Evidence map›Paper›PMID 29237504›Full record

ArticleEnvironmental health : a global access science source2017

A multivariate analysis of CalEnviroScreen: comparing environmental and socioeconomic stressors versus chronic disease.

Ben K Greenfield, Jayant Rajan, Thomas E McKone

Open access · goldAbstract read
In one paragraph

Article in Environmental health : a global access science source, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
6.0field-weighted citation impact, top 4% 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

15 citing papers in PubMed, 24 citations in OpenAlex.

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  8. County-Level Social Vulnerability Is Positively Associated with Cardiometabolic Disease in Colorado.International journal of environmental research and public health · 2022
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  9. Article
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  13. Utilization of the Maryland Environmental Justice Screening Tool: A Bladensburg, Maryland Case Study.International journal of environmental research and public health · 2019
    Article
  14. Air Pollutant and Health-Efficiency Evaluation Based on a Dynamic Network Data Envelopment Analysis.International journal of environmental research and public health · 2018
    Article
  15. Article
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.

Ben K GreenfieldEnvironmental Health Sciences Division, School of Public Health, University of California, Berkeley, 50 University Hall #7360, Berkeley, CA, 94720, USA. begreen@siue.edu.
Jayant RajanDepartment of Medicine, Zuckerberg San Francisco General Hospital, University of California, San Francisco, 1001 Potrero Avenue, San Francisco, CA, 94110, USA.
Thomas E McKoneEnvironmental Health Sciences Division, School of Public Health, University of California, Berkeley, 50 University Hall #7360, Berkeley, CA, 94720, USA.
University of California, Berkeley · USUniversity of California, San Francisco · US

Funding

National Science Foundation 1144885U.S. Environmental Protection Agency (US) FP917287
6 · The paper itself

Abstract

backgroundThe health-risk assessment paradigm is shifting from single stressor evaluation towards cumulative assessments of multiple stressors. Recent efforts to develop broad-scale public health hazard datasets provide an opportunity to develop and evaluate multiple exposure hazards in combination.

methodsWe performed a multivariate study of the spatial relationship between 12 indicators of environmental hazard, 5 indicators of socioeconomic hardship, and 3 health outcomes. Indicators were obtained from CalEnviroScreen (version 3.0), a publicly available environmental justice screening tool developed by the State of California Environmental Protection Agency. The indicators were compared to the total rate of hospitalization for 14 ICD-9 disease categories (a measure of disease burden) at the zip code tabulation area population level. We performed principal component analysis to visualize and reduce the CalEnviroScreen data and spatial autoregression to evaluate associations with disease burden.

resultsCalEnviroScreen was strongly associated with the first principal component (PC) from a principal component analysis (PCA) of all 20 variables (Spearman ρ = 0.95). In a PCA of the 12 environmental variables, two PC axes explained 43% of variance, with the first axis indicating industrial activity and air pollution, and the second associated with ground-level ozone, drinking water contamination and PM

conclusionsThis study supports the use of CalEnviroScreen for its intended purpose of screening California regions for areas with high environmental exposure and population vulnerability. Study results further suggest a hypothesis that, compared to environmental pollutant exposure, socioeconomic status has greater impact on overall burden of disease.

Indexed as

Cost of IllnessEnvironmental ExposureModels, TheoreticalSocioeconomic FactorsVulnerable PopulationsCaliforniaChronic DiseaseEnvironmental PollutantsHospitalizationHumansMultivariate AnalysisPrincipal Component AnalysisEnvironmental PollutantsCalEnviroScreenCensusEnvironmental exposureEnvironmental justiceHealth hazardMultivariate analysisPrincipal component analysisSocioeconomic statusSpatial regressionVulnerable populations

Identifiers

PMID29237504
PMCPMC5729424
OpenAlexW2773523782

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.