[Development of a prediction model for the incidence of type 2 diabetic kidney disease and its application based on a regional health data platform].

Objective: To construct a risk prediction model for diabetes kidney disease (DKD). Methods: Patients newly diagnosed with type 2 diabetes mellitus (T2DM) between January 1, 2015, and December 31, 2022, were selected as study subjects from the Yinzhou Regional Health Information Platform in Ningbo City. The Lasso method was used to screen the risk factors, and the DKD risk prediction model was established using Cox proportional hazard regression models. Bootstrap 500 resampling was applied for internal validation. Results: The study included 49 706 subjects, with an median (Q1, Q3) age of 60.00 (50.00, 68.00) years old, and 55% were male. A total of 4 405 subjects eventually developed DKD. Age at first diagnosis of T2DM, BMI, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, past medical history (hyperuricemia, rheumatic diseases), triglycerides, and estimated glomerular filtration rate were included in the final model. The final model's C-index was 0.653, with an average of 0.654 after Bootstrap correction. The final model's area under the receiver operating characteristic curve for predicting 4-year, 5-year, and 6-year was 0.657, 0.659, and 0.664, respectively. The calibration curve was closely aligned with the ideal curve. Conclusions: This study constructed a DKD risk prediction model for newly diagnosed T2DM patients based on real-world data that is simple, easy to use, and highly practical. It provides a reliable basis for screening high-risk groups for DKD.

[Development and application of a prediction model for incidence of diabetic retinopathy in newly diagnosed type 2 diabetic patients based on regional health data platform].

Objective: To develop a prediction model for the risk of diabetic retinopathy (DR) in patients with newly diagnosed type 2 diabetes mellitus (T2DM). Methods: Patients with new diagnosis of T2DM recorded in Yinzhou Regional Health Information Platform between January 1, 2015 and December 31, 2022 were included in the study. The predictor variables were selected by using Lasso-Cox proportional hazards regression model. Cox proportional hazards regression models were used to establish the prediction model for the risk of DR. Bootstrap method (500 resamples) was used for internal validation, and the performance of the model was assessed by C-index, the receiver operating characteristic curve and area under the curve (AUC), and calibration curve. Results: The predictor variables included in the final model were age of T2DM onset, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, estimated glomerular filtration rate, and history of lipid-lowering agent and angiotensin converting enzyme inhibitor uses. The C-index of the final model was 0.622, and the mean corrected C-index was 0.623 (95%CI: 0.607-0.634). The AUC values for predicting the risk of DR after 3, 5, and 7 years were 0.631, 0.620, and 0.624, respectively, with a high degree of overlap of the calibration curves with the ideal curves. Conclusion: In this study, a simple and practical risk prediction model for DR risk prediction was developed, which could be used as a reference for individualized DR screening and intervention in newly diagnosed T2DM patients.

[Identification process of time-related bias in pharmacoepidemiologic research based on a scoping review].

Objective: To summarize the characteristics of pharmacoepidemiologic research involving diabetes patients, which were published in recent years, in terms of study design and analysis, and develop an identification process for time-related biases in pharmacoepidemiologic research. Methods: PubMed, Embase, CNKI and Wanfang were used for a systematical literature retrieval of relevant study papers published between January 1,2012 and September 26, 2022. Literature screening and data extraction were performed independently by two reviewers. Based on the mechanisms of different time-related biases and the characteristics of included study papers in terms of study design and analysis methods, an identification process for all types of time-related biases was developed. Results: A total of 281 study papers were included, of which 58 (20.64%) specifically mentioned certain time-related biases considered in the study. Based on the scoping review results, key points to identify time-related biases were summarized, involving data source, study design, control selection, comparator drugs, matching the duration of diabetes, identification of the washout period, identification of the induction/latency period, identification of the initiation of follow-up, identification of time window, statistical analysis methods, sensitivity analysis, and other design and analytical elements, in the identification process for time-related biases in pharmacoepidemiologic research. Conclusions: Time-related biases are common in pharmacoepidemiologic research and might significantly impact the study results. Based on scoping review results, this study further developed an identification process for time-related biases in pharmacoepidemiologic research, which will help researchers identify and avoid time-related biases and improve the reliability of related evidence in pharmacoepidemiologic research.

[Development of a prediction model for incidence of diabetic foot in patients with type 2 diabetes and its application based on a local health data platform].

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.

[Application of virtual simulation technology in epidemiology education: a systematic review].

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.

[Epidemioloical characteristics and economic burden analysis of palmoplantar pustulosis in urban areas of China].

Objective: To analyze the epidemiological characteristics and economic burden of palmoplantar pustulosis (PPP) in China. Methods: A population-based retrospective study was conducted using the data from China's Urban Basic Medical Insurance data from January 1, 2012, to December 31, 2016. International Classification of Diseases code and diagnoses in Chinese for PPP were used to identify cases and estimate the prevalence, incidence, and cost. Subgroup analyses were performed according to age and sex, and sensitivity analyses were conducted to evaluate the robustness of the results. Age-adjusted prevalence rates were calculated based on the 2010 national census data. Results: The crude prevalence and incidence rate of PPP in 2016 were 2.730/100 000 (95%CI: 2.218/100 000-3.242/100 000) and 1.556/100 000 (95%CI: 1.154/100 000-1.958/100 000), and the prevalence rate of females (2.910/100 000) was higher than that of males (2.490/100 000, χ2=97.48, P=0.001). The incidence rate of females (1.745/100 000) was also higher than that of males (1.418/100 000, χ2=85.02, P=0.001). The age peak of incidence and prevalence of patients with PPP was in the 30-39-year age group and a small peak existed in the 0-3-year age group among people under 20 years old. From 2012 to 2016, the average number of visits was (2.44±0.04) per patient, and the total per-capita cost per year was (982.40±39.19) yuan. Conclusion: In 2016, the prevalence and incidence rate of PPP in China were higher in females than in males, and the highest age peak was in the 30-39-year age group.

[Progress in methodological research on bridging the efficacy-effectiveness gap of clinical interventions(2): to improve the extrapolation of efficacy].

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 31st 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.

[Application and advancement of digital teaching materials in teaching epidemiology].

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.

[Analysis methods and case analysis of effect modification (3): effect modification in individual patient data Meta-analysis].

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.

[Analysis methods and case analysis of effect modification (2): effect modification in network Meta-analysis].

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.

[Progress in methodological research on bridging the efficacy-effectiveness gap of clinical interventions (1): to improve the validity of real-world evidence].

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 31st 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.

[Analysis methods and case analysis of effect modification (1): effect modification in epidemiology and traditional Meta-analysis].

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.

[Research on indicators of ideological and political resource database construction for curriculum of "Epidemiology"].

Objective: To construct indicators of the ideological and political resource database construction for the curriculum of "Epidemiology". Methods: Two rounds of expert consultation were conducted in 15 experts from 4 universities and 1 textbook publishing house using the Delphi method, and the importance and feasibility scores of the indicators were calculated with the degree of concentration and coordination of experts' opinions. Results: In the two rounds of consultation, the experts' positive coefficient of the two questionnaires were both 100.00% (15/15), the authoritative coefficients of experts were both 0.83, and the Kendall's W was 0.27 (P<0.05) and 0.33 (P<0.05), respectively. Consensus was reached on 4 primary indicators and 31 secondary indicators. Conclusion: The process of this study is scientific, and the indicators for the construction of ideological and political resource database for the curriculum of "Epidemiology" are authoritative, which can promote the establishment of ideological and political resource database for the curriculum of "Epidemiology".

[Introduction of a tool to assess Risk of Bias in Non-randomized Studies-of Exposure (2022)].

This article introduces the contents of the latest edition Risk of Bias in Non-randomized Studies-of Exposure (ROBINS-E) published in June 2022 [ROBINS-E (2022)], and gives some examples about its usage. ROBINS-E is a tool for assessing the risk of bias in non-randomized studies-of exposure. Compared with ROBINS-E (2019), ROBINS-E (2022) adds more bias for observational studies, covers a more comprehensive range of bias, and adds the assessment of the external authenticity of the study. ROBINS-E (2022) adds a preliminary evaluation process to improve the efficiency of evaluation. In addition, ROBINS-E (2022) visualizes and instrumentalizes the use of signal problems in the form of path graph, making it more convenient to use. ROBINS-E (2022), although more consideration has been given to the issue of co-exposure, still does not address the problem of effect modification in co-exposure, and there is still room to expand the applicable research.

[Methods for controlling and evaluating residual confounding in the association analysis of observational study with a multicenter database].

The observational research based on big data in healthcare has attracted increasing attention, with the control and evaluation of residual confounding being the critical issue that needs to be solved urgently. This review summarized the methods for statistical adjustment and sensitivity analysis of residual confounding in the association analysis with a multicenter database. Based on individual-level data, the residual confounding can be adjusted in each subcenter using methods such as regression discontinuity design, while the pooled estimate can be obtained as a weighted average. Based on the center-level results, the Bayesian Meta-analysis method can adjust the pooled estimates. The sensitivity analysis of residual confounding can also be carried out using center-level data to calculate the E-value, p^(q), T^(r, q) and G^r,q. The abovementioned methods should be selected reasonably according to the requirements for practical applications, advantages, and disadvantages. For example, the use of subcenter individual data for residual confounding adjustment usually needs strict study design and frequent coordination; the Bayesian Meta-analysis is based on some strong assumptions; the interpretation of the results in the sensitivity analysis, such as E-value requires professional judgment to assess the risk of residual confounding. Therefore, the methods for controlling and evaluating residual confounding in association analysis based on multicenter databases still need further development and improvement.

[Methodology and progress in adjusting time-dependent covariates in clinical prediction models].

Adjusting time-dependent covariates into prediction models may help improve model performance and expand clinical applications. The methodology of handling time-dependent covariates is limited in traditional regression strategies (i.e., landmark model, joint model). For example, the number of predictors and practical situations which can be handled are restricted when using regression models. One new strategy is to use machine learning (i.e., neural networks). This review summarizes the methodology of handling time-dependent covariates in prediction models, such as applicable scenarios, strengths, and limitations, to offer methodological enlightenment for processing time-dependent covariates.

[Epidemiological study of incidence of systematic lupus erythematosus in Yinzhou, Ningbo, 2016-2021].

Objective: To characterize the incidence density of systematic lupus erythematosus (SLE) in Yinzhou District of Ningbo from 2016 to 2021, and compare the age and gender specific differences. Methods: A retrospective cohort study was conducted based on the related data from 2015 to 2021 collected from the Health Information Platform of Yinzhou. Suspected SLE cases in local residents were identified by fuzzy matching of International Classification of Diseases 10th edition code "M32" or Chinese text "lupus". The classification criteria from Systemic Lupus International Collaboration Clinics-2012 and The European League Against Rheumatism/American College of Rheumatology-2019 were used for case verification. SLE cases were identified with specific algorithm based on verification results, and new cases were identified with 1 year as the washout period. The incidence density and 95%CI were estimated by Poisson distribution. Results: From 2016 to 2021, a total of 1 551 921 permanent residents were registered in Yinzhou, in whom 51.52% were women. The M(Q1,Q3) age at enrollment was 40.38 (27.54, 53.54) years. The M(Q1,Q3) of follow-up person-years was 3.83 (0.41, 5.83) years. There were 451 new SLE cases, in which 352 were women (78.05%). The 6-year incidence density was 8.14/100 000 person-years (95%CI: 7.41/100 000 person-years-8.93/100 000 person-years) for the total population, 3.68/100 000 person-years (95%CI: 2.99/100 000 person-years-4.48/100 000 person-years) for men and 12.37/100 000 person-years (95%CI: 11.11/100 000 person-years- 13.73/100 000 person-years) for women. The incidence density in men appeared a small peak at 20-29 years old, and began to increase with age from 40 years old. The incidence density in women was highest in age group 20-29 years (16.57/100 000 person-years) and remained to be high until 30-79 years old. The incidence density of SLE in Yinzhou show no significant temporal trend from 2016 to 2021 (men: P=0.848; women: P=1.000). Conclusions: The incidence density of SLE in Yinzhou from 2016 to 2021 was similar to those of other areas in China. SLE has a high incidence in women, especially in the young and elderly, suggesting that more attention should be paid to the diagnosis and treatment of SLE in women.

[A risk prediction model of cervical cancer developed based on nested case-control design].

Objective: To construct a cervical cancer risk prediction model based on nested case-control study design and Yinzhou Health Information Platform in Ningbo, and provide reliable reference for self-risk assessment of cervical cancer in local women. Methods: In local women aged 25-75 years old who had no history of cervical cancer registered in Yinzhou before October 31, 2018, a follow up was conducted for at least three years, the patients who developed cervical cancer during the follow up period were selected as the case group and matched with a control group at a ratio of 1∶10. The prediction indicators before the onset was used in model construction. Variables were selected by Lasso-logistic regression, the variables with non-zero ß were selected to fit the logistic regression model and Bootstrap was used for internal validation. The discrimination of the model was evaluated by area under the receiver operating characteristic curve(AUROC), and the calibration was evaluated by calibration curve and Hosmer-Lemeshow test. Results: The prediction indicators included in the final model were age, smoking status, history of cervicitis, history of adenomyosis, HPV testing, and thinprep cytologic test. The AUROC calculated in the internal validation was 0.740 (95%CI:0.739-0.740), and the calibration curve was almost identical with the ideal curve, P=0.991 in Hosmer-Lemeshow test, indicating that the model discrimination and calibration were good. Conclusions: In this study, a simple and practical cervical cancer risk prediction model was developed. The model can be used in general population with strong interpretability, good discrimination and calibration in internal validation, which can provide a reference for women to assess their risk of cervical cancer.

[Real-world evidence and randomized controlled trials: the initiation, implementation, progress interpretation and revelation of RCT DUPLICATE (part 1)].

In recent years, researchers, pharmaceutical companies, and political makers gradually using more real-world data (RWD) to produce real-world evidence (RWE) for policy-making. A research team of Harvard University launched the RCT DUPLICATE project in 2018, aiming to replicate 30 randomized controlled trials using the medical claims database in order to explore methods for quantifying the efficacy-effectiveness gap and explain its potential sources, to enhance the credibility of the RWE. This paper reviews the background of RCT DUPLICATE Initiative, highlights the research purposes, research design and implementation process of the RCT DUPLICATE Initiative, to help domestic scholars better understand the scope and application value of RWE.

[Real-world evidence and randomized controlled trials: the initiation, implementation, progress interpretation and revelation of RCT DUPLICATE (part 2)].

With the promotion and application of big medical data, non-interventional real-world evidence (RWE) has been used by regulators to assess the effectiveness of medical products. This paper briefly introduces the latest progress and research results of the RCT DUPLICATE Initiative launched by the research team of Harvard University in 2018 and summarizes relevant research experience based on the characteristics of China's medical service to provide inspiration and reference for domestic scholars to conduct related RWE research in the future.