Evidence map›Paper›PMID 23407325›Full record

SynthesisJournal of preventive medicine and public health = Yebang Uihakhoe chi2013

PM₁₀ exposure and non-accidental mortality in Asian populations: a meta-analysis of time-series and case-crossover studies.

Hye Yin Park, Sanghyuk Bae, Yun-Chul Hong

Abstract readMeta-Analysis
In one paragraph

Synthesis in Journal of preventive medicine and public health = Yebang Uihakhoe chi, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
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  6. Review
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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.

Hye Yin ParkDepartment of Preventive Medicine, Seoul National University College of Medicine, Seoul, Korea.
Sanghyuk Bae
Yun-Chul Hong

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesWe investigated the association between particulate matter less than 10 µm in aerodynamic diameter (PM₁₀) exposure and non-accidental mortality in Asian populations by meta-analysis, using both time-series and case-crossover analysis.

methodsAmong the 819 published studies searched from PubMed and EMBASE using key words related to PM₁₀ exposure and non-accidental mortality in Asian countries, 8 time-series and 4 case-crossover studies were selected for meta-analysis after exclusion by selection criteria. We obtained the relative risk (RR) and 95% confidence intervals (CI) of non-accidental mortality per 10 µg/m³ increase of daily PM₁₀ from each study. We used Q statistics to test the heterogeneity of the results among the different studies and evaluated for publication bias using Begg funnel plot and Egger test.

resultsTesting for heterogeneity showed significance (p<0.001); thus, we applied a random-effects model. RR (95% CI) per 10 µg/m³ increase of daily PM₁₀ for both the time-series and case-crossover studies combined, time-series studies relative risk only, and case-crossover studies only, were 1.0047 (1.0033 to 1.0062), 1.0057 (1.0029 to 1.0086), and 1.0027 (1.0010 to 1.0043), respectively. The non-significant Egger test suggested that this analysis was not likely to have a publication bias.

conclusionsWe found a significant positive association between PM₁₀ exposure and non-accidental mortality among Asian populations. Continued investigations are encouraged to contribute to the health impact assessment and public health management of air pollution in Asian countries.

Indexed as

Environmental ExposureMortalityAir PollutantsAsian PeopleCross-Over StudiesDatabases, FactualHumansModels, TheoreticalParticulate MatterTime FactorsAir PollutantsParticulate MatterAsiaMeta-analysisNon-accidental mortalityPM

Identifiers

PMID23407325
PMCPMC3567321

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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.