Jian-Yun Lu1, Zhou-Bin Zhang2, Qing He1, Xiao-Wei Ma3, Zhi-Cong Yang4. 1. Department of Infectious Disease Control and Prevention, Guangzhou Center for Disease Control and Prevention, Baiyun District Qi De Road in Guangzhou, Guangdong Province 510440, China. 2. Guangzhou Center for Disease Control and Prevention, Baiyun District Qi De Road in Guangzhou, Guangdong Province 510440, China. 3. Department of Public Health Emergency Preparedness and Response, Guangzhou Center for Disease Control and Prevention, Baiyun District Qi De Road in Guangzhou, Guangdong Province 510440, China. 4. Guangzhou Center for Disease Control and Prevention, Baiyun District Qi De Road in Guangzhou, Guangdong Province 510440, China. Electronic address: yangzc@gzcdc.org.cn.
Abstract
OBJECTIVE: To analyze the correlation between climatic factors and the incidence of varicella in Guangzhou, and improve the prevention measures about public health. METHODS: Data for daily climatic variables and varicella incidence from 2006 to 2018 in Guangzhou were collected from the Guangzhou Meteorological Bureau and the National Notifiable Disease Report System. Distributed lag nonlinear models were applied to evaluate the association between climatic factors and varicella incidence. RESULTS: The nonlinear effects of meteorological factors were observed. At lag day21，when the mean temperature was 31.8 °C, the relative risk was the highest as 1.11 (95% CI: 1.07-1.16). When the diurnal temperature range was 24.0 °C at lag day 20, the highest RR was 1.11 (95% CI: 1.05-1.17). For rainfall, the highest RR was 1.09 (95% CI: 1.01-1.19) at lag day 21，when the aggregate rainfall was 160 mm. When air pressure was 1028 hPa, the highest RR was 1.08 (95% CI: 1.04-1.13) at lag day 21. When wind speed was 0.7 m/s, the highest RR was 1.07 (95% CI: 1.04-1.11) at lag day 7. When the hours of sunshine were 9.0 h at lag day 21, the RR was highest as 1.04 (95% CI: 1.02-1.05). Aggregate rainfall, air pressure, and sunshine hours were positively correlated with the incidence of varicella, which was inconsistent with the wind velocity. Mean temperature showed a reverse U-shape curve relationship with varicella, while the diurnal temperature range showed a binomial distribution curve. The extreme effect of climatic factors on the varicella cases was statistically significant, apart from the extremely low effect of rainfall. CONCLUSION: Our preliminary results offered fundamental knowledge which might be benefit to give an insight into epidemic trends of varicella and develop an early warning system. We could use our findings about influential factors to strengthen the intervention and prevention of varicella.
OBJECTIVE: To analyze the correlation between climatic factors and the incidence of varicella in Guangzhou, and improve the prevention measures about public health. METHODS: Data for daily climatic variables and varicella incidence from 2006 to 2018 in Guangzhou were collected from the Guangzhou Meteorological Bureau and the National Notifiable Disease Report System. Distributed lag nonlinear models were applied to evaluate the association between climatic factors and varicella incidence. RESULTS: The nonlinear effects of meteorological factors were observed. At lag day21，when the mean temperature was 31.8 °C, the relative risk was the highest as 1.11 (95% CI: 1.07-1.16). When the diurnal temperature range was 24.0 °C at lag day 20, the highest RR was 1.11 (95% CI: 1.05-1.17). For rainfall, the highest RR was 1.09 (95% CI: 1.01-1.19) at lag day 21，when the aggregate rainfall was 160 mm. When air pressure was 1028 hPa, the highest RR was 1.08 (95% CI: 1.04-1.13) at lag day 21. When wind speed was 0.7 m/s, the highest RR was 1.07 (95% CI: 1.04-1.11) at lag day 7. When the hours of sunshine were 9.0 h at lag day 21, the RR was highest as 1.04 (95% CI: 1.02-1.05). Aggregate rainfall, air pressure, and sunshine hours were positively correlated with the incidence of varicella, which was inconsistent with the wind velocity. Mean temperature showed a reverse U-shape curve relationship with varicella, while the diurnal temperature range showed a binomial distribution curve. The extreme effect of climatic factors on the varicella cases was statistically significant, apart from the extremely low effect of rainfall. CONCLUSION: Our preliminary results offered fundamental knowledge which might be benefit to give an insight into epidemic trends of varicella and develop an early warning system. We could use our findings about influential factors to strengthen the intervention and prevention of varicella.
Authors: Muhammad Khalid Anser; Zahid Yousaf; Muhammad Azhar Khan; Xuan Hinh Voo; Abdelmohsen A Nassani; Saad M Alotaibi; Muhammad Moinuddin Qazi Abro; Khalid Zaman Journal: Air Qual Atmos Health Date: 2020-07-11 Impact factor: 3.763