Evidence map›Paper›PMID 41446473›Full record

ArticleCureus2025

COVID-19 and Social Vulnerabilities in Virginia ZIP Code Tabulation Areas.

Parthay H Patel, Nikhil Amin, Bhaumik Patel

Abstract read
In one paragraph

Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Parthay H PatelSchool of Business, Virginia Commonwealth University, Richmond, USA.
Nikhil AminDepartment of Biology, University of Virginia, Charlottesville, USA.
Bhaumik PatelDivision of Hematology, Oncology and Palliative Care, Virginia Commonwealth University, Richmond, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background We retrospectively reviewed demographic determinants (e.g., area-level ethnic and racial composition, income, and household crowding) of COVID-19 prevalence in Virginia. Data spanning May 15, 2020, to August 6, 2020, during the first COVID-19 peak in the state, were obtained from the Virginia Department of Health (VDH). Methods Associations between COVID-19 cases and socioeconomic variables were examined according to Virginia ZIP Code Tabulation Areas (VZCTAs) in Virginia's health planning regions (VHPRs). Multivariate regression analysis and Pearson correlations were employed in data analyses to understand the relationship between various socioeconomic factors. Results The prevalence of COVID-19 was highest in the suburban Northern region and lowest in the rural-predominant southwestern region (p < 0.05). Overall, area-level ethnic and racial composition (Black/Hispanic populations), median household income, and household crowding were the most important predictors of COVID-19 prevalence in VZCTA communities in univariate and multivariate analyses, with a few important regional differences (multivariate analyses: overall R = 0.612, northern VHPR R = 0.903). Conclusions The study highlights area-level racial and ethnic composition, used as a proxy for structural inequalities, in the context of VHPRs as an important indicator of social vulnerability to COVID-19. The findings of this study can guide critical public health decisions, e.g., vaccine distribution or implementation of critical health policies based on social vulnerability in smaller population units.

Indexed as

covid-19pandemic preparednesspublic healthsocial vulnerabilitiesvirginia

Identifiers

PMID41446473
PMCPMC12726741

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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.