Evidence map›Paper›PMID 40371287›Full record

ArticleFrontiers in public health2025

Study on the impact of air pollutants on childhood asthma in Nanjing based on a distributed lag non-linear model.

Wenjing Li, Ting Ding, Yahui Feng, Jin Xu, Zhuo Li

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Observational
  3. 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

5 authors.

Wenjing LiDepartment of Pharmacy, Children's Hospital of Nanjing Medical University, Nanjing, China.
Ting DingNational Climate Center, China Meteorological Administration, Beijing, China.
Yahui FengDepartment of Emergency, Children's Hospital of Nanjing Medical University, Nanjing, China.
Jin Xu *Department of Pharmacy, Children's Hospital of Nanjing Medical University, Nanjing, China.
Zhuo Li *Department of Emergency, Children's Hospital of Nanjing Medical University, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Asthma, a chronic respiratory disease, is a significant public health concern globally. Air pollution has been identified as one of the key risk factors exacerbating respiratory conditions, particularly in children. Previous studies have shown a correlation between air pollution levels and asthma visits. However, the impact of air pollutants on childhood asthma visits stratified by gender, age and season remains a topic of interest, and further investigation is necessary to comprehend this complex relationship. Objective: This study aimed to explore the association between air pollution and childhood asthma visits in Nanjing from 2013 to 2021. It focused on the effects of various pollutants, including PM Methods: Data on childhood asthma visits and concentrations of air pollutants were collected and analyzed. The lag effects of pollutants on asthma visits were assessed, and the impacts were stratified by gender, age group (0-5 years, 6-11 years, and over 11 years), and season. Statistical methods were used to identify significant correlations and the timing of maximum effects of pollutants. Results: A positive correlation was found between childhood asthma visits and concentrations of AQI, PM Conclusion: The findings indicate a substantial effect of air pollutants, particularly PM

Indexed as

Air PollutantsAir PollutionAsthmaEnvironmental ExposureChildChild, PreschoolChinaFemaleHumansInfantInfant, NewbornMaleNonlinear DynamicsParticulate MatterSeasonsAir PollutantsParticulate Matterair pollutantschildhood asthmadistributed lag non-linear modelgeneralized additive modellag effect

Identifiers

PMID40371287
PMCPMC12075223

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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.