Qihuan Li1, Mengyang Liu2, Yingjie Zhang3, Shangwu Wu4, Yang Yang5, Yue Liu6, Endawoke Amsalu7, Lixin Tao8, Xiangtong Liu9, Feng Zhang10, Yanxia Luo11, Xinghua Yang12, Weimin Li13, Xia Li14, Wei Wang15, Xiaonan Wang16, Xiuhua Guo17. 1. School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China. Electronic address: liqihuankk@163.com. 2. School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China. Electronic address: 18031864301@163.com. 3. Chinese Center for Disease Control and Prevention, Beijing 102206, China. Electronic address: zhangyj@chinacdc.cn. 4. Department of Statistics, University of Florida, Gainesville, FL 32610-7450, USA. Electronic address: samwu@biostat.ufl.edu. 5. Department of Statistics, University of Florida, Gainesville, FL 32610-7450, USA. Electronic address: yangyang@ufl.edu. 6. School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China. Electronic address: mugglesblue@163.com. 7. School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China. Electronic address: indexmar@outlook.com. 8. School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China. Electronic address: 13426176692@163.com. 9. School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China. Electronic address: lxiangtong@163.com. 10. School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China. Electronic address: zhangfeng@ccmu.edu.cn. 11. School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China. Electronic address: lyx100@ccmu.edu.cn. 12. School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China. Electronic address: xinghuay@263.net. 13. National Tuberculosis Clinical Lab of China, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China; Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing 101149, China. Electronic address: lwm_18@aliyun.com. 14. Department of Mathematics and Statistics, La Trobe University, Bundoora, Victoria 3086, Australia. Electronic address: lixia_new@163.com. 15. Global Health and Genomics, School of Medical Sciences and Health, Edith Cowan University, Perth WA6027, Australia. Electronic address: wei.wang@ecu.edu.au. 16. School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China. Electronic address: hnaywxn@163.com. 17. School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China. Electronic address: statguo@ccmu.edu.cn.
Abstract
BACKGROUND: Tuberculosis is still one of the most infectious diseases in China. This study aimed to explore the spatio-temporal distribution of TB and the associated factors in mainland China from 2009 to 2015. METHODS: A Bayesian spatio-temporal model was utilized to analyse the correlation of socio-economic, healthcare, demographic and meteorological factors with the population level number of TB. RESULTS: The Bayesian spatio-temporal analysis showed that for the population level number of TB, the estimated parameters of the ratio of males to females, the number of beds in medical institutions, the population density, the proportion of the population that is rural, the amount of precipitation, the largest wind speed and the sunshine duration were 0.556, 0.197, 0.199, 29.03,0.1958, 0.0854 and 0.2117, respectively, demonstrating positive associations. However, health personnel, per capita annual gross domestic product, minimum temperature and humidity indicated negative associations, and the corresponding parameters were -0.050, -0.095, -0.0022 and -0.0070, respectively. CONCLUSIONS: Socio-economic, number of health personnel, demographic and meteorological factors could affect the case notification number of TB to different degrees and in different directions.
BACKGROUND:Tuberculosis is still one of the most infectious diseases in China. This study aimed to explore the spatio-temporal distribution of TB and the associated factors in mainland China from 2009 to 2015. METHODS: A Bayesian spatio-temporal model was utilized to analyse the correlation of socio-economic, healthcare, demographic and meteorological factors with the population level number of TB. RESULTS: The Bayesian spatio-temporal analysis showed that for the population level number of TB, the estimated parameters of the ratio of males to females, the number of beds in medical institutions, the population density, the proportion of the population that is rural, the amount of precipitation, the largest wind speed and the sunshine duration were 0.556, 0.197, 0.199, 29.03,0.1958, 0.0854 and 0.2117, respectively, demonstrating positive associations. However, health personnel, per capita annual gross domestic product, minimum temperature and humidity indicated negative associations, and the corresponding parameters were -0.050, -0.095, -0.0022 and -0.0070, respectively. CONCLUSIONS: Socio-economic, number of health personnel, demographic and meteorological factors could affect the case notification number of TB to different degrees and in different directions.
Authors: M Xu; Y Li; B Liu; R Chen; L Sheng; S Yan; H Chen; J Hou; L Yuan; L Ke; M Fan; P Hu Journal: Epidemiol Infect Date: 2020-12-28 Impact factor: 2.451