crashes involving sand and gravel roads and single-vehicle crashes have lower casualties, but migrant workers and self-employed individuals are more likely to die or be seriously injured in urban road crashes.

Conclusion: To reduce the impact of road traffic risk factors on at-fault crash behaviors and serious casualties in the rural areas of developing countries, targeted measures should be implemented for drivers, vehicles, and roads. These measures may include focusing on illegal drivers, trucks, and unsafe vehicles for enhanced supervision, as well as prioritizing the construction and improvement in transportation infrastructure such as roads, lighting, and safety mechanisms in rural areas.

Background: Infections caused by multidrug-resistant organisms (MDROs) continue to pose serious challenges for hospital infection control, often resulting in longer hospitalizations, increased patient morbidity, and higher healthcare costs. While time series forecasting has gained traction as a tool for anticipating MDROs trends, there remains a lack of real-world studies comparing the effectiveness of different modeling approaches using hospital-based data.

Objective: This study aimed to evaluate and compare the predictive performance of four time series models-SARIMA, ETS, Prophet, and NNETAR-using monthly MDROs infection data collected from a tertiary hospital in China between 2014 and 2023, with the goal of forecasting trends for 2024.

Methods: Monthly MDROs infection rates from January 2014 to December 2023 were analyzed using R software. Stationarity was assessed through unit root tests, and appropriate differencing was applied as needed. Each model was fitted to the training dataset and used to forecast infection rates for the year 2024. Model accuracy was assessed by comparing forecasted values with actual 2024 data using root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), symmetric mean absolute percentage error (sMAPE), and mean absolute scaled error (MASE).

Results: Among the models, SARIMA produced the most consistent and reliable forecasts (RMSE = 0.0469, MAE = 0.0424, MAPE = 20.74%, sMAPE = 21.27%, MASE = 0.932), with residuals satisfying tests for independence and normality. Although the ETS model achieved lower numerical point errors (RMSE = 0.0367, MAE = 0.0305, MAPE = 14.46%, sMAPE = 14.81%, MASE = 0.670), its residual diagnostics raised concerns regarding robustness. The Prophet (RMSE = 0.0499, MAE = 0.0439, MAPE = 20.41%, sMAPE = 22.15%, MASE = 0.563) and NNETAR (RMSE = 0.0697, MAPE = 30.60%, sMAPE = 30.60%, MASE = 0.072) models captured certain aspects of the data dynamics but showed lower overall robustness compared with SARIMA.

Conclusion: Based on its overall robustness and diagnostic consistency, SARIMA is recommended for short- to medium-term forecasting of MDROs infection trends. The other models, while less reliable on their own, may still be valuable for validating trends and conducting sensitivity analyses to support hospital infection control planning.

Here, we summarize the work that Microsoft's philanthropic Artificial Intelligence (AI) for Good Lab has completed in the realm of promoting public and population health. In particular, after providing examples of how the AI for Good Lab has articulated the value of using AI to improve public and population health, we provide examples and references of the work demonstrating how the Lab has: applied Artificial Intelligence (AI) to improve maternal, fetal, and infant health; leveraged large language models to improve population health; and applied AI to improve rural health and healthcare. We also summarize what we have learned through our work, finding that: getting the question right and ensuring the limitations of any analysis are understood is important; collaboration across public, private, and educational institutions with subject matter experts will be the most effective and efficient way to harness this new technology; and that focusing on metrics that reflect health, and not just the accuracy of the model, is the most impactful way to improve the health of populations, worldwide.

To address health disparities, community engagement (CE) approaches meaningfully and actively partner with affected communities for the long term. While the ultimate goal of CE is to affect population health, intermediate outcomes, like capacity building, can be used to assess progress toward achieving and sustaining health aims. The Diabetes Impact Project - Indianapolis Neighborhoods (DIP-IN) is a CE initiative aimed at reducing diabetes disparities through multi-sector collaboration and resident-driven decision-making. This study evaluates capacity building in DIP-IN through qualitative interviews with 28 partners, including residents, project staff, and organizational leaders. Partners across roles reported increased capacity, including enhanced leadership, professional development, improved data practices, the development of staffing structures, and increased investment in community health. Facilitators included DIP-IN CE principles of long-term commitment, respect, valuing resident expertise, and transparency. Slow progress toward goals was a main barrier. Findings suggest that the project is progressing towards ultimate aims around health equity, with the potential for long-lasting impacts. The study underscores the importance of sustained, resident-driven CE supported by formal structures like steering committees. Further, it demonstrates that capacity building is a critical indicator of progress toward achieving and sustaining health improvement goals. Practical tools are provided to support robust evaluation of capacity building in a variety of settings.