Background The value of targeting people with a high risk of stroke based on electronic health records (EHRs) in Shanghai is largely undiscovered.Aim To test the hypothesis that EHRs might be developed into an evidence-based support system to identify people who are at high risk of stroke.Methods We performed a screen analysis utilizing EHRs to target the population with stroke risk factors, such as hypertension, diabetes mellitus, obesity, smoking and physical inactivity. We calculated the distribution of each risk factor and the combinations of risk factors.Results In the Jiading District of Shanghai, 46,580 hypertensive patients with complete baseline information joined the hypertensive patient management system from 2014 to 2017. The majority of the patients were older than 60 years. Physical inactivity (83.24%), smoking (24.07%), diabetes (16.87%), and obesity (12.23%) were highly prevalent in hypertension participants. Approximately 4377 patients had hypertension only, accounting for 9.70% of the total patients in this study. Approximately 52.47% of the patients had two risk factors at the same time, and 38.13% of the patients had hypertension, which means that 17,762 patients could be identified as a high-risk population for stroke according to the criteria established by the National Stroke Screening Survey.Conclusion Our exploratory findings suggest the feasibility of targeting populations with a high risk of stroke using the EHRs of hypertensive patients.

Performance pay in county-level public hospitals is an important part of the reform of public hospitals, which largely determines the success or failure of the reform of public hospitals. This paper reviews the concept of performance pay, the recognition of hospitals and staff, the main performance pay model, current situation, design principles and ideas in County-level Public hospitals, and provides some reference for future research and reform direction of performance pay in County-level Public hospitals.

Concrete can be recycled after certain processing technologies for use in pavement engineering but the flexural strength (FS) is difficult to predict accurately in the design process. This study proposes a novel systematic and evolved approach to estimate the FS of recycled concrete. The proposed methods are conducted based on the random forest (RF) model as well as the firefly algorithm (FA), where the latter is employed to tune the hyperparameters of the RF model. For this purpose, data sets were collected from previously published literature for the training and verification of the model, and the accuracy of the model was verified by the fitting effect of the predicted and actual values. The results showed that the proposed hybrid machine learning model has a good fitting effect on the predicted and actual values; the calculation and evaluation process demonstrated fast convergence and significantly lower values of RMSE for the proposed model to determine the FS of the recycling concrete. In addition, the study analyzed the sensitivity of the FS of recycled concrete to input variables, and the results showed that effective water-cement ratio (WC), water absorption of recycling concrete (WAR), and water absorption of natural aggregate (WAN) show more obvious influences on FS, so these factors should be paid more attention in future pavement design using the recycling of concrete.

Background: The value of identifying and targeting population demographics at high risk of stroke based on patient-reported outcomes (PROs) with electronic health records (EHRs) in Shanghai is largely undiscovered. Aim: To test the hypothesis that establishing an evidence-based support system composed of PROs integrated with EHRs could be effective at identifying individuals at high risk of suffering from stroke. Methods: The patients included in this study joined the hypertensive patient management system from 2014 to 2018. We merged the Hypertension Patients Management Database and the Diabetes Mellitus Patients Management Database of Shanghai Jiading district, then kept the hypertension patients with or without diabetes. We subsequently performed a screen analysis utilizing EHRs to target the population with any risk factor for stroke, namely, hypertension, diabetes mellitus, obesity, smoking and physical inactivity. We also calculated the distribution of each risk factor and the combinations of risk factors. Results: In the Jiading District of Shanghai, 46,580 hypertensive patients with complete baseline information joined the hypertensive patient management system from 2014 to 2018. The majority of the patients were aged above 60 years old. Physical inactivity (83.24%), smoking (24.07%), diabetes (16.87%), and obesity (12.23%) were highly prevalent in hypertensive participants. Approximately 4377 patients were diagnosed with hypertension exclusively, accounting for 9.70% of the total number of patients in this study. Meanwhile, approximately 52.47% of the patients were diagnosed with two concurrent risk factors, and 38.13% of the patients had hypertension, meaning that 17,762 patients could be labeled as the high-risk population for stroke according to the criteria established by the National Stroke Screening Survey. Conclusion: Our exploratory findings demonstrate the feasibility of pinpointing and targeting populations at high risk of stroke using the EHRs of hypertensive patients.