Evidence map›Paper›PMID 33673819›Full record

ArticleBMC infectious diseases2021

Spatiotemporal heterogeneity and its determinants of COVID-19 transmission in typical labor export provinces of China.

Li Wang, Chengdong Xu, Jinfeng Wang, Jiajun Qiao, Mingtao Yan, Qiankun Zhu

Abstract read
In one paragraph

Article in BMC infectious diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 2 pooled it
–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

22 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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

Li Wang *College of Environment and Planning, Henan University, KaiFeng, 475001, China.
Chengdong Xu *State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resource Research, Chinese Academy of Sciences, Beijing, 100101, China.
Jinfeng WangState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resource Research, Chinese Academy of Sciences, Beijing, 100101, China. wangjf@Lreis.ac.cn.ORCID http://orcid.org/0000-0002-6687-9420
Jiajun QiaoCollege of Environment and Planning, Henan University, KaiFeng, 475001, China. jjqiao@henu.edu.cn.
Mingtao YanCollege of Environment and Planning, Henan University, KaiFeng, 475001, China.
Qiankun ZhuCollege of Environment and Planning, Henan University, KaiFeng, 475001, China.

Funding

Young Scientists Fund 41901331
6 · The paper itself

Abstract

backgroundPrevious studies have indicated that the risk of infectious disease spread is greatest in locations where a population has massive and convenient access to the epicenter of an outbreak. However, the spatiotemporal variations and risk determinants of COVID-19 in typical labor export regions of China remain unclear. Understanding the geographical distribution of the disease and the socio-economic factors affecting its transmission is critical for disease prevention and control.

methodsA total of 2152 COVID-19 cases were reported from January 21 to February 24, 2020 across the 34 cities in Henan and Anhui. A Bayesian spatiotemporal hierarchy model was used to detect the spatiotemporal variations of the risk posed by COVID-19, and the GeoDetector q statistic was used to evaluate the determinant power of the potential influence factors.

resultsThe risk posed by COVID-19 showed geographical spatiotemporal heterogeneity. Temporally, there was an outbreak period and control period. Spatially, there were high-risk regions and low-risk regions. The high-risk regions were mainly in the southwest areas adjacent to Hubei and cities that served as economic and traffic hubs, while the low-risk regions were mainly in western Henan and eastern Anhui, far away from the epicenter. The accessibility, local economic conditions, and medical infrastructure of Wuhan in Hubei province all played an important role in the spatiotemporal heterogeneity of COVID-19 transmission. The results indicated that the q statistics of the per capita GDP and the proportion of primary industry GDP were 0.47 and 0.47, respectively. The q statistic of the population flow from Wuhan was 0.33. In particular, the results showed that the q statistics for the interaction effects between population density and urbanization, population flow from Wuhan, per capita GDP, and the number of doctors were all greater than 0.8.

conclusionsCOVID-19 showed significant spatiotemporal heterogeneity in the labor export regions of China. The high-risk regions were mainly located in areas adjacent to the epicenter as well as in big cities that served as traffic hubs. Population access to the epicenter, as well as local economic and medical conditions, played an important role in the interactive effects of the disease transmission.

Indexed as

COVID-19Disease Transmission, InfectiousRisk AssessmentChinaCommunicable Disease ControlHumansRisk FactorsSARS-CoV-2Social Determinants of HealthSocioeconomic FactorsSpatio-Temporal AnalysisTransients and MigrantsUrbanizationCOVID-19Labor export regionSocioeconomic risk factorsSpatiotemporal pattern

Identifiers

PMID33673819
PMCPMC7935008

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LicenceCC BY
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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.