Evidence map›Paper›PMID 38654228›Full record

ArticleEnvironmental health : a global access science source2024

Associations between long-term exposure to air pollution and kidney function utilizing electronic healthcare records: a cross-sectional study.

David Dillon, Cavin Ward-Caviness, Abhijit V Kshirsagar, Joshua Moyer, Joel Schwartz, Qian Di, Anne Weaver

Abstract read
In one paragraph

Article in Environmental health : a global access science source, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

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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

7 authors.

David DillonCenter for Public Health and Environmental Assessment, United States Environmental Protection Agency, Research Triangle Park, NC, USA.
Cavin Ward-CavinessCenter for Public Health and Environmental Assessment, United States Environmental Protection Agency, Research Triangle Park, NC, USA.
Abhijit V KshirsagarDivision of Nephrology and Hypertension, University of North Carolina School of Medicine, Chapel Hill, NC, USA.
Joshua MoyerCenter for Public Health and Environmental Assessment, United States Environmental Protection Agency, Research Triangle Park, NC, USA.
Joel SchwartzT.H. Chan School of Public Health, Harvard University, Boston, MA, USA.
Qian DiResearch Center for Public Health, School of Medicine, Tsinghua University, Beijing, China.
Anne WeaverCenter for Public Health and Environmental Assessment, United States Environmental Protection Agency, Research Triangle Park, NC, USA. Weaver.anne@epa.gov.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChronic kidney disease (CKD) affects more than 38 million people in the United States, predominantly those over 65 years of age. While CKD etiology is complex, recent research suggests associations with environmental exposures.

methodsOur primary objective is to examine creatinine-based estimated glomerular filtration rate (eGFR

resultsWe found 44,872 serum creatinine measurements among 7,722 patients. An IQR increase in PM2.5 was associated with a 1.63 mL/min/1.73m

conclusionsOne-year average PM

Indexed as

Air PollutantsAir PollutionElectronic Health RecordsEnvironmental ExposureGlomerular Filtration RateNitrogen DioxideOzoneParticulate MatterRenal Insufficiency, ChronicAdultAgedAged, 80 and overCreatinineCross-Sectional StudiesFemaleHumansAir PollutantsCreatinineNitrogen DioxideOzoneParticulate MatterAir pollutionChronic kidney diseaseElectronic healthcare recordsFine particulate matterNitrogen dioxideOzone

Identifiers

PMID38654228
PMCPMC11036746

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.