Prevalence of Dry Eye Disease, Associated Factors and Impact on Quality of Life among Medical Students during the Covid-19 pandemic

doi:10.21203/rs.3.rs-4818851/v1

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Prevalence of Dry Eye Disease, Associated Factors and Impact on Quality of Life among Medical Students during the Covid-19 pandemic

https://doi.org/10.21203/rs.3.rs-4818851/v1

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This cross-sectional study aimed to evaluate the prevalence of dry eye disease (DED), its associated factors, and the impact on quality of life (QOL) and mental health among medical students during the COVID-19 era using online surveys. Participants completed questionnaires covering demography, medical history, and personal data. The Thai version of the Dry Eye-related Quality of Life Score (DEQS-Th) was used for DED screening. The QOL and mental health challenges (support, coping, stress, and depression) were evaluated by the EuroQoL-5 dimensions-5 levels (EQ-5D-5L) and Thymometer questionnaires. A total of 449 participants were analyzed, with a mean age of 21.8 years and 61.5% female. The prevalence of DED was 60.4% (95%CI 55.7–64.8). Preexisting DED (p = 0.001) and refractive errors (p = 0.007) were significantly associated with DED. The DEQS-Th score, the EQ-5D-5L score, and all aspects of mental health challenges were significantly worse in DED participants compared to non-DED participants. The DEQS-Th score was significantly correlated with the EQ-5D-5L index score and all issues of mental health challenges, though these correlations were not strong (r = 0.094 to 0.337, all p < 0.05). This study highlighted the high prevalence of DED among medical students during the pandemic and its negative impact on QoL and mental health.

Health sciences/Diseases

Health sciences/Health care

Dry eye disease

quality of life

mental health

COVID-19

Dry eye disease (DED) is a multifactorial disease involving tear film and the ocular surface, resulting in ocular discomfort and visual disturbance. Two major subtypes of DED are aqueous deficiency and increased tear evaporation, which contribute to tear film instability, hyper-osmolarity, inflammation, and ocular surface damage.¹ The ocular symptoms of DED include dryness, discomfort, irritation, fatigue, increase in sensitivity to light, and fluctuating visual disturbances.

Due to its chronic nature, ocular symptoms and visual impairment have been shown to have a multifaceted impact on an individual with DED.² These adverse effects on visual and physical functions lead to a decreased ability to perform everyday tasks such as reading, driving, and working.² Previous studies demonstrated that patients with symptomatic DED encountered greater challenges with mobility and work-related functions, as well as an increased likelihood of psychological disorders such as anxiety or depression. ^3–5 In addition, DED has a substantial economic burden resulting from direct medical costs and indirect costs including loss of productivity and impaired quality of life. ^5–7

According to the 2017 International Dry Eye Workshop, the global prevalence of DED varied between 5–50%. ⁸ While the definition of the disease varied across studies, its prevalence tended to rise with age, with females being more frequently affected than males. ⁸ Consistent and non-modifiable risk factors for DED include aging, female, Asian race, meibomian gland dysfunction, connective tissue diseases, and Sjogren syndrome. While computer use, contact lens wear, androgen deficiency, hormone replacement therapy, hematopoietic stem cell transplantation, environment, and medications are modifiable risk factors for DED. ⁸ Interestingly, there appears to be a high reported prevalence of DED (60.5%-90%) among young populations and school children. ^9–11 Potential risk factors in youth may be associated with screen usage, contact lenses, inadequate refractive correction, topical medication, and poor sleep quality. ^9–14 DED has a strong association with children who use smartphones. ¹² In addition, other co-existing ocular conditions (such as ocular allergy, meibomian gland dysfunction, Stevens-Johnson syndrome, and vitamin A deficiency) also pose a risk for DED in the pediatric population. ^10,15

During the global Coronavirus disease 2019 (COVID-19) pandemic, many countries implemented lockdowns or other restrictions to prevent the spread of the virus. As a result, solutions such as online studying or remote work were adopted to maintain productivity in schools and workplaces. However, the increased use of visual display terminal (VDT) associated with these activities may have adverse effects on the quality of life (QOL) related to vision, particularly in DED patients. ¹⁶ Studies conducted among university students in Chile during the COVID-19 pandemic revealed that 77.5% of students experienced dry eye symptoms, with the duration of VDT use increasing to 15.9 hours during online classes, compared to 9.8 hours before the pandemic. ¹⁷ Lifestyle changes such as increased screen device usage, air conditioning, and adherence to social distancing measures can trigger or worsen dry eye symptoms. An online survey conducted among DED patients during the initial wave of COVID-19 indicated that exacerbation of dry eye symptoms had a detrimental effect on QOL, especially among individuals reporting increased screen time (59%), elevated stress levels (50%), and sleep deprivation (43%). ¹⁸

Given that DED is primarily a symptomatic disease, chronic dry eye symptoms can lead to complications including impaired QOL and a deterioration in mental health.^19,20 Fortunately, there are several questionnaires with sufficient psychometric properties for assessing dry eye symptoms, and their impact on an individual’s QOL as well as the patient-reported outcome after treatment. ²⁰ These assessment tools can be valuable for studying DED prevalence, especially during a pandemic when performing ocular examinations may be challenging.

This study aimed to evaluate the prevalence of DED among medical students in Thailand, identify associated risk factors, and examine its impact on QOL and mental health during the COVID-19 pandemic. We hope the findings of this study can help increase awareness among medical students and other young individuals about the risks associated with DED and could also assist in implementing appropriate management strategies to alleviate its consequences.

This study was a cross-sectional study using an internet-based survey conducted between February to April 2022. The study protocol was approved by the Research and Ethics Committee, Faculty of Medicine, Chiang Mai University (Study code: OPT-2546-08534) and adhered to the Declaration of Helsinki. The survey was launched online using the REDCap (Research Electronic Data Capture; Vanderbilt University, Tennessee) platform version 7.6.5.

Participants. The inclusion criteria specified medical students from Chiang Mai University's Faculty of Medicine, aged 18 years or older. Exclusion criteria included other chronic ocular diseases (such as glaucoma, uveitis); ocular infection and inflammation within 3 months; previous ocular or refractive surgery within 6 months; and systemic disease or disabilities that affect daily life activities and psychological disorders. Patients who agreed to participate had given written informed consent online before answering the questionnaires.

Measurement tools. The questionnaire and measurements used in the online survey include:

Self-developed Questionnaire This part was to gather general information and details associated with physical health such as demography (as gender, and age); refractive error and methods of correction (glasses, contact lenses, or refractive surgery); previous diagnosis of ocular disease and treatment; other underlying systemic diseases; current medication; exercise frequency; VDT use (time and type of screen); sleep duration (hours/day); mask use (duration, hours/day; and type); and frequency of artificial tears used.

Thai version of the Dry Eye-related Quality-Of-Life Score (DEQS-Th) This questionnaire was

developed from the English version. ²¹ It contains 15 questions addressing two subscales: “Bothersome Ocular Symptoms” (6 items) and “Impact on Daily Life” (9 items). Each question was evaluated for frequency and severity, based on a 5-point scale, ranging from “none of the time” (0) to “all of the time” (4) for the frequency, and a 4-point scale, ranging from “no affect” (1) to “high affect” (4) for the severity. The DEQS score is calculated using the following formula: (sum of the severity scores of all questions answered) x 25/ (total number of questions answered). The higher scores indicated more severe symptoms and poorer QOL. The Thai version of the DEQS (DEQS-Th) has been validated for its psychometric properties in normal and DED participants. The cut-off DEQS-Th score of 18 or more is the criterion for a diagnosis of DED. ²²

EuroQoL-5 dimensions-5 levels (EQ-5D-5L) This self-assessment questionnaire evaluated health-related QOL across 5 dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/ depression. ²³ Each dimension was rated on a 5-level scale: no problems (Level 1), slight, moderate, severe, and extreme problems (Level 5). The EQ-5D has two parts, 1) a descriptive system that calculates a five-digit code specifying a specific health state to the index score. The score ranges from 0 (death) to 1(complete health), and negative values indicate a health state considered worse than death (total utility score = 1 - proportion in each dimension). 2) a visual analog scale, ranging from 0 (worst imaginable health state) to 100 (best imaginable health state). In this study, we utilized the Thai version of the EQ-5D-5L and its corresponding index score. ²⁴

Thymometer This single rating scale, assessing depression, perceived stress, perceived social

support, and coping, was used to evaluate mental health status. For stress and depression, the score ranges from 1 (no stress/depression) to 10 (stressful), whereas in the case of support and coping, the score ranges from 1 (nothing) to 10 (helpful/good support).²⁵ The final scores of perceived social support and coping were calculated from the inversion of the raw scores. Hence, in all four dimensions, the higher the score the worse the mental health.

Statistical analysis. The data extracted from the online platform were analyzed using SPSS program version 25 (IBM Corp., Armonk, NY, USA). Continuous data were presented as mean (standard deviation, SD) or median (interquartile range, IQR) depending on the distribution of the data. Categorial data were analyzed and presented as a proportion. To compare participant characteristics, the QOL scores, and mental health issues between participants with DED and non-DED, Chi-square, Fisher's exact, Mann Whitney-U, and t-test were used, depending on the types of variables and the distribution of the data. A p-value under 0.05 was considered statistically significant. In case an association was statistically significant in a univariable logistic regression model, the variable was selected for the multivariable regression model. Pearson’s correlation was used to determine any correlations between DED and health-related QOL, and DED and mental health challenges.

Demographic data of the participants

The response rate of this online survey was 50.6% (648 respondents from a total of 1,281 medical students). However, 197 responses were incomplete or duplicated, and 1 participant was subsequently excluded due to previous glaucoma treatment. The characteristics of the remaining 449 eligible participants are summarized in Table 1.

Table 1

Participants Characteristics
Variables	Overall (n = 449)	DED (n = 271)	Non-DED (n = 178)	P values
Age (years, mean ± SD)	21.8 ± 1.8	21.7 ± 1.8	21.9 ± 1.7	0.169
Gender (%) Male Female	173 (38.5) 276 (61.5)	95 (35.1) 176 (64.9)	78 (43.8) 100 (56.2)	0.062
Activities ((hours, mean ± SD)
Total study time/week	29.8 ± 18.3	28.8 ± 19.4	31.3 ± 16.5	0.152
Total screen time/day	10.8 ± 4.3	11 ± 4.9	10.5 ± 3.3	0.180
Screen time for study/day	6.8 ± 2.9	6.9 ± 3.0	6.6 ± 2.9	0.380
Sleep hours/day	6.5 ± 1.0	6.5 ± 0.9	6.4 ± 1.0	0.555
Mask-wearing time	8.4 ± 3.2	8.2 ± 3.1	8.6 ± 3.2	0.215
Medical history (n, %)
Hypertension	2 (0.4)	1 (0.4)	1 (0.6)	1.000*
Diabetes mellitus	2 (0.4)	1 (0.4)	1 (0.6)	1.000*
Systemic allergic diseases	131 (29.2)	88 (32.5)	43 (24.2)	0.058
Cerebrovascular diseases	1 (0.2)	1 (0.4)	0 (0)	1.000*
Ocular history (n, %)
Dry eye	59 (13.1)	52 (19.2)	7 (3.9)	< 0.001
Allergic conjunctivitis	20 (4.5)	17 (6.3)	3 (1.7)	0.021
Refractive error and correction (n = 378,%) Glasses Contact lens Refractive surgery	345 (76.8) 67 (14.9) 7 (1.6)	219 (80.8) 45 (16.6) 5 (1.8)	126 (70.8) 22 (12.4) 2 (1.1)	0.014 0.217 0.546
Mask types Surgical mask Fabric mask N95 mask ≥ 2 layers of mask	381 (84.9) 12 (2.7) 21 (4.7) 35 (7.8)	226 (83.4) 10 (3.7) 14 (5.2) 21 (7.7)	155 (87.1) 2 (1.1) 7 (3.9) 7 (3.9)	0.366
DED: dry eye disease, N95: N95 respirators mask, SD: standard deviations.
Continuous variables with normal distribution are presented as mean + SD and are compared between DED and non-DED participants by Student’s t-test.
Categorical variables are presented as numbers with percent and are compared by the chi-square test or Fisher’s Exact Test*.

Among the participants, 271 were diagnosed as DED based on the DEQS-Th criteria, thus the prevalence of DED was 60.4%, (95% CI 55.7–64.8). The mean age of all participants was 21.8 ± 1.8 years, with females being predominant (61.5%). There was no significant difference in characteristics between DED and non-DED participants including satisfaction with income, and underlying conditions (such as hypertension, diabetes mellitus, systemic allergic diseases, and cerebrovascular diseases). In addition, there was no significant difference in the time spent on visual-related activities (overall mean ± SD) between DED and non-DED groups such as total study time (29.8 ± 18.3 hours/week), total screen time (10.8 ± 4.3 hours/day), and screen time for study (6.8 ± 2.9 hours/day) or mean sleep hours (6.5 ± 1.0 hours/day). Regarding ocular history, DED participants had a higher prevalence of pre-existing dry eye and allergic conjunctivitis compared to non-DED participants (19.2% vs 3.9%, p < 0.001, and 6.3% vs 1.7%, p = 0.021, respectively). In the DED group, the proportion of participants with refractive errors who used corrective spectacles was higher than in the non-DED group (80.8% vs 70.8%, p = 0.014) whereas correction with contact lenses and refractive surgery were comparable between groups. In addition, the DED group had more frequent artificial tear use than participants in the non-DED. Use of a surgical mask was the most common mask type (overall 84.9%) and there was no difference in mask-wearing hours among students with DED and non-DED (8.4 ± 3.2 vs 8.2 ± 3.1 hours/day, p = 0.215). (Table 1)

Associated factors for dry eye disease

The results from the multivariable logistic regression analysis are shown in Table 2. Preexisting dry eye was associated with DED (OR = 4.45, 95% CI 1.90-10.44). Interestingly, refractive error was associated with higher odds of becoming DED (OR = 2.16, 95%CI 1.23–3.78). Notably, female gender, contact lens wear, allergic conjunctivitis, and screen time did not show a significant association with DED.

Table 2

Factors Associated with Dry Eye Disease Determined by Multivariable Logistic Regression
Variables	OR (95% CI)	P value
Female	1.27 (0.83–1.95)	0.275
Age	0.96 (0.84–1.10)	0.570
Diabetes mellitus	0.54 (0.17–17.66)	0.729
Systemic allergic diseases	1.32 (0.82–2.11)	0.255
Preexisting dry eye	4.45 (1.90-10.44)	0.001*
Allergic conjunctivitis	2.86 (0.77–10.71)	0.118
Refractive error	2.16 (1.23–3.78)	0.007*
Contact lens use	1.21 (0.66–2.20)	0.544
Total study time	1.00 (0.99–1.01)	0.511
Screen time	1.04 (0.98–1.10)	0.245
Devices Tablet Smartphone	0.99 (0.63–1.54) 1.19 (0.34–4.13)	0.959 0.785
Sleep duration	1.00 (0.80–1.25)	0.976
Mask-wearing time	0.97 (0.91–1.05)	0.470
DED: dry eye disease, LASIK: laser-assisted in situ keratomileusis, OR: odd ratio, VDT: visual display terminal, CI: confidence interval.

Association of DED, QOL, and mental health

In Table 3, the QOL and mental health status of DED and non-DED participants were compared using the DEQS-Th, EQ-5D-5L, and Thymometer criteria. DED participants had significantly higher DEQS-Th scores compared to non-DED participants (total score of 32.12 ± 12.73 vs 9.11 ± 4.67, p < 0.001). The QOL determined by the EQ-5D-5L was significantly lower in DED compared to non-DED participants (0.97 ± 0.07 vs 0.99 ± 0.03, p < 0.001). Coping (with inversion score), stress, and depression scores in the Thymometer were significantly higher in DED participants compared to those in the non-DED group (all p < 0.01).

Table 3

Mental Health-associated Scores in DED vs Non-DED Participants
Questionnaires	DED (n = 271) (mean ± SD)	Non-DED (n = 178) (mean ± SD)	P value*
DEQS-Th Total score Ocular symptoms score Impact on daily life score	32.18 ± 12.73 13.13 ± 5.82 19.05 ± 8.81	9.11 ± 4.67 4.65 ± 2.79 4.46 ± 3.55	< 0.001 < 0.001 < 0.001
EQ-5D-5L	0.97 ± 0.07	0.99 ± 0.03	< 0.001
Thymometer Support Coping Stress Depression	3.22 ± 1.63 3.78 ± 1.56 5.12 ± 2.07 3.44 ± 2.10	2.99 ± 1.59 3.40 ± 1.49 4.37 ± 2.22 2.72 ± 1.84	0.152 0.01 < 0.001 < 0.001
*The comparison was conducted by Wilcoxon’s Rank-sum test.
DED: dry eye disease, non-DED: non-dry eye disease, DEQS-TH: Thai-version of Dry Eye-related Quality of Life Score, EQ-5D-5L: EuroQoL-5D-5L.

Table 4 shows the correlations between the ocular symptoms and the impact on daily life subscale scores of DEQS-Th and the EQ-5D-5L, as well as the Thymometer. EQ-5D-5L score had a negative correlation with DEQS-Th in both subscale scores and total scores (r -0.329 to -0.337, all p < 0.001). Additionally, almost all aspects of the Thymometer (with the conversion of support and coping) were statistically significantly positively correlated with both subscale scores and total score of the DEQS-Th (r = 0.094 to 0.210, all p < 0.05), except for the support part and the symptom subscale of the DEQS-Th (r = 0.005, p = 0.245).

Table 4

Correlation between the DEQS-Th and EQ-5D-5L Questionnaires, and the DEQS-Th and Thymometer Questionnaires
	Symptoms		Impact on daily life		Total score
	R	P value	R	P value	R	P value
EQ-5D-5L	-0.329	< 0.001	-0.337	< 0.001	-0.337	< 0.001
Thymometer Support Coping Stress Depression	0.055 0.111 0.180 0.191	0.245 0.018 < 0.001 < 0.001	0.094 0.135 0.200 0.210	0.045 0.004 < 0.001 < 0.001	0.094 0.135 0.200 0.210	0.045 0.004 < 0.001 < 0.001
By Pearson correlation.
DEQS-Th: Thai version of the Dry Eye-related Quality of Life Score, EQ-5D-5L: EuroQoL-5dimensions-5levels.

During the COVID-19 pandemic, university students, including medical students, had to shift most study programs to online platforms. This study ascertained the prevalence of DED in young people and its associated factors, along with its impact on QoL during the pandemic. This online survey was conducted during the third wave of the COVID-19 pandemic in Thailand in 2022 and the prevalence of DED among medical students was 60.4%, determined using the DEQS-Th questionnaire. This figure was comparatively lower than those reported in previous studies conducted among high school students and university students during the early onset of the pandemic, where prevalence ranged from 70.5–77.5%. ^17,26,27 These variations may partly differ due to the diagnostic criteria for DED across studies, as well as the timing of the research. However, it is noteworthy that the prevalence of DED among this specific young population remains high across all studies compared to the pre-COVID-19 era (8.2–26.6%).^13,14,28

The use of digital devices for education such as laptops, tablets, mobile phones, e-readers, as well as video conference platforms has increased since the pandemic. Previous studies found an association between VDT use and DED among young adults and children, including primary school students.^10–12 In addition, VDT use increased significantly in comparison to before the pandemic. ¹⁷ Although the average screen time in this study was high (> 10 hours/day), there was no significant difference between DED and non-DED participants. This might be because digital devices have been increasingly used in the past decade and have become a norm in people’s lifestyles. Excessive exposure to VDT can lead to the deterioration of dry eye symptoms. ²⁹ While using digital screens, or when paying attention to something, the blink rate usually decreases, or incomplete blinking occurs which leads to an increase in tear evaporation and tear film instability. ^12,30 In addition, prolonged use of digital devices or exposure to screen illumination (> 4 hours) may contribute to ocular surface-related symptoms such as burning sensation, grittiness, watering, itching, dryness, and eye fatigue known as “digital eye strain (DES)”, which may overlap with dry eye symptoms.³¹ DES symptoms can be accommodation-related such as difficulty refocusing from one distance to another, or can be extraocular symptoms such as headache, neck or shoulder, and back pain.³¹ Moreover, myopic progression has been linked to DES in the pediatric population.³² Thus, both DES and DED are associated with prolonged screen exposure and may co-occur. Uwimana et al found a concurrent rise of DES and DED symptoms with a strong and significant positive correlation between both conditions (r = 0.695, p < 0.001) among university students during the COVID-19 pandemic. ³³

Other known associated risk factors for DED include female gender, contact lens use, and allergic diseases.^8,34 However, only preexisting dry eye and refractive error correction with spectacles were statistically significantly associated factors for DED in this study. Although we found that female students were predominant in the DED group, the difference was not statistically significant. Among the students using glasses to correct refractive error, particularly individuals with improper correction, using accommodation may lead to ocular symptoms (i.e. eye pain, strain, and discomfort), which overlap with dry eye symptoms.^12,17 Our findings are contrary to previous evidence that contact lens use is a consistent risk factor for DED in students.^9,10,13 This is probably due to a relatively small population of contact lens wearers among our study population that might affect the analysis.

During the pandemic, a face mask became the most common personal prevention equipment to protect against viral infection. Various studies found evidence to show an increase in dry eye symptoms and ocular discomfort related to face mask users. The incidence of mask-associated dry eye (MADE) varied from 7.9–18.3%. ^35,36 The possible pathophysiological mechanisms associated with MADE may be related to an unnatural upward airflow towards the ocular surface during expiration leading to increased tear evaporation and inducing stress which may increase in improperly fitted face masks. ^36,37

This study found that the surgical mask was the most common type of mask used (85%) in all students and there was no significant difference in mean mask-wearing time among the DED and non-DED students with overall mask-wearing time was more than 8 hours/day. This may be because medical students might have been trained in proper mask-wearing as they needed to wear masks for their work.

Evidence showed that symptoms of DED can significantly impair visual function, limit activities, and reduce work productivity.^2,4,5,7 Our previous study demonstrated a significant association between perceived stress, neuroticism, and QOL in DED patients.³⁸ In addition, the personality of the patients may influence the symptoms and QOL in DED patients.³⁸ During the pandemic, stress levels and sleep disturbance have been identified as factors exacerbating dry eye symptoms, along with a decrease in pain threshold and inflammation.¹⁸ Lin et al studied high school students in China and found that apart from prolonged VDT use and female gender, stress and poor sleep quality were factors significantly associated with DED during the COVID-19 outbreak.²⁷ Increased online studying and a decrease in normal learning activities may heighten stress levels beyond typical circumstances, potentially leading to increased stress and depression among medical students. In this study, DED symptoms exhibited a significant negative correlation with QOL and mental health, particularly regarding perceived stress and depression, although these correlations were not strong. Our findings underscore that DED is among the ocular disorders that significantly affect the QOL and mental well-being of an individual. In a younger population, chronic dry eye symptoms and impaired mental health may potentially affect the learning ability of individuals as well as their personalities. However, further studies are needed to explore these relationships.

The main strength of this study lies in the use of validated questionnaires with adequate psychometric properties to assess DED symptoms, in addition to QOL and mental health. Participants voluntarily and anonymously completed the survey. The results of this study will enable clinicians and carers to become aware of this ocular problem, which can significantly impact the QOL of students. However, this study also has some limitations. Firstly, it was conducted during the pandemic using an online platform, lacking clinical evaluations for DED such as tear film break-up time, ocular surface staining, or the Schirmer test. Thus, asymptomatic DED participants may have been overlooked. Furthermore, a reliance on self-conducted questionnaire surveys might introduce recall bias. This study is a cross-sectional study, and further investigations are required to determine the causal relationship between individual risk factors and DED.

In conclusion, the prevalence of symptomatic DED among medical students was notably high during the COVID-19 pandemic. Significant associated factors for DED in this young population included preexisting DED and the use of spectacles for correction of refractive errors. Interestingly, female gender, ocular allergy, contact lens wear, screen time, and mask-wearing were not significantly associated with DED. Dry eye symptoms had adverse effects on both quality of life and mental health, particularly regarding perceived stress, and depression. Our findings may contribute to the development of preventive and management strategies to identify students at risk and mitigate the consequences of chronic DED.

COVID-19

Coronavirus disease 2019

DED

dry eye disease

DEQS-Th

Dry Eye-related Quality of Life Score

DES

digital eye strain

EQ-5D-5L

EuroQoL-5 dimensions-5 levels

MADE

mask-associated dry eye

QOL

quality of life

VDT

visual display terminal

Completing interest statement

The authors declare no competing interests.

Additional Information

This study was presented in part as a poster at the 8th Asia Cornea Society Biennial Scientific Meeting, 23–25 November 2022, Bangkok, Thailand.

Correspondence and requests for materials should be addressed to N.T.

Funding

This work was granted by the Faculty of Medicine Endowment Fund, Faculty of Medicine, Chiang Mai University (Grant Number: 089/2565). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author Contribution

Study concept and design: A.K., N.T., P.U., N.W., and T.W. Acquisition of data: A.K., N.T., and P.U. Analysis and interpretation of data: A.K., N.T., P.U., N.W., and T.W. Drafting of the manuscript: A.K. and N.T.Statistical analysis: A.K., and P.U. Critical revision of the manuscript: N.T., P.U., N.W., and T.W.All authors read and approved the final manuscript.

Acknowledgement

The authors wish to express our gratitude to all students who participated in this study by responding to the online survey

Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request

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No competing interests reported.

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Prevalence of Dry Eye Disease, Associated Factors and Impact on Quality of Life among Medical Students during the Covid-19 pandemic

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Abstract

Introduction

Methods

Results

Demographic data of the participants

Associated factors for dry eye disease

Association of DED, QOL, and mental health

Discussions

Abbreviations

Declarations

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Author Contribution

Acknowledgement

Data Availability

References

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