Considering spatial heterogeneity in the distributed lag non-linear model when analyzing spatiotemporal data

The distributed lag non-linear (DLNM) model has been frequently used in time series environmental health research. However, its functionality for assessing spatial heterogeneity is still restricted, especially in analyzing spatiotemporal data. This study proposed a solution to take a spatial functio...

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Bibliographic Details
Published in:Journal of exposure science & environmental epidemiology 2018-01, Vol.28 (1), p.13-20
Main Authors: Chien, Lung-Chang, Guo, Yuming, Li, Xiao, Yu, Hwa-Lung
Format: Article
Language:English
Subjects:
704/158/1144
704/172/169/895
Air pollution
Analysis
Case studies
Children
Data processing
Environmental health
Environmental research
Epidemiology
Health aspects
Heterogeneity
Management
Markov processes
Medicine
Medicine & Public Health
Nonlinear analysis
original-article
Particulate matter
Preschool children
Public health
Spatial distribution
Spatial heterogeneity
Spatiotemporal data
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Summary:The distributed lag non-linear (DLNM) model has been frequently used in time series environmental health research. However, its functionality for assessing spatial heterogeneity is still restricted, especially in analyzing spatiotemporal data. This study proposed a solution to take a spatial function into account in the DLNM, and compared the influence with and without considering spatial heterogeneity in a case study. This research applied the DLNM to investigate non-linear lag effect up to 7 days in a case study about the spatiotemporal impact of fine particulate matter (PM 2.5 ) on preschool children’s acute respiratory infection in 41 districts of northern Taiwan during 2005 to 2007. We applied two spatiotemporal methods to impute missing air pollutant data, and included the Markov random fields to analyze district boundary data in the DLNM. When analyzing the original data without a spatial function, the overall PM 2.5 effect accumulated from all lag-specific effects had a slight variation at smaller PM 2.5 measurements, but eventually decreased to relative risk significantly
ISSN:1559-0631
1559-064X
DOI:10.1038/jes.2016.62