ObjectiveTo systematically review the purchase willingness rate and influencing factors of long-term care insurance in Chinese population.MethodsCNKI, VIP, WanFang Data, EMbase and PubMed databases were electronically searched to collect cross-sectional studies on the purchase willingness rate of long-term care insurance in China from inception to March 2021. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Meta-analysis was then performed using Stata 16.0 software.ResultsA total of 66 cross-sectional studies involving 151 231 subjects were included. The results of the meta-analysis showed that the purchase willingness rate of long-term care insurance in China was 52.4% (95%CI 48.1% to 56.8%). Subgroup analysis showed that: among the sample characteristic factors, residents who were from the central region of China (56.4%), being studied after 2016 (53.3%), and residing in pilot regions (53.1%) had a higher willingness rate to purchase long-term care insurance. Among demographic factors, the research considered factors of residence and family size (56.2%) contributed to a higher willingness to purchase long-term care insurance, and residents with monthly income from 1 000 yuan to 5 000 yuan (55.4%) and who were unmarried (55.3%) had a higher willingness to purchase long-term care insurance. Among health and concept factors, the research considered factors of insurance and government trust (57.3%), factor of number of chronic diseases (55.0%), and factor of health risk cognition (52.4%) contributed to a higher willingness to purchase long-term care insurance. Among the factors of long-term care insurance system, factor of the government subsidy (60.6%), factor of long-term care insurance price (58.0%) and factor of payment methods (56.2%) contributed a higher willingness to purchase long-term care insurance.ConclusionsCurrent evidence shows that over half of residents are willing to purchase long-term care insurance. However, different factors still affect their purchase willingness. The influencing factors reflect numerous difficulties in the current long-term care insurance system, which requires attention and continuous improvement of policy formulators and related researchers.
Objective To evaluate the predictive effect of three machine learning methods, namely support vector machine (SVM), K-nearest neighbor (KNN) and decision tree, on the daily number of new patients with ischemic stroke in Chengdu. Methods The numbers of daily new ischemic stroke patients from January 1st, 2019 to March 28th, 2021 were extracted from the Third People’s Hospital of Chengdu. The weather and meteorological data and air quality data of Chengdu came from China Weather Network in the same period. Correlation analyses, multinominal logistic regression, and principal component analysis were used to explore the influencing factors for the level of daily number of new ischemic stroke patients in this hospital. Then, using R 4.1.2 software, the data were randomly divided in a ratio of 7∶3 (70% into train set and 30% into validation set), and were respectively used to train and certify the three machine learning methods, SVM, KNN and decision tree, and logistic regression model was used as the benchmark model. F1 score, the area under the receiver operating characteristic curve (AUC) and accuracy of each model were calculated. The data dividing, training and validation were repeated for three times, and the average F1 scores, AUCs and accuracies of the three times were used to compare the prediction effects of the four models. Results According to the accuracies from high to low, the prediction effects of the four models were ranked as SVM (88.9%), logistic regression model (87.5%), decision tree (85.9%), and KNN (85.1%); according to the F1 scores, the models were ranked as SVM (66.9%), KNN (62.7%), decision tree (59.1%), and logistic regression model (57.7%); according to the AUCs, the order from high to low was SVM (88.5%), logistic regression model (87.7%), KNN (84.7%), and decision tree (71.5%). Conclusion The prediction result of SVM is better than the traditional logistic regression model and the other two machine learning models.
HtrA serine peptidase 2 (HTRA2) is a serine protease existing in the mitochondrial gap. Among the four members of the human HtrA serine peptidase family, HTRA2 is the only protease with clear localization in the cell. It plays a dual role in the maintenance of mitochondrial homeostasis and the promotion of cell apoptosis. HTRA2 has been found to be associated with a variety of tumors. Meanwhile, the expression of HTRA2 can enhance the sensitivity of chemotherapy and radiotherapy, and can be used as a diagnostic and prognostic marker for malignant tumors and a target for combined therapy. This article reviews the structure, biological function and role of HTRA2 in malignant tumors, in order to provide clues and basis for early diagnosis and individualized treatment of tumor patients.
Objective To explore the efficacy of endovascular therapy in elderly patients with acute ischemic stroke. Methods The acute ischemic stroke patients who received endovascular therapy between January 2020 and January 2023 were retrospectively enrolled. According to age, patients were divided into the elderly group (≥ 80 years old) and other age groups (<80 years old). The baseline data, green channel data, nerve function deficit, recanalization and complication information were collected, and the patients were followed up. Modified Rankin Scale (mRS) was used to evaluate patients prognosis at 3 months after onset. Score less than or equal to 2 points was defined as good prognosis and over 2 points was defined as poor prognosis. Results A total of 138 patients were included, and 7 patients were lost to follow-up. Finally, 131 patients were included. Among them, there were 50 cases in the elderly group and 81 cases in the other age group. There were statistically significant differences in age, hypertension, atrial fibrillation, and vascular recanalization between the elderly group and the other age group (P<0.05). There was no statistically significant difference in the other baseline data, complications, 3-month prognosis, or mortality between the two groups (P>0.05). The results of multivariate logistic regression analysis showed that the National Institute of Health Stroke Scale score at admission [odds ratio (OR)=1.150, 95% confidence interval (CI) (1.033, 1.281), P=0.011], pulmonary infection [OR=2.933, 95%CI (1.109, 7.758), P=0.030], and hypoproteinemia [OR=3.716, 95%CI (1.226, 11.264), P=0.020] affected the mRS score at 3 months after onset. Conclusions Among the patients with acute ischemic stroke undergoing endovascular therapy, there is no difference in the occurrence of complications or short-term prognosis between elderly patients and other age patients. However, the attention should still be paid to reducing the occurrence of complications in patients, strengthening their nutritional support, and thereby improving their prognosis.