tetano
Editor, Senior Moderator
Sci Rep
. 2024 Feb 6;14(1):3079.
doi: 10.1038/s41598-024-53767-w. Heterogeneity of influenza infection at precise scale in Yinchuan, Northwest China, 2012-2022: evidence from Joinpoint regression and spatiotemporal analysis
Lu Zhang[SUP] #[/SUP][SUP] 1 2 [/SUP], Yan Li[SUP] #[/SUP][SUP] 3 [/SUP], Ning Ma[SUP] 1 2 [/SUP], Yi Zhao[SUP] 1 2 [/SUP], Yu Zhao[SUP] 4 5 [/SUP]
Affiliations
Identifying high-risk regions and turning points of influenza with a precise spatiotemporal scale may provide effective prevention strategies. In this study, epidemiological characteristics and spatiotemporal clustering analysis at the township level were performed. A descriptive study and a Joinpoint regression analysis were used to explore the epidemiological characteristics and the time trend of influenza. Spatiotemporal autocorrelation and clustering analyses were carried out to explore the spatiotemporal distribution characteristics and aggregation. Furthermore, the hotspot regions were analyzed by spatiotemporal scan analysis. A total of 4025 influenza cases were reported in Yinchuan showing an overall increasing trend. The tendency of influenza in Yinchuan consisted of three stages: increased from 2012 to the first peak in 2019 (32.62/100,000) with a slight decrease in 2016; during 2019 and 2020, the trend was downwards; then it increased sharply again and reached another peak in 2022. The Joinpoint regression analysis found that there were three turning points from January 2012 to December 2022, namely January 2020, April 2020, and February 2022. The children under ten displayed an upward trend and were statistically significant. The trend surface analysis indicated that there was a shifting trend from northern to central and southern. A significant positive spatial auto-correlation was observed at the township level and four high-incidence clusters of influenza were detected. These results suggested that children under 10 years old deserve more attention and the spatiotemporal distribution of high-risk regions of influenza in Yinchuan varies every year at the township level. Thus, more monitoring and resource allocation should be prone to the four high-incidence clusters, which may benefit the public health authorities to carry out the vaccination and health promotion timely.
. 2024 Feb 6;14(1):3079.
doi: 10.1038/s41598-024-53767-w. Heterogeneity of influenza infection at precise scale in Yinchuan, Northwest China, 2012-2022: evidence from Joinpoint regression and spatiotemporal analysis
Lu Zhang[SUP] #[/SUP][SUP] 1 2 [/SUP], Yan Li[SUP] #[/SUP][SUP] 3 [/SUP], Ning Ma[SUP] 1 2 [/SUP], Yi Zhao[SUP] 1 2 [/SUP], Yu Zhao[SUP] 4 5 [/SUP]
Affiliations
- PMID: 38321190
- DOI: 10.1038/s41598-024-53767-w
Identifying high-risk regions and turning points of influenza with a precise spatiotemporal scale may provide effective prevention strategies. In this study, epidemiological characteristics and spatiotemporal clustering analysis at the township level were performed. A descriptive study and a Joinpoint regression analysis were used to explore the epidemiological characteristics and the time trend of influenza. Spatiotemporal autocorrelation and clustering analyses were carried out to explore the spatiotemporal distribution characteristics and aggregation. Furthermore, the hotspot regions were analyzed by spatiotemporal scan analysis. A total of 4025 influenza cases were reported in Yinchuan showing an overall increasing trend. The tendency of influenza in Yinchuan consisted of three stages: increased from 2012 to the first peak in 2019 (32.62/100,000) with a slight decrease in 2016; during 2019 and 2020, the trend was downwards; then it increased sharply again and reached another peak in 2022. The Joinpoint regression analysis found that there were three turning points from January 2012 to December 2022, namely January 2020, April 2020, and February 2022. The children under ten displayed an upward trend and were statistically significant. The trend surface analysis indicated that there was a shifting trend from northern to central and southern. A significant positive spatial auto-correlation was observed at the township level and four high-incidence clusters of influenza were detected. These results suggested that children under 10 years old deserve more attention and the spatiotemporal distribution of high-risk regions of influenza in Yinchuan varies every year at the township level. Thus, more monitoring and resource allocation should be prone to the four high-incidence clusters, which may benefit the public health authorities to carry out the vaccination and health promotion timely.