Evidence map›Paper›PMID 31699184›Full record

ArticleParasitology2020

Spatiotemporal pattern analysis of schistosomiasis based on village level in the transmission control stage in lake and marshland areas in China.

Yanyan Chen, Jianbing Liu, Ying Xiao, Chenhui Zhong, Fenghua Wei, Si Liu

Abstract read
In one paragraph

Article in Parasitology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

  1. 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

6 authors.

Yanyan ChenHubei Provincial Center for Disease Control and Prevention, Wuhan, China.ORCID 0000-0001-8061-5651
Jianbing LiuHubei Provincial Center for Disease Control and Prevention, Wuhan, China.
Ying XiaoHubei Provincial Center for Disease Control and Prevention, Wuhan, China.
Chenhui ZhongHubei Provincial Center for Disease Control and Prevention, Wuhan, China.
Fenghua WeiHubei Provincial Center for Disease Control and Prevention, Wuhan, China.
Si LiuHubei Provincial Center for Disease Control and Prevention, Wuhan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hubei Province is one of the endemic regions with severe schistosomiasis in China. To eliminate schistosomiasis in lake and marshland regions, this study detected hotspots of schistosomiasis cases both spatially and spatiotemporally on the basis of spatial autocorrelation; clustering and outlier, purely spatial and spatiotemporal cluster analyses at the village level from 2013 to 2017 in Hubei Province. The number of cases confirmed positive by an immunodiagnostic test and etiological diagnosis and advanced schistosomiasis cases dramatically declined during the study period. Significant global spatial autocorrelation of schistosomiasis patients was found at the village level in the whole province in 5 years. Clustering and outlier analysis showed that most HH villages were mainly concentrated along the Yangtze River, especially in Jianghan Plain. Spatial and spatiotemporal cluster analyses showed that significant clusters of the schistosomiasis cases were detected at the village level. In general, space and spatiotemporal clustering of schistosomiasis cases at the village level demonstrated a downward trend from 2013 from 2017 in Hubei Province. High-risk regions included Jianghan Plain along the middle reach of Yangtze River and Yangxin County in the lower reaches of the Yangtze River in Hubei Province. To eliminate schistosomiasis, precise control and management of schistosomiasis cases should be strictly implemented. Moreover, comprehensive prevention and control measures should be continuously strengthened in these regions.

Indexed as

LakesWetlandsChinaCluster AnalysisDemographyHumansSchistosomiasis japonicaSpatio-Temporal AnalysisChinaHubei provinceschistosomiasisspatial clusteringspatiotemporal analysis

Identifiers

PMID31699184
PMCPMC10317698

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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.