Evidence map›Paper›PMID 41085938›Full record

ArticleJournal of racial and ethnic health disparities2025

Masking Mandate Policies and Community-Level Factors Associated with COVID-19 Cases Spread in the State of Texas.

Eduardo Pérez, Samuel K Greer, Mary Van, Kenneth Skidmore, Kenroy Williams, Francis A Méndez Mediavilla

Abstract read
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In one paragraph

Article in Journal of racial and ethnic health disparities, 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

6 authors.

Eduardo PérezIngram School of Engineering, Texas State University, 601 University Drive, San Marcos, TX, 78666, USA. eduardopr@txstate.edu.ORCID http://orcid.org/0000-0001-8481-4332
Samuel K GreerDepartment of Information Systems and Analytics, Texas State University, San Marcos, USA.
Mary VanIngram School of Engineering, Texas State University, 601 University Drive, San Marcos, TX, 78666, USA.
Kenneth SkidmoreDepartment of Information Systems and Analytics, Texas State University, San Marcos, USA.
Kenroy WilliamsDepartment of Information Systems and Analytics, Texas State University, San Marcos, USA.
Francis A Méndez MediavillaDepartment of Information Systems and Analytics, Texas State University, San Marcos, USA.

Funding

National Science Foundation 2030511
6 · The paper itself

Abstract

This exploratory study investigates the intersection of county demographics, healthcare disparities, and COVID-19 policies with COVID-19 outcomes in Texas. Using hierarchical clustering, random forest, and multinomial logistic regression algorithms, the study analyzes COVID-19 case data from March 4, 2020, to December 15, 2020, across 254 Texas counties. The findings reveal six distinct clusters, with key differentiating factors including the number of days under mask mandate, number of churches, population density, water area, and proportion of African American population. Longer mask mandates are associated with higher case rates, suggesting remedial rather than preventive implementation. Higher population density and more churches per person correlate with steeper case increases. Access to bodies of water and the proportion of the African American population were also associated with case trends. These insights helped us formulate hypotheses that can inform tailored public health interventions to address regional characteristics within Texas.

Indexed as

Cluster analysisCOVID-19 casesExploratory data analysisMasking mandatesRandom forestTime series analysis

Identifiers

PMID41085938

What OpenQuestion holds

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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.