Next Article in Journal
Effects of Ultrafine Particles in Ambient Air on Primary Health Care Consultations for Diabetes in Children and Elderly Population in Ljubljana, Slovenia: A 5-Year Time-Trend Study
Next Article in Special Issue
Pollution Assessment Based on Element Concentration of Tree Leaves and Topsoil in Ayutthaya Province, Thailand
Previous Article in Journal
Promoting a Safe Environment in Our Cities: Towards a Theoretical Model of “Moral Deficit” for Appropriate Psychopathic Therapy
Previous Article in Special Issue
Comparative Study of the Composition of Sweat from Eccrine and Apocrine Sweat Glands during Exercise and in Heat
 
 
Article

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

1
Department of Environmental Engineering, Inha University, Incheon 22212, Korea
2
Department of Ophthalmology, Hallym University, Dongtan Sacred Heart Hospital, Hwaseong-si 18450, Korea
3
School of Industrial Management Engineering, Korea University, Seoul 02841, Korea
4
Division of Energy Resources Engineering and Industrial Engineering, Kangwon National University, Chuncheon-si 24341, Korea
*
Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(14), 4969; https://doi.org/10.3390/ijerph17144969
Received: 4 June 2020 / Revised: 22 June 2020 / Accepted: 2 July 2020 / Published: 10 July 2020
(This article belongs to the Special Issue Advances in the Field of Human Health and Environment)
Here, we develop a dry eye syndrome (DES) incidence rate prediction model using air pollutants (PM10, NO2, SO2, O3, 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 (R2 = 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 NO2 and relative humidity are the factors impacting regional deviations in the prediction model. View Full-Text
Keywords: dry eye syndrome; air pollutants; meteorological factors; prediction model; regional deviation dry eye syndrome; air pollutants; meteorological factors; prediction model; regional deviation
Show Figures

Figure 1

MDPI and ACS Style

Youn, J.-S.; Seo, J.-W.; Park, W.; Park, S.; Jeon, K.-J. Prediction Model for Dry Eye Syndrome Incidence Rate Using Air Pollutants and Meteorological Factors in South Korea: Analysis of Sub-Region Deviations. Int. J. Environ. Res. Public Health 2020, 17, 4969. https://doi.org/10.3390/ijerph17144969

AMA Style

Youn J-S, Seo J-W, Park W, Park S, Jeon K-J. Prediction Model for Dry Eye Syndrome Incidence Rate Using Air Pollutants and Meteorological Factors in South Korea: Analysis of Sub-Region Deviations. International Journal of Environmental Research and Public Health. 2020; 17(14):4969. https://doi.org/10.3390/ijerph17144969

Chicago/Turabian Style

Youn, Jong-Sang, Jeong-Won Seo, Wonjun Park, SeJoon Park, and Ki-Joon Jeon. 2020. "Prediction Model for Dry Eye Syndrome Incidence Rate Using Air Pollutants and Meteorological Factors in South Korea: Analysis of Sub-Region Deviations" International Journal of Environmental Research and Public Health 17, no. 14: 4969. https://doi.org/10.3390/ijerph17144969

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop