Dry eye disease (DED), also referred to as dry eye syndrome or keratoconjunctivitis sicca, is one of the most common reasons for patients to seek ophthalmic care. The Tear Film and Ocular Surface Society (TFOS) defines DED as “a multi-factorial disease of the tears and ocular surface that results in symptoms of discomfort, visual disturbance and tear film instability with potential damage to the ocular surface, accompanied by increased osmolarity of the tear film and inflammation of the ocular surface” [1]. The TFOS Dry Eye Workshop II (DEWS II) further refined this definition, describing DED as a disorder of tear film homeostasis in which tear film instability, hyperosmolarity, ocular surface inflammation and neurosensory abnormalities play key etiological roles [2,3]. Multiple risk factors have been implicated in the development of DED, including female gender, advanced age, excessive use of digital devices, smoking, air pollution and climatic influences. Systemic conditions such as diabetes and hypertension, medications including antihistamines, antidepressants and oral contraceptives, as well as ocular diseases such as blepharitis, conjunctivitis and meibomitis also contribute to its pathogenesis [4–6]. Additionally, DED may occur secondary to systemic autoimmune disorders such as Sjögren’s syndrome, lupus and Stevens–Johnson syndrome, or be exacerbated by hormonal therapy and environmental exposures [7].

The reported prevalence of DED varies widely from 5% to 50%, with a higher burden observed in Asian compared to Caucasian populations [7]. Prolonged exposure to digital display terminals (DDTs) such as computers, smartphones and tablets has been increasingly recognized as a major risk factor, largely due to impaired blinking patterns [8]. With the growing reliance on DDTs for education, work and entertainment—further accelerated by the COVID-19 pandemic and the shift toward remote or hybrid learning—the frequency of DED is rising, particularly among younger populations [9]. DED remains a significant cause of ocular morbidity, presenting with symptoms such as dryness, irritation, redness and foreign body sensation, which can impair quality of life. Advances in diagnostic modalities and a better understanding of etiological factors have improved detection; however, the rising prevalence underscores the need for comprehensive evaluation of risk factors and accurate diagnostic testing to enable timely management of this chronic condition and enhance long-term patient outcomes.

OBJECTIVES

1. To determine the prevalence of dry eye among symptomatic patients attending the ophthalmology outpatient department (OPD).
2. To evaluate the associated risk factors and assess the diagnostic tests used in the evaluation of dry eye.

Study Design and Setting

This was a cross-sectional observational study conducted in the Department of Ophthalmology between July 2023 and June 2025. Ethical approval was obtained from the Institutional Ethics Committee and written informed consent was secured from all participants prior to enrolment.

Sample Size

The minimum required sample size was calculated using the standard formula for prevalence studies:

where Z_1−α/2 is the Z-score at 95% confidence level (1.96), P is the estimated prevalence and d is the absolute precision. Based on an assumed prevalence of 61.9%, confidence level of 95% and precision of 6.2%, the calculated sample size was 237 eyes. Accordingly, 237 eyes of eligible patients were screened.

Study Population

Patients aged 18–70 years presenting to the ophthalmology outpatient department (OPD) with symptoms suggestive of dry eye were considered.

• Inclusion criteria: Adults (18–70 years) of either sex who provided informed consent.
• Exclusion criteria: History of ocular surgery or trauma, ocular infection within the past three months, pregnancy, or unwillingness to participate.

Clinical Examination

All participants underwent a complete ocular examination. Dry eye evaluation included:

• Tear Film Break-Up Time (T-BUT): Measured after fluorescein instillation; values <10 seconds were considered abnormal [18].
• Schirmer’s Test: Performed using standardized filter paper strips; readings ≤10 mm indicated dry eye and <5 mm indicated severe dry eye [18].
• Severity Grading: Classified according to DEWS II criteria.

Statistical Analysis

Categorical variables were expressed as frequencies and percentages, while continuous variables were summarized as mean ± standard deviation. Associations were analyzed using paired t-test and one-way ANOVA as appropriate. Data were entered in Microsoft Excel and analyzed using SPSS version 21.0. A p-value <0.05 was considered statistically significant.

A total of 237 patients were included in the study. Of these, 68 patients (28.7%) were diagnosed with dry eye, while 169 (71.3%) did not have the disease, giving an overall prevalence of 28.7%. When analyzed by age, the prevalence of dry eye was found to increase in middle and older age groups. The highest proportion was observed in the 41–50 years group (19 patients, 8.0%) and in the 51–60 and 61–70 years groups (18 patients each, 7.6%). In contrast, no cases of dry eye were recorded in patients younger than 21 years. Gender-wise distribution showed a female predominance, with 45 out of 125 females (18.9%) having dry eye, compared to 23 out of 112 males (9.7%). With regard to laterality of symptoms, most patients presented with bilateral complaints. A total of 64 patients (27.0%) had involvement of both eyes, whereas unilateral dry eye was uncommon, being observed in only three patients (1.3%) in the left eye and one patient (0.4%) in the right eye. Assessment of severity according to the DEWS II classification showed that moderate dry eye was the most common, affecting 44 patients (18.6%). Severe dry eye was present in 14 patients (5.9%), while 10 patients (4.2%) had mild disease. The remaining 169 patients (71.3%) had no evidence of dry eye.

Occupational analysis revealed that the highest prevalence was among farmers and labourers (44.4%), followed by housewives (38.3%) and office workers (28.3%). The student group had the lowest prevalence, with only one patient (2.3%) affected. The “others” category, which included miscellaneous occupations, showed a prevalence of 29.2%. When systemic illness was considered, dry eye was significantly associated with depression, where 12 o:13 patients (92.3%) were affected (p < 0.00001). In contrast, no statistically significant association was observed with diabetes mellitus (29.6%, p = 0.909), hypertension (30.0%, p = 0.865), or ischemic heart disease (16.7%, p = 0.057). More than half of the study population (52.7%) did not report any systemic illness.

Table 1. Demographic distribution and prevalence of dry eye (n = 237)

Table 2. Clinical presentation and laterality of dry eye (n = 237)

Table 3. Severity of dry eye (DEWS II classification, n = 237)

Table 5: Dry eye in relation to occupation

Table 6: Dry eye in relation to systemic illness

* Significant at p < 0.05

Dry Eye Disease (DED) has been widely studied across different populations, with numerous studies highlighting its prevalence and associated risk factors. Our study aimed to assess the prevalence and contributing factors of DED within a selected population and compare our findings with existing literature. Our study found that 28.69% of the participants had dry eyes, a prevalence rate that is consistent with other population-based studies. Betiku AO et al. (2022) [10] reported a 28.2% prevalence rate in a Nigerian population, while Chevuturu M et al. (2020) [11] found a higher prevalence of 61.9% in an Indian hospital-based study. Similarly, Castro JSD et al. (2018) [12] reported a prevalence of 12.8% in Brazil, indicating variation across geographic regions.

Our findings suggest that the highest prevalence of dry eyes was observed in the 41–50 years age group (8.02%), followed by 51–60 years (7.59%) and the 31–40 years group (4.22%). This aligns with the study by Shanti Y et al. (2020) [13], which found that older age was a significant risk factor for DED. Additionally, Sahai et al. (2005) [14] reported that the prevalence of DED increased with age, particularly above 40 years, which is consistent with our observations. Our study also found that females (18.99%) had a higher prevalence of dry eyes than males (9.70%), supporting previous studies that identified female gender as a significant risk factor for DED. Shanti Y et al. (2020) [13] and Gupta N et al. (2010) [15] confirmed that females had a higher prevalence, which could be attributed to hormonal changes influencing tear production. Similarly, Moss SE et al. (2008) [16] and Schaumberg et al. (2017) [17] found that postmenopausal hormonal changes contributed to increased dry eye symptoms in women.

Occupation was also found to be a contributing factor, with the highest prevalence among farmers/laborers (44.44%) and housewives (38.30%). This is in line with findings by Pankaj Choudhary et al. (2007–09) [18], which reported that environmental factors such as dust exposure and air pollution contribute to DED among rural populations. Office workers also showed a notable prevalence (28.30%), which corresponds with findings from Titiyal JS et al. (2016) [19], who demonstrated that prolonged screen exposure is a major risk factor for DED. Our study observed a significant association between depression and dry eye disease (p < 0.00001), while other systemic conditions such as diabetes mellitus (DM), hypertension (HTN) and ischemic heart disease (IHD) were not statistically significant. This finding aligns with Megdum et al. (2022) [20], who found that depression was significantly associated with dry eye symptoms. However, previous studies such as the Beaver Dam Eye Study (2000) [21] and Gupta N et al. (2010) [15] indicated that diabetes and hypertension were linked to increased dry eye prevalence, suggesting the need for further research to clarify these associations.

In our study, 4.22% of patients had mild DED, 18.57% had moderate DED and 5.91% had severe DED. This aligns with findings from Titiyal JS et al. (2016) [19], who reported that nearly 28.9% of their study population had severe DED, particularly among individuals with prolonged screen exposure. Similarly, Almasoudi EA et al. (2023) [22] found that DED was more prevalent among medical students with prolonged reading habits. Furthermore, studies by Neti N et al. (2021) [23] and Fjaervoll K et al. (2022) [24] suggested that increased screen time, particularly in the post-COVID-19 era, has contributed to a higher incidence of DED due to reduced blink rates and altered tear film dynamics. Our findings support these conclusions, reinforcing the importance of limiting screen exposure and adopting preventive measures such as regular blinking exercises and artificial tear supplementation.

Our study underscores the significant burden of Dry Eye Disease (DED) in the studied population, with an overall prevalence of 28.69%. The findings demonstrate that age, female gender, occupation and systemic illnesses—particularly depression—are important contributors to the development of DED. The association between prolonged digital screen exposure and dry eye symptoms further emphasizes the need for awareness and preventive strategies in occupational and lifestyle settings. A considerable proportion of patients exhibited moderate to severe disease, highlighting the importance of timely diagnosis and management through lifestyle modifications, environmental adaptations and targeted therapeutic interventions. Preventive measures, such as optimizing workplace ergonomics, promoting regular blinking habits and the judicious use of artificial tears, may help mitigate symptoms and improve patient outcomes. Future research should focus on larger, multicenter studies to better delineate the influence of systemic diseases, environmental exposures and emerging therapeutic options on DED. Addressing these aspects will not only enhance patient care but also reduce the overall burden of this increasingly prevalent condition.

Limitations

This study has certain limitations that must be acknowledged. The sample size, although adequate for preliminary observations, was relatively small and may not be fully representative of the general population, thereby limiting the generalizability of the findings. The reliance on self-reported symptoms and clinical tests such as Schirmer’s test and Tear Break-Up Time (TBUT) may have introduced variability, as these assessments are inherently subjective. More objective diagnostic tools, including ocular surface imaging, could provide more precise evaluations. In addition, environmental factors such as air pollution, humidity and digital screen exposure were not comprehensively assessed, although they are known to influence the prevalence and severity of dry eye disease (DED). Systemic conditions and detailed medication histories were also not extensively analyzed, which may have limited the ability to establish stronger associations. Furthermore, as this was a cross-sectional study, it provides only a snapshot of DED prevalence without accounting for long-term disease progression, seasonal variation, or causal inferences.

IEC Approval: Taken

Conflict of Interest: Nil

Funding: Nil

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