Prediction Model for Dry Eye Syndrome Incidence Rate Using Air Pollutants and Meteorological Factors in South Korea: Analysis of Sub-Region Deviations

Here, we develop a dry eye syndrome (DES) incidence rate prediction model using air pollutants (PM(10), NO(2), SO(2), O(3), and CO), meteorological factors (temperature, humidity, and wind speed), population rate, and clinical data for South Korea. The prediction model is well fitted to the incidenc...

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Autores principales: Youn, Jong-Sang, Seo, Jeong-Won, Park, Wonjun, Park, SeJoon, Jeon, Ki-Joon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7399894/
https://www.ncbi.nlm.nih.gov/pubmed/32664192
http://dx.doi.org/10.3390/ijerph17144969
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author Youn, Jong-Sang
Seo, Jeong-Won
Park, Wonjun
Park, SeJoon
Jeon, Ki-Joon
author_facet Youn, Jong-Sang
Seo, Jeong-Won
Park, Wonjun
Park, SeJoon
Jeon, Ki-Joon
author_sort Youn, Jong-Sang
collection PubMed
description Here, we develop a dry eye syndrome (DES) incidence rate prediction model using air pollutants (PM(10), NO(2), SO(2), O(3), and CO), meteorological factors (temperature, humidity, and wind speed), population rate, and clinical data for South Korea. The prediction model is well fitted to the incidence rate (R(2) = 0.9443 and 0.9388, p < 2.2 × 10(−16)). To analyze regional deviations, we classify outpatient data, air pollutant, and meteorological factors in 16 administrative districts (seven metropolitan areas and nine states). Our results confirm NO(2) and relative humidity are the factors impacting regional deviations in the prediction model.
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spelling pubmed-73998942020-08-17 Prediction Model for Dry Eye Syndrome Incidence Rate Using Air Pollutants and Meteorological Factors in South Korea: Analysis of Sub-Region Deviations Youn, Jong-Sang Seo, Jeong-Won Park, Wonjun Park, SeJoon Jeon, Ki-Joon Int J Environ Res Public Health Article Here, we develop a dry eye syndrome (DES) incidence rate prediction model using air pollutants (PM(10), NO(2), SO(2), O(3), and CO), meteorological factors (temperature, humidity, and wind speed), population rate, and clinical data for South Korea. The prediction model is well fitted to the incidence rate (R(2) = 0.9443 and 0.9388, p < 2.2 × 10(−16)). To analyze regional deviations, we classify outpatient data, air pollutant, and meteorological factors in 16 administrative districts (seven metropolitan areas and nine states). Our results confirm NO(2) and relative humidity are the factors impacting regional deviations in the prediction model. MDPI 2020-07-10 2020-07 /pmc/articles/PMC7399894/ /pubmed/32664192 http://dx.doi.org/10.3390/ijerph17144969 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Youn, Jong-Sang
Seo, Jeong-Won
Park, Wonjun
Park, SeJoon
Jeon, Ki-Joon
Prediction Model for Dry Eye Syndrome Incidence Rate Using Air Pollutants and Meteorological Factors in South Korea: Analysis of Sub-Region Deviations
title Prediction Model for Dry Eye Syndrome Incidence Rate Using Air Pollutants and Meteorological Factors in South Korea: Analysis of Sub-Region Deviations
title_full Prediction Model for Dry Eye Syndrome Incidence Rate Using Air Pollutants and Meteorological Factors in South Korea: Analysis of Sub-Region Deviations
title_fullStr Prediction Model for Dry Eye Syndrome Incidence Rate Using Air Pollutants and Meteorological Factors in South Korea: Analysis of Sub-Region Deviations
title_full_unstemmed Prediction Model for Dry Eye Syndrome Incidence Rate Using Air Pollutants and Meteorological Factors in South Korea: Analysis of Sub-Region Deviations
title_short Prediction Model for Dry Eye Syndrome Incidence Rate Using Air Pollutants and Meteorological Factors in South Korea: Analysis of Sub-Region Deviations
title_sort prediction model for dry eye syndrome incidence rate using air pollutants and meteorological factors in south korea: analysis of sub-region deviations
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7399894/
https://www.ncbi.nlm.nih.gov/pubmed/32664192
http://dx.doi.org/10.3390/ijerph17144969
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