Objective To analyze and verify the factors influencing the prediction model of suicidal ideation of burn patients in hospital based on international scale.Methods The clinical data of 194 burn patients treated in hospi-tal were retrospectively analyzed.General data questionnaire,ISI,HAMD,HAMA,ASDS and BSHS-B were used to evaluate the influencing factors of suicidal ideation.According to the presence or absence of suicidal ideation,the patients were divided into the suicidal ideation group and the non-suicidal ideation group.The baseline data be-tween the groups were compared,univariate screening of meaningful variables was conducted,and multivariate Lo-gistic regression modeling was further conducted.ROC analysis evaluated model differentiation,and internal verifi-cation was conducted.Results According to the baseline data analysis results,there were no statistically signifi-cant differences in age,BMI,years of education,smoking history,estimated percentage of burned area,head and neck burns,hip and perineal burns,and pain scores in the suicidal ideation group(21/194)compared with the non-suicidal ideation group(173/194).Gender(P=0.047),presence or absence of trunk burn(P=0.022),severity of burn(moderate burn:P=0.002;severe burn:P=0.458;extremely severe burn:P=0.169),ISI score(P=0.001),HAMD score(P=0.001),HAMA score(P＜0.001),ASDS score(P=0.003),BSHS-B score(P=0.011)had statistical significance.Multivariate Logistic regression analysis showed that the severity of burn(moderate burn:OR=0.103,P=0.009;severe burn:OR=0.351,P=0.223;extremely severe burn:OR=0.103,P=0.095)and HAMA score(OR=1.136,P=0.007)were independent influencing factors for burn patients with suicidal ideation.The Logistic regression prediction model was established by two independent influ-encing factors.ROC analysis results showed that the model had good differentiation(AUC=0.880,95％CI:0.808-0.952,P＜0.001)and the internal verification accuracy was 79.38％.Conclusion The prediction model built on the basis of two independent influencing factors,burn severity and HAMA score,has a good predic-tion accuracy,which is helpful for clinicians to intervene as soon as possible for burn patients with suicidal ideation in hospital,in order to reduce the incidence and enrich clinical psychological research.

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 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.

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.

Objective: To investigate the spatio-temporal clustering characteristics of influenza in Yinzhou District, Ningbo City, Zhejiang Province from 2017 to 2021, so as to provide insights into prevention and control of influenza. Methods Data of influenza in Yinzhou District from 2017 to 2021 were collected from the Chinese Disease Prevention and Control Information System. The software ArcGIS 10.8 was employed for spatial autocorrelation analysis, and SaTScan 10.1 was employed for spatio-temporal scanning to analyze the temporal and spatial clustering characteristics of influenza incidence in Yinzhou District. Methods: Data of influenza in Yinzhou District from 2017 to 2021 were collected from the Chinese Disease Prevention and Control Information System. The software ArcGIS 10.8 was employed for spatial autocorrelation analysis, and SaTScan 10.1 was employed for spatio-temporal scanning to analyze the temporal and spatial clustering characteristics of influenza incidence in Yinzhou District. Results: Totally 60 543 influenza cases were reported in Yinzhou District from 2017 to 2021, with an incidence of 0.76%. The incidence of influenza peaked in December 2019 (9.35%) and January 2020 (9.28%) during the period between 2017 and 2021. Spatial autocorrelation analysis showed that there was a positive spatial correlation of influenza incidence in Yinzhou District from 2018 to 2021 (all P<0.05), and a high clustering in 2019 and 2021. Zhonghe Street showed a low-high clustering from 2017 to 2020; Jiangshan Town showed a low-high clustering in 2017 and 2020, and a high-high clustering in 2019 and 2021; Shounan Street showed a high-high clustering from 2018 to 2020; Yunlong Street showed a high-high clustering in 2021. Spatio-temporal scanning analysis showed that the class Ⅰ clusters were located in the central region which centered in Dongqianhu Town, with aggregation time in August 2017, in the northwest region with aggregation time in December and January from 2018 to 2020, and in the west region with aggregation time in August 2021. Conclusion The incidence of influenza in Yinzhou District from 2017 to 2021 showed a spatio-temporal clustering in the northwestern region in winter and summer.

Objective:To validate the performance of cardiovascular risk prediction models based on the Sweden National Diabetes Register (NDR) and Diabetes Lifetime-perspective prediction (DIAL) model for assessing risks of 5-year and 10-year cardiovascular disease (CVD) among Chinese patients with type 2 diabetes.Methods:Based on the Chinese Electronic Health Records Research in Yinzhou study, 83 503 patients with type 2 diabetes aged 30-75 years without a history of CVD at baseline were included from January 1, 2010 to December 31, 2020. Recalibrated NDR model was used to estimate 5-year risk, while the recalibrated DIAL model was used to predict 5-year and 10-year risks. The competing events adjusted Kaplan-Meier analysis was used to obtain the observed cardiovascular events. Discrimination C statistics evaluated model accuracy, calibration χ2 value, and calibration plots. Results:Through a median follow-up of 7.0 years, 7 326 cardiovascular events, and 2 937 non-vascular deaths were identified among a total of 83 503 subjects. The recalibrated NDR model overestimated 5-year risk by 39.4% in men and 8.6% in women, whereas the overestimation for the recalibrated DIAL model was 14.6% in men and 50.1% in women. The DIAL model had a better discriminative ability ( C-statistic=0.681, 95% CI: 0.672-0.690) than NDR model ( C-statistic=0.667, 95% CI: 0.657-0.677) in 5-year risk prediction for men, and the models had a similar ability for women ( C-statistic=0.699, 95% CI: 0.690-0.708 for NDR and C-statistic=0.698, 95% CI: 0.689-0.706 for DIAL). The prediction accuracy of the DIAL model was improved in the 10-year risk, with the underestimation being 1.6% for men and the overestimation being 12.8% for women. Conclusions:Both recalibrated NDR and DIAL models overestimated 5-year cardiovascular risk in Chinese patients with type 2 diabetes, while the higher overestimation was shown using the DIAL model. However, the improvement was found in predicting 10-year CVD risk using the DIAL model, which suggested the value of lifetime risk prediction and indicated the need for research on the lifetime risk prediction model for cardiovascular risk assessment in Chinese patients with type 2 diabetes.

With the rapid development of Internet technology and the continuous advancement of medical informatization, big data in healthcare has gradually become an important resource to innovate health management and meet the growing health needs of people and the application of big data in healthcare has been one of the indispensable parts of national big data strategy in China. Based on the established healthcare big data platform and the application of big data technology, Yinzhou district has made innovative efforts to explore a new model driven by big data for the prevention and control of communicable and non-communicable diseases and the management of vaccination programs. It is expected that the "Internet plus healthcare" model will strengthen the disease prevention and control and public health management in local area, create a new business form and provide strong support for Healthy China 2030. This article introduces this new model driven by big data in Yinzhou and discusses the preliminary efficiency of this model in public health practice.

Objective:To understand the epidemiological characteristics of COVID-19 monitoring cases in Yinzhou district based on health big data platform to provide evidence for the construction of COVID-19 monitoring system.Methods:Data on Yinzhou COVID-19 daily surveillance were collected. Information on patients’ population classification, epidemiological history, COVID-19 nucleic acid detection rate, positive detection rate and confirmed cases monitoring detection rate were analyzed.Results:Among the 1 595 COVID-19 monitoring cases, 79.94% were community population and 20.06% were key population. The verification rate of monitoring cases was 100.00%. The total percentage of epidemiological history related to Wuhan city or Hubei province was 6.27% in total, and was 2.12% in community population and 22.81% in key population ( P<0.001). The total COVID-19 nucleic acid detection rate was 18.24% (291/1 595), and 53.00% in those with epidemiological history and 15.92% in those without ( P<0.001).The total positive detection rate was 1.72% (5/291) and the confirmed cases monitoring detection rate was 0.31% (5/1 595). The time interval from the first visit to the first nucleic acid detection of the confirmed monitoring cases and other confirmed cases was statistically insignificant ( P>0.05). Conclusions:The monitoring system of COVID-19 based on the health big data platform was working well but the confirmed cases monitoring detection rate need to be improved.

During the prevention and control of the COVID-19 epidemic, identifying and controlling the source of infection has become one of the most important prevention and control measures to curb the epidemic in the absence of vaccines and specific therapeutic drugs. While actively taking traditional and comprehensive "early detection" measures, Yinzhou district implemented inter-departmental data sharing through the joint prevention and control mechanism. Relying on a healthcare big data platform that integrates the data from medical, disease control and non-health sectors, Yinzhou district innovatively explored the big data-driven COVID-19 case finding pattern with online suspected case screening and offline verification and disposal. Such effort has laid a solid foundation and gathered experience to conduct the dynamic and continuous surveillance and early warning for infectious disease outbreaks more effectively and efficiently in the future. This article introduces the exploration of this pattern in Yinzhou district and discusses the role of big data-driven disease surveillance in the prevention and control of infectious diseases.

OBJECTIVE:To introduce the research and application of sentinel hospital pharmacovigilance system,and provide technical reference for hospital pharmacovigilance working in China. METHODS:A sentinel hospital pharmacovigilance system based on hospital information system was established,the architecture and functionality were introduced and its effects were ana-lyzed. RESULTS:The sentinel hospital pharmacovigilance system formed by hospital business information management platform and data center. Its main functions included drug data arrangement,the auxiliary reporting of ADR,active monitoring,pharmaco-vigilance information inquiry,monitoring and warning and statistical analysis,which successfully achieved the rapid reporting and active monitoring of hospital ADR. The system had applied in 20 sentinel hospitals,and the ADR reporting quantity was obviously increased after applying the system. Compared with 2015,ADR reporting in a sample sentinel hospital was increased 120.6％ since it used the system in early 2016. Besides,the system had improved the ADR reporting process,operation and input standard for the ADR reporter,shortened the reporting time and improved the efficiency of the reporting staff. CONCLUSIONS:The establish-ment and application of sentinel hospital pharmacovigilance system has greatly improved the hospital ADR monitoring management level,and it is of great significance to further strengthen the pharmaovigilance in China.