Health Care Science最新文献

Introduction: This study protocol specifies the primary research line and theoretical framework of the 2023 Survey of the Psychology and Behavior of the Chinese Population. It aims to establish a consistent database of Chinese residents' psychological and behavioral surveys through multi-center and large-sample cross-sectional surveys to provide robust data support for developing research in related fields. It will track the public's physical and psychological health more comprehensively and systematically.

Methods: The study was conducted from June 20, 2023 to August 31, 2023, using stratified and quota sampling methods. A total of 150 cities across 800 communities/villages were surveyed, selected from China (Despite extensive coordination, we have been unable to contact our counterparts in the Taiwan region of China to obtain relevant statistical data). The questionnaires were distributed to the public one-on-one and face-to-face by trained surveyors. The questionnaires included basic information about the individual, personal health status, basic information about the family, the social environment in which the individual lives, psychological condition scales, behavioral level scales, other scales, and attitudes towards topical social issues. Supervisors conducted quality control during the distribution process and returned questionnaires, logically checked and cleaned for data analysis.

Discussion: Data collection has been finished, and scientific outputs based on this data will support the development of health promotion strategies in China and globally. In the aftermath of the pandemic, it will guide policymakers and healthcare organizations to improve their existing policies and services to maximize the physical and mental health of the Chinese population.

Trial registration: This study was filed in the National Health Security Information Platform (Record No.: MR-37-23-017876) and officially registered in the China Clinical Trials Registry (Registration No.: ChiCTR2300072573).

Population aging presents a growing societal challenge and imposes a heavy burden on the healthcare system in many Asian countries. Given the limited availability of formal long-term care (LTC) facilities and personnel, family caregivers play a vital role in providing care for the increasing population of older adults. While awareness of the challenges faced by caregivers is rising, discussions often remain within academic circles, resulting in the lived experiences, well-being, and needs of family caregivers being frequently overlooked. In this review, we identify four key priority areas to advance research, practice, and policy related to family caregivers in Asia: (1) Emphasizing family caregivers as sociocultural navigators in the healthcare system; (2) addressing the mental and physical health needs of family caregivers; (3) recognizing the diverse caregiving experiences across different cultural backgrounds, socioeconomic status, and countries of residence; and (4) strengthening policy support for family caregivers. Our review also identifies deficiencies in institutional LTC and underscores the importance of providing training and empowerment to caregivers. Policymakers, practitioners, and researchers interested in supporting family caregivers should prioritize these key areas to tackle the challenge of population aging in Asian countries. Cross-country knowledge exchange and capacity development are crucial for better serving both the aging population and their caregivers.

The COVID-19 pandemic presented unparalleled challenges to prompt and adaptive responses from nations worldwide. This review examines China's multifaceted approach to the crisis, focusing on five key areas of response: infrastructure and system design, medical care and treatment, disease prevention and control, economic and social resilience, and China's engagement in global health. This review demonstrates the effectiveness of a top-down command system at the national level, intersectoral coordination, a legal framework, and public social governance. This study also examines medical care and treatment strategies, highlighting the importance of rapid emergency response, evidence-based treatment, and well-planned vaccination rollout. Further discussion on disease prevention and control measures emphasizes the importance of adaptive measures, timely infection control, transmission interruption, population herd immunity, and technology applications. Socioeconomic impact was also assessed, detailing strategies for disease prevention, material supply, livelihood preservation, and social economy revival. Lastly, we examine China's contributions to the global health community, with a focus on knowledge-sharing, information exchange, and multilateral assistance. While it is true that each nation's response must be tailored to its own context, there are universal lessons to be drawn from China's approach. These insights are pivotal for enhancing global health security, especially as the world navigates evolving health crises.

Background: Pneumothorax is a medical emergency caused by the abnormal accumulation of air in the pleural space-the potential space between the lungs and chest wall. On 2D chest radiographs, pneumothorax occurs within the thoracic cavity and outside of the mediastinum, and we refer to this area as "lung + space." While deep learning (DL) has increasingly been utilized to segment pneumothorax lesions in chest radiographs, many existing DL models employ an end-to-end approach. These models directly map chest radiographs to clinician-annotated lesion areas, often neglecting the vital domain knowledge that pneumothorax is inherently location-sensitive.

Methods: We propose a novel approach that incorporates the lung + space as a constraint during DL model training for pneumothorax segmentation on 2D chest radiographs. To circumvent the need for additional annotations and to prevent potential label leakage on the target task, our method utilizes external datasets and an auxiliary task of lung segmentation. This approach generates a specific constraint of lung + space for each chest radiograph. Furthermore, we have incorporated a discriminator to eliminate unreliable constraints caused by the domain shift between the auxiliary and target datasets.

Results: Our results demonstrated considerable improvements, with average performance gains of 4.6%, 3.6%, and 3.3% regarding intersection over union, dice similarity coefficient, and Hausdorff distance. These results were consistent across six baseline models built on three architectures (U-Net, LinkNet, or PSPNet) and two backbones (VGG-11 or MobileOne-S0). We further conducted an ablation study to evaluate the contribution of each component in the proposed method and undertook several robustness studies on hyper-parameter selection to validate the stability of our method.

Conclusions: The integration of domain knowledge in DL models for medical applications has often been underemphasized. Our research underscores the significance of incorporating medical domain knowledge about the location-specific nature of pneumothorax to enhance DL-based lesion segmentation and further bolster clinicians' trust in DL tools. Beyond pneumothorax, our approach is promising for other thoracic conditions that possess location-relevant characteristics.

Background: The global impact of the highly contagious COVID-19 virus has created unprecedented challenges, significantly impacting public health and economies worldwide. This research article conducts a time series analysis of COVID-19 data across various countries, including India, Brazil, Russia, and the United States, with a particular emphasis on total confirmed cases.

Methods: The proposed approach combines auto-regressive integrated moving average (ARIMA)'s ability to capture linear trends and seasonality with long short-term memory (LSTM) networks, which are designed to learn complex nonlinear dependencies in the data. This hybrid approach surpasses both individual models and existing ARIMA-artificial neural network (ANN) hybrids, which often struggle with highly nonlinear time series like COVID-19 data. By integrating ARIMA and LSTM, the model aims to achieve superior forecasting accuracy compared to baseline models, including ARIMA, Gated Recurrent Unit (GRU), LSTM, and Prophet.

Results: The hybrid ARIMA-LSTM model outperformed the benchmark models, achieving a mean absolute percentage error (MAPE) score of 2.4%. Among the benchmark models, GRU performed the best with a MAPE score of 2.9%, followed by LSTM with a score of 3.6%.

Conclusions: The proposed ARIMA-LSTM hybrid model outperforms ARIMA, GRU, LSTM, Prophet, and the ARIMA-ANN hybrid model when evaluating using metrics like MAPE, symmetric mean absolute percentage error, and median absolute percentage error across all countries analyzed. These findings have the potential to significantly improve preparedness and response efforts by public health authorities, allowing for more efficient resource allocation and targeted interventions.

Background: Frailty in older adults is linked to increased risks and lower quality of life. Pre-frailty, a condition preceding frailty, is intervenable, but its determinants and assessment are challenging. This study aims to develop and validate an explainable machine learning model for pre-frailty risk assessment among community-dwelling older adults.

Methods: The study included 3141 adults aged 60 or above from the China Health and Retirement Longitudinal Study. Pre-frailty was characterized by one or two criteria from the physical frailty phenotype scale. We extracted 80 distinct features across seven dimensions to evaluate pre-frailty risk. A model was constructed using recursive feature elimination and a stacking-CatBoost distillation module on 80% of the sample and validated on a separate 20% holdout data set.

Results: The study used data from 2508 community-dwelling older adults (mean age, 67.24 years [range, 60-96]; 1215 [48.44%] females) to develop a pre-frailty risk assessment model. We selected 57 predictive features and built a distilled CatBoost model, which achieved the highest discrimination (AUROC: 0.7560 [95% CI: 0.7169, 0.7928]) on the 20% holdout data set. The living city, BMI, and peak expiratory flow (PEF) were the three most significant contributors to pre-frailty risk. Physical and environmental factors were the top 2 impactful feature dimensions.

Conclusions: An accurate and interpretable pre-frailty risk assessment framework using state-of-the-art machine learning techniques and explanation methods has been developed. Our framework incorporates a wide range of features and determinants, allowing for a comprehensive and nuanced understanding of pre-frailty risk.

Background: Skin cancer poses a significant global health threat, with early detection being essential for successful treatment. While deep learning algorithms have greatly enhanced the categorization of skin lesions, the black-box nature of many models limits interpretability, posing challenges for dermatologists.

Methods: To address these limitations, SkinSage XAI utilizes advanced explainable artificial intelligence (XAI) techniques for skin lesion categorization. A data set of around 50,000 images from the Customized HAM10000, selected for diversity, serves as the foundation. The Inception v3 model is used for classification, supported by gradient-weighted class activation mapping and local interpretable model-agnostic explanations algorithms, which provide clear visual explanations for model outputs.

Results: SkinSage XAI demonstrated high performance, accurately categorizing seven types of skin lesions-dermatofibroma, benign keratosis, melanocytic nevus, vascular lesion, actinic keratosis, basal cell carcinoma, and melanoma. It achieved an accuracy of 96%, with precision at 96.42%, recall at 96.28%, f ₁ score at 96.14%, and an area under the curve of 99.83%.

Conclusions: SkinSage XAI represents a significant advancement in dermatology and artificial intelligence by bridging gaps in accuracy and explainability. The system provides transparent, accurate diagnoses, improving decision-making for dermatologists and potentially enhancing patient outcomes.

We summarized the unique challenges that China faced in digital health due to its large population, regional disparities, and uneven distribution of medical resources. Under the guidance of the Global Initiative on Digital Health (GIDH) released by WHO, we proposed corresponding solutions that address infrastructure, data, terminology, technology and security.