Evidence map›Paper›PMID 33264282›Full record

ArticlePLoS neglected tropical diseases2020

Risk mapping of scrub typhus infections in Qingdao city, China.

Hualei Xin, Peng Fu, Junling Sun, Shengjie Lai, Wenbiao Hu, Archie C A Clements, Jianping Sun, Jing Cui, Simon I Hay, Xiaojing Li and 1 more

Open access · goldAbstract read
In one paragraph

Article in PLoS neglected tropical diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.8field-weighted citation impact, top 31% 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

9 citing papers in PubMed, 14 citations in OpenAlex.

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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 5 countries.

Hualei XinDivision of Infectious Disease, Qingdao City Center for Disease Control and Prevention, Qingdao, Shandong, China.ORCID 0000-0001-8944-3980
Peng FuDepartment of Anesthesiology, Qingdao Fuwai Cardiovascular Hospital, Qingdao, Shandong, China.
Junling SunKey Laboratory of Surveillance and Early Warning on Infectious Disease, Division of Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China.
Shengjie LaiKey Laboratory of Surveillance and Early Warning on Infectious Disease, Division of Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China.
Wenbiao HuSchool of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Queensland, Australia.
Archie C A ClementsFaculty of Health Sciences, Curtin University, Bentley, Western Australia, Australia.
Jianping SunDivision of Infectious Disease, Qingdao City Center for Disease Control and Prevention, Qingdao, Shandong, China.
Jing CuiDivision of Infectious Disease, Qingdao City Center for Disease Control and Prevention, Qingdao, Shandong, China.
Simon I HayInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, United States of America.ORCID 0000-0002-0611-7272
Xiaojing LiDivision of Infectious Disease, Qingdao City Center for Disease Control and Prevention, Qingdao, Shandong, China.
Zhongjie LiKey Laboratory of Surveillance and Early Warning on Infectious Disease, Division of Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China.
Chinese Center For Disease Control and Prevention · CNQingdao Municipal Center for Disease Control and Prevention · CNCurtin University · AUQueensland University of Technology · AUUniversity of Washington · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe emergence and re-emergence of scrub typhus has been reported in the past decade in many global regions. In this study, we aim to identify potential scrub typhus infection risk zones with high spatial resolution in Qingdao city, in which scrub typhus is endemic, to guide local prevention and control strategies. METHODOLOGY/PRINCIPAL

findingsScrub typhus cases in Qingdao city during 2006-2018 were retrieved from the Chinese National Infectious Diseases Reporting System. We divided Qingdao city into 1,101 gridded squares and classified them into two categories: areas with and without recorded scrub typhus cases. A boosted regression tree model was used to explore environmental and socioeconomic covariates associated with scrub typhus occurrence and predict the risk of scrub typhus infection across the whole area of Qingdao city. A total of 989 scrub typhus cases were reported in Qingdao from 2006-2018, with most cases located in rural and suburban areas. The predicted risk map generated by the boosted regression tree models indicated that the highest infection risk areas were mainly concentrated in the mid-east and northeast regions of Qingdao, with gross domestic product (20.9%±1.8% standard error) and annual cumulative precipitation (20.3%±1.1%) contributing the most to the variation in the models. By using a threshold environmental suitability value of 0.26, we identified 757 squares (68.7% of the total) with a favourable environment for scrub typhus infection; 66.2% (501/757) of the squares had not yet recorded cases. It is estimated that 6.32 million people (72.5% of the total population) reside in areas with a high risk of scrub typhus infection. CONCLUSIONS/SIGNIFICANCE: Many locations in Qingdao city with no recorded scrub typhus cases were identified as being at risk for scrub typhus occurrence. In these at-risk areas, awareness and capacity for case diagnosis and treatment should be enhanced in the local medical service institutes.

Indexed as

Models, BiologicalRural PopulationUrban PopulationChinaEcosystemHumansRetrospective StudiesRisk FactorsScrub TyphusSocioeconomic FactorsSpatio-Temporal Analysis

Identifiers

PMID33264282
PMCPMC7735632
OpenAlexW3110077978

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.