tetano
Editor, Senior Moderator
Front Public Health
. 2024 Jan 8:11:1268073.
doi: 10.3389/fpubh.2023.1268073. eCollection 2023. The role of meteorological factors on influenza incidence among children in Guangzhou China, 2019-2022
Zhitao Chen[SUP] #[/SUP][SUP] 1 [/SUP], Yanhui Liu[SUP] #[/SUP][SUP] 2 3 [/SUP], Haiyan Yue[SUP] 4 [/SUP], Jinbin Chen[SUP] 5 [/SUP], Xiangzhi Hu[SUP] 1 [/SUP], Lijuan Zhou[SUP] 2 3 [/SUP], Boheng Liang[SUP] 2 3 [/SUP], Guozhen Lin[SUP] 2 3 [/SUP], Pengzhe Qin[SUP] 2 3 [/SUP], Wenru Feng[SUP] 2 3 [/SUP], Dedong Wang[SUP] 2 3 [/SUP], Di Wu[SUP] 2 3 [/SUP]
Affiliations
Objective: Analyzing the epidemiological characteristics of influenza cases among children aged 0-17 years in Guangzhou from 2019 to 2022. Assessing the relationships between multiple meteorological factors and influenza, improving the early warning systems for influenza, and providing a scientific basis for influenza prevention and control measures.
Methods: The influenza data were obtained from the Chinese Center for Disease Control and Prevention. Meteorological data were provided by Guangdong Meteorological Service. Spearman correlation analysis was conducted to examine the relevance between meteorological factors and the number of influenza cases. Distributed lag non-linear models (DLNM) were used to explore the effects of meteorological factors on influenza incidence.
Results: The relationship between mean temperature, rainfall, sunshine hours, and influenza cases presented a wavy pattern. The correlation between relative humidity and influenza cases was illustrated by a U-shaped curve. When the temperature dropped below 13°C, Relative risk (RR) increased sharply with decreasing temperature, peaking at 5.7°C with an RR of 83.78 (95% CI: 25.52, 275.09). The RR was increased when the relative humidity was below 66% or above 79%, and the highest RR was 7.50 (95% CI: 22.92, 19.25) at 99%. The RR was increased exponentially when the rainfall exceeded 1,625 mm, reaching a maximum value of 2566.29 (95% CI: 21.85, 3558574.07) at the highest rainfall levels. Both low and high sunshine hours were associated with reduced incidence of influenza, and the lowest RR was 0.20 (95% CI: 20.08, 0.49) at 9.4 h. No significant difference of the meteorological factors on influenza was observed between males and females. The impacts of cumulative extreme low temperature and low relative humidity on influenza among children aged 0-3 presented protective effects and the 0-3 years group had the lowest RRs of cumulative extreme high relative humidity and rainfall. The highest RRs of cumulative extreme effect of all meteorological factors (expect sunshine hours) were observed in the 7-12 years group.
Conclusion: Temperature, relative humidity, rainfall, and sunshine hours can be used as important predictors of influenza in children to improve the early warning system of influenza. Extreme weather reduces the risk of influenza in the age group of 0-3 years, but significantly increases the risk for those aged 7-12 years.
Keywords: children; distributed lag non-linear models; incidence; influenza; meteorological factors.
. 2024 Jan 8:11:1268073.
doi: 10.3389/fpubh.2023.1268073. eCollection 2023. The role of meteorological factors on influenza incidence among children in Guangzhou China, 2019-2022
Zhitao Chen[SUP] #[/SUP][SUP] 1 [/SUP], Yanhui Liu[SUP] #[/SUP][SUP] 2 3 [/SUP], Haiyan Yue[SUP] 4 [/SUP], Jinbin Chen[SUP] 5 [/SUP], Xiangzhi Hu[SUP] 1 [/SUP], Lijuan Zhou[SUP] 2 3 [/SUP], Boheng Liang[SUP] 2 3 [/SUP], Guozhen Lin[SUP] 2 3 [/SUP], Pengzhe Qin[SUP] 2 3 [/SUP], Wenru Feng[SUP] 2 3 [/SUP], Dedong Wang[SUP] 2 3 [/SUP], Di Wu[SUP] 2 3 [/SUP]
Affiliations
- PMID: 38259781
- PMCID: PMC10800649
- DOI: 10.3389/fpubh.2023.1268073
Objective: Analyzing the epidemiological characteristics of influenza cases among children aged 0-17 years in Guangzhou from 2019 to 2022. Assessing the relationships between multiple meteorological factors and influenza, improving the early warning systems for influenza, and providing a scientific basis for influenza prevention and control measures.
Methods: The influenza data were obtained from the Chinese Center for Disease Control and Prevention. Meteorological data were provided by Guangdong Meteorological Service. Spearman correlation analysis was conducted to examine the relevance between meteorological factors and the number of influenza cases. Distributed lag non-linear models (DLNM) were used to explore the effects of meteorological factors on influenza incidence.
Results: The relationship between mean temperature, rainfall, sunshine hours, and influenza cases presented a wavy pattern. The correlation between relative humidity and influenza cases was illustrated by a U-shaped curve. When the temperature dropped below 13°C, Relative risk (RR) increased sharply with decreasing temperature, peaking at 5.7°C with an RR of 83.78 (95% CI: 25.52, 275.09). The RR was increased when the relative humidity was below 66% or above 79%, and the highest RR was 7.50 (95% CI: 22.92, 19.25) at 99%. The RR was increased exponentially when the rainfall exceeded 1,625 mm, reaching a maximum value of 2566.29 (95% CI: 21.85, 3558574.07) at the highest rainfall levels. Both low and high sunshine hours were associated with reduced incidence of influenza, and the lowest RR was 0.20 (95% CI: 20.08, 0.49) at 9.4 h. No significant difference of the meteorological factors on influenza was observed between males and females. The impacts of cumulative extreme low temperature and low relative humidity on influenza among children aged 0-3 presented protective effects and the 0-3 years group had the lowest RRs of cumulative extreme high relative humidity and rainfall. The highest RRs of cumulative extreme effect of all meteorological factors (expect sunshine hours) were observed in the 7-12 years group.
Conclusion: Temperature, relative humidity, rainfall, and sunshine hours can be used as important predictors of influenza in children to improve the early warning system of influenza. Extreme weather reduces the risk of influenza in the age group of 0-3 years, but significantly increases the risk for those aged 7-12 years.
Keywords: children; distributed lag non-linear models; incidence; influenza; meteorological factors.