Endawoke Amsalu1, Mengyang Liu1, Qihuan Li1, Xiaonan Wang1, Lixin Tao1, Xiangtong Liu1, Yanxia Luo1, Xinghua Yang1, Yingjie Zhang2, Weimin Li3, Xia Li4, Wei Wang5, Xiuhua Guo6. 1. Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, 100069, China. 2. Chinese Center for Disease Control and Prevention, Beijing, 102206, China. 3. Beijing Chest Hospital, Beijing, 101149, China. 4. Department of Mathematics and Statistics, La Tribe University, Melbourne, 3086, Australia. 5. Global Health and Genomics, School of Medical Sciences and Health, Edith Cowan University, Joondalup, Perth, WA6027, Australia. 6. Department of Epidemiology and Health Statistics, 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 (TB) remains a clinical and epidemiological challenge in the geriatric population. We aim to examine the spatial-temporal pattern of TB in the geriatric population and its relationship with meteorological & sociodemographic factors using the Bayesian conditional autoregressive (CAR) model. METHOD: An ecological design was used in the geriatric (age > = 65 years) population from 2005 to 2015. Spatial autocorrelation and hot spots were explored using geographical information system (GIS) statistics. The Bayesian CAR model was used for modeling TB to estimate the parameters using the WinBUGS software. Deviance information criteria (DIC) were used to select the best performing model. RESULTS: Spatially, TB was clustered in Central China and southeast of China. Temporally, an increasing trend and high peak of TB was detected during the spring. TB was significantly associated with air temperature at the posterior mean: -0.165 (95%CI: -0.235, -0.108), and it was negatively associated with average wind speed: -0.028 (95%CI: -0.043, -0.018) and positively associated with rainfall: 0.095 (95%CI: 0.045, 0.163). TB was significantly and positively associated with population density: 0.088(95%CI: 0.031, 0.129) and sex ratio (M: F): 0.162 (95%CI: 0.091, 0.284) and was negatively related with gross domestic product (GDP): -0.046(95%CI: -0.156, -0.037). Out of 31 provinces, 17 provinces had a higher risk for TB. CONCLUSION: TB shows a clear spatial and seasonal variation; it is geographically aggregated, and more men are affected than women. Areas with an underprivileged economy, high population density, high rainfall, low wind speed, and low temperature have a higher risk for TB.
BACKGROUND:Tuberculosis (TB) remains a clinical and epidemiological challenge in the geriatric population. We aim to examine the spatial-temporal pattern of TB in the geriatric population and its relationship with meteorological & sociodemographic factors using the Bayesian conditional autoregressive (CAR) model. METHOD: An ecological design was used in the geriatric (age > = 65 years) population from 2005 to 2015. Spatial autocorrelation and hot spots were explored using geographical information system (GIS) statistics. The Bayesian CAR model was used for modeling TB to estimate the parameters using the WinBUGS software. Deviance information criteria (DIC) were used to select the best performing model. RESULTS: Spatially, TB was clustered in Central China and southeast of China. Temporally, an increasing trend and high peak of TB was detected during the spring. TB was significantly associated with air temperature at the posterior mean: -0.165 (95%CI: -0.235, -0.108), and it was negatively associated with average wind speed: -0.028 (95%CI: -0.043, -0.018) and positively associated with rainfall: 0.095 (95%CI: 0.045, 0.163). TB was significantly and positively associated with population density: 0.088(95%CI: 0.031, 0.129) and sex ratio (M: F): 0.162 (95%CI: 0.091, 0.284) and was negatively related with gross domestic product (GDP): -0.046(95%CI: -0.156, -0.037). Out of 31 provinces, 17 provinces had a higher risk for TB. CONCLUSION: TB shows a clear spatial and seasonal variation; it is geographically aggregated, and more men are affected than women. Areas with an underprivileged economy, high population density, high rainfall, low wind speed, and low temperature have a higher risk for TB.
Authors: Gina Polo; Diego Soler-Tovar; Luis Carlos Villamil Jimenez; Efraín Benavides-Ortiz; Carlos Mera Acosta Journal: Spat Spatiotemporal Epidemiol Date: 2022-03-25
Authors: Nadav L Sprague; Ariana N Gobaud; Christina A Mehranbod; Christopher N Morrison; Charles C Branas; Ahuva L Jacobowitz Journal: Int J Environ Res Public Health Date: 2022-04-22 Impact factor: 4.614