Evidence map›Paper›PMID 35270383›Full record

ArticleInternational journal of environmental research and public health2022

Statistical Methods for Assessing the Explained Variation of a Health Outcome by a Mixture of Exposures.

Hua Yun Chen, Hesen Li, Maria Argos, Victoria W Persky, Mary E Turyk

Open access · goldAbstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 33% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Review
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

5 authors at 1 institution in 1 country.

Hua Yun ChenDivision of Epidemiology & Biostatistics, School of Public Health, University of Illinois at Chicago, 1603 West Taylor Street, Chicago, IL 60612, USA.
Hesen LiDivision of Epidemiology & Biostatistics, School of Public Health, University of Illinois at Chicago, 1603 West Taylor Street, Chicago, IL 60612, USA.
Maria ArgosDivision of Epidemiology & Biostatistics, School of Public Health, University of Illinois at Chicago, 1603 West Taylor Street, Chicago, IL 60612, USA.ORCID 0000-0003-4234-252X
Victoria W PerskyDivision of Epidemiology & Biostatistics, School of Public Health, University of Illinois at Chicago, 1603 West Taylor Street, Chicago, IL 60612, USA.
Mary E TurykDivision of Epidemiology & Biostatistics, School of Public Health, University of Illinois at Chicago, 1603 West Taylor Street, Chicago, IL 60612, USA.
University of Illinois Chicago · US

Funding

Pilot Program CoreP30ES027792 · NIEHS · UNIVERSITY OF CHICAGO · PI Gokhan M. Mutlu, Gail S Prins · 2017 to 2026
$13.6M
Innovative Methodologic Advances for Mixtures Research in EpidemiologyR01ES028790 · NIEHS · UNIVERSITY OF ILLINOIS AT CHICAGO · PI CHEN, HUA YUN, TURYK, MARY ELLEN · 2018 to 2021
$1.9M
NIEHS NIH HHS P30 ES027792NIEHS NIH HHS R01 ES028790NIEHS NIH HHS R01ES028790
6 · The paper itself

Abstract

Exposures to environmental pollutants are often composed of mixtures of chemicals that can be highly correlated because of similar sources and/or chemical structures. The effect of an individual chemical on a health outcome can be weak and difficult to detect because of the relatively low level of exposures to many environmental pollutants. To tackle the challenging problem of assessing the health risk of exposure to a mixture of environmental pollutants, we propose a statistical approach to assessing the proportion of the variation of an outcome explained by a mixture of pollutants. The proposed approach avoids the difficult task of identifying specific pollutants that are responsible for the effects and may also be used to assess interactions among exposures. Extensive simulation results demonstrate that the proposed approach has very good performance. Application of the proposed approach is illustrated by investigating the main and interaction effects of the chemical pollutants on systolic and diastolic blood pressure in participants from the National Health and Nutrition Examination Survey.

Indexed as

Environmental ExposureEnvironmental PollutantsComputer SimulationHumansNutrition SurveysOutcome Assessment, Health CareEnvironmental Pollutantsenvironmental healthestimating equationlinear modelmixture of pollutantsrandom matrix

Identifiers

PMID35270383
PMCPMC8910055
OpenAlexW4214560063

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.