Evidence map›Paper›PMID 33561504›Full record

ArticleThe Journal of hospital infection2021

Spatiotemporal characteristics and factor analysis of SARS-CoV-2 infections among healthcare workers in Wuhan, China.

P Wang, H Ren, X Zhu, X Fu, H Liu, T Hu

Open access · bronzeAbstract read
In one paragraph

Article in The Journal of hospital infection, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
0.6field-weighted citation impact, top 35% 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, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review.International journal of environmental research and public health · 2022
    Pooled it
  2. Article
  3. 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 at 3 institutions in 2 countries.

P WangState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China.
H RenState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China.
X ZhuState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China; Collaborative Innovation Center of Geospatial Technology, Wuhan, China; Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, Wuhan University, Wuhan, China. Electronic address: xinyanzhu@whu.edu.cn.
X FuState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China.
H LiuCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, China.
T HuCenter for Geographic Analysis, Harvard University, Cambridge, MA, USA. Electronic address: taohu@g.harvard.edu.
State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing · CNCenter for Policy Analysis · USShandong University of Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundStudying the spatiotemporal distribution of SARS-CoV-2 infections among healthcare workers (HCWs) can aid in protecting them from exposure.

aimTo describe the spatiotemporal distributions of SARS-CoV-2 infections among HCWs in Wuhan, China.

methodsIn this study, an open-source dataset of HCW diagnoses was provided. A geographical detector technique was then used to investigate the impacts of hospital level, type, distance from the infection source, and other external indicators of HCW infections.

findingsThe number of daily HCW infections over time in Wuhan followed a log-normal distribution, with its mean observed on January 23

conclusionHCW infections displayed significant spatial autocorrelation and dependence. Factor analysis revealed that hospital level and type had an even greater impact on HCW infections; third-class and general hospitals closer to infection sources were correlated with especially high risks of infection.

Indexed as

AdultChinaCOVID-19Disease OutbreaksFactor Analysis, StatisticalFemaleHealth PersonnelHumansMaleMiddle AgedOccupational DiseasesSpatio-Temporal AnalysisFactor analysisHealthcare worker infectionSARS-CoV-2Spatiotemporal patternViral outbreak

Identifiers

PMID33561504
PMCPMC7985129
OpenAlexW3128236850

What OpenQuestion holds

Textmetadata
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.