Evidence map›Paper›PMID 39277661›Full record

ArticleScientific reports2024

Spatial-temporal evolution patterns of influenza incidence in Xinjiang Prefecture from 2014 to 2023 based on GIS.

Zhe Yin, Yan Dong, Qi Wang, Yuanyuan Ma, Zhenguo Gao, Zhang Ling, Xiapikatijiang Aihaiti, Xiayidanmu Abudusaimaiti, Ruiying Qiu, Zihan Chen and 1 more

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Zhe YinXinjiang Uygur Autonomous Region Center for Disease Control and Prevention, No. 380, Jianquan First Street, Tianshan District, Ürümqi, 830002, Xinjiang Uygur Autonomous Region, China.
Yan DongXinjiang Uygur Autonomous Region Center for Disease Control and Prevention, No. 380, Jianquan First Street, Tianshan District, Ürümqi, 830002, Xinjiang Uygur Autonomous Region, China.
Qi WangXinjiang Uygur Autonomous Region Center for Disease Control and Prevention, No. 380, Jianquan First Street, Tianshan District, Ürümqi, 830002, Xinjiang Uygur Autonomous Region, China.
Yuanyuan MaXinjiang Uygur Autonomous Region Center for Disease Control and Prevention, No. 380, Jianquan First Street, Tianshan District, Ürümqi, 830002, Xinjiang Uygur Autonomous Region, China.
Zhenguo GaoXinjiang Uygur Autonomous Region Center for Disease Control and Prevention, No. 380, Jianquan First Street, Tianshan District, Ürümqi, 830002, Xinjiang Uygur Autonomous Region, China.
Zhang LingXinjiang Uygur Autonomous Region Center for Disease Control and Prevention, No. 380, Jianquan First Street, Tianshan District, Ürümqi, 830002, Xinjiang Uygur Autonomous Region, China.
Xiapikatijiang AihaitiXinjiang Uygur Autonomous Region Center for Disease Control and Prevention, No. 380, Jianquan First Street, Tianshan District, Ürümqi, 830002, Xinjiang Uygur Autonomous Region, China.
Xiayidanmu AbudusaimaitiXinjiang Uygur Autonomous Region Center for Disease Control and Prevention, No. 380, Jianquan First Street, Tianshan District, Ürümqi, 830002, Xinjiang Uygur Autonomous Region, China.
Ruiying QiuXinjiang Uygur Autonomous Region Center for Disease Control and Prevention, No. 380, Jianquan First Street, Tianshan District, Ürümqi, 830002, Xinjiang Uygur Autonomous Region, China.
Zihan ChenXinjiang Uygur Autonomous Region Center for Disease Control and Prevention, No. 380, Jianquan First Street, Tianshan District, Ürümqi, 830002, Xinjiang Uygur Autonomous Region, China.
Fuerhati WushouerXinjiang Uygur Autonomous Region Center for Disease Control and Prevention, No. 380, Jianquan First Street, Tianshan District, Ürümqi, 830002, Xinjiang Uygur Autonomous Region, China. 1092963@qq.com.

Funding

Xinjiang Uygur Autonomous Region science and technology Department key research and development project 2021B03003
6 · The paper itself

Abstract

Using GIS technology, this study investigated the spatiotemporal distribution pattern of influenza incidence in Xinjiang from 2014 to 2023 based on influenza surveillance data. The study revealed a noticeable fluctuation trend in influenza incidence rates in Xinjiang, particularly notable spikes observed in 2019 and 2023. The results of the 3-year moving average showed a significant long-term upward trend in influenza incidence rates, confirmed by Theil-Sen method (MAD = 2.202, p < 0.01). Global spatial autocorrelation analysis indicated significant positive spatial autocorrelation in influenza incidence rates from 2016 and from 2018 to 2023 (Moran's I > 0, P < 0.05). Local spatial autocorrelation analysis further revealed clustering patterns in different regions, with high-high clustering and low-high clustering predominating in northern Xinjiang, and low-low clustering predominating in southern Xinjiang. Hotspot analysis indicated a progressive rise in the number of influenza incidence hotspots, primarily concentrated in northern Xinjiang, particularly in Urumqi, Ili Kazakh Autonomous Prefecture, and Hotan Prefecture. Standard deviation ellipse analysis and the trajectory of influenza incidence gravity center migration showed that the transmission range of influenza in Xinjiang has been expanding, with the epidemic center gradually moving northward. The spatiotemporal heterogeneity of influenza incidence in Xinjiang highlights the need for differentiated and precise influenza prevention and control strategies in different regions to address the changing trends in influenza prevalence.

Indexed as

Geographic Information SystemsInfluenza, HumanSpatio-Temporal AnalysisChinaHumansIncidenceGISInfluenzaSpatiotemporal analysis

Identifiers

PMID39277661
PMCPMC11401927

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.