Evidence map›Paper›PMID 30370331›Full record

ArticleAIMS environmental science2018

The status of geo-environmental health in Mississippi: Application of spatiotemporal statistics to improve health and air quality.

Swatantra R Kethireddy, Grace A Adegoye, Paul B Tchounwou, Francis Tuluri, H Anwar Ahmad, John H Young, Lei Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in AIMS environmental science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Swatantra R KethireddyDepartment of Natural Sciences and Environmental Health, Mississippi Valley State University, 14000 Highway 82 W, Itta Bena, MS 38941, USA.
Grace A AdegoyeDepartment of Natural Sciences and Environmental Health, Mississippi Valley State University, 14000 Highway 82 W, Itta Bena, MS 38941, USA.
Paul B TchounwouCollege of Science, Engineering and Technology, Jackson State University, 1400 John R Lynch St., Jackson, MS 39217, USA.
Francis TuluriCollege of Science, Engineering and Technology, Jackson State University, 1400 John R Lynch St., Jackson, MS 39217, USA.
H Anwar AhmadCollege of Science, Engineering and Technology, Jackson State University, 1400 John R Lynch St., Jackson, MS 39217, USA.
John H YoungCollege of Science, Engineering and Technology, Jackson State University, 1400 John R Lynch St., Jackson, MS 39217, USA.
Lei ZhangOffice of Health Data and Research, Mississippi State Department of Health, 570 East Woodrow Wilson Avenue, Jackson, MS 39215, USA.

Funding

STEERING COMMITTEEU54MD008149 · NIMHD · MOREHOUSE SCHOOL OF MEDICINE · PI OFILI, ELIZABETH O. · 2012 to 2018
$21.1M
RCMI-Center for Environmental HealthG12MD007581 · NIMHD · JACKSON STATE UNIVERSITY · PI TCHOUNWOU, PAUL B. · 2012 to 2017
$11.2M
NIMHD NIH HHS G12 MD007581NIMHD NIH HHS U54 MD008149
6 · The paper itself

Abstract

Data enabled research with a spatial perspective may help to combat human diseases in an informed and cost-effective manner. Understanding the changing patterns of environmental degradation is essential to help in determining the health outcomes such as asthma of a community. In this research, Mississippi asthma-related prevalence data for 2003-2011 were analyzed using spatial statistical techniques in Geographic Information Systems. Geocoding by ZIP code, choropleth mapping, and hotspot analysis techniques were applied to map the spatial data. Disease rates were calculated for every ZIP code region from 2009 to 2011. The highest rates (4-5.5%) were found in Prairie in Monroe County for three consecutive years. Statistically significant hotspots were observed in urban regions of Jackson and Gulf port with steady increase near urban Jackson and the area between Jackson and meridian metropolis. For 2009-2011, spatial signatures of urban risk factors were found in dense population areas, which was confirmed from regression analysis of asthma patients with population data (linear increase of R

Indexed as

air qualityasthmaenvironmental healthgeocodingGISkrigingregression analysisspatial analysisspatiotemporal statistics

Identifiers

PMID30370331
PMCPMC6201236

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.