%0 Journal Article
%@ 2369-2960
%I JMIR Publications
%V 9
%N 
%P e49859
%T Trend of the Tuberculous Pleurisy Notification Rate in Eastern China During 2017-2021: Spatiotemporal Analysis
%A Zhou,Ying
%A Luo,Dan
%A Liu,Kui
%A Chen,Bin
%A Chen,Songhua
%A Pan,Junhang
%A Liu,Zhengwei
%A Jiang,Jianmin
%+ Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, 3399 Binsheng Road, BinJiang District, Hangzhou, 310000, China, 86 057187115009, jmjiang@cdc.zj.cn
%K tuberculous pleurisy
%K spatio-temporal
%K epidemiology
%K prediction
%K time series
%D 2023
%7 30.10.2023
%9 Original Paper
%J JMIR Public Health Surveill
%G English
%X Background: Tuberculous pleurisy (TP) presents a serious allergic reaction in the pleura caused by Mycobacterium tuberculosis; however, few studies have described its spatial epidemiological characteristics in eastern China. Objective: This study aimed to determine the epidemiological distribution of TP and predict its further development in Zhejiang Province. Methods: Data on all notified cases of TP in Zhejiang Province, China, from 2017 to 2021 were collected from the existing tuberculosis information management system. Analyses, including spatial autocorrelation and spatial-temporal scan analysis, were performed to identify hot spots and clusters, respectively. The prediction of TP prevalence was performed using the seasonal autoregressive integrated moving average (SARIMA), Holt-Winters exponential smoothing, and Prophet models using R (The R Foundation) and Python (Python Software Foundation). Results: The average notification rate of TP in Zhejiang Province was 7.06 cases per 100,000 population, peaking in the summer. The male-to-female ratio was 2.18:1. In terms of geographical distribution, clusters of cases were observed in the western part of Zhejiang Province, including parts of Hangzhou, Quzhou, Jinhua, Lishui, Wenzhou, and Taizhou city. Spatial-temporal analysis identified 1 most likely cluster and 4 secondary clusters. The Holt-Winters model outperformed the SARIMA and Prophet models in predicting the trend in TP prevalence. Conclusions: The western region of Zhejiang Province had the highest risk of TP. Comprehensive interventions, such as chest x-ray screening and symptom screening, should be reinforced to improve early identification. Additionally, a more systematic assessment of the prevalence trend of TP should include more predictors. 
%M 37902822
%R 10.2196/49859
%U https://publichealth.jmir.org/2023/1/e49859
%U https://doi.org/10.2196/49859
%U http://www.ncbi.nlm.nih.gov/pubmed/37902822