Objective: To analyze the spatio-temporal distribution of pulmonary tuberculosis (PTB) among students in Suzhou City, Jiangsu Province from 2015 to 2023, so as to provide the evidence for the prevention and control of PTB in schools. Methods: Data of PTB cases among students in Suzhou City from 2015 to 2023 were collected from Chinese Disease Prevention and Control Information System and Suzhou Report of Investigation and Disposal of Tuberculosis in Schools. The seasonal incidence of PTB among students was analyzed using seasonal index (SI). The spatio-temporal clustering characteristics of PTB among students were analyzed using spatial autocorrelation and retrospective spatio-temporal permutation scanning. Results: Totally 1 374 PTB cases among students were reported in Suzhou City from 2015 to 2023. PTB cases were reported in each month, and the SIs were 100.69%, 124.38%, 108.98%, 135.04%, 106.61% and 106.61% in April, May, July, September, October and November, respectively, indicating the prevalence of PTB among students. Spatial autocorrelation analysis showed there was a positive spatial correlation of PTB among students in 2019 and 2020 (Moran's I=0.053 and 0.089, both P<0.05). From 2015 to 2023, there were high-high clustering sites mainly in Hengtang Street and Shishan Street. Retrospective spatio-temporal permutation scanning showed a primary cluster in Hengtang Street, with aggregation time in 2017, and 6 secondary clusters covering 25 towns (streets). Conclusion From 2015 to 2023, the PTB cases among students in Suzhou City were mainly concentrated in summer and autumn, and were predominantly clustered in Hengtang Street and Shishan Street.

Objective: To analyze delay in student pulmonary tuberculosis(PTB) case finding and associated factors in Suzhou, and to provide a reference for tuberculosis outbreak prevention and control in schools. Methods: A total of 1 148 students with PTB who registered and were treated in Suzhou from 2011 to 2020 were included. Kruskal Wallis H test, 2 test and Cochran Armitage trend test were used to analyze the time trend of case finding delay. Logistic regression was used to analyze the correlation between admission characteristics and case finding delay. Results: Among the students with PTB, a total of 569 cases were found to be delayed. The rate of delay was 49.6%, and the median delay time was 26(11-49) days. From 2011 to 2020, the difference in case finding interval of students with PTB was statistically significant( Hc=54.62, P <0.05), and the difference in case finding rate was also statistically significant( χ 2=53.69, P <0.05). The rate of delay fluctuated, with an overall upward trend over time( Z=-3.67, P < 0.05). Clinical consultation( OR=5.57, 95%CI =1.91-16.27), positive etiology ( OR=1.46, 95%CI =1.14-1.86) were positively correlated with case finding delay(all P <0.05). Conclusion There are significant delays in case finding among students with PTB in Suzhou. Clinical consultation and positive etiology are associated with case finding delay. In response to the growing problems in daily school tuberculosis prevention and control, multiple departments should cooperate to implement relevant measures and to reduce the occurrence of case finding delay.

Objective: To analyze the relationship between the risk of tuberculosis outbreaks in schools and the visit interval of index cases, so as to provide a scientific reference for predicting the risks of tuberculosis outbreak and making preventive measures. Methods: A total of 630 index cases from school tuberculosis outbreaks were studied during January, 2015 to December, 2022. Data on demographics, consultation history, etiological diagnosis, and methods of detection were collected. Restricted Cubic Splines (RCS), unconditional Logistic regression, and the receiver operating characteristic curve (ROC curve) were used for analysis. Results: The RCS fitted curve showed that the risk of a tuberculosis outbreak linearly increased when the consultation interval for etiologically negative patients exceeded 5.79 days, or for etiologically positive patients exceeded 8.37 days. After multi factor adjustment, for every additional day in the visit interval of the index case, the odds ratio ( OR ) value for a high risk outbreak was 1.10 (95% CI =1.07-1.13)( P <0.05). When analyzed by tertiles of visit intervals, compared to an interval of <14 days, the OR values (95% CI ) for high risk outbreaks in schools with intervals of 14-<28 days and ≥28 days were 10.32(3.04-35.10) and 82.58( 28.42 -239.95), respectively( P <0.01), indicating a trend of increasing outbreak risk with longer visit intervals. Based on the ROC curve analysis, the optimal threshold for predicting a high risk school tuberculosis outbreak was 23.5 days, with an area under the curve ( AUC ) of 0.93 (95% CI =0.89-0.98). Conclusion An extended visit interval of index cases is a good early warning indicator for high risk tuberculosis outbreaks in schools and could be considered a key factor in early intervention and risk control strategies.

OBJECTIVE： To investigate the current situation of medication safety in 24 public medical institutions（referred to as “hospital”） from Linyi city and the differences in medication safety between urban and rural areas. METHODS： ISMP self-assessment scale [including 10 key elements （Ⅰ-Ⅹ，such as “Ⅰ patient’s information” “Ⅱ drug information”）， 20 key characteristics and 270 evaluation projects] developed by Institute of Safe Medication Practices was used to investigate 24 hospitals in Linyi city. The implementation rates of 10 key elements in urban and rural hospitals were analyzed statistically， and the top 10 evaluation projects were listed for the key elements with the lowest implementation rate. The key elements and top 10 evaluation projects with the largest difference in the implementation rate were compared between urban and rural hospitals. Radar maps were used for comparison and analysis intuitively. RESULTS： A total of 24 hospitals were surveyed， including 12 in urban and 12 in rural areas； there were significant differences in the implementation rate of 10 key factors among 24 hospitals； the elements with the highest implementation rate were “Ⅶ environmental factors， workflow and staffing pattern”（56.55％）；the elements with the lowest implementation rate was “Ⅱ drug information” （26.77％）. Among 33 evaluation projects of “Ⅱ drug information”， the implementation rate of No. 36 project （12.50％， related to opioids） was the lowest. Among the implementation rates of 10 key elements in 12 urban hospitals and 12 rural hospitals， the key elements with the greatest difference was “Ⅳ drug label， packaging and naming” （differing by 44.44％，59.72％ vs. 15.28％）； Among“Ⅸ patient education”evaluation project with the greatest gap， there was the greatest difference in No. 199 project （related to patients’ active participation in medication， 58.33％ in urban， 4.17％ in rural）. CONCLUSIONS： The results of medication safety investigation in 24 hospitals from Linyi city show that all the item in each hospital needs to be improved expecially in the implementation of “Ⅱ drug information”. Rural hospitals should strengthen medication education for patients.