Hongchao Qi1, Yu Li2, Jun Zhang3, Yue Chen4, Yuming Guo5, Shuang Xiao3, Jian Hu3, Wenge Wang3, Wenyi Zhang6, Yi Hu3, Zhongjie Li7, Zhijie Zhang8. 1. Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, 138 Yixueyuan Rd, Xuhui District, Shanghai, China; Key Laboratory of Public Health Safety, Ministry of Education, 138 Yixueyuan Rd, Xuhui District, Shanghai, China; Department of Biostatistics, Erasmus University Medical Center, Doctor Molewaterplein 40, 3015 GD Rotterdam, Netherlands. 2. Division of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, 155 Changbai Rd, Changping District, Beijing, China. 3. Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, 138 Yixueyuan Rd, Xuhui District, Shanghai, China; Key Laboratory of Public Health Safety, Ministry of Education, 138 Yixueyuan Rd, Xuhui District, Shanghai, China. 4. School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, 75 Laurier Ave E, Ottawa, ON K1N 6N5, Canada. 5. Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, 27 Rainforest Walk, Clayton, VIC 3800, Australia. 6. Chinese PLA Center for Disease Control and Prevention, Academy of Military Medical Sciences, 27 Taiping Rd, Haidian District, Beijing, China. 7. Division of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, 155 Changbai Rd, Changping District, Beijing, China. Electronic address: lizj@chinacdc.cn. 8. Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, 138 Yixueyuan Rd, Xuhui District, Shanghai, China; Key Laboratory of Public Health Safety, Ministry of Education, 138 Yixueyuan Rd, Xuhui District, Shanghai, China. Electronic address: epistat@gmail.com.
Abstract
BACKGROUND: Hand, foot, and mouth disease (HFMD) is a widespread infectious disease in China. Associated meteorological factors have been widely studied, but their attributable risks have not been well quantified. OBJECTIVES: The study aimed to quantify the HFMD burden attributable to temperature and other meteorological factors. METHODS: The daily counts of HFMD and meteorological factors in all 574 counties of East China were obtained for the period from 2009 to 2015. The exposure-lag-response relationships between meteorological factors and HFMD were quantified by using a distributed lag non-linear model for each county and the estimates from all the counties were then pooled using a multivariate mete-regression model. Attributable risks were estimated for meteorological variables according to the exposure-lag-response relationships obtained before. RESULTS: The study included 4,058,702 HFMD cases. Non-optimal values of meteorological factors were attributable to approximately one third of all HFMD cases, and the attributable numbers of non-optimal ambient temperature, relative humidity, wind speed and sunshine hours were 815,942 (95% CI: 796,361-835,888), 291,759 (95% CI: 226,183-358,494), 92,060 (95% CI: 59,655-124,738) and 62,948 (95% CI: 20,621-105,773), respectively. The exposure-response relationship between temperature and HFMD was non-linear with an approximate "M" shape. High temperature had a greater influence on HFMD than low temperature did. There was a geographical heterogeneity related to water body, and more cases occurred in days with moderate high and low temperatures than in days with extreme temperature. The effects of meteorological factors on HFMD were generally consistent across subgroups. CONCLUSIONS: Non-optimal temperature is the leading risk factor of HFMD in East China, and moderate hot and moderate cold days had the highest risk. Developing subgroup-targeted and region-specific programs may minimize the adverse consequences of non-optimum weather on HFMD risk.
BACKGROUND: Hand, foot, and mouth disease (HFMD) is a widespread infectious disease in China. Associated meteorological factors have been widely studied, but their attributable risks have not been well quantified. OBJECTIVES: The study aimed to quantify the HFMD burden attributable to temperature and other meteorological factors. METHODS: The daily counts of HFMD and meteorological factors in all 574 counties of East China were obtained for the period from 2009 to 2015. The exposure-lag-response relationships between meteorological factors and HFMD were quantified by using a distributed lag non-linear model for each county and the estimates from all the counties were then pooled using a multivariate mete-regression model. Attributable risks were estimated for meteorological variables according to the exposure-lag-response relationships obtained before. RESULTS: The study included 4,058,702 HFMD cases. Non-optimal values of meteorological factors were attributable to approximately one third of all HFMD cases, and the attributable numbers of non-optimal ambient temperature, relative humidity, wind speed and sunshine hours were 815,942 (95% CI: 796,361-835,888), 291,759 (95% CI: 226,183-358,494), 92,060 (95% CI: 59,655-124,738) and 62,948 (95% CI: 20,621-105,773), respectively. The exposure-response relationship between temperature and HFMD was non-linear with an approximate "M" shape. High temperature had a greater influence on HFMD than low temperature did. There was a geographical heterogeneity related to water body, and more cases occurred in days with moderate high and low temperatures than in days with extreme temperature. The effects of meteorological factors on HFMD were generally consistent across subgroups. CONCLUSIONS: Non-optimal temperature is the leading risk factor of HFMD in East China, and moderate hot and moderate cold days had the highest risk. Developing subgroup-targeted and region-specific programs may minimize the adverse consequences of non-optimum weather on HFMD risk.
Authors: Suyan Yi; Hongwei Wang; Shengtian Yang; Ling Xie; Yibo Gao; Chen Ma Journal: Int J Environ Res Public Health Date: 2021-02-17 Impact factor: 3.390
Authors: Zhicheng Du; Yong Huang; Wayne R Lawrence; Jianxiong Xu; Zhicong Yang; Jianyun Lu; Zhoubin Zhang; Yuantao Hao Journal: Int J Environ Res Public Health Date: 2021-01-02 Impact factor: 3.390
Authors: Rui Zhang; Zhen Guo; Yujie Meng; Songwang Wang; Shaoqiong Li; Ran Niu; Yu Wang; Qing Guo; Yonghong Li Journal: Int J Environ Res Public Health Date: 2021-06-07 Impact factor: 3.390