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Language: English

Conflict of Interest: In relation to this article, we declare that there is no conflict of interest.

Publication history: Received June 13, 2023
Accepted September 4, 2023

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Incidence Rate Prediction Model for Keratitis, Conjunctivitis, and Dry Eye Syndrome Using Air Pollutants and Meteorological Factors

Jong-Sang Youn Jeong-Won Seo¹ Poong-Mo Park^{2 3} Jin-Woo Huh⁴ SeJoon Park⁴ Ki-Joon Jeon^{2 3 5}

Department of Energy and Environmental Engineering , The Catholic University of Korea ¹Department of Ophthalmology , Hallym University, Dongtan Sacred Heart Hospital ²Department of Environmental Engineering , Inha University ³Particle Pollution Research and Management Center ⁴Division of Energy Resources Engineering and Industrial Engineering , Kangwon National University ⁵Program in Environmental and Polymer Engineering , Inha University

Korean Journal of Chemical Engineering, March 2024, 41(3), 819-828(10), https://doi.org/10.1007/s11814-024-00085-6

Abstract

The eye is a sensory organ with a large area exposed to the atmosphere and, thus, substantially aff ected by air quality. In this

study, we developed a prediction model for keratitis, conjunctivitis, and dry eye syndrome (DES) based on the air quality,

using hospital visit records and data for air pollution, weather, and population. Male and female populations were used as

independent variables to improve the accuracy of the model. Moreover, developed model was applied to air pollutants and

meteorological data using nonlinear regression. The results of the incidence rate prediction model for keratitis, conjunctivitis,

and DES were compared with actual data. Each model is statistically signifi cant ( p < 0.05). Based on the nationwide prediction

model, regional prediction models for 16 administrative districts were analyzed. In the cases of Incheon and Daegu,

the model showed high accuracy. However, in the cases of Chungnam and Jeju, the model showed lowest accuracy. Further

research is necessary for the optimization of regional predictive models for keratitis, conjunctivitis, and DES.

Keywords

Air pollutants · Prediction model · Keratitis · Conjunctivitis · Dry eye syndrome