Associations of PM2.5 and aspergillosis: ambient fine particulate air pollution and population-based big data linkage analyses View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-05-19

AUTHORS

Po-Yu Liu, Yu-Tse Tsan, Yu-Wei Chan, Wei-Chen Chan, Zhi-Yuan Shi, Chao-Tung Yang, Ben-Shen Lou

ABSTRACT

A substantial amount of reports has been published on the association between the environment Aspergillus conidia and fine particulates, while limited studies have focused on the association between invasive aspergillosis and ambient fine particulate air pollution. The aim of this study is to explore the population-level association between ambient fine particulate PM2.5 air pollution and invasive aspergillosis. We designed multiple powerful systems applied to a unique collection of long-term and nationwide database of daily ambient PM2.5 levels and invasive aspergillosis incidence in Taiwan. Two data sets were leveraged for this study; the National Health Insurance Research Database and the Taiwan air quality monitoring network. The National Health Insurance Research Database was used to define invasive aspergillosis while the Taiwan air quality monitoring network was used to profile the PM2.5 concentration in Taiwan. We adopted the cases of invasive aspergillosis infection from the system with PM2.5 levels. A total of 1,000,000 patients during the study period (1999–2009) were included in the data set.The findings of this study suggest positive association between PM2.5 concentration and incidence of aspergillosis. Furthermore, monthly-wise invasive aspergillosis case number potentially demonstrated lagged pattern following peaking of PM2.5 concentration. More... »

PAGES

1-11

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12652-018-0852-x

DOI

http://dx.doi.org/10.1007/s12652-018-0852-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1104124754


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s12652-018-0852-x'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s12652-018-0852-x'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s12652-018-0852-x'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s12652-018-0852-x'


 

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