Evidence map›Paper›PMID 36517796›Full record

ArticleBMC public health2022

Investigating the relationships between concentrated disadvantage, place connectivity, and COVID-19 fatality in the United States over time.

Fengrui Jing, Zhenlong Li, Shan Qiao, Jiajia Zhang, Bankole Olatosi, Xiaoming Li

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC public health, 2022. 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
1.0field-weighted citation impact, top 24% 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

2 citing papers in PubMed, 6 citations in OpenAlex.

  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 at 1 institution in 1 country.

Fengrui JingDepartment of Geography, Geoinformation and Big Data Research Lab, University of South Carolina, Columbia, SC, 29208, USA. fengrui@sc.edu.
Zhenlong LiDepartment of Geography, Geoinformation and Big Data Research Lab, University of South Carolina, Columbia, SC, 29208, USA.
Shan QiaoBig Data Health Science Center, University of South Carolina, Columbia, SC, 29208, USA.
Jiajia ZhangBig Data Health Science Center, University of South Carolina, Columbia, SC, 29208, USA.
Bankole OlatosiBig Data Health Science Center, University of South Carolina, Columbia, SC, 29208, USA.
Xiaoming LiBig Data Health Science Center, University of South Carolina, Columbia, SC, 29208, USA.
University of South Carolina · US

Funding

Multilevel Determinants of Racial and Ethnic Disparities in Maternal Morbidity and Mortality in the Context of COVID-19 PandemicR01AI127203 · NIAID · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI LI, XIAOMING, LIU, JIHONG · 2017 to 2021
$5.2M
NIAID NIH HHS R01 AI127203NIH HHS 3R01AI127203-04S1
6 · The paper itself

Abstract

backgroundConcentrated disadvantaged areas have been disproportionately affected by COVID-19 outbreak in the United States (US). Meanwhile, highly connected areas may contribute to higher human movement, leading to higher COVID-19 cases and deaths. This study examined the associations between concentrated disadvantage, place connectivity, and COVID-19 fatality in the US over time.

methodsConcentrated disadvantage was assessed based on the spatial concentration of residents with low socioeconomic status. Place connectivity was defined as the normalized number of shared Twitter users between the county and all other counties in the contiguous US in a year (Y = 2019). COVID-19 fatality was measured as the cumulative COVID-19 deaths divided by the cumulative COVID-19 cases. Using county-level (N = 3,091) COVID-19 fatality over four time periods (up to October 31, 2021), we performed mixed-effect negative binomial regressions to examine the association between concentrated disadvantage, place connectivity, and COVID-19 fatality, considering potential state-level variations. The moderation effects of county-level place connectivity and concentrated disadvantage were analyzed. Spatially lagged variables of COVID-19 fatality were added to the models to control for the effect of spatial autocorrelations in COVID-19 fatality.

resultsConcentrated disadvantage was significantly associated with an increased COVID-19 fatality in four time periods (p < 0.01). More importantly, moderation analysis suggested that place connectivity significantly exacerbated the harmful effect of concentrated disadvantage on COVID-19 fatality in three periods (p < 0.01), and this significant moderation effect increased over time. The moderation effects were also significant when using place connectivity data from the previous year.

conclusionsPopulations living in counties with both high concentrated disadvantage and high place connectivity may be at risk of a higher COVID-19 fatality. Greater COVID-19 fatality that occurs in concentrated disadvantaged counties may be partially due to higher human movement through place connectivity. In response to COVID-19 and other future infectious disease outbreaks, policymakers are encouraged to take advantage of historical disadvantage and place connectivity data in epidemic monitoring and surveillance of the disadvantaged areas that are highly connected, as well as targeting vulnerable populations and communities for additional intervention.

Indexed as

COVID-19HumansSARS-CoV-2Spatial AnalysisUnited StatesVulnerable PopulationsConcentrated disadvantageCOVID-19 fatalityModerationPlace connectivityTwitter

Identifiers

PMID36517796
PMCPMC9748905
OpenAlexW4311493376

What OpenQuestion holds

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LicenceCC BY
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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.