Background: Recurrent pregnancy loss undermines the physical and mental health of women. Recent randomized controlled trials have reported some effects of traditional Chinese medicine (TCM); however, whether various TCM methods have different effectiveness remains unclear. Objective: To comprehensively evaluate the efficacy and adverse events of TCM for patients with RPL and to explore whether various TCM methods have different effectiveness. Methods: Ten databases were searched up to May 27, 2024. The risk of bias was assessed using the RoB2 tool. The certainty of the evidence was evaluated using the grading of Recommendations, Assessment, Development, and Evaluation tool. Pairwise and network analyses were conducted using Stata 18.0. Results: A total of 47 randomized controlled trials enrolling 6678 women with RPL were included. Pairwise analysis showed that use of TCM had a significantly lower miscarriage rate (RR 0.50 [95% CI 0.45, 0.55]), lower preterm birth rate (RR 0.81 [95% CI 0.67, 0.98), and lower adverse event rate (RR 0.46 [95% CI 0.37, 0.58]). Moreover, use of TCM was associated with a higher alive-fetus rate (RR 1.21 [95% CI 1.15, 1.26]), live-birth rate (RR 1.20 [95% CI 1.15, 1.25]), and full-term rate (RR 1.37 [95% CI 1.23, 1.53]) compared with nonuse of TCM. Network analysis demonstrated that Bushenshugan combined with conventional Western medicine was ranked the best for the reduction of miscarriage rate. Discussion: Use of TCM is more likely to improve pregnancy outcomes and reduce adverse events compared with nonuse of TCM in patients with RPL. Different TCM methods have differences in reducing the miscarriage rate. The Bushenshugan method might be a potential optimal TCM therapy, but more high-quality evidence is needed to further validate and evaluate the efficacy and safety.

The major obstacle to optimizing the design of rare disease coverage is the fragmented decision-making process among medical services, pharmaceuticals, and medical insurance departments. There is an urgent need to realize data sharing and digital empowerment, as well as to adopt top-level design and systematic decision-making. It is also crucial to establish mechanisms, facilitated by digital intelligence, for sharing power and responsibilities, and assessing rewards and punishments. Furthermore, there is an urgent need to incorporate the theories of collaborative governance, digital governance, and the full life cycle into the entire process, which includes patient classification, diagnosis and treatment, medical assistance, medication protection, and health insurance fund management for rare diseases. This integration aims to provide theoretical reference for the effective linkage of medical services, pharmaceuticals, and medical insurance, and to improve the efficiency and equity of resource allocation in the public sector.

Objective:To understand the characteristics of faculty in high-level public health schools in China, and analyze the differences in age, area and school level.Methods:Based on the internet information, the faculty information of 18 high-level public health schools was collected for a descriptive analysis on faculty characteristics.Results:There were 1 642 faculty members in the schools of public health in China, in whom 51.8% were women, 92.8% had doctorate, 32.4% had postdoctoral experience and 56.8% were former students staying to teach. The average age of the faculty members was (45.6±9.8) years. Meanwhile the top three study subjects were epidemiology and health statistics (31.0%), occupational health and environmental sanitation (16.5%), and health toxicology (16.3%). In the faculty members aged >40 years, 90.2% had doctorate, 62.6% were former students staying to teach, and 24.7% had no educational background of public health. The proportions of faculty members aged ≤40 years in the three groups mentioned above were 98.2%, 45.8% and 39.1% respectively. In terms of study subject, big data study were mainly conducted in the schools with top subject ranking and the schools in developed areas.Conclusions:The public health faculty was characterized by cross education background and high capability. The study subjects and sub-disciplines varied with schools and areas.

Objective:To comprehensively evaluate the application of Common Data Model (CDM) of Observational Medical Outcomes Partnership (OMOP) in China, and provide reference for the implementation of data standardization and evidence sharing in China.Methods:PubMed, Embase, Web of Science, CNKI, VIP, WanFang and SinoMed databases were used for literature retrieval to collect the research papers of OMOP CDM application for data standardization in China until March 15, 2023. The information about institutions, types and numbers of patients were extracted.Results:A total of 14 research papers, including 9 in English and 5 in Chinese, were selected. The research papers published since 2018 were collected, which focused on patients with hypertension, diabetes, and depression. A total of 12 institutions or platforms transformed data into OMOP CDM. Jiangsu Provincial People's Hospital was the first one to apply the CDM and demonstrated its feasibility in China. Additionally, the regional information system in Yinzhou District of Ningbo, Zhejiang Province, standardized the multi-dimensional data of patients with diabetes and hypertension. Based on this platform, a series of prediction models for complications in patients with diabetes were constructed. Another major database in Beijing Anding Hospital applied OMOP CDM to analyze the characteristics of patients with late-life depression and dementia.Conclusions:This study analyzed the application of OMOP CDM in China. Through in-depth analysis of specific cases, the study provided guidance for the future cross-regional evidence sharing and collaboration.

Objective:To systematically review the progress in the method development and application of distributed learning in the estimation of epidemiological effect and provide methodological reference for multi-center studies.Methods:We conducted a literature retrieval for English papers published up to December 31, 2023 by using keywords of "health/medical big data" and "distributed/federated learning". After consulting experts, we set criteria of paper inclusion and exclusion and created a framework for data extraction. We collected information about basic study details, including method, application, and evaluation. Two researchers independently screened the papers and extracted information. We used EndNote 20 for the management of literatures and EpiData for the management of data.Results:A total of 3 444 papers were collected, and 29 papers were included in the final analysis. Most of the papers (25, 86.2%) were published in or after 2019, and the papers were mainly from the United States (21/29, 72.4%). For the estimation of epidemiological effects, 22 distributed learning methods had been developed, including methods for logistic regression (8), Cox regression (8), Poisson regression (2), and generalized linear mixed model (GLMM) (4), as well as three platforms for distributed analysis (VLP, Vantage6, AusCAT). The 29 papers described 45 applications, with 20 (44.4%) focusing on the establishment of prediction model and 25 (55.6%) on association analysis. Importantly, except for GLMM, current distributed learning methods can estimate effects with little bias in 1-3 rounds of communication. These methods show less bias compared with meta-analysis, especially in the address of data heterogeneity and rare outcomes. However, less studies examined how differences in data structure and sparse data affect results, an area that requires further research.Conclusion:While distributed learning shows promise in epidemiological effect estimation, it is still in early development, requiring further research on data heterogeneity handling and communication efficiency improvement.

Objective:To analyze the current management status of real-world studies (RWS) in the medical institutions in China and suggest improvement focus for the management optimization.Methods:Surveys were conducted in 81 medical institutions nationwide. Convenience sampling was used to recruit survey subjects, and data were collected through self-administered questionnaires, followed by statistical analysis using descriptive methods.Results:The survey results indicated that 92.6% (75/81) of the medical institutions surveyed had undertaken RWS projects, with electronic medical records being the primary data source (89.3%, 67/75). Retrospective and prospective observational studies were the main types of study designs. Additionally, 96.3% (78/81) of the research subjects indicated that their medical institution expressed willingness to participate in or undertake RWS projects in the future. In terms of management, all types of RWS projects were managed by clinical trial center (24.0, 18/75), but differences existed in the management practices among medical institutions. Moreover, the challenges in data quality and standardization, study design and staff training, data and privacy protection and information technology support appeared in the management of RWS projects.Conclusions:It suggests to optimize the management processes of RWS projects in medical institutions and improve relevant laws and regulations to promote the development of RWS in China.

In the past decade, real-world data (RWD) research has undergone significant transformations due to data aggregation and processing technologies. However, there is still a lack of consensus regarding the scope of RWD applications and the value of real-world evidence (RWE). This study briefly outlined the origins of the concept of RWD study and its early research scope to promote further development in this area. We also reviewed the understanding of RWD applications and research models from the five perspectives of healthcare professionals, medical institutions, decision-making departments, cross-regional cooperation model, and the practice of the One-Health model. Finally, we systematically summarized the renewed understanding of the value of RWE while looking ahead to the challenges and future developments in this field.

Evidence-based public health, as the forefront of modern public health practice, has increasingly important in public health field. However, a significant gap remains between the available evidence and its practical application. Effectively disseminating and implementing evidence-based public health practice in real-world settings has become a key challenge in contemporary public health research. In this context, Implementation Science has emerged as a vital discipline. This paper explores the critical role of Implementation Science in public health, reviews the origins and core components of the Consolidated Framework for Implementation Research (CFIR), and analyzes the current application of CFIR in public health through bibliometric methods. Additionally, it discusses specific examples to further elucidate the steps involved in using the CFIR and its application contexts. The findings indicate that since 2015, research on CFIR in public health has progressively increased, showing a continuous upward trend. CFIR applications mainly address context-specific facilitators, health decision-making, barrier and facilitator identification, and community-based participatory evaluation, predominantly employing qualitative and mixed-methods research. This paper not only reviews and analyzes the current use of CFIR in public health but also provides a detailed discussion on its application. The goal is to offer valuable insights for the development of Implementation Science research within China's public health sector.

With the rise of personalized medicine and the rapid development of big data technology, medical prediction models have become increasingly important in disease diagnosis, prognosis assessment, and risk stratification. However, class imbalance is a common problem in medical data, which can result in models being overly trained toward the majority class rather than the minority class, influencing the detection power and clinical application value. This paper systematically summarizes traditional methods in addressing class imbalance, including data pre-processing and algorithm level strategies, and introduces the applications of new technologies such as generative adversarial networks and transfer learning and suggests key considerations and potential research focus for addressing class imbalance to provide reference for researchers to select appropriate strategies.

Objective:To describe the hotspots and application trends of artificial intelligence (AI) in epidemiology in the past decade and analyze its advantages and challenges.Methods:The literatures with AI and epidemiology related keywords were systematically retrieved from Web of Science and China National Knowledge Infrastructure from 2014 to 2024. CiteSpace was used for bibliometric analysis of publication volume, keyword co-occurrence, clustering, emergence and cited literature co-occurrence analysis.Results:A total of 5 389 English papers and 1 659 Chinese papers were included, showing an increasing publication trend. High-frequency Chinese keywords included prediction, influencing factor, and machine learning, while English keywords frequently used were machine learning, prediction, and artificial intelligence. The Chinese keywords formed 14 clusters such as epidemiological characteristic, dietary pattern, and elderly individual, and the English keywords formed 21 clusters including prediction model, risk factor, and adult. In international studies, health policy, COVID-19, and digital health were the emerging frontier keywords. Eleven core papers were selected, covering key areas like traffic accident risk assessment, public health big data application, and deep learning in medical diagnosis.Conclusions:This study systematically summarized the research hotspots and development trends of AI applications in epidemiology over the past decade by using bibliometric methods, which indicated that current AI-based epidemiological studies are still in the exploratory phase, with the coexisting of both advantages and challenges. Continued attention should be paid to the future development of this field.