Objective To screen the influencing factors of hepatitis C cirrhosis patients progressing to hepatocellular carcinoma(HCC)using commonly used laboratory testing indicators,establish a prediction model using these indicators and validate them.Methods A total of 231 patients with hepatitis C cirrhosis and 179 patients with hepatitis C HCC hospitalized at the First Affiliated Hospital of Xi'an Jiaotong University between June 2020 and May 2023 were enrolled as the training set,and 105 patients with hepatitis C cirrhosis and 86 patients with hepatitis C HCC hospitalized between June 2023 and February 2024 were enrolled as the validation set.The routine laboratory test indexes of the study subjects in the two groups within the training set were compared,and logistic regression analysis was applied to screen the independent predictors of hepatocellular carcinoma occurrence.Receiver operating characteristic(ROC)curve was used to construct the curve model and validate the model.Results The age,male ratio,ALT,AST,AFP,WBC,NEU,MO,PLT,MPV,PDW,Fbg,NLR and PLR levels of the HCC group were higher than those of the cirrhosis group in the training set(H=-9.07～-2.19),while the levels of INR and LMR were lower than those of the cirrhosis group(H=-4.49,-2.65),and the differences were significant(all P＜0.05).The differences in TP,eGFR,LY and AST/ALT values between the two groups of patients were not significant(H=-1.46～-0.15,all P＞0.05).Multifactorial Logistic regression analysis showed that age(OR=1.048,95％CI:1.023～1.074),Male(OR=1.467,95％CI:1.413～1.765),AST(OR=1.010,95％CI:1.002～1.019),NEU(OR=1.186,95％CI:1.018～1.382)and Fbg(OR=2.245,95％CI:1.639～3.076)were independent risk factors for hepatocellular carcinoma patients(all P＜0.05),and these five independent risk factors were used to construct the HCC column-line graph prediction model,with the AUC for the training set and the validation set AUC(95％Cl)were 0.813(0.771～0.854)and 0.712(0.639～0.784),respectively,and the Hosmer-Lemeshow test showed a good fit of the model with P=0.650 for the training set and P=0.310 for the validation set.Conclusion The prediction model of HCC based on age,gender,AST,NEU and Fbg can have good predictive efficacy and clinical application value.

Objective:To assess the trends in case-fatality rates for acute myocardial infarction (AMI) in China from 2015 to 2019.Methods:This study employed a population-based surveillance. Data from the China Registry of Acute Cardiovascular Event (China RACE) were utilized, including AMI cases reported by Grade Ⅱ and Grade Ⅲ hospitals at the disease surveillance sites across China from January 1 st 2015 to December 31 st 2019. The 28-day mortality outcome for reported AMI events was obtained by linking to the national death certificate registry system. The study analyzed the overall and age-standardized case-fatality rates, as well as their annual percent change (APC), during the study period, stratified by gender, age, and region. Results:The overall 28-day case fatality rate for AMI was 28.97% (22 532/77 764) from 2015 to 2019. The age-standardized case-fatality rate for AMI declined significantly from 37.53% in 2015 to 18.58% in 2019, with an APC of -14.33% ( P=0.018). We observed a significant downward trend in case-fatality rates of AMI in both genders (both P<0.05). Among males, the case-fatality rate decreased more steeply in younger males compared to elder counterparts. The most marked decreases were seen in males aged<35 years and 35 to 44 years, with APC of -27.63% ( P=0.007) and -22.65% ( P=0.004), respectively. In females, we observed a relatively stable decrease in case-fatality across age groups. The age-standardized case-fatality rate of AMI in eastern and central China decreased significantly from 2015 to 2019, with the APC of -19.22% ( P=0.006) and -15.62% ( P=0.032) respectively. However, the age-standardized case-fatality rate of AMI in western China remained stable ( P=0.227). Conclusions:The prognosis of AMI has considerably improved from 2015 to 2019 in China, regardless of ages and gender. Inequality in case-fatality rates among geographic regions highlights the need for targeted strategies in AMI prevention in western regions.

New quality productive forces with innovation and efficient utilization of resources as its core,is a new engine driving the innovative development of the national healthcare system.Healthcare big data has become the core motivity of the new quality productive forces,with the rapid development of industrial technologies including data governance and cloud storage,digital intelligence technology,and artificial intelligence,etc.From the perspective of new quality productive forces,it expounds on how to drive the innovative development of health economics and pharmacoeconomics research based on big data,including aspects of developing the innovative instruments for health outcome measurement,building the innovative frameworks of value assessment for innovative medications,and enhancing the precise evidence of healthcare policy evaluation.It describes how to use Healthcare big data as a driver to promote the innovative development of scientific research in related fields,and give full play to the role of economics evidence generation and decision-making support,so as to promote health care decision-making and optimal allocation of resources towards a new stage of improving quality and efficiency.

New quality productive forces with innovation and efficient utilization of resources as its core,is a new engine driving the innovative development of the national healthcare system.Healthcare big data has become the core motivity of the new quality productive forces,with the rapid development of industrial technologies including data governance and cloud storage,digital intelligence technology,and artificial intelligence,etc.From the perspective of new quality productive forces,it expounds on how to drive the innovative development of health economics and pharmacoeconomics research based on big data,including aspects of developing the innovative instruments for health outcome measurement,building the innovative frameworks of value assessment for innovative medications,and enhancing the precise evidence of healthcare policy evaluation.It describes how to use Healthcare big data as a driver to promote the innovative development of scientific research in related fields,and give full play to the role of economics evidence generation and decision-making support,so as to promote health care decision-making and optimal allocation of resources towards a new stage of improving quality and efficiency.

Objective To explore the Helicobacter pylori (Hp) infection rate and antibody typing of 1111 physical examination people in plateau area, and to analyze the risk factors of Hp infection by logistics regression analysis. Methods 1111 healthy people with physical examination in plateau area from January 2022 to December 2022 were selected as the research subjects. The Hp infection rate and antibody typing were calculated, and the risk factors of Hp infection were analyzed by logistics regression analysis. Results The Hp infection rate of physical examination people in plateau area was 62.47% (694/1 111). The infection rate of type I HP in infected patients was higher than that of type Ⅱ HP(75.50% vs 24.50%) (χ²=361.141, P<0.001). The AUC of CagA in the diagnosis of Hp infection was higher than that of antibody VacA or Ure positive diagnosis alone (Z=6.740, 7.608, P<0.001). The proportions of people with male gender, often eating pickled or barbecued foods, history of chronic gastric disease and family members living together≥4 in infected group were higher than those in uninfected group (χ²=4.418, 8.708, 16.565, 32.583, P=0.036, 0.003, <0.001, <0.001) while the proportion of people with regular garlic consumption was lower than that in uninfected group (χ²=5.153, P=0.023). Often eating pickled or barbecued foods [OR (95%CI)=2.038 (1.049-3.961)], history of chronic gastric disease [OR(95%CI)=1.706 (1.132-2.569)] and family members living together≥4 [OR (95%CI)=1.857 (1.135-3.037)] were risk factors of Hp infection, and regular garlic consumption [OR (95%CI)=0.559 (0.346-0.903)] was a protective factor (P=0.036, 0.011, 0.014, 0.018). Conclusion The Hp infection rate and antibody Ure positive rate are higher in physical examination people in plateau area, and chronic gastric disease history and often eating pickled or barbecued foods are risk factors of Hp infection.

New quality productive forces with innovation and efficient utilization of resources as its core,is a new engine driving the innovative development of the national healthcare system.Healthcare big data has become the core motivity of the new quality productive forces,with the rapid development of industrial technologies including data governance and cloud storage,digital intelligence technology,and artificial intelligence,etc.From the perspective of new quality productive forces,it expounds on how to drive the innovative development of health economics and pharmacoeconomics research based on big data,including aspects of developing the innovative instruments for health outcome measurement,building the innovative frameworks of value assessment for innovative medications,and enhancing the precise evidence of healthcare policy evaluation.It describes how to use Healthcare big data as a driver to promote the innovative development of scientific research in related fields,and give full play to the role of economics evidence generation and decision-making support,so as to promote health care decision-making and optimal allocation of resources towards a new stage of improving quality and efficiency.

New quality productive forces with innovation and efficient utilization of resources as its core,is a new engine driving the innovative development of the national healthcare system.Healthcare big data has become the core motivity of the new quality productive forces,with the rapid development of industrial technologies including data governance and cloud storage,digital intelligence technology,and artificial intelligence,etc.From the perspective of new quality productive forces,it expounds on how to drive the innovative development of health economics and pharmacoeconomics research based on big data,including aspects of developing the innovative instruments for health outcome measurement,building the innovative frameworks of value assessment for innovative medications,and enhancing the precise evidence of healthcare policy evaluation.It describes how to use Healthcare big data as a driver to promote the innovative development of scientific research in related fields,and give full play to the role of economics evidence generation and decision-making support,so as to promote health care decision-making and optimal allocation of resources towards a new stage of improving quality and efficiency.

New quality productive forces with innovation and efficient utilization of resources as its core,is a new engine driving the innovative development of the national healthcare system.Healthcare big data has become the core motivity of the new quality productive forces,with the rapid development of industrial technologies including data governance and cloud storage,digital intelligence technology,and artificial intelligence,etc.From the perspective of new quality productive forces,it expounds on how to drive the innovative development of health economics and pharmacoeconomics research based on big data,including aspects of developing the innovative instruments for health outcome measurement,building the innovative frameworks of value assessment for innovative medications,and enhancing the precise evidence of healthcare policy evaluation.It describes how to use Healthcare big data as a driver to promote the innovative development of scientific research in related fields,and give full play to the role of economics evidence generation and decision-making support,so as to promote health care decision-making and optimal allocation of resources towards a new stage of improving quality and efficiency.

New quality productive forces with innovation and efficient utilization of resources as its core,is a new engine driving the innovative development of the national healthcare system.Healthcare big data has become the core motivity of the new quality productive forces,with the rapid development of industrial technologies including data governance and cloud storage,digital intelligence technology,and artificial intelligence,etc.From the perspective of new quality productive forces,it expounds on how to drive the innovative development of health economics and pharmacoeconomics research based on big data,including aspects of developing the innovative instruments for health outcome measurement,building the innovative frameworks of value assessment for innovative medications,and enhancing the precise evidence of healthcare policy evaluation.It describes how to use Healthcare big data as a driver to promote the innovative development of scientific research in related fields,and give full play to the role of economics evidence generation and decision-making support,so as to promote health care decision-making and optimal allocation of resources towards a new stage of improving quality and efficiency.

New quality productive forces with innovation and efficient utilization of resources as its core,is a new engine driving the innovative development of the national healthcare system.Healthcare big data has become the core motivity of the new quality productive forces,with the rapid development of industrial technologies including data governance and cloud storage,digital intelligence technology,and artificial intelligence,etc.From the perspective of new quality productive forces,it expounds on how to drive the innovative development of health economics and pharmacoeconomics research based on big data,including aspects of developing the innovative instruments for health outcome measurement,building the innovative frameworks of value assessment for innovative medications,and enhancing the precise evidence of healthcare policy evaluation.It describes how to use Healthcare big data as a driver to promote the innovative development of scientific research in related fields,and give full play to the role of economics evidence generation and decision-making support,so as to promote health care decision-making and optimal allocation of resources towards a new stage of improving quality and efficiency.