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Objective: To understand the occupational burnout status of tuberculosis prevention and treatment medical staff in Beijing, and analyze the influencing factors of different degrees of occupational burnout. Methods: From April to May 2021, an anonymous questionnaire survey was conducted among the medical staff of tuberculosis prevention and control in Beijing and 16 districts under its jurisdiction. A total of 313 questionnaires were issued, 311 were recovered, and 311 were valid, with an effective recovery rate of 99.36%. The General Information Questionnaire and Maslach Burnout Inventory Generalized Scale (MBI-GS) were used to collect social demographic data and the occurrence of occupational burnout, analyze the occurrence degree of occupational burnout, and identify the influencing factors of the occurrence degree of occupational burnout by using the orderly multiple logistic regression model. Results: Among 311 tuberculosis prevention and control medical staff, the total detection rate of occupational burnout was 62.70% (195/311), and the detection rates of mild, moderate and severe occupational burnout were 22.19% (69/311), 38.59% (120/311) and 1.93% (6/311), respectively. Orderly multiple logistic regression analysis showed that medical staff in prevention and control positions ( OR =1.616, 95% CI : 1.030-2.534, P =0.037) and not meeting expectations for title promotion ( OR =2.969, 95% CI : 1.675-5.262, P <0.001), and not getting along well with colleagues ( OR =2.177, 95% CI : 1.362-3.480, P =0.001) were the main factors affecting the occurrence and severity of occupational burnout among tuberculosis prevention and treatment medical staff. Conclusion: The main manifestations of tuberculosis prevention and control medical staff in Beijing are mild to moderate occupational burnout. It is suggested to pay attention to the occupational needs of different positions of tuberculosis prevention and control medical staff, cultivate professional achievement, carry out psychological counseling, and reduce the degree of occupational burnout.

Objective: To understand the spatiotemporal distribution of pulmonary tuberculosis (TB) and influencing factors in Beijing from 2008 to 2018. Methods: The incidence data of pulmonary TB in Beijing from 2008 to 2018 were from Tuberculosis Information Management System of Chinese Disease Prevention and Control Information System. Software ArcGIS 10.2 was used to visualize the spatiotemporal distribution of pulmonary TB incidence. Getis's G i * The reported pulmonary TB incidence showed a downward trend in the past 11 years in Beijing, from 58.64/100 000 to 30.43/100 000. The incidences were higher in Tongzhou, Changping and other newly developed urban districts, with the hot spots concentrated in local areas of these districts. The incidences of pulmonary TB were lower in Dongcheng, Xicheng and other old urban districts-with the cold spots also concentrated in these area. The risk for the incidence of pulmonary TB was associated with the urbanization rate and the permanent migrant population. For every 1% increase in the urbanization rate, the relative risk of pulmonary TB would increase by 1%. For every 10 000 person increase of permanent migrant population, the relative risk of pulmonary TB would increase by 0.6%. Results: In Beijing, the current pulmonary TB prevention and control needs to be focused on the newly developed urban areas. Due to the accelerated process of urbanization, it is necessary to strengthen TB prevention and control in permanent migrant population to reduce the incidence of TB in Beijing.Conclusions: In Beijing, the current pulmonary TB prevention and control needs to be focused on the newly developed urban areas. Due to the accelerated process of urbanization, it is necessary to strengthen TB prevention and control in permanent migrant population to reduce the incidence of TB in Beijing.

Objective: To analyze the spatial distribution and identify the high risk areas of pulmonary tuberculosis at the township level in Beijing during 2005-2015. Methods: Data on pulmonary tuberculosis cases was collected from the tuberculosis information management system. Global autocorrelation analysis, local indicators of spatial association and Kulldorff's Scan Statistics were applied to map the spatial distribution and detect the space-time clusters of the pulmonary tuberculosis cases during 2005-2015. Results: Spatial analysis on the incidence of pulmonary tuberculosis at the township level demonstrated that the spatial autocorrelation was positive during the study period. The values of Moran's I ranged from 0.224 3 to 0.291 8 with all the P values less than 0.05. Hotspots were primarily distributed in 8 towns/streets as follows: Junzhuang, Wangping, Yongding and Tanzhesi in Mentougou district, Yancun in Fangshan district, Wangzuo town in Fengtai district, Tianqiao street in Xicheng district and Tianzhu town in Shunyi district. Spatiotemporal clusters across the entire study period were identified by using Kulldorff's spatiotemporal scan statistic. The primary cluster was located in Chaoyang and Shunyi districts, including 17 towns/streets, as follows: Cuigezhuang, Maizidian, Dongfeng, Taiyanggong, Zuojiazhuang, Hepingjie, Xiaoguan, Xiangheyuan, Dongba, Jiangtai, Wangjing, Jinzhan, Jiuxianqiao, Laiguangying, Sunhe towns/streets in Chaoyang district, Houshayu and Tianzhu town in Shunyi district, during January to December 2005. Conclusion: Incidence rates of pulmonary tuberculosis displayed spatial and temporal clusterings at the township level in Beijing during 2005-2015, with high risk areas relatively concentrated in the central and southern parts of Beijing.