Association of air pollutants and hospital admissions for respiratory diseases in Lanzhou, China, 2014-2019.
Humans
Male
Female
Air Pollutants
/ toxicity
Air Pollution
/ adverse effects
Nitrogen Dioxide
/ analysis
Environmental Exposure
/ analysis
Particulate Matter
/ toxicity
Hospitalization
Respiratory Tract Diseases
/ chemically induced
Respiration Disorders
/ chemically induced
Environmental Pollutants
/ analysis
China
/ epidemiology
Gases
/ analysis
Hospitals
Air pollutants
Distributed lag nonlinear model
Hospital admissions
Respiratory disease
Journal
Environmental geochemistry and health
ISSN: 1573-2983
Titre abrégé: Environ Geochem Health
Pays: Netherlands
ID NLM: 8903118
Informations de publication
Date de publication:
Mar 2023
Mar 2023
Historique:
received:
11
10
2021
accepted:
07
03
2022
pubmed:
7
4
2022
medline:
17
3
2023
entrez:
6
4
2022
Statut:
ppublish
Résumé
The aim of this study was to assess the effects of air pollutants on hospital admissions for respiratory disease (RD) by using distributed lag nonlinear model (DLNM) in Lanzhou during 2014-2019. In this study, the dataset of air pollutants, meteorological, and daily hospital admissions for RD in Lanzhou, from January 1st, 2014 to December 31st, 2019, were collected from three national environmental monitoring stations, China meteorological data service center, and three large general hospitals, respectively. A time-series analysis with DLNM was used to estimate the associations between air pollutants and hospital admissions for RD including the stratified analysis of age, gender, and season. The key findings were expressed as the relative risk (RR) with a 95% confidence interval (CI) for single-day and cumulative lag effects (0-7). A total of 90, 942 RD hospitalization cases were identified during the study period. The highest association (RR, 95% CI) of hospital admissions for RD and PM
Identifiants
pubmed: 35384572
doi: 10.1007/s10653-022-01256-2
pii: 10.1007/s10653-022-01256-2
pmc: PMC8985563
doi:
Substances chimiques
Air Pollutants
0
Nitrogen Dioxide
S7G510RUBH
Particulate Matter
0
Environmental Pollutants
0
Gases
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
941-959Subventions
Organisme : Scientific Research and Innovation Foundation of Gansu University of Chinese Medicine
ID : 2020KCYB-4
Organisme : National Science Foundation of China
ID : 41975019
Organisme : Fundamental Research Funds for the Central University
ID : lzujbky-2019-42
Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Nature B.V.
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