Evidence map›Paper›PMID 36277104›Full record

ArticleOne health (Amsterdam, Netherlands)2022

Co-effects of global climatic dynamics and local climatic factors on scrub typhus in mainland China based on a nine-year time-frequency analysis.

Junyu He, Yong Wang, Ping Liu, Wenwu Yin, Xianyu Wei, Hailong Sun, Yuanyong Xu, Shanshan Li, Ricardo J Soares Magalhaes, Yuming Guo and 1 more

Open access · goldAbstract read
In one paragraph

Article in One health (Amsterdam, Netherlands), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.3field-weighted citation impact, top 17% of its field
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

4 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. Article
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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 at 5 institutions in 2 countries.

Junyu HeOcean Academy, Zhejiang University, Zhoushan, China.
Yong WangChinese PLA Center for Disease Control and Prevention, Beijing, China.
Ping LiuDepartment of General Practice, Chinese PLA General Hospital-Sixth Medical Center, Beijing, China.
Wenwu YinChinese Center for Disease Control and Prevention, Beijing, China.
Xianyu WeiChinese PLA Center for Disease Control and Prevention, Beijing, China.
Hailong SunChinese PLA Center for Disease Control and Prevention, Beijing, China.
Yuanyong XuChinese PLA Center for Disease Control and Prevention, Beijing, China.
Shanshan LiDepartment of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
Ricardo J Soares MagalhaesSpatial Epidemiology Laboratory, School of Veterinary Science, The University of Queensland, Brisbane, Australia.
Yuming GuoDepartment of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
Wenyi ZhangChinese PLA Center for Disease Control and Prevention, Beijing, China.
Monash University · AUChinese Center For Disease Control and Prevention · CNChinese PLA General Hospital · CNUniversity of Queensland · AUZhejiang Ocean University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Scrub Typhus (ST) is a rickettsial disease caused by Methods: We obtained monthly documented ST cases greater than 54 cases in 434 counties of China during 2012-2020. Spatiotemporal wavelet analysis was conducted to identify the ST clusters with similar pattern of the temporal variation and explore the association between ST variation and El Niño and La Niña events. Wavelet coherency analysis and partial wavelet coherency analysis was employed to further explore the co-effects of global and local climatic factors on ST. Results: Wavelet cluster analysis detected seven clusters in China, three of which are mainly distributed in Eastern China, while the other four clusters are located in the Southern China. Among the seven clusters, summer and autumn-winter peak of ST are the two main outbreak periods; while stable and fluctuated periodic feature of ST series was found at 12-month and 4-(or 6-) month according to the wavelet power spectra. Similarly, the three-character bands were also found in the associations between ST and El Niño and La Niña events, among which the 12-month period band showed weakest climate-ST association and the other two bands owned stronger association, indicating that the global climate dynamics may have short-term effects on the ST variations. Meanwhile, 12-month period band with strong association was found between the four local climatic factors (precipitation, pressure, relative humidity and temperature) and the ST variations. Further, partial wavelet coherency analysis suggested that global climatic dynamics dominate annual ST variations, while local climatic factors dominate the small periods. Conclusion: The ST variations are not directly attributable to the change in large-scale climate. The existence of these plausible climatic determinants stimulates the interests for more insights into the epidemiology of ST, which is important for devising prevention and early warning strategies.

Indexed as

ClimateClustersENSO, El Niño Southern OscillationMEI, multivariate El Niño Southern Oscillation indexNSI, Niño 3.4 SST indexPartial wavelet coherencyScrub typhusSST, Sea surface temperatureST, Scrub TyphusWavelet power spectra

Identifiers

PMID36277104
PMCPMC9582591
OpenAlexW4304893385

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.