Evidence map›Paper›PMID 40884682›Full record

ArticleJournal of epidemiology and global health2025

Medium-Term Lag-Response Associations Between PM

Bin Zhou, Katrin Gohlsch, Surendra Ranpal, Jiancong Wang, Christoph Knote

Abstract read
In one paragraph

Article in Journal of epidemiology and global health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Bin ZhouChair of Model-Based Environmental Exposure Science, Faculty of Medicine, University of Augsburg, Augsburg, Germany.ORCID 0000-0001-8853-0724
Katrin GohlschChair of Model-Based Environmental Exposure Science, Faculty of Medicine, University of Augsburg, Augsburg, Germany.
Surendra RanpalChair of Model-Based Environmental Exposure Science, Faculty of Medicine, University of Augsburg, Augsburg, Germany.ORCID 0000-0003-2018-8488
Jiancong Wang *Chair of Model-Based Environmental Exposure Science, Faculty of Medicine, University of Augsburg, Augsburg, Germany. Jiancong.Wang@med.uni-augsburg.de.ORCID 0000-0001-6284-9702
Christoph Knote *Chair of Model-Based Environmental Exposure Science, Faculty of Medicine, University of Augsburg, Augsburg, Germany.ORCID 0000-0001-9105-9179

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAir pollution is among the top five environmental risk factors for human health worldwide. However, our understanding of the physiological responses to PM

methodsWe used a time-stratified case-crossover design; building on the methodologies of Tobias et al. and Bhaskaran et al., we applied a fixed-effects conditional quasi-Poisson regression model to quantify the association between PM

resultsWe found distinct differences in the relative risk (RR) patterns of PM

conclusionsThis study highlights the critical importance of addressing medium-term lag-response associations and methodological variations in environmental epidemiology. The findings have important clinical and public health implications and offer insights for risk assessment, healthcare planning, and the development of policies to mitigate the health impacts of PM

Indexed as

Air PollutantsAir PollutionEnvironmental ExposureMortalityParticulate MatterAdultAgedCross-Over StudiesFemaleHumansLondonMaleMiddle AgedRisk FactorsTime FactorsAir PollutantsParticulate MatterAll cause mortalityEnvironmental epidemiologyEuropeLagging time windows of 21 daysMedium term lag response associationsPM10 exposureTemporal lag methodological structures

Identifiers

PMID40884682
PMCPMC12398449

What OpenQuestion holds

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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.