Objective To establish a time series model suitable for predicting the number of cases of brucellosis in China, and provide a scientific basis for preventing and controlling this disease.Methods This study utilized data on the number of cases from January 2015 to December 2021 to establish SARIMA and prophet models, respectively. Subsequently, the predictive performance of these two models was validated by using data from January 2022 to April 2023, and the results of the model predictions were compared by using three indicators: RMSE, MAPE, and MAE. Finally, the prophet model with higher accuracy was used to predict the number of cases from May 2023 to April 2024.Results The overall number of cases of brucellosis in China is rising, reaching its peak from June to July each year, showing a clear seasonal trend. Compared to the SARIMA model, the RMSE, MAPE, and MAE values of the prophet model are lower, indicating that the prophet model has higher accuracy in predicting brucellosis. The predicted peak of the number of cases in 2023 is lower than the actual peak in 2021 and 2022.Conclusion The prophet model can better fit the monthly reported number of cases of brucellosis in the country and can be used for short-term prediction.

Objective To explore a data statistical analysis and visualization display method based on Power BI under the background of performance evaluation in tertiary public hospitals, so as to improve the efficiency of hospital statisticians′ data statistics and analysis; to promote the efficiency and quality of medical management by using visualization, to assist in daily indicator monitoring, and to assist management and decision-making.Methods The first page data of medical records were extracted from a tertiary public hospital from January 1, 2022 to June 30, 2023 to establish a power BI based first page index data query system for performance evaluation of tertiary public hospitals based on the statistical caliber of the first page index data of medical records for performance evaluation of tertiary public hospitals, for data analysis and visual display.Results The performance evaluation medical record homepage indicator data query system for tertiary public hospitals based on Power BI can quickly query multi-dimensional and multi scenario indicator data, which is practical and easy to operate.Conclusion Power BI can be used as a tool for hospital statistical staff to conduct data analysis and visual display, which is worth promoting and using. Hospital statistical staff can combine this study to apply Power BI to practical work, improve work efficiency, and assist daily statistical work.

Objective To take 57 tertiary general hospitals in Shandong Province as study samples, to evaluate the medical quality and operation efficiency, and to provide references for strengthening connotation construction and improving the refined management level.Methods Entropy weighted technique for order preference by similarity to ideal solution (TOPSIS) and data envelopment analysis (DEA) were respectively used to evaluate the medical quality and operation efficiency in sample hospitals, by constructing the evaluation index system.Results In terms of medical quality, there was significant difference in highlevel surgical proportion and CMI among sample hospitals, with the maximum weight of both. The top ranked hospitals were A1, A2, A3, A4 and A29, all of which were tertiary and mostly provincial hospitals. In terms of operation efficiency, the overall efficiency of 57 sample hospitals was relatively high, but imbalance development still existed, with 22 hospitals DEA effectiveness, 12 hospitals DEA weakly effectiveness, and 23 hospitals DEA ineffectiveness. There was a positive correlation between medical quality and  technical efficiency.Conclusion There is a big space for improvement of the medical quality and operation efficiency in some sample hospitals. They should focus on functional orientation, continuously enhance medical technology level and internal management capability, optimize resources allocation, and transform from scale expansion to quality and efficiency improvement, in order to promote the high-quality development of hospitals. 

Colorectal Cancer (CRC) statistics are updated by American Cancer Society every 3 years based on morbidity provided by the population cancer registry and mortality provided by the National Center for Health Statistics. In 2023, about 153 020 people will be diagnosed with CRC and 52 550 will die from the disease, including 19 550 cases and 3 750 deaths among people under 50 years old. The magnitude of the decline in CRC incidence has changed, from 3%4% per year in 20002010 to 1% per year in 20112019. The proportion of rectal cancer increased from 27% in 1995 to 31% in 2019, and CRC incidence was migrating to the left colon. From 2011 to 2020, CRC mortality overall decreased by 2% per year, but CRC mortality increased by 0.5%3.0% per year in the native Americans under 50 years old and under 65 years old. In summary, despite the continual decline in overall mortality, CRC is rapidly shifting towards younger, more advanced stages and development in the left colon/rectum. In this paper, the important contents of the updated data are sorted out and briefly interpreted. At the same time, compared with the present situation of CRC in our country, we can reveal and analyze the cause of the rising incidence of CRC to provide reference for CRC diagnosis and treatment.

Objective To measure the cost efficiency, technical efficiency and allocation efficiency of each clinical ward of a tertiary hospital, so as to provide reference for realizing its high-quality development and fine management.Methods CCR, BCC and COST-DEA models in data envelopment method were used to measure the relevant efficiency values of clinical wards in the tertiary hospital.Results In 2019, the average cost efficiency of each clinical ward of the tertiary hospital was 0.675, the average allocation efficiency was 0.782, the average technical efficiency was 0.853, the average pure technical efficiency was 0.902 , and the average scale efficiency was 0.944; 7 (13.73%) clinical wards were in the state of increasing returns to scale, 23 (45.10%) clinical wards were in the state of unchanged returns to scale, and 21 (41.17%) clinical wards were in the state of decreasing returns to scale.Conclusion The cost efficiency of clinical wards in the tertiary hospital is mainly affected by the allocation efficiency, which requires reasonable allocation of medical resources. Most clinical wards need to improve the internal fine management level to increase output.

Objective To understand the trend of nosocomial infection prevalence in China, and to predict the nosocomial infection prevalence in hospitals of different scales in China with the gray GM (1,1) model, so as to provide data support and new ideas for prevention and control of nosocomial infection.Methods Descriptive epidemiological method was used to analyze the trend of nosocomial infection prevalence in China. The grey GM (1,1) model was constructed with data on nosocomial infection prevalence in China from 2008 to 2016, and the model was validated with data from 2018 to 2020. The constructed grey GM (1,1) model was used to predict the prevalence of nosocomial infection in China from 2022 to 2024.Results The prevalence of nosocomial infection in China showed a downward trend. The prevalence of nosocomial infection increased with the increase of hospital scales. The grey GM (1,1) model for the prevalence of nosocomial infection has good accuracy and high fitting effect. In 2024, the prevalence of nosocomial infection in China, in hospitals with<300 beds, in hospitals with 300-599 beds, in hospitals with 600-899 beds, and in hospitals with≥ 900 beds can be reduced to 1.00%, 0.49%, 0.90%, 1.13%, and 2.05%, respectively.Conclusion The prevention and control effect of nosocomial infection in China is obvious, and the grey GM (1,1) model has a good prediction effect on the prevalence of nosocomial infection in China.

Objective The complexity of the concept of multivariate statistics can easily lead to difficulties in students′ understanding. This article uses simulation experiments to examine the impact of various characteristics of data on the analysis results, in order to strengthen students′ understanding of the principles of multivariate statistics and facilitate their correct application of multivariate statistical methods.Methods Six simulation experiments were designed, and R 4.2.2 was used to generate simulation data and conduct statistical analysis to explore the impact of data characteristics on the 6 statistical analysis methods.Results According to the statistical analysis results of simulation experiments, the multicollinearity among independent variables in multilinear regression will make the regression coefficient estimation of the regression equation inaccurate. The correlation structure between variables in principal component and factor analysis affects the dimensionality reduction effect. In logistic regression, the nonlinear influence of independent variables on dependent variables should be considered, otherwise the results will affect the accuracy of the estimation results. When the proportion of deletion in Cox regression is too large, especially after reaching 60%, the results of the Cox regression model have a significant deviation. The correlation structure between variables affects the results of canonical correlation analysis. When there is a large difference in the proportion of categories, Bayesian discrimination and logistic regression are more appropriate for discrimination, because Bayesian discrimination with prior probability is more flexible.Conclusion The application of multivariate statistical methods needs to fully consider applicable conditions such as data characteristics. Through simulation experiments, students are encouraged to independently run simulation experiments, intuitively understand abstract multivariate concepts, and facilitate the understanding and application of multivariate statistical methods.

Objective To investigate the factors affecting hospitalization costs of hospitalized patients with chronic obstructive pulmonary disease (COPD), so as to reduce the burden of patients, promote the rational allocation of medical resources, and provide a basis for formulating healthcare policies.Methods The medical record homepage data of chronic obstructive pulmonary hospitalized patients in a tertiary general hospital in Beijing from 2019 to 2022 were selected to study the influencing factors of the hospitalization costs through descriptive analysis, rank sum test, correlation analysis, and multifactor regression analysis.Results Male patients (66.0%) outnumbered female patients (34.0%) among 1 053 COPD patients, the proportion of patients aged 60-89 years old was the highest (82.8%), the number of medicare-paid patients was the highest (88.2%), emergency admissions (57.1%) were higher than outpatient admissions (42.9%), and the percentage of hospitalization days with 10-19 days was the the most (45.3%). The high incidence seasons of chronic obstructive pulmonary disease were fall and winter (51.9%), and common comorbidities were coronary heart disease (78.3%), hypertension (73.0%), and pulmonary heart disease (48.5%). Chinese and Western medicine (36.75%), medical services (29.57%), and diagnosis (22.82%) accounted for the largest share of hospitalization costs. There was a statistical difference in hospitalization costs for variables of age, medical payment method, number of hospitalization days, comorbidities of hypertension, coronary heart disease, respiratory failure, pulmonary heart disease, treatment with antimicrobials, and ventilator-assisted therapy (P<0.05) . Regression analysis showed that the older the age, the longer the hospitalization days, the higher the number of comorbidities, the more use of antimicrobial drugs and ventilator therapy, the higher the hospitalization costs. Compared to medical insurance patients, public medical care was positively correlated with hospitalization costs (P<0.05).Conclusion Health education should be strengthened, early screening and intervention for elderly patients and COPD patients with a history of cardiovascular disease should be carried out, and multidisciplinary diagnostic and treatment modes and improved clinical pathways should be utilized to standardize the diagnostic and treatment processes, logically shorten the average number of hospitalization days, reduce the waste of medical resources and alleviate the patients′ economic burden.

Objective To analyze the death cases in a tertiary general hospital from 2015 to 2022, and to provide a basis for rational allocation of medical resources and improvement of medical service quality.Methods Death data of a tertiary hospital in Hangzhou from January 1, 2015 to December 31, 2022 were extracted by using the population information registration system of the Chinese Center for Disease Control and Prevention (CDC). Minitab 18 software was used to draw Pareto charts to analyze the main factors and order of the patients′ death cause disease spectrum. Excel 2016 and SPSS 21.0 software were used for data sorting and analysis, and statistical description and chi-square test were for retrospective analysis.Results A total of  1 938-deaths were reported in the hospital from 2015 to 2022, including 287 inpatients and 1 651 non-inpatients. The male to female ratio was 2.22∶1, and there were statistically significant differences between genders in age, marital status, educational level, and distribution of death cases (P<0.05). According to the Pareto plot, the causes of death were circulatory system diseases (633, 32.66%), injury-poisoning (547, 28.22%), tumors (286, 14.76%), and respiratory system diseases (203, 10.47%), with a cumulative composition ratio of 86.12%. The main causes of death in patients were sudden cardiac death, acute-myocardial infarction, cerebral hemorrhage, severe traumatic brain injury, multiple injuries, thoracic injury, lung cancer, liver cancer, and pulmonary infection.Conclusion The hospital should improve the level of prehospital and in-hospital first aid, strengthen the treatment of circulatory system diseases, injury-poisoning, tumors, and respiratory system diseases, and allocate medical resources reasonably.

Objective To investigate the risk factors of early diabetic nephropathy and construct a risk prediction model based on BP neural network algorithm. Methods A total of 1 048 diabetic patients admitted to Yongkang Hospital of Traditional Chinese Medicine from January 2020 to December 2022 were retrospectively analyzed, including 115 diabetic nephropathy patients (10.97%), and were divided into the DKD group (115 diabetic nephropathy patients) and the DM group (933 diabetic patients). The relevant data of patients were collected and matched according to 1∶1 nearest proximity method after the confounding factors were excluded by propensity score matching (PSM). The prediction model was built based on the correlation factors by using the statistically significant indicators in the single factor analysis and BP neural network algorithm. Mean absolute error (MAE) was used to analyze the model efficacy, the predictive value of the risk prediction model was evaluated by receiver operating characteristic curve (ROC), and external validation was performed. The model consistency was evaluated by calibration curve.Results The confounding factors were gender, hypertension, fasting blood glucose and uric acid. After the modeling set was 1∶1 and PSM was performed by the nearest method, the confounding factors were excluded: 95 cases in DKD group and 95 cases in DM group. Univariate results indicated that there were significant differences in age, type 2 diabetes, total cholesterol, urinary protein excretion rate, diabetes course, and cystatin C(CysC) between groups (P<0.05). The prediction accuracy was BP neural network algorithm, decision tree, support vector machine and logistic regression in the descending order. The results of BP neural network showed that the top 4 factors affecting the occurrence of early diabetic nephropathy were proteinuria excretion rate, age, diabetes course and cystatin C(CysC) in order. The AUC  of the prediction model was 0.959 (95%CI: 0.917-1.000), the Yoden index was 0.867, and the corresponding sensitivity and specificity were 0.867 and 1.000, respectively. The external validation-AUC was 0.958 (95%CI: 0.922-0.995), and its sensitivity and specificity were 0.804 and 1.000, respectively. The calibration curve in the calibration diagram was close to the standard curve.Conclusion The BP neural network algorithm model based on machine learning, which takes age, disease course, urinary protein excretion rate, TC, CysC and type 2 diabetes as predictive features, has good predictive value for early diabetic nephropathy, and can be clinically applied to the management and identification of high-risk population.

Objective To construct a machine learning model to predict the risk of deep venous thrombosis (DVT) in patients undergoing total hip arthroplasty (THA), and to identify key risk factors influencing DVT using shapley additive explanations (SHAP) method.Methods We retrospectively analyzed data from 416 patients who underwent THA in Wenzhou People′s Hospital from January 1, 2017 to July 31, 2022, and randomly divided them into a training set and a test set in a 4∶〖KG-*2/3〗1 ratio. Recursive feature elimination and five-fold cross-validation were used to select the best features. Six machine learning algorithms were utilized to develop predictive models, and various performance metrics were employed to evaluate them. The SHAP method was used to analyze the interpretability of the optimal model.Results Four hundred and sixteen patients were included in the final study, including 333 in the training set and 83 in the test set. The XGBoost model was the most accurate on the test dataset, achieving a sensitivity of 0.817, specificity of 0.783, F1 score of 0.860, ROC-AUC of 0.800, and a Brier score of 0.106. SHAP summary plots showed that age, cholesterol, postoperative bed time, fibrinogen, and preoperative plasma D-dimer levels were the top five determinants for post-THA DVT. SHAP values feature dependence plots revealed complex non-linear effects of these factors on DVT risk, with age, bed rest, and fibrinogen showing an inverted U-shaped relationship, and cholesterol displaying a positive correlation. Individual SHAP values offered insights into each predictor′s role in DVT risk.Conclusion This study developed an efficient and interpretable machine learning model to predict DVT risk in THA patients, which is helpful for clinical health professionals in identifying high-risk patients and providing personalized intervention.

Objective To analyze the disease distribution of pediatric inpatients in a tertiary hospital in the past five years, and to provide basis for the dynamic allocation of pediatric medical resources and the development of the discipline.Methods According to the National Clinical Edition 2.0 diagnosis code (ICD-10) , the diseases were classified, and the hospitalized pediatric patients from 2018 to 2022 were taken as the original data. Excel 16.0 and SPSS 26.0 statistical analysis software were used to analyze gender, age, seasonal distribution and disease composition of hospitalized children retrospectively.Results From 2018 to 2022, a total of 35 714 pediatric patients were admitted, with a male to female ratio of 1.51∶〖KG-*2/3〗1. There was a gender difference in the distribution of hospitalized children (χ2=20.472，P<0.001) . On the whole, the infants were the most (42.26%) , but the proportion of hospitalized infants was decreasing year by year, and the proportion of hospitalized infants in winter was the highest (30.57%) , followed by summer (24.04%) , spring (23.27%) and autumn (22.12%). The first five kinds of diseases were respiratory system diseases (51.21%) , some diseases originated from perinatal period (15.75%) , some infectious diseases and parasitic diseases (11.81%) , gastrointestinal disease diseases (8.87%) and neurological disorder diseases (4.36%). There were annual and seasonal differences in the order of the first five diseases (P<0.001). Respiratory diseases, conditions originating in the perinatal period, and gastrointestinal disease in winter all accounted for the highest proportion.Conclusion The hospital should dynamically adjust the allocation of pediatric medical resources according to the seasonal changes, and improve the diagnosis and treatment techniques and nursing level of common and frequently-occurring diseases in children of different genders and ages, to strengthen the management of key pediatric diseases and discipline construction.

Objective To understand the influencing factors of perioperative death of elective surgery patients in a hospital, and to provide data reference for hospitals to strengthen perioperative management and reduce postoperative mortality.Methods The information of patients who underwent elective surgery from January 1, 2020 to December 31, 2022 was retrieved from the medical record homepage management system of a tertiary general hospital, and the main diagnosis, surgical method, surgical department and causes of postoperative death were retrospectively analyzed.Results From 2020 to 2022, 81 722 patients underwent elective surgery in the hospital, of which 103 patients died after the surgery, with a mortality rate of 0.13%; the top three primary diseases were circulatory system disease, tumor minor and congenital disease, accounting for 85.43%; the main surgical types of patients who died after elective surgery cardiac surgery, aortic surgery and craniocerebral surgery, accounting for 46.07%; postoperative death cases were mainly concentrated in the departments of cardiothoracic surgery, cardiovascular surgery, cardiovascular medicine, vascular surgery, neurosurgery and intervention; the main cause of death was surgical complications, accounting for 44.66%, followed by primary disease aggravation and other diseases after the surgery, accounting for 32.04% and 23.30% respectively.Conclusion The relevant surgical departments should strengthen the management of preoperative, intraoperative and postoperative perioperative patients. The functional departments should carry out special management for the departments with more deaths of elective postoperative patients, which is of great significance to reduce the postoperative mortality of patients undergoing elective surgery.

Objective To illuminate the application of cumulative odds logit model and partial proportional odds model in ordinal categorical data.Methods The cumulative odds logit model and partial proportional odds model were used to fit the relationship between hospital costs and relative factors of lung cancer surgery. The goodness of fit of the two models was compared by the log likelihood value (-2ln L) of the models.Results When the independent variables did not fully satisfy the proportional odds assumption (gender χ2=15.888, P＜0.001; surgical procedure χ2=35.874，P＜0.001), the fitting results of the two methods were different. At different segmentation points, there was a significant difference in the relationship between hospitalization costs and surgical procedures. The partial proportional odds model (-2ln L=5 797.112) was better than the cumulative odds logit model (-2ln L=5 852.420). The difference of the log likelihood values was statistically significant (P<0.05).Conclusion In order to analyze the ordinal categorical data, the proportional odds assumption should be verified firstly. If all the independent variables satisfy the condition, the cumulative odds logit model is selected, otherwise the partial proportional odds model is used to study the data.

Objective To develop a quality control system for medical record homepage data based on VB6.0 programming software to improve the quality of medical record homepage data.Methods The data quality control system of the first page of medical records was constructed by calling four stored procedures of the SQL Server database: provincial rules, dictionary rules, data logic rules and hospital rules.Results After the implementation and application of the system, according to the monthly rating feedback of the homepage data quality evaluation system, the monthly average number of violations of the verification rule was 3.29 from October 2019 to July 2022, and decreased to 0.23 from August 2022 to August 2023; the full score rate of medical record homepage quality increased from 17.65% to 84.62%, with a significant improvement; the full score rate of medical record quality and the full score rate of verification rules reached 100.00% and 84.62% respectively, and the full score rate was also significantly improved.Conclusion Currently, many hospitals are limited by factors such as low level of hospital informationization, inadequate knowledge of doctors regarding relevant medical records, and poor collaboration between hospital information and medical record personnel. This method can be used to assist hospital medical record personnel in improving the quality of medical record data.

Objective To analyze the coding quality of ICD for appendicitis and provide theoretical basis for the reform of medical insurance payment methods.Methods A coding quality control team consisting of 2 coding personnel with intermediate titles in medical record information technology and 1 attending physician in gastrointestinal surgery was formed to retrieve the medical insurance settlement data of appendicitis patients in the hospital in 2022. The medical records were reviewed and analyzed one by one, and the coding errors were recoded. Excel was used to record error types of appendicitis for statistical analysis.Results The number of appendicitis discharge cases in 2022 was 630, and the number of coding error cases was 154, with a coding error rate of 24.44%. Among them, 87 cases (56.49%) were not corrected according to pathological results, 22.73% were not combined with coding, 18.83% were acute and chronic appendicitis, and 1.95% were not distinguished.Conclusion Medical institutions need to strengthen the supervision of the quality of medical record coding. Coders should strengthen the study of coding theory and related clinical knowledge to improve the quality of coders. Clinicians should standardize the writing of diagnosis and related medical documents to do the basic work for the accuracy of coding.

Objective To explore the structure and importance ranking of average hospitalization costs for patients with depression, and to provide a basis for improving the structure of patients′ hospitalization costs and effective cost control.Methods Information on the hospitalization costs of patients with depression in a tertiary hospital in Guangzhou City from 2018 to 2023 was collected, and the degree of association, composition ratio, and change of each cost with the average hospitalization costs per-time were evaluated by using the degree of structural variation and new gray correlation analysis.Results From 2018 to 2023, the average hospitalization cost per-time of patients with first gradually increased and then decreased, the proportion of diagnostic fee and examination fee continued to increase, and the proportion of nursing fee and drug fee continued to decrease. The degree of structural change of average hospitalization cost per-time during the 6-year period was 33.84%, and the degree of annual average structural change was 5.64%. The top 3 contributors to the structural change were drug fee (26.77%), diagnostic fee (23.43%) and medical service fee (19.77%), and the cumulative contribution of the three amounted to 69.97%. The results of the new gray correlation analysis showed that the treatment fee (0.939) had the greatest impact on the average hospitalization cost per-time of depressed patients, followed by the diagnostic fee (0.883), the examination fee (0.866) and the medical service fee (0.817).Conclusion Treatment fee, diagnostic fee and examination fee are the main factors affecting the hospitalization cost of depressed patients, and the income of nursing fee and drug fee continues to decrease. It is recommended to continuously optimize the cost structure and establish a reasonable, stable and efficient cost control program.

Objective To analyze the effect of nitrogen dioxide (NO₂) on hospital visits for respiratory diseases in Jiaxing.Methods We collected daily air NO₂ monitoring data, meteorological factors and hospital visits for respiratory diseases in Jiaxing from 2019 to 2021．We used Wilcoxon rank sum test and generalized addition model (GAM) to analyze the acute effects of air NO₂ on hospital visits for respiratory diseases.Results The average daily concentration of NO₂ exceeded the standard for 9 days. The daily hospital visits for respiratory diseases and childhood respiratory diseases on days when air NO₂ exceeded the national standard were higher than those on qualified days. In the single-day lag effect analysis, the excess risk (ER) of daily hospital visits for respiratory diseases and adult respiratory diseases both reached the maximum on lag4, while the ER of daily hospital visits for childhood respiratory diseases reached the maximum on lag3 with ER(95%CI) being 2.23% (1.52%-2.94%), 1.73%(1.06%-2.41%) and 2.73%(1.73%-3.74%) with NO₂ concentration increase per 10 μg/m3, respectively. In the cumulative lag effect analysis, the ER of daily hospital visits for respiratory diseases, adult and childhood respiratory diseases all reached the maximum on lag07, with ER(95%CI) being 5.35%(4.06%6.66%), 3.47%(2.25%-4.70%) and 7.17%(5.31%-9.07%) with NO₂ concentration increase per 10 μg/m³, respectively. The maximum ER of hospital visits for childhood respiratory diseases was higher than that for adults.Conclusion The increase of NO₂ concentration in Jiaxing may lead to an increase in hospital visits for respiratory diseases with a lag effect, and the health protection of children′s respiratory system should be especially strengthened.

Objective To use Power BI Desktop for multidimensional comparative analysis and data visualization of hospital DIP subgroup details, in order to identify the causes of DIP losses in hospitals.Methods The data were extracted from the DIP subgroup discharge settlement details of a tertiary oncology hospital in 2022. Methods such as data import, data type conversion, data relationship establishment, data modeling, and data visualization were used to build dynamic analysis reports for different dimensions such as the entire hospital, departments, and disease categories.Results Power BI Desktop can effectively enhance the level of fine management for DIP, improve work efficiency, accurately analyze the causes of losses, guide departments in making improvements, and effectively reduce the DIP insurance fund losses for the hospital.Conclusion Power BI Desktop is characterized by its low cost, customization, high level of visualization and automation, as well as a user-friendly interface, making it suitable for promotion in hospitals, especially those with low levels of informatization, insufficient specialized operational personnel, and limited budget for DIP fine management.

Objective To study the medical records of patients with malignant tumors in a tertiary hospital, to grasp the composition and change trend of the main malignant tumor diseases, and to provide scientific reference for the prevention and treatment of malignant tumors in the local area.Methods Data of the first page of medical records of malignant tumor patients in a hospital from 2017 to 2021 were collected and retrospectively analyzed in terms of disease composition, sequence structure, age and gender distribution of patients with malignant tumors in the hospital.Results Totally 19 198 patients with malignant tumor were admitted, and the gender ratio was 1.3∶1. The proportion of hospitalized patients aged 50-69 was the highest (50.66%), and the mortality rate of malignant tumor was 4.96%. The top 5 malignant tumors were lung cancer, liver cancer, nasopharynx cancer, breast cancer, and colon cancer. The average medical cost of malignant tumor patients was 23 711.55 yuan, the average medicine cost was 4 851.93 yuan and the average hospitalization days were 13.03 days.Conclusion It should be aimed at highrisk groups and common types of malignant tumors. It is necessary to formulate comprehensive prevention and treatment measures for local population with malignant tumors over 50 years old, carry out cancer screening for early diagnosis and treatment, and strengthen control over medical expenses.

Objective To analyze the epidemiological characteristics of statutory infectious diseases in Tongxiang City and provide scientific basis for the development of effective prevention and control measures.Methods Descriptive epidemiological methods were used to analyze the data on the incidence of statutory infectious diseases reported in Tongxiang Ctity from 2013 to 2022.Results There were no reports of class A infectious diseases in the city. There were 47 944 cases of statutory infectious diseases reported, with an average annual incidence rate of 546.69 per 100 000. There were 43 reported deaths, with an average annual mortality rate of 0.49 per 100 000. The top five diseases in terms of incidence were other infectious diarrheal diseases, hand, foot and mouth disease, gonorrhea, syphilis, and tuberculosis, accounting for 86.64% of all reported cases of infectious diseases. The spatial distribution of outbreaks conformed to the characteristics of infectious diseases. The composition ratio of transmission route from high to low was as follows: intestinal infectious diseases accounted for 61.03%, bloodborne and sexually transmitted infectious diseases accounted for 23.11%, respiratory tract infectious diseases accounted for 15.41%, natural foci and vectorborne infectious diseases accounted for 0.39%, and other routes accounted for 0.06%. The incidence of other infectious diarrheal diseases and hand, foot and mouth disease were the highest in the low age group (0＜15 years old).Conclusion Attention should be paid to the prevention and control of intestinal and respiratory tract infectious diseases such as other infectious diarrheal diseases, hand, foot and mouth disease, and influenza in densely populated collective units such as childcare institutions and schools. Adults should be aware of sexually transmitted diseases such as gonorrhea, syphilis, and intestinal infectious diseases such as other infectious diarrheal diseases. Strengthening health education and promoting disease prevention awareness can help reduce the incidence of infectious diseases.

Objective To analyze the research hotspots and development trends of the teaching model of Medical Statistics.Methods NoteExpress software was used to modify and integrate the 569 valid literatures related to the teaching model of Medical Statistics into a form that could be recognized by CiteSpace software, and then CiteSpace 6.1.R2 software was used for visual analysis, including the growth trend of related literatures, high-yield institutions, core authors, high-frequency keywords, keyword clustering, emerging words, etc.Results The number of publications related to the teaching model of Medical Statistics showed an overall upward trend. There was almost no academic cooperation among institutions in this research field. The top three authors published 26, 23 and 12 papers respectively. A total of 378 nodes and 912 connections were identified in the network map, and the network density was 0.013, and the top 3 keywords were "teaching reform" with 129 times, "teaching methods" with 53 times, and "teaching" with 50 times. The keywords were clustered into 11 classes, of which the module value was 0.623 and the average outline value was 0.858. A total of 19 emergent words were identified, among which the top 3 keywords for emergent intensity were "teaching methods", "flipped classroom" and "medical statistics".Conclusion At present, scholars′ research on the teaching model of Medical Statistics is in a period of stable development. There is little academic cooperation among institutions in this field, but there is close cooperation between prolific scholars. Research hot topics include teaching reform, teaching methods, teaching, teaching effects, and teaching modes. Medical schools are the main research trend of the teaching reform model of Medical Statistics.

Objective To understand the influence of economic burden on the demoralization of prostate cancer patients, and to provide a theoretical basis for the formulation of intervention measures.Methods A total of 249 patients with prostate cancer who were hospitalized in a hospital from December 2020 to December 2022 were selected as subjects by convenience sampling method. The Comprehensive Scores for Financial Toxicity Based on the PatientReported Outcome Measures, and the Chinese Version of the Demoralization Scale-Ⅱ were used for the questionnaire survey.Results The economic burden score of prostate cancer patients was (27.05±8.14) points, and the demoralization score was (14.93±4.87) points. There were statistically significant differences in demoralization scores among prostate cancer patients with different ages, educational backgrounds, per capita monthly family income, working position, years of diagnosis, clinical stages of cancer and complications (P<0.05). Economic burden score of prostate cancer patients was positively correlated with the total score of demoralization and scores of all dimensions (P<0.01). The clinical staging of tumor, economic burden, educational background and complications of prostate cancer patients were the main influencing factors of patients with demoralization (P<0.05).Conclusion The economic burden of prostate cancer patients is heavy, and the level of demoralization is high. Therefore, appropriate intervention measures should be taken to alleviate the level of demoralization of patients according to the diagnosis years, economic burden, educational background and complications of patients.

Objective To apply the risk matrix to assess the risk of nosocomial infection management in upper second-class general hospitals.Methods The incidence rate, infection site, increased length of hospital stay, increased hospitalization cost, outcome, and monthly incidence variation coefficient of patients with nosocomial infection in each department in 2021 were used to quantify the probability of occurrence and consequence indicators, and the risk identification of internal medicine, surgery, and obstetrics and gynecology and children systems was carried out. Percentile method (P₂₀, P₄₀, P₆₀, P₈₀) was used to grade the occurrence possibility and consequence, establish the risk matrix table, and analyze and determine the high-risk departments.Results The high-risk departments in internal medicine were oncology department, geriatric disease department, general medicine department and infectious disease department; the high-risk departments in surgical medicine were neurosurgery department, otolaryngology department and orthopedics department; the high-risk departments in gynecology and pediatrics system were obstetrics and gynecology.Conclusion The risk assessment model of nosocomial infection based on risk matrix truly and objectively reflects the risk of nosocomial infection in various departments of primary hospitals, which suggests that primary hospitals should give priority to the prevention and control strategy of nosocomial infection in high-risk general departments, and pay attention to key departments such as neonatology.

Objective To analyze the disease characteristics of inpatient deaths in a tertiary general hospital from 2018 to 2022, and provide reference for the rational allocation of medical resources and the strengthening of medical quality management.Methods All inpatient information from the hospital information system from January 1, 2018 to December 31, 2022 were extracted for statistical analysis from the aspects of gender，age，death disease，case fatality rate, and death disease composition of hospitalized deaths with the use of Excel 2007 and SPSS 18.Results The mortality rate of inpatients was 0.64%. There were 443 male cases (65.63%) and 232 female cases(34.37%)  among the 675 deaths. The top three causes of death were circulatory system diseases, respiratory system diseases, and tumors accounting for 28.44%，28.30%，and 27.56% of total deaths, respectively. The gender ratio of male to female deaths was 1.91∶1, the fatality rate was higher in males than in females, and the difference was statistically significant (χ2=46.068， P<0.001). The higher the age, the higher the fatality rate. The mortality rate rapidly increased after the age of 50.Conclusion The mortality rate of inpatients in the hospital is generally low, and the elderly are at high risk of death. Circulatory system diseases, malignant tumors and respiratory system diseases were the main causes of death．In medical work, medical resources should be scientifically and reasonably allocated based on patient gender, age, high incidence of mortality, and department characteristics to reduce case fatality rate.

Objective To analyze the coupling coordination of medical resources supply and utilization in Guangdong Province in the past five years, and to provide theoretical support for optimizing resources supply and spatial distribution and promoting the coordinated development of supply and utilization in Guangdong Province.Methods The supply and utilization level of medical resources in 21 cities in Guangdong Province in 2018 and 2022 were analyzed by coupling coordination model and GIS spatial analysis.Results The comprehensive level of medical resource supply in Guangdong Province increased from 0.37 to 0.45, and the comprehensive level of utilization decreased from 0.48 to 0.39. In 2018 and 2022, the medical resources supply subsystem and utilization subsystem were in a high-level coupling state, with the mean values of 0.97 and 0.96, respectively. However, the mean values of the coupling coordination degree did not change much in the past five years, 0.64 and 0.63 respectively, and were in a moderate coordination state, showing a trend of high in the west and low in the east, and high in the north and low in the south, with spatial aggregation.Conclusion The coupling coordination degree between medical resource supply and utilization is not high, and the regional differences are obvious. In formulating policies to optimize the distribution of medical resources, attention should be paid to the coordinated development of the two subsystems, and corresponding strategies should be proposed according to the different coupling coordination degrees and lagging types of different cities and regional heterogeneity. At the same time, the advantages of the Guangdong-Hong Kong-Macao Greater Bay Area should be taken into account to strengthen inter-regional cooperation and realize the optimal allocation of resources in different regions.

Objective To understand the average hospitalization cost structure of patients with hepatitis B cirrhosis at decompensated stage from January, 2018 to August, 2023, so as to provide a reference for reducing the medical burden of patients, optimizing the hospitalization cost structure and controlling the growth of medical costs.Methods The medical costs of 7 459  patients with hepatitis B cirrhosis at decompensated stage in a tertiary hospital in Guangdong Province from a January, 2018 to August, 2023 were selected. Grey correlation and structural variation degree analysis method were used to analyze the correlation degree, variation degree and contribution rate of each item cost of patients.Results The average hospitalization cost of patients with decompensated post-hepatitis B cirrhosis increased year by year from 2018 to 2021, with an average annual growth of 7.01%, and then decreased in 2022 and 2023, with a decline rate of 7.64% and 8.04%, respectively. The top four costs were the drug cost, the diagnosis cost, the consumables cost, and the treatment cost. The drug cost (0.915), the diagnosis cost (0.909) and the consumables cost (0.770) had the highest correlation coefficient. The top three items with the highest structural contribution rate were drug cost (43.27%), diagnostic cost (19.12%), and consumables cost (17.91%), and the cumulative structural change contribution rate of the three was over 80%.Conclusion The drug cost, diagnostic cost and consumables cost were the main factors affecting hospitalization costs, and were the key points to reduce the hospitalization cost of patients. It is necessary to further adjust and optimize the structure of hospitalization costs, reflect the labor and technology value of medical staff, and reduce the burden of patients′ medical expenses.

Objective To investigate the influencing factors affecting the level of hospital revenue and expenditure balance, and construct a regression prediction model under the payment of diagnosis related groups (DRGs) point method in Ningbo City.Methods The income and expenditure balance level of a Hospital of Ningbo city from January to December 2021 and 20 related factors indicators were collected, typical indicators were selected by similarity cluster analysis, and the weights of the typical indicators were calculated by using gray correlation analysis, so as to construct a gray system multivariate regression prediction model.Results The key indicators influencing the balance of revenue and expenditure included two positive indicators which were the proportion of lowmagnification cases and total weight, and 3 negative indicators which were the proportion of normal cases, cost consumption index, and proportion of DRG settlement cases. The correlation and weight of each indicator were in the following descending order: proportion of low-magnification cases, total weight, proportion of DRG settlement cases, proportion of normal cases, and cost consumption index. By applying these five indicators, we constructed a grey system multivariate regression prediction model, which was proven to meet the criteria for effectiveness and accuracy testing. This model could accurately fit and predict the level of income and expenditure balance in hospitals.Conclusion The multi-factor gray system regression prediction model is beneficial for hospital management to accurately formulate strategic goals and decisions, improve the level of hospital revenue and expenditure balance, and ensure the long-term stable development of hospitals.

Objective To establish a decision tree model of deep vein thrombosis associated with peripheral inserted central catheters (PICC) in elderly patients.Methods A total of 400 elderly cancer patients receiving PICC in a hospital from March 2017 to May 2021 were selected as the model group, and 120 elderly cancer patients receiving PICC in a hospital from June 2021 to February 2023 were selected as the validation group. Logistic regression was used to screen the risk factors of PICC-related deep vein thrombosis in elderly cancer patients. SPSS Modeler software was used to build a decision tree model of PICC-related deep vein thrombosis in elderly cancer patients, and the prediction efficiency of the decision tree model was analyzed.Results Among 400 elderly tumor patients, 74 cases developed PICC-associated DVT, and the incidence of DVT was 18.50%. Logistic regression analysis showed that body mass index, puncture times, catheter retention time, diabetes and chronic renal insufficiency were the risk factors for PICC-related deep vein thrombosis in elderly tumor patients (P<0.05). The classification nodes of the decision tree model of PICC-related deep vein thrombosis in elderly tumor patients were diabetes, catheter retention time, chronic renal insufficiency, puncture times and body mass index, among which diabetes was the most important predictor. The AUC of the decision tree model (AUC=0.749, 95%CI: 0.688~0.811) was higher than that of the logistic regression model (AUC=0.701, 95%CI: 0.633~0.770) (P<0.05), and that of the verification group was 0.812 (95%CI: 0.783~0.841).Conclusion Body mass index, puncture times, catheter retention time, diabetes and chronic renal insufficiency are risk factors for PICC-related DVT in elderly cancer patients. The decision tree model of PICC-related DVT in elderly cancer patients established in this study has high accuracy.

In recent years, there has been a rapid increase in the clinical use of implantable cardiac electronic devices. These devices consist mainly of electronic components such as pulse generators and electrode leads, encompassing artificial cardiac pacemakers and implantable cardioverter defibrillators. This article focuses on the clinical understanding of cardiac pacing and defibrillation technology, providing an analysis of the operational proce dures. It adheres to the classification principles of ICD-9-CM-3 for organizing and summarizing corresponding codes, offering a valuable reference for coding work. Coding personnel are required to maintain a thorough grasp of the latest clinical technological advancements, adhere to the principles of surgical classification, and diligently review medical records to ensure accurate coding and comprehensive representation of medical information.

Objective To analyze the hotspots and trends of Diagnosis-Intervention Packet (DIP) research in China in order to provide reference for the research direction.Methods The CNKI was used to retrieve the DIP-related literature, and descriptive analysis was conducted on the basic information such as the publication time of the literature. The social network method was used to analyze the core authors and high-frequency keywords, and visualize the connection between network nodes.Results From 2011 to 2023, a total of 545 related DIP literatures were published. There were 178 core journal literatures (32.66%) and 367 non-core journal literatures (67.34%). Since 2016, the DIP literature has rapidly increased, with over 200 articles published in 2023. There were 71 core authors in this field, forming 5 multi-person cooperation groups, led by Gao Guangying and Tian Jiashuai, with a total of 278 articles published. The network cograph and centrality analysis of high-frequency keywords showed that "medical insurance payment", "public hospitals", "fine management", "payment methods", and "cost control" ranked at the top of the degree centrality and proximity centrality rankings; while the intermediary centrality of "fine management" and "medical insurance payment" was relatively large.Conclusion The literature in DIP field is showing a continuous growth trend in China, with multiple research teams formed by core authors who have a greater influence and academic leadership role. The hotspots mainly focus on medical insurance payment and fine management in public hospitals. The development trend will go deep into many fine links such as cost control of public hospitals.

Objective To analyze the operation index data of public hospitals in Jiangsu Province, in order to provide insights for optimizing resource allocation and management of medical resources in public hospitals in Jiangsu Province.Methods Data envelopment analysis (DEA) was employed to assess the operational efficiency of second-level and above public hospitals in sub-cities of Jiangsu from 2015 to 2022.Results From 2015 to 2019, the technical efficiency, scale efficiency and comprehensive efficiency of public hospitals in Jiangsu Province were all 1, indicating that DEA was strong and effective, while the return to scale remained unchanged. From 2020 to 2022, except for 2021, when the technical efficiency was 1, the scores of technical efficiency, scale efficiency and comprehensive efficiency were less than 1, and in 3 years, the scale efficiency and comprehensive efficiency were not effective. Notably, Suzhou, Zhenjiang, and Suqian maintained a scale compensation coefficient of 1 from 2015 to 2022, while other areas experienced fluctuations in scale compensation.Conclusion The operational efficiency of public hospitals in Jiangsu Province varies over time and across regions, indicating uneven development. There is still room for optimizing the allocation of medical resources and scale among public hospitals in many cities.

Objective To predict the outpatient volume of a tertiary general hospital in Zhejiang Province by establishing the seasonal ARIMA model, and to provide a basis for the rational allocation of outpatient human resources.Methods Based on the outpatient visits data of a tertiary general hospital in Zhejiang Province from January 2013 to June 2023, the seasonal ARIMA model was constructed by SPSS software to predict the annual outpatient visits from July 2023 to December 2023. By comparing the measured outpatient visits, the accuracy of the seasonal ARIMA model was evaluated.Results The outpatient volume of the general hospital showed an increasing trend year by year, and showed the characteristics of periodic fluctuations. The optimal seasonal ARIMA model fitted was ARIMA(0,1,1)(1,0,1)12, BIC (Bayesian information criterion) was 5.273, MAPE (mean absolute percentage error) was 14.265, R2 (module determination coefficient) was 0.408, and the overall relative error was 1.83%, indicating good prediction results.Conclusion The seasonal ARIMA model can simulate the change trend of the outpatient volume in the time series of the tertiary general hospital well, and provide a theoretical basis for the short-term forecast of the outpatient volume in the hospital.

Objective To explore the screening effect of different obesity indicators on diabetes in middle-aged and elderly people, and determine the optimal cutting point values and optimal indicators of different obesity indicators, so as to provide data support and scientific basis for early screening, prevention and control of diabetes.Methods A total of 12 630 people aged 45-79 years old with physical examination in a hospital were investigated by questionnaire. Receiver operating characteristic curve (ROC) was used to determine the optimal index and the optimal cutting point. Delong test was used to compare the screening effect of different obesity indicators on diabetes, and the logistic regression was used to evaluate the predictive effect of different obesity indicators at different cutting points.Results  (1) The prevalence of diabetes in males was higher than that in females; (2) the area under the receiver operating characteristic curve (AUROC) of waist-to-height ratio (WHtR) index of different genders was the largest, and the AUROC of WC and WHtR was significantly different from that of body mass index (BMI) and waist-to-hip ratio (WHR); (3) at different cutting points, WC and WHtR had higher -OR-values and AUROC values.Conclusion For middleaged and elderly people, WC and WHtR have better screening ability and prediction effect for diabetes than other indicators.

Objective To analyze the risk of acute coronary events in patients with type 2 diabetes mellitus, and to provide basis for the management of patients.Methods Based on the data of Wenzhou chronic disease collaborative management system, a retrospective cohort study was conducted. A total of 277 205 newly diagnosed type 2 diabetes mellitus patients from 2010 to 2020 were included in the analysis. The incidence and death information were obtained by matching the ID number with the data of acute coronary events and deaths. COX regression model was used to analyze the relationship between the complications of diabetes patients at the time of diagnosis and the risk of acute coronary events.Results During the followup period, acute- coronary events occurred in 3 241 cases, and the incidence rate of coronary heart disease was 1 169.17/100 000; 1 083 diabetes patients died from coronary heart disease, with a fatality rate of 390.69/100 000. Taking the patients diagnosed without complications as the reference, the HR of acute coronary disease in patients with one complication and two or more was 1.382 (95%CI:1.266-1.508) and 1.661 (95%CI:1.221-2.259) respectively.Conclusion Type 2 diabetes patients have a high risk of morbidity and mortality from acute coronary events , so intervention should be strengthened in patient management to actively prevent and treat complications.

Objective To understand the level of nutrition and health knowledge of adult residents in Wuning County, Jiangxi Province under the background of healthy China, analyze the existing shortcomings of nutrition and health knowledge, and discuss the relevant influencing factors of residents′ nutrition and health knowledge awareness, so as to provide a scientific basis for the formulation of nutrition intervention policies.Methods A total of 895 local residents aged 18-64 years old in Wuning County, Jiangxi Province were selected to carry out a questionnaire survey on nutrition and health knowledge, the awareness rate was calculated, the acquisition channels of nutrition knowledge were analyzed and compared, and the main factors affecting the awareness rate of nutrition and health knowledge were analyzed by variance analysis and multivariate logistic regression method.Results The average age of 895 respondents was 39.49±12.08 years old, and males accounted for 51.62%. The awareness rate of high-oil and high-salt food was the highest (85.59%), followed by the best calcium supplement food (55.75%), and the daily salt intake of healthy adults (41.68%). The average score of nutrition knowledge was 11.01±2.38 points. The most extensive way for respondents to obtain nutrition knowledge was WeChat /Weibo /Tiktok /Kuaishou, accounting for 91.28% of the total number of respondents. There were statistically significant differences in the average scores and awareness rates of different age groups (F=30.80, P<0.001), different education level groups (F=22.26, P<0.001), different occupational groups (F=20.06, P<0.001), and different health status groups (F=5.34, P=0.005). Logistics regression analysis showed that education level and occupation type were the influencing factors of nutrition knowledge score (P<0.05).ConclusionThe awareness rate of nutrition and health knowledge among adult residents in Wuning County, Jiangxi Province is related to age, occupation, education level and health status, and it is necessary to focus on the weak links and take corresponding measures according to the characteristics of different groups of people, and strengthen the dissemination of health and nutrition knowledge through multiple channels.

Metabolomics is a disciplinary field that investigates all small-molecule metabolites within the biological organism. Utilizing advanced technological platforms such as mass spectrometry and nuclear magnetic resonance, comprehensive and high-throughput detection of metabolites can offer new avenues for early disease diagnosis, elucidation of pathogenic mechanisms, and personalized therapeutic approaches. Due to the diversity, high dimensionality, dynamics, noise, and variability inherent in metabolomics data, it poses formidable challenges for the research and development of data analysis methods. This paper aims to provide a comprehensive overview of recent advancements of machine learning methods for statistical analysis on metabolomics data, with particular focus on improved approaches. By synthesizing these methodologies, the intention is to offer a crucial foundation for effective data analysis and to fully harness the potential of metabolomics in medical research.