Pattern analysis of asthma and particulate matter considering spatial relationship
Jeongeun Lee1 · Taeyoung Heo2 · Jonghwa Na3
123Department of Information & Statistics, Chungbuk National University
Correspondence to: 1 Graduate student, Department of Information & Statistics, Chungbuk National University, Cheongju 28644, Korea.
2 Professor, Department of Information & Statistics, Chungbuk National University, Cheongju 28644, Korea.
3 Professor, Department of Information & Statistics, Chungbuk National University, Cheongju 28644, Korea. E-mail: cherin@cbnu.ac.kr
2 Professor, Department of Information & Statistics, Chungbuk National University, Cheongju 28644, Korea.
3 Professor, Department of Information & Statistics, Chungbuk National University, Cheongju 28644, Korea. E-mail: cherin@cbnu.ac.kr
Received June 19, 2024; Revised July 10, 2024; Accepted July 11, 2024.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Abstract
- This study analyzed asthma patients data in Korea from 2017 to 2022 using spatial statistics techniques. To understand the effect of particulate matter on asthma in consideration of spatial patterns, a generalized linear mixed model reflecting spatial correlation was used, and the generalized linear mixed model assuming a negative binomial distribution was found to be the best in terms of model explanation ability. As a result of pattern analysis from the suitable model, it was confirmed that asthma patients occurred frequently in March-April and October-December. In addition, after COVID-19, the incidence of asthma showed a decreasing trend. As for regional characteristics, the incidence of asthma was high in the western and coastal areas, which is thought to be the effect of industrialization. In addition, the time when particulate matter had a significant effect on asthma and the incidence rate of asthma had a similar temporal pattern, and it was confirmed that the number of significant ultrafine dust increased year by year over time, which had a great influence on the incidence of asthma.
- Keywords : Asthma, generalized linear mixed model, particulate matter, spatial statistics, spatio-temporal patterns
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