| Objective:To explore the epidemiological characteristics of tuberculosis among students in Hengyang City,Hunan Province from 2010 to2019.Apply the Exponential Smoothing method and ARIMA model to predict the incidence of tuberculosis among students in Hengyang City,and determine the best model.Understand the current status of the knowledge,attitudes and behaviors of tuberculosis prevention and evaluate the effect of health education,to provide a basis for scientific and efficient control of tuberculosis epidemics in schools.Methods:Collect the data on the pulmonary tuberculosis epidemic among students in Hengyang City from 2010 to 2020,and use descriptive epidemiological methods to describe the epidemiological characteristics of pulmonary tuberculosis among students in Hengyang City;Apply the Exponential Smoothing method and ARIMA model to predict the incidence of pulmonary tuberculosis among students in Hengyang City;Before and after health education,a stratified cluster sampling method was used to conduct a questionnaire survey among college students in Hengyang City.Results1.From 2010 to 2019,the average annual reported incidence of pulmonary tuberculosis among students in Hengyang City was 13.74/100,000,showing a trend of first decreasing and then increasing.The male-to-female sex ratio was 1.65:1,mainly in the age group of 15 to 20 years old,with peak incidence in March and April.The epidemic of pulmonary tuberculosis among students in 4 urban areas including Zhengxiang District and Shigu District continued to decrease after 2018,but the reported tuberculosis epidemic among students in Leiyang City,Qidong County and other places increased sharply in 2018;48.17%of the cases were traced to the source,and 26.58%of the cases were treated due to illness.The rate of delay in seeking medical treatment was within the range of 37.42%to 60.58%,with median delays from33 to 48 days;the rate of delay in diagnosis was within the range of 15.85%to47.10%,with median delays from 30 to 44 days.2.The Holt-Winter additive model in the Exponential Smoothing model has the best fitting effect.The fitted R2,stationary R2,RMSE,MAPE,MAE,and normalized BIC are 0.666,0.469,5.716,31.276,3.873,3.606,respectively.Ljung-Box Q=20.741,P=0.145,verifying that the average relative error of the forecast from January to December 2020 is 39.98%;the best fitting ARIMA model is ARIMA(0,1,1)×(0,1,1)12model,the fitted stationary R2,R2,RMSE,MAPE,MAE,and normalized BIC are 0.500,0.603,6.532,34.623,4.443,3.885,respectively,Ljung-Box Q=15.611,P=0.480,verifying that the average relative error of the forecast from January to December 2020 is 120.76%.3.The awareness rate of different majors,grades,gender,ethnicity,household registration before enrollment,parental education level,monthly family income,health education,and whether they have had tuberculosis increased significantly after health education;the awareness rate of core information of respondents before health education was 68.36%,the total information awareness rate was 57.56%,the core information awareness rate of the respondents after health education was 81.46%,and the total information awareness rate was 70.97%.Conclusion1.From 2010 to 2019,The reported incidence of pulmonary tuberculosis among students in Hengyang City first decreased and then increased.Boys and students in the age group of 15-20 are the key monitoring targets for controlling the TB epidemic in schools.The peak incidence is from March to April every year.Leiyang City and Qidong County are Students in high-incidence areas of pulmonary tuberculosis are mainly found through passive detection.2.The Exponential Smoothing model is more effective in fitting the number of tuberculosis among students in Hengyang City,and the prediction accuracy is higher.3.In the future,school tuberculosis prevention and control work,we should vigorously carry out tuberculosis health education activities in a planned way,improve students’awareness of tuberculosis prevention and health knowledge,and establish positive prevention and control attitudes,thereby generating positive prevention and control behaviors. |