Evidence map›Paper›PMID 34377879›Full record

ArticleGeoHealth2021

Spatiotemporal Associations Between Social Vulnerability, Environmental Measurements, and COVID-19 in the Conterminous United States.

Daniel P Johnson, Niranjan Ravi, Christian V Braneon

Open access · goldAbstract read
In one paragraph

Article in GeoHealth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 4 pooled it
2.4field-weighted citation impact, top 11% 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

19 citing papers in PubMed, 4 syntheses or guidelines pooled it, 34 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. What Makes Urban Communities More Resilient to COVID-19? A Systematic Review of Current Evidence.International journal of environmental research and public health · 2022
    Pooled it
  4. Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review.International journal of environmental research and public health · 2022
    Pooled it
  5. Review
  6. Article
  7. Mortality from type 2 diabetes mellitus across municipalities in Mexico.Archives of public health = Archives belges de sante publique · 2024
    Article
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  17. Temporal Geospatial Analysis of COVID-19 Pre-Infection Determinants of Risk in South Carolina.International journal of environmental research and public health · 2021
    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

3 authors at 2 institutions in 1 country.

Daniel P JohnsonDepartment of Geography Indiana University-Purdue University at Indianapolis Indianapolis IN USA.ORCID https://orcid.org/0000-0002-9949-7584
Niranjan RaviDepartment of Electrical and Computer Engineering Indiana University-Purdue University at Indianapolis Indianapolis IN USA.
Christian V BraneonNASA Goddard Institute for Space Studies New York NY USA.ORCID https://orcid.org/0000-0003-1878-1397
University of Indianapolis · USGoddard Institute for Space Studies · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study summarizes the results from fitting a Bayesian hierarchical spatiotemporal model to coronavirus disease 2019 (COVID-19) cases and deaths at the county level in the United States for the year 2020. Two models were created, one for cases and one for deaths, utilizing a scaled Besag, York, Mollié model with Type I spatial-temporal interaction. Each model accounts for 16 social vulnerability and 7 environmental variables as fixed effects. The spatial pattern between COVID-19 cases and deaths is significantly different in many ways. The spatiotemporal trend of the pandemic in the United States illustrates a shift out of many of the major metropolitan areas into the United States Southeast and Southwest during the summer months and into the upper Midwest beginning in autumn. Analysis of the major social vulnerability predictors of COVID-19 infection and death found that counties with higher percentages of those not having a high school diploma, having non-White status and being Age 65 and over to be significant. Among the environmental variables, above ground level temperature had the strongest effect on relative risk to both cases and deaths. Hot and cold spots, areas of statistically significant high and low COVID-19 cases and deaths respectively, derived from the convolutional spatial effect show that areas with a high probability of above average relative risk have significantly higher Social Vulnerability Index composite scores. The same analysis utilizing the spatiotemporal interaction term exemplifies a more complex relationship between social vulnerability, environmental measurements, COVID-19 cases, and COVID-19 deaths.

Indexed as

Bayesian spatiotemporal disease modelingCOVID‐19 pandemicenvironmental determinants of COVID‐19remote sensing and COVID‐19social vulnerabilityspatial epidemiology

Identifiers

PMID34377879
PMCPMC8335698
OpenAlexW3136471166

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.