Evidence map›Paper›PMID 33282812›Full record

ArticleFrontiers in public health2020

Analysis of COVID-19 Cases' Spatial Dependence in US Counties Reveals Health Inequalities.

T Saffary, Oyelola A Adegboye, E Gayawan, F Elfaki, Md Abdul Kuddus, R Saffary

Abstract read
In one paragraph

Article in Frontiers in public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 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, 1 synthesis or guideline pooled it.

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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.

T SaffaryDepartment of Mathematics, Engineering and Computer Science, Chemeketa Community College, Salem, OR, United States.
Oyelola A AdegboyeEvolution Equations Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
E GayawanDepartment of Statistics, Federal University of Technology, Akure, Nigeria.
F ElfakiDepartment of Mathematics, Physics and Statistics, Qatar University, Doha, Qatar.
Md Abdul KuddusDepartment of Mathematics, University of Rajshahi, Rajshahi, Bangladesh.
R SaffaryDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

On March 13, 2020, the World Health Organization (WHO) declared the 2019 coronavirus disease (COVID-19) caused by the novel coronavirus SARS-CoV2 a pandemic. Since then the virus has infected over 9.1 million individuals and resulted in over 470,000 deaths worldwide (as of June 24, 2020). Here, we discuss the spatial correlation between county population health rankings and the incidence of COVID-19 cases and COVID-19 related deaths in the United States. We analyzed the spread of the disease based on multiple variables at the county level, using publicly available data on the numbers of confirmed cases and deaths, intensive care unit beds and socio-demographic, and healthcare resources in the U.S. Our results indicate substantial geographical variations in the distribution of COVID-19 cases and deaths across the US counties. There was significant positive global spatial correlation between the percentage of Black Americans and cases of COVID-19 (Moran

Indexed as

Health Status DisparitiesPandemicsSpatial AnalysisCOVID-19Databases, FactualDiabetes MellitusHumansIntensive Care UnitsObesitySARS-CoV-2United StatescoronavirusCOVID-19health rankingsneighborhoodspatial autocorrelation

Identifiers

PMID33282812
PMCPMC7690561

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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.