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.

This paper briefly introduces the definition, classification and significance of effect modification in epidemiological studies, summarizes the difference between effect modifier and confounders, and analyze the influence as well as the role of effect modification in epidemiological studies and Meta-analysis. In this paper, the possible scenarios of effect modification and related analysis strategy in Meta-analysis are indicated by graphics, aiming to arouse researchers' attention to effect modification. This paper also demonstrates how to identify and deal with effect modification in Meta-analysis through a study case of "Efficacy of sodium-glucose cotransporter 2 inhibitors in patients with type 2 diabetes", and shows the analysis process and interpretation of results of subgroup analysis and Meta-regression methods respectively. The advantages and disadvantages of these two methods are summarized to provide reference for the method selection of future research.

This paper briefly introduces the characteristics, research significance, and global reporting status of effect modification in network Meta-analysis, demonstrates the heterogeneity caused by effect modification in network Meta-analysis, and emphasizes the importance of exploring effect modification in network Meta-analysis. This paper also summarizes the normalized description and analysis strategies of effect modification in network Meta-analysis. Finally, by the case of "comparison of efficacy of three new hypoglycemic drugs in reducing body weight in type 2 diabetes patients", this paper demonstrates the realization of subgroup analysis and network Meta-regression in exploring effect modification, summarizes the advantages and disadvantages of the two methods, to provide references for future researchers.

Objective:Differences between randomized controlled trial (RCT) results and real world study (RWS) results may not represent a true efficacy-effectiveness gap because efficacy-effectiveness gap estimates may be biased when RWS and RCT differ significantly in study design or when there is bias in RWS result estimation. Secondly, when there is an efficacy- effectiveness gap, it should not treat every patient the same way but assess the real-world factors influencing the intervention's effectiveness and identify the subgroup likely to achieve the desired effect.Methods:Six databases (PubMed, Embase, Web of Science, CNKI, Wanfang Data, and VIP) were searched up to 31 st December 2022 with detailed search strategies. A scoping review method was used to integrate and qualitatively describe the included literature inductively. Results:Ten articles were included to discuss how to use the RCT research protocol as a template to develop the corresponding RWS research protocol. Moreover, based on correctly estimating the efficacy-effectiveness gap, evaluate the intervention effect in the patient subgroup to confirm the subgroup that can achieve the expected benefit-risk ratio to bridge the efficacy-effectiveness gap.Conclusion:Using real-world data to simulate key features of randomized controlled clinical trial study design can improve the authenticity and effectiveness of study results and bridge the efficacy-effectiveness gap.

This paper briefly introduces the unique advantages, overall analysis ideas and existing analysis methods of individual patient data Meta-analysis in terms of effect modification. In addition to Meta-regression and subgroup analysis, this paper also introduces the analysis methods based on part of individual patient data integrated with aggregated data and summarizes the current reporting of the above mentioned methods. In addition, the application and results interpretation of the above mentioned methods in individual patient data Meta-analysis are presented in this paper by taking "Effects of sodium-glucose cotransporter 2 inhibitors on SBP in patients with type 2 diabetes" as an example and by introducing their advantages and limitations.

Objective:Randomized controlled trials (RCT) usually have strict implementation criteria. The included subjects' characteristics of the conditions for the intervention implementation are quite different from the actual clinical environment, resulting in discrepancies between the risk-benefit of interventions in actual clinical use and the risk-benefit shown in RCT. Therefore, some methods are needed to enhance the extrapolation of RCT results to evaluate the real effects of drugs in real people and clinical practice settings.Methods:Six databases (PubMed, Embase, Web of Science, CNKI, Wanfang Data, and VIP) were searched up to 31 st December 2022 with detailed search strategies. A scoping review method was used to integrate and qualitatively describe the included literature inductively. Results:A total of 12 articles were included. Three methods in the included literature focused on: ①improving the design of traditional RCT to increase population representation; ②combining RCT Data with real-world data (RWD) for analysis;③calibrating RCT results according to real-world patient characteristics.Conclusions:Improving the design of RCT to enhance the population representation can improve the extrapolation of the results of RCT. Combining RCT data with RWD can give full play to the advantages of data from different sources; the results of the RCT were calibrated against real-world population characteristics so that the effects of interventions in real-world patient populations can be predicted.

This study aims at examining the application and development of digital teaching materials in the field of epidemiology, encompassing both China and international contexts. The research involved conducting search on websites and literature databases to assess the status of digital teaching materials in epidemiology, nationally and internationally. At present, in China, digital teaching materials used in epidemiology are primarily presented in the form of printed books with added QR codes, providing teaching resources such as videos and exercises. However, issues with the level of interactivity have been identified. In foreign countries, with stronger emphasis placed on personalization, interactivity, and the use of rich media technologies in the digital teaching materials, epidemiologically. Enhanced digitization regarding materials and learning outcomes is achieved through features such as real-time notes, interactive animations, and quizzes. These approaches are considered worth considering for adoption. This study provides valuable insights for the digital transformation of epidemiology education.

Objective:To construct a diabetes foot prediction model for adult patients with type 2 diabetes based on retrospective cohort study using data from a regional health data platform.Methods:Using Yinzhou Health Information Platform of Ningbo, adult patients with newly diagnosed type 2 diabetes from January 1, 2015 to December 31, 2022 were included in this study and divided randomly the train and test sets according to the ratio of 7∶3. LASSO regression model and bidirectional stepwise regression model were used to identify risk factors, and model comparisons were conducted with net reclassification index, integrated discrimination improvement and concordance index. Univariate and multivariate Cox proportional hazard regression models were constructed, and a nomogram plot was drawn. Area under the curve (AUC) was calculated as a discriminant evaluation indicator for model validation test its calibration ability, and calibration curves were drawn to test its calibration ability.Results:No significant difference existed between LASSO regression model and bidirectional stepwise regression model, but the better bidirectional stepwise regression model was selected as the final model. The risk factors included age of onset, gender, hemoglobin A1c, estimated glomerular filtration rate, taking angiotensin receptor blocker and smoking history. AUC values (95% CI) of risk outcome prediction at year 5 and 7 were 0.700 (0.650-0.749) and 0.715(0.668-0.762) for the train set and 0.738 (0.667-0.801) and 0.723 (0.663-0.783) for the test set, respectively. The calibration curves were close to the ideal curve, and the model discrimination and calibration powers were both good. Conclusions:This study established a convenient prediction model for diabetic foot and classified the risk levels. The model has strong interpretability, good discrimination power, and satisfactory calibration and can be used to predict the incidence of diabetes foot in adult patients with type 2 diabetes to provide a basis for self-assessment and clinical prediction of diabetic foot disease risk.

Objective:To systematically review the progress, advantages, disadvantages, precautions and future trends of virtual simulation technology used in epidemiology teaching.Methods:A systematical literature retrieval was conducted by using PubMed, Web of Science, Embase, China National Knowledge Infrastructure, Wanfang Data and VIP Paper Check System with key words of epidemiology, teaching and virtual simulation, and the literatures included were screened and classified with narrative integration method. Chinese virtual simulation teaching platforms were used to select the literatures about existing epidemiology virtual simulation teaching projects for integration and analysis.Results:A total of 22 articles were included (7 in Chinese and 15 in English), most of which were teaching projects for students majoring in Public Health. We also found 24 national first-class courses and 21 provincial first-class courses in virtual simulation of epidemiology in China. The application of virtual simulation technology in epidemiology education is still in its infancy, and the interaction degree is mostly moderate. It is mainly used in three scenarios: improving the visualization degree of complex concepts and structures, training the operational skills through low-risk and low-cost virtual environment, serving as an effective supplement to the teaching of epidemiological field investigation and response to public health emergencies. In terms of effect, it is conducive to students' understanding of epidemiology related phenomena and principles, and has the advantages of breaking through time and space constraints, reducing teaching costs and risks, improving students' attention and satisfaction and so on. However, it also faces the lack of foundation in the initial stage, and some students have problems such as psychological and physiological discomfort. In the future, we need to optimize the development process, program design and so on. At the same time, we should conduct more in-depth research on cost calculation, effect evaluation and curriculum integration.Conclusions:The application of virtual simulation technology in epidemiology education is an important part of training high-level applied public health talents. We encourage to actively carry out virtual simulation teaching in epidemiology, and train public health talents with Chinese characteristics.

Traditional evidence-based medicine has been essential in medical practice and health decision-making. However, it has also continuously exposed shortcomings such as low efficiency in evidence generation, narrow scope of coverage, and imperfect integration strategies, making it challenging to serve clinical diagnosis and treatment and regulatory decision-making. Therefore, it is urgent to adapt to the development of cutting-edge technology and to expand and improve the concept of evidence-based medicine. Deep evidence-based medicine proposed in 2023 aims to advocate the innovative use of the latest artificial intelligence and natural language processing technologies, comprehensively expanding the breadth, depth, and integrability of evidence and improving the efficiency of evidence generation and integration. Building a new generation of evidence-based decision-making ecosystems based on deep evidence-based medicine has broad prospects for practical application. It can promote the development of evidence retrieval, generation, integration, dissemination, transformation, and application, deeply explore imaging, multi-omics, and real-world data to increase the utilization potential of real-world evidence, establish dynamic literature management platforms and decision support tools, reduce resource waste, and promote evidence flow. Utilizing this system can help obtain individual-centered comprehensive clinical evidence and play a significant role in talent training, reforming evidence-based teaching, popularizing science, and ultimately promoting the goal of "One Health".