Evidence map›Paper›PMID 39580430›Full record

ArticleBMC public health2024

Association between air pollution and hypertension hospitalizations: a time series analysis in Lanzhou.

Anning Zhu, Miaoxin Liu, Jingze Yu, Runping Zhang, Yaoyi Zhang, Rentong Chen, Ye Ruan

Abstract read
In one paragraph

Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
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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.

Anning ZhuSchool of Public Health, Lanzhou University, Lanzhou, 730000, P.R. China.
Miaoxin LiuSchool of Public Health, Lanzhou University, Lanzhou, 730000, P.R. China.
Jingze YuSchool of Public Health, Lanzhou University, Lanzhou, 730000, P.R. China.
Runping ZhangGansu Provincial Health Supervision and Guarantee Center, Lanzhou, 730013, P.R. China.
Yaoyi ZhangSchool of Public Health, Lanzhou University, Lanzhou, 730000, P.R. China.
Rentong ChenSchool of Public Health, Lanzhou University, Lanzhou, 730000, P.R. China.
Ye RuanSchool of Public Health, Lanzhou University, Lanzhou, 730000, P.R. China. ruany@lzu.edu.cn.

Funding

Fundamental Research Funds for the Central Universities, China lzujbky-2020-9Science and Technology Program of Gansu Province, China 20YF3FA027
6 · The paper itself

Abstract

backgroundAir pollution is a major environmental risk factor. Hypertension is one of the most important modifiable risk factors for cardiovascular diseases. However, few studies have evaluated the impact of exposure to ambient air pollution on hypertension hospitalizations.

objectiveThis study aims to explore the correlation between exposure to air pollution and hospital admissions for hypertension, to evaluate the short-term effects of air pollution on hypertension hospitalizations and its lag effects.

methodsWe collected air pollution concentration and hypertension hospitalization data from 2013 to 2020. Distributed lag non-linear models were employed to assess the impact of air pollution on hypertension hospitalizations in Lanzhou City. We also performed subgroup analyses and sensitivity analyses.

resultsA total of 47,884 cases of hypertension hospitalizations were included. Short-term exposure to NO

conclusionShort-term exposure to ambient air pollution increases risk of hypertension hospitalization.

Indexed as

Air PollutantsAir PollutionEnvironmental ExposureHospitalizationHypertensionAdultAgedChinaFemaleHumansMaleMiddle AgedParticulate MatterRisk FactorsTime FactorsAir PollutantsParticulate MatterAir pollutionHospitalizationHypertensionRelative risk

Identifiers

PMID39580430
PMCPMC11585948

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.