Objective:To analysis the incentive level of family doctors in primary medical and health institutions in Beijing, and to explore its influencing factors, so as to provide references for promoting family doctors′ contract service.Methods:From October to December 2021, 40 family doctors were randomly selected from 135 urban community health service centers in 8 districts of Beijing, and a survey was conducted on basic demographic information, institutional organizational capacity evaluation, and family doctor incentive level evaluation (including four dimensions of work value, organizational environment, personal development, and reward compensation), and the influencing factors of family doctor incentive level was analyzed. Chi-square test and Pearson correlation analysis were used for univariate analysis, and multiple linear regression analysis was used for multivariate analysis.Results:A total of 4 568 family doctors were included, and the score of family doctors′ incentive level was 3.75±0.81, among which the work value score was the highest(3.89±0.77), followed by the organizational environment score(3.69±0.92) and personal development score(3.75±0.90), and the lowest score was reward(3.75±0.90). Age, education, professional title, working years and average total working hours per week negatively affected the incentive level( P<0.05), while the average monthly income, the frequency of attending training and further study, and the organizational ability of the organization positively affected the incentive level( P<0.05). Conclusions:The overall incentive level of family doctors in primary medical and health institutions in Beijing needed improvement, and there were many factors that affect the incentive level of family doctors. It was recommended to appropriately increase the salary and benefits of family doctors, reasonably arrange the workload of family doctors, strengthen the training mechanism of family doctors, and strengthen the organizational capacity building of primary medical and health institutions.

Objective:To study the relationship between hemoglobin glycation index (HGI) and blood lipid indices such as low-density lipoprotein cholesterol (LDL-C), non-high-density lipoprotein cholesterol (non-HDL-C), and plasma atherogenic index (AIP).Methods:This cross-sectional study included 16 049 participants from the Beijing Apple Garden community between December 2011 and August 2012. The subjects were divided into three groups based on the HGI quartile: low ( n=5 388), medium ( n=5 249), and high ( n=5 412). The differences in blood lipid indicators between different HGI groups were compared and multivariate logistic regression model was established to analyze the association between HGI and dyslipidemia. And multivariate logistic regression model was established to analyze the relationship between HGI and blood lipid indicators in different glucose metabolism populations. Results:There were 16 049 participants in all (mean age: 56 years), including 10 452 women (65.1%). They were classified into normal glucose tolerance (9 093 cases), prediabetes (4 524 cases), and diabetes (2 432 cases) based on glucose tolerance status. In the general population, with the increase of HGI, LDL-C, non-HDL-C, and AIP gradually increased (all P values for trends were <0.05), and the proportion of abnormalities increased significantly ( χ2=101.40, 42.91, 39.80; all P<0.001). A multivariate logistic regression model was established, which suggested a significant correlation between HGI and LDL-C, non-HDL-C, and AIP (all P<0.05), after adjusting for factors such as age, sex, fasting blood glucose, hypertension, body mass index, smoking, and alcohol consumption. In the overall population, normal glucose tolerance group, and diabetes group, HGI had the highest correlation with non-HDL-C ( OR values of 1.325, 1.678, and 1.274, respectively); in the prediabetes group, HGI had a higher correlation with LDL-C ( OR value: 1.510); and in different glucose metabolism groups, AIP and HGI were both correlated ( OR: 1.208-1.250), but not superior to non-HDL-C and LDL-C. Conclusion:HGI was closely related to LDL-C, non HDL-C, and AIP in the entire population and people with different glucose metabolism, suggesting that HGI may be a predictor of atherosclerotic cardiovascular disease.

Objective: To evaluate the practical efficacy of a colloidal gold (CG) detection reagent of rabies virus antibody. Methods: Series dilutions of rabies immunoglobulin and serum samples of rabies vaccine immunized population were tested by a CG detection reagent of rabies virus antibody and rapid fluorescent focus inhibition test (RFFIT). The consistency of the qualitative results of rabies virus antibodies between the two methods were compared. The comparison of rates was made by Chi-square test. Results: For rabies immunoglobulin diluent, the detection limit of the rabies virus antibody CG detection reagent was higher than 6.53 IU/ml but lower than 9.53 IU/ml. For the serum samples, the detection limit of the rabies virus antibody CG detection reagent was higher than 2.80 IU/ml but lower than 3.30 IU/ml. The positive rates of serum rabies virus antibodies detected by CG and RFFIT were 26% and 67% respectively, and the difference was statistically significant (χ² =13.66, P=0.000). The results of RFFIT were regarded as gold standard, false negative results but no false positive results were observed when CG was used to detect serum rabies virus antibodies. Conclusions The sensitivity of the rabies virus antibody CG detection reagent is poor, and attention should be paid to the phenomenon of missing some practically positive results in practical application of CG detection reagent.