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. 2023 Apr 24;18(4):e0284928.
doi: 10.1371/journal.pone.0284928. eCollection 2023.

Prevalence and associated factors for self-reported symptoms of dry eye among Thai school children during the COVID-19 outbreak

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Prevalence and associated factors for self-reported symptoms of dry eye among Thai school children during the COVID-19 outbreak

Danai Tonkerdmongkol et al. PLoS One. .

Erratum in

Abstract

Purpose: COVID-19 pandemic caused an increase in digital screen time, which seemed to increase the prevalence of dry eye symptoms among the population with abnormally high digital screen usage hours. However, there are no reports of dry eye symptoms in school children with high digital usage hours. Therefore, the present study aimed to assess the prevalence of dry eye symptoms and evaluate the associated factors among school children aged 12 to 18 years during the COVID-19 outbreak.

Methods: Multistage cluster sampling was applied, and six sections of online questionnaires were distributed to selected respondents in November 2021. The odds ratio (OR) with confidence intervals (CIs) for the factors was calculated using binary logistic regression. All statistical significance was determined at p < 0.05.

Results: The findings revealed that 62.5% of 603 students showed symptoms of dry eye (DEQ-5 score ≥ 6). Significant associated factors included being female (adjusted OR (aOR) 1.54; 95% CIs 1.05-2.25), higher-grade student (aOR 1.77; 95% CIs 1.23-2.57), digital screen time use (6 to < 12 hours: aOR 2.00; 95% CIs 1.12-3.57, ≥12 hours: aOR 2.54; 95% CIs 1.39-4.76), and perceived stress (aOR 1.12; 95% CIs 1.08-1.16). The Thai-Perceived Stress Scale-10 scores were positively correlated with the scores on the 5-item dry eye questionnaire (Spearman's r = 0.38, p-value < 0.01).

Conclusion: A high prevalence of dry eye symptoms might be common among school children during the COVID-19 outbreak. Significant risk factors include being female, being a higher-grade level student, prolonged use of digital screens, and perceived stress. However, contact lens use, smoking, and the most common digital device usage patterns were not found to be contributing factors.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Distribution of T-PSS-10 and DEQ-5 scores.
(A) Density scatter-plot with a correlation line between the T-PSS-10 and DEQ-5 scores of 603 respondents. Yellow indicates a higher density of points and blue indicates a lower density. (B) Box plot indicates the median T-PSS-10 among healthy, mild-to-moderate, and severe symptoms of dry eye. The asterisk shows statistically significant differences analyzed using the Mann-Whitney U test at p-value < 0.01.
Fig 2
Fig 2. The prevalence and associated factors of symptomatic dry eye classified using different 5-item dry eye questionnaire (DEQ-5) cut-off scores.
The associated factors were computed by univariate and multivariate logistic regression. The yellow color indicated omissions in the analysis, green color indicated p-values ≥ 0.05 and light green color indicated p-values < 0.05.

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