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.

Standardized research reporting is crucial for the translation of pharmacoepidemiology research findings,and visual reporting can significantly enhance the clarity,understandability,and transparency of research results.Based on the Guide on Methodological Standards in Pharmacoepidemiology(2nd edition),this article systematically explains the key points for writing each component of a research report(including title,abstract,introduction,research methods,research results,discussion and conclusions,acknowledgments,conflict of interest statement,and references).This article also summarizes recognized international and domestic standards for pharmacoepidemiology research reporting,providing a reference for researchers.Furthermore,real-world cases will be used to demonstrate common forms of visualized reports and their interpretation methods.Finally,it further explores strategies for communicating research results.This study aims to provide pharmacoepidemiology researchers with detailed guidance on visually presenting research results and writing high-quality research reports,thereby enhancing the integrity and impact of their research.

The emergence of artificial intelligence(AI)has had a significant impact on medical research and practice,both in terms of the number of studies and research paradigms,and has become an important tool for the development of pharmacoepidemiology.However,traditional AI has faced many challenges,while facilitating pharmacoepidemiology research,such as complex data processing,difficulty in identifying drug exposures and potential outcomes,and time-consuming and laborious study design and implementation.The rapid development of generative AI,represented by large language models(LLMs),has demonstrated a unique potential to enhance research efficiency,shift research paradigms,and facilitate knowledge discovery.LLMs are equipped with natural language understanding and generation capabilities.Through deep mining of multi-dimensional data resources,LLMs can quickly and accurately extract,analyze,summarize,and present the required information,which can not only help drug discovery,drug repurposing,pharmacovigilance and other pharmacoepidemiological tasks,but also provide powerful support for the whole process of research protocol design,data analysis,result interpretation and paper publication.Driven by LLMs,pharmacoepidemiology research is gradually moving into a new stage based on big data and automated analysis.Of course,LLMs also have problems of data bias,"illusion"of results,and ethical and legal regulation.By strengthening interdisciplinary cooperation,establishing a standardized evaluation system,improving ethical and regulatory guidance,enhancing data quality,strengthening practitioner training and capacity building,and promoting human-machine collaborative research modes,it is expected that the potential of LLMs in pharmacoepidemiology will be fully released,and it will provide a more scientific,rapid,and efficient technological support for drug regulation and public health decision-making.

Appropriate data sources are the cornerstone of pharmacoepidemiological research.Based on the Guide on Methodological Standards in Pharmacoepidemiology in China(2nd edition),hereinafter referred to as"the Guideline(2nd edition)",this article provides an in-depth interpretation of the classification and selection of data sources in pharmacoepidemiological research.It begins with a brief overview of the evolution of data sources in pharmacoepidemiological studies,followed by a detailed analysis of the definitions,applicable scopes,conditions of use,and strengthens and limitations of primary and secondary data sources.It further introduces the characteristics of common data sources and relevant classic domestic and international databases.Subsequently,integrating aspects of the Guideline(2nd edition),including research questions,study design,study population,sample size,exposure or intervention,outcomes,and covariates,the article discusses the criteria and strategies for choosing data sources in pharmacoepidemiological research.Ultimately,a data source selection methodology is established based on clear classification,guided by research questions,tailored to research methods,and supported by data quality,aiming to promote the development of high-quality pharmacoepidemiological research in China.

Pharmacoepidemiology is a scientific discipline that applies epidemiological principles and methods to study the utilization and effects of pharmaceuticals in populations.It plays a crucial role in promoting rational drug use and improving public health outcomes.High-quality pharmacoepidemiological research relies on rigorous methodological frameworks.Chinese Pharmaceutical Association(CPA)has officially initiated the development/revision of the of the Guide on Methodological Standards in Pharmacoepidemiology in China(2nd edition)in 2024.This revision project was supported by the Pharmacoepidemiology Committee of CPA,and was led by the Department of Epidemiology and Biostatistics at Peking University School of Public Health,which provided methodological guidance,in collaboration with multidisciplinary experts from across the country.Building on the experience of the 1st edition's development,the working group conducted a qualitative systematic review,to extract key information from global pharmacoepidemiology-related guidelines,laying the foundation for the development/revision proposals of the 2nd edition.Subsequently,through two rounds of Delphi method expert consultations and qualitative interviews,alongside extensive discussions within the working group,the draft revision of the guideline was created.Finally,through an expert consensus meeting using the Nominal Group Technique(NGT),a consensus was reached,resulting in a methodological guideline that integrates international perspectives with local applicability for pharmacoepidemiological research in China.The 2nd edition consists of 41 entries,including 8 primary entries,20 secondary entries,and 13 tertiary entries.It comprehensively covers key aspects such as study protocol development,study implementation,adverse event reporting,research report writing,and dissemination of study findings.Additionally,it addresses specific application scenarios and topics,as well as the application of artificial intelligence and real-world studies.

Pharmacoepidemiology is an interdisciplinary discipline that applies epidemiological research methods to evaluate the application and effect of drugs within population.Standardizing pharmacoepidemiological research methods is crucial for ensuring the quality of research and promoting the development of the discipline.The Pharmacoepidemiology Professional Committee of Chinese Pharmaceutical Association developed the Guide on Methodological Standards in Pharmacoepidemiology(1st edition),in 2019,but has not been updated in over five years.Other,countries/regions such as Europe,America,Japan and South Korea have made a lot of progress in developing and updating the guidelines.Therefore,the rapid development of pharmacoepidemiology in our country and the growing need to align with international standards make it essential to update the guidelines.The second edition of the guidelines was developed through a process that included systematic reviews,practical surveys,and multidisciplinary collaboration.Compared with the first edition,the updated version has made many important changes in research design,data sources,specific application scenarios and more,and added new contents such as the application of artificial intelligence.The purpose of this paper is to emphasize the necessity of updating the guidelines,selecting important changes and formulating interpretation plans.It aims to provide references for relevant professionals to comprehensively understand and apply the new guidelines,and to promote the standardized development and quality of pharmacoepidemiologic research in our country,further advancing the field.

Objective To systematically review the safety of disialoganglioside(GD2)targeted therapies in the treatment of pediatric neuroblastoma(NB).Methods PubMed,Embase,Cochrane Library,CNKI,WanFang Data,SinoMed databases and ClinicalTrials.gov clinical trial registration system were electronically searched to collect clinical studies of GD2-targeted therapies for pediatric NB from inception to November 30,2024.Two reviewers independently screened the literature,extracted data and assessed the risk of bias of the included studies.Meta-analysis was performed by using R 4.3.1 software.Results A total of 21 studies involving 2,438 patients were included.Meta-analysis results showed that the overall incidence(95％CI)of serious adverse drug events(SADEs)in GD2-targeted therapies for pediatric NB was 51.67％(35.43％to 67.92％).The serious fever and alanine aminotransferase(ALT)increase were common with the incidence(95％CI)of 7.80％(0.80％to 18.85％)and 5.10％(2.02％to 10.55％)respectively.The incidences(95％CI)of grade 3 to 5 adverse drug reactions(ADRs):fever 18.81％(8.00％to 29.61％),pain 30.32％(16.21％to 44.44％),hypokalemia 18.96％(6.44％to 31.49％),ALT increased 10.41％(2.96％to 17.86％),hypotension 8.48％(3.78％to 13.18％),anemia 16.62％(5.97％to 27.26％),platelet count decreased 16.46％(5.55％to 27.37％),lymphocyte count decreased 10.42％(0.97％to 26.09％),neutrophil count decreased 13.37％(3.12％to 23.62％).Among the intervention subgroups,the incidences of grade 3 to 5 ADRs were relatively low in dinutuximab β group and GD2-chimeric antigen receptor T cells(GD2 CART)group.Conclusions The incidences of SADEs and grade 3 to 5 ADRs are high in pediatric NB patients receiving GD2-targeted therapy,which should be paid attention to.The safety profile of GD2 targeted therapies under development for pediatric NB are even better than those of GD2 targeted drugs already on the market,especially GD2 CART,which shows good safety potential.

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.

The selection of an appropriate control group is a critical component of pharmacoepidemiologic research.This article provides an interpretation of the control selection methods outlined in the Guide on Methodological Standards in Pharmacoepidemiology in China(2nd edition).According to the 2nd edition,studies are categorized into interventional and non-interventional research.In interventional research,control group options include placebo controls,no-treatment controls,active controls,and dose-response controls.For non-interventional research,the gold standard design is the active comparator new user(ACNU)design.When the ACNU design is not feasible,alternative control group strategies should be selected based on the research objective,data sources,exposure characteristics,and potential confounding.These alternatives may include non-user comparators,prevalent user comparators,self-controlled comparators,and external controls.Finally,this article compares the applicability,strengths,and limitations of various control group types.It aims to provide methodological guidance for the scientific selection of control groups in pharmacoepidemiologic studies and to support the conduct of high-quality research.

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.