title: Considering spatial heterogeneity in the distributed lag non-linear model when analyzing spatiotemporal data.
abstract: 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 (PM2.5) 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 PM2.5effect accumulated from all lag-specific effects had a slight variation at smaller PM2.5measurements, but eventually decreased to relative risk significantly <1 when PM2.5increased. While analyzing spatiotemporal imputed data without a spatial function, the overall PM2.5effect did not decrease but increased in monotone as PM2.5increased over 20 μg/m3. After adding a spatial function in the DLNM, spatiotemporal imputed data conducted similar results compared with the overall effect from the original data. Moreover, the spatial function showed a clear and uneven pattern in Taipei, revealing that preschool children living in 31 districts of Taipei were vulnerable to acute respiratory infection. Our findings suggest the necessity of including a spatial function in the DLNM to make a spatiotemporal analysis available and to conduct more reliable and explainable research. This study also revealed the analytical impact if spatial heterogeneity is ignored.
authors.affiliation: Department of Biostatistics, The University of Texas School of Public Health at San Antonio Regional Campus, San Antonio, Texas, USA.|Research to Advance Community Health Center, The University of Texas Health Science Center at San Antonio Regional Campus, San Antonio, Texas, USA.
authors.forename: Lung-Chang
authors.initials: LC
authors.lastname: Chien
authors.affiliation: Division of Epidemiology and Biostatistics, School of Public Health, The University of Queensland, Brisbane, Queensland, Australia.
authors.forename: Yuming
authors.initials: Y
authors.lastname: Guo
authors.affiliation: Department of Biostatistics, The University of Texas School of Public Health, Houston, Texas, USA.
authors.forename: Xiao
authors.initials: X
authors.lastname: Li
authors.affiliation: Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan.
authors.forename: Hwa-Lung
authors.initials: HL
authors.lastname: Yu
mesh_terms: D000393:Air Pollutants / Q000009:adverse effects*; D000397:Air Pollution / Q000009:adverse effects*; D002675:Child, Preschool; D002947:Cities; D016208:Databases, Factual; D004784:Environmental Monitoring / Q000379:methods; D005260:Female; D005843:Geography; D006801:Humans; D008297:Male; D008390:Markov Chains; D017711:Nonlinear Dynamics; D010316:Particle Size; D052638:Particulate Matter / Q000009:adverse effects*; D012141:Respiratory Tract Infections / Q000139:chemically induced* / Q000453:epidemiology*; D062211:Spatio-Temporal Analysis; D013624:Taiwan / Q000453:epidemiology; D013997:Time Factors
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter