Prevalence, pattern and associated risk factors of Dry eye disease: Results of a prospective patient database from a tertiary eye care centre in Central India

doi:10.21203/rs.3.rs-4725136/v1

Download PDF

Research Article

Prevalence, pattern and associated risk factors of Dry eye disease: Results of a prospective patient database from a tertiary eye care centre in Central India

https://doi.org/10.21203/rs.3.rs-4725136/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Purpose:

To assess the prevalence, pattern, and associated risk factors of dry eye disease at the tertiary eye care centre in Central India.

Methods:

This prospective cross-sectional study, over 24 months, enrolled patients with systematic random sampling. Evaluation of dry eye disease (DED) was done with subjective and objective methods comprising of Ocular Surface Disease Index (OSDI) questionnaire, slit lamp examination, Schirmer’s and FTBUT test. Patients were categorised into levels 1–4 DED. Association with various etiological factors like age, sex, occupation, diabetes mellitus, autoimmune disorders, and other systemic illnesses was assessed. The chi-square test/Fischer exact test was used to analyse categorical data. Bivariate logistic regression analysis was used to calculate the odds ratio (OR). Multivariate analysis was performed to identify independent risk factors.

Results:

A total of 2560 patients were evaluated, of which 640 patients (25%) had DED. Prevalence was higher in patients aged > 50 years, household workers, students, government employees and farmers. Uncorrected refractive status, cigarette smoking, ocular allergy and contact lens usage were associated with increasing odds of developing DED.

Conclusion:

The prevalence of DED was estimated to be 25%. Certain occupations like household workers, students, government employees, farmers, uncorrected refractive errors, cigarette smoking, ocular allergy and contact lens users were significant risk factors for dry eyes.

Dry eye disease

Prevalence

Risk factors

Central India

Dry eye is a multifactorial disease of the ocular surface characterised by a loss of homeostasis of the tear film and accompanied by ocular symptoms in which tear film instability and hyperosmolarity, ocular surface inflammation and damage, and neurosensory abnormalities play etiological roles [1]. It is the most frequent disorder in ophthalmology practice. The prevalence of dry eyes ranges from 10.8–57.1%, showing a wide variation in demography, ethnicity, geographical location, climatic factors and occupation. Such disparity can also occur due to non-standardized criteria for patient selection, diagnosis and stratification of dry eyes [2].

Patients with DED experience varying grades of visual impairment and other ocular symptoms with consecutive difficulties in daily routine activities, thus compromising their quality of life [1, 2]. Thus, it is imperative to thoroughly assess patients with signs and symptoms because prompt treatment can significantly improve long-term quality of life, relieve ocular symptoms, and improve therapeutic outcomes.

Epidemiologic studies are helpful in the determination of prevalence and risk factors for valuable insights regarding prevention and treatment. Although many studies on the epidemiological pattern of DED have been reported from different parts of the world, very few studies are available from India [2–6]. This study concentrates on the most recent information on the epidemiology of DED. This is the first reported prospective series of disease prevalence, pattern, and associated risk factors of DED in alignment with TFOS DEWS II. The primary objective of this study was to evaluate the prevalence of dry eye disease and to study the pattern, predisposing causes and risk factors associated with DED at a major tertiary eye centre in central India, which caters to around 30 lakh population of the district and is the tertiary referral centre for the entire state. DED accounted for around 1% of our hospital-based daily ophthalmological outpatient visits, around two to three per day.

A hospital-based cross-sectional study was conducted at a tertiary eye care centre in Central India. The study duration was 24 months and was approved by RRB and IHEC in accordance with the tenets of the Declaration of Helsinki. Patients attending the outpatient services of the department were selected by systematic random sampling. [Figure]

Every 15th patient on the three OPD days per week was enrolled in the study. The next patient was taken if the selected subject was unwilling to participate in the study. Subjects unwilling to participate in the study, aged < 10 years, patients with acute infection, those with undiagnosed systemic disorders requiring treatment and patients with incomplete medical records were excluded from the study. Considering a 95% confidence level with a margin of error of 2%, a design effect of 2 and a prevalence of 15% of dry eye disease from a previous study, the sample size was around 2500 participants.

Written informed consent was obtained from all the participants/parents or guardians of minor subjects. Detailed ocular history was recorded with reference to onset, severity, laterality, duration, progression of symptoms and nature of the disease. History of any associated pain, irritation, redness and burning/stinging, tearing, contact lens intolerance, increased frequency of blinking, itchy eyes, foreign body sensation, blurred vision, photophobia and thick sticky mucous discharge was taken. A detailed evaluation of systemic signs and symptoms was done to look for other associated autoimmune or connective tissue diseases.

A slit lamp examination of the lid was done for the presence of any anatomical abnormalities, blepharitis, vascularisation, or foreign body. Meibomian orifices were examined for pouting, presence of foam, secretion and plugging. Tarsal conjunctiva was examined for papillae, congestion, symblepharon, conjunctival xerosis, and scarring.

The cornea was examined to evaluate the epithelial defect, conjuctivalisation, limbal ischemia, vascularisation, and dystrophy or degeneration. Each recruited subject was investigated using OSDI [7]. A single observer completed the questionnaire, which was explained in their local language for those who needed to be more conversant in English. The objective test was done in patients with OSDI scores > 10 by a single examiner in the same room to maintain similar temperature and humidity conditions for all patients. Schirmer’s test I and II, Fluorescein tear-film breakup time (FTBUT), was done, and patients were graded into level 1, level 2, level 3 and level 4 DED by TFOS DEWS II [8].

Statistical analysis

Statistical analyses were performed using Statistical Package for the Social Sciences software (Version SPSS 22.0/ IBM, Chicago, USA). The chi-square test, Fischer exact test and linear regression analyses were used to establish the association between categorical data. Bivariate logistic regression analysis was used to calculate the odds ratio (OR). Multivariate analysis was performed to identify independent risk factors. To identify factors related to the predisposition of disease, P values of 0.05 or less within 95% CI were considered statistically significant.

A total of 2560 subjects were enrolled in the study. 1396 (54.53%) were male and 1164 (45.47%) were female (M: F = 1.19:1). Mean age of study participants was computed to be 48.1 ± 19.5 years. The study population was divided into four groups as per their age, i.e., 10–30 years, 31–50 years, 51–70 years & above 70 years age group. Demographic details of patients are summarised in Table 1.

Table 1

Correlation between demography, refractive error and occupation with the severity of DED
DED severity level	LEVEL I	LEVEL II	LEVEL III	LEVEL 1V	TOTAL NO.	% OF DED
Gender
Male	117	105	68	18	308	22.1%
Female	123	149	53	7	332	28.5%
Age (years)
10–30	60	77	27	3	167	26.7%
31–50	85	75	28	9	197	29.9%
51–70	74	88	51	19	232	35.8%
> 70	12	16	12	4	44	7%
Residential status
Rural	103	95	59	43	300	27.9%
Urban	109	131	67	33	340	22.9%
Refractive Error
Emmetrope	73	47	13	11	143	20.6
Uncorrected Refractive Error	72	131	43	27	273	31.8
Corrected Refractive Error	125	57	23	19	224	22.3
Occupation
Farmer	24	36	12	8	80	12.5
Teacher	16	0	4	0	20	3.125
Tailor	0	24	16	4	44	6.9
Software worker	0	16	4	0	20	3.1
Laborers	4	0	12	0	16	2.5
House Hold worker	100	96	36	0	232	36.25
Factory Worker	4	0	4	0	8	1.25
Student	40	56	16	4	116	18.1
Govt Employee (computer work)	52	24	16	12	104	16.2

Out of 2560 study subjects, 640 (25%) were diagnosed to have DED. Patients diagnosed with DED were categorised further into severity grades as level 1 [240 (37.5%)], level 2 [252 (39.37%)], level 3 [120 (18.75%)], 28 (4.37%) level 4 DED. Maximum prevalence of DED was found among 51–70 years age group (232/648; 35.8%), followed by 31–50 years age group (197/658; 29.9%), 10–30 years age group (167/627; 26.7%) and above 70 years age group (44/627; 7%). The association was highly significant, with a P value of 0.0001. However, the association between age and disease severity was not found to be statistically significant (P = 0.1). The prevalence of DED was observed to be 27.9% (300/1075) among the rural population, while it was 22.9% (340/1485) among the urban population, which was statistically insignificant (P = 0.2).

The most common symptom of DED was observed to be watering 235 (36.8%), followed by redness 160 (25%), gritty sensation 128 (20%) and burning sensation 120 (18.7%). Itching and blurring of vision were observed in 84 (13.1%) and 43 (6.8%) subjects, respectively.

Prevalence of DED was higher among the female subjects (332/1164, 28.5%) than the male subjects (308/1396, 22.1%); the association, however, was not found to be significant statistically (p = 0.07). Both the groups were further analysed for association with the severity of the disease. Among males, a maximum number of cases were found in level 1 (116), while among females, the maximum cases were under level 2 (148). In both genders, a minimum number of cases were seen in the level 4 category, i.e. 20 and 8, respectively, in males and females. On statistical analysis, the correlation between gender and disease severity was not significant (P = 0.42).

Prevalence was seen to be maximum among household workers (232/640, 36.2%), followed by students with screen time > 6hrs (116/640, 18.1%), government employees (104/640, 16.2%) and farmers (80/640, 12.5%) [Table 1]. The level 3 disease was prevalent more amongst household workers and students, Govt employees, tailors and farmers. The probability of level 4 disease was higher in Govt employees, farmers, tailors, and students. The association between occupation and disease severity was statistically significant (P = 0.02).

Concerning refractive errors, the prevalence of DED was computed to be (857/273,31.8% ) among uncorrected refractive errors, (1004/224, 22.3%) among corrected refractive errors and (699/144, 20.6%) among emmetropes. The association between refractive status and DED occurrence was statistically significant (P = 0.02).

In patients who were diagnosed as having DED, 14.4% (92/640) of subjects were shown to have various systemic comorbidities. 40 (6.2%) patients had type 2 DM, 16 (2.5%) had hypertension, 12 (1.9%) had rheumatoid arthritis, 16 had (2.5%) thyroid disease and 5 (1.2%) had Sjogren syndrome.

Study subjects were assessed for any risk factors associated with dry eye disease. Major risk factors observed were near work of more than 6 hours, menopause and smoking, which constituted approximately 70% of DED patients. A bivariate analysis of the risk factors associated with the development of severe DED and a multivariate analysis of risk factors associated with DED was undertaken [Table 2].

Table 2

Multivariate analysis of risk factors with severity of DED.
Risk Factor	Severe DED	Non-severe DED	P	Unadjusted OR (95% CI)	Adjusted OR (95% CI)
Smoking	48	36	< 0.001	1.5 (0.6–3.4)	1.79 (0.93–3.4)
Alcohol consumption	0	28	0.18	0.4 (0.19-1.0)	2.5 (2.1–2.9)
Post ocular Surgery	4	28	0.88	1.31 (0.5–3.1)	1.89 (1.10–3.51)
Post menopause	8	88	0.20	1.17 (0.9–1.5)	2.3(1.9–2.8)
Pterygium	0	16	0.13	0.80 (0.7–1.1)	1.6(.9-1.8)
Ocular allergy	32	16	< 0.001	1.5 (1.19–1.88)	2.14 (1.6–2.7)
Topical medication	4	40	0.50	2.4 (1.90–3.66)	3.5 (2.40–5.72)
Post viral Keratitis	0	12	0.29	1.89 (1.5–2.90)	2.14 (1.6–2.7)
Systemic illness	16	76	0.19	1.21(1.1–1.22)	2.32(1.11–2.83)
Contact lens use	16	0	< 0.001	6.4 (3.3–12.6)	5.1 (2.1–12.4)
Computer work	24	80	0.37	1.31 (0.5–3.1)	1.52(1.4–1.9)
Near work	12	148	0.49	1.5 (1.1–1.9)	1.46 (1.1–1.9)

Significant odds of having severe DED were associated with smoking, contact lens users and ocular allergy. Cigarette smoking (P < 0.001, OR 1.5; 95% CI 0.6–3.4), ocular allergy (P < 0.001, OR 1.5; 95% CI 1.19–1.88) and contact lens usage (P < 0.001, OR 6.4; 95% CI 3.3–12.6) were identified as significant risk factors for severe DED. [Table 3]

Table 3

Risk factors with significant statistical correlation.
Parameter	Statistical test	Significance	Significant Study parameter
1.Age	Chi square test	P value = 0.0001(HS)	Age group:51-70years was most commonly affected
2.Occupation	Chi square test	P value = 0.026(S)	Maximum occurrence of DED was seen in household workers,,students,,government employees and farmers.
3.Refractive status	Chi square test	P value = 0.018(S)	Occurrence of DED among uncorrected refractive error was significantly higher than in corrected refractive error patients and emmetropes.
4.Risk factors	Bivariate and multivariate analysis	P value < 0.001(S)	Cigarette smoking, ocular allergy and contact lens usage were identified as significant risk factors for severe DED

On logistic multivariate analysis, we did not observe any significant association between more than one coexistent risk factor and the occurrence of DED.

Dry eye is a multifactorial ocular surface disease whose prevalence varies according to the operational definition and the characteristics of the population studied. Early detection and timely treatment are of immense importance in managing DED. In different parts of the world, varying prevalence patterns are most likely due to variations in geographic, genetic, sociodemographic and treatment-related factors. This study is intended to evaluate prevalence and associated risk factors in a hospital-based setting, with consequent insights into preventive measures and timely management. Of the 2560 study subjects, 640 (25%) were diagnosed with dry eye. The lower prevalence of 25% in the present study is contrary to 32% and 34.2%, as reported by Titiyal et al. and Shilpy et al. [4,9]. These studies relied only on symptoms to estimate the prevalence of DED, which may have resulted in an overestimation of the prevalence of DED. The prevalence varies with the subject’s geographical location, environmental conditions and lifestyle; hence, places with extreme temperatures and dry weather conditions report a higher prevalence of DED. Factors relating to lifestyle, like sitting in air-conditioned rooms, using digital devices and smoking, increase the likelihood of DED. Past studies suggest that dry eye prevalence ranges from 10.8–57.1% [2]. Such disparity can be attributed to a lack of standardisation of dry eye diagnostic criteria and cut-off values for diagnostic tests.

Association with age

The prevalence of DED in different age groups is important in determining the disease burden in categorised subjects and is known to increase with age. The maximum prevalence of DED (35.8%) was found to be in the 51–70 years age group, which is statistically significant. These results agree with studies which reported that the prevalence of dry eye was higher in increasing age groups [10–12]. This is likely the result of a pathological decrease in tear production and tear stability associated with advancing age and an age-related decrease in meibomian gland secretion due to atrophy of acinar cells [13]. In contrast, the severity of DED had no significant association with different age groups.

The lowest prevalence (7%) was in extreme age group (patients > 70 years). In the more than 70-year age group, the prevalence of DED was paradoxically less as compared to increased prevalence in patients in the younger age group. We also found a significant decrease in associated risk factors comprising visual display terminals (VDT) use, electronic gadgets, and mobile phones in this age group compared to the younger age group, which could be the possible explanation for decreased prevalence in this age group.

Association with Gender

Although the prevalence of DED was higher in females, the difference was not statistically significant. These results are similar to those of the study by Sherry et al. [14]. Contrary to these findings, a significant association between gender and the prevalence of DED have been reported in several studies [10, 15, 16]. A Study by Moss et al. reported a lower prevalence in men, which was explained by the effect of hormonal loss during menopause on ocular dryness in women [15]. Furthermore, the severity of DED was not significantly associated with the gender of the studied population, which indicated a lack of temporal association between disease severity and gender. This could be due to a higher proportion of non-elderly subjects in our study. Elderly females and rural populations living in far areas are less likely to visit hospitals due to sociocultural factors, which could be the potential cause of decreased prevalence.

Association with locality

The association between the prevalence of DED and residential location (rural vs urban) was found to have a non-significant correlation. These results contrast with the findings of studies done by Donthineni et al., Um et al., and Moon et al. [11, 17, 18]. However, our study is similar to Alshamrani et al. [19]. This can be attributed to the use of electronic gadgets and VDTs in equal frequency among both rural and urban populations in India.

Association with occupation

The prevalence of disease was observed to bear a significant association with the occupation of studied subjects. The maximum prevalence of DED was in household workers, followed by students and Govt. employees. Maximum cases of level 4 DED were found in Govt. employees engaged in computer work. Most of the household workers belonged to rural and low socioeconomic backgrounds; hence, elderly females had a history of exposure to smoke and dust while cooking food on “chulha”. Due to their low socioeconomic background, they were deprived of good dietary intake containing essential nutrients. Frequent use of electronic gadgets, VDT use, and air-conditioned atmosphere could be the reason for increased prevalence in urban and higher socioeconomic backgrounds, which also agrees with other studies [4, 11, 20–22].

Association with refractive status

Prevalence of DED was found to be significantly associated with refractive status and was highest in uncorrected refractive error (31.8%), followed by corrected refractive error (22.3%) and emmetropes (20.6%). Sahai et al. have reported a similar high incidence of DED in patients with refractive error [2]. It is postulated that persons with refractive errors have an increased tendency to rub their eyes, which could cause the lodgement of particulate foreign substances, contaminants, sebum and sweat into the eye, predisposing to tear film instability [15]. This result contrasts with Gupta et al., who reported that refractive error does not affect the occurrence of DED [3]. Patients more than 50 years of age group had a coexistent refractive error, accounting for the increased prevalence of DED in the referenced age group. At the same time, the incidence of using electronic gadgets was significantly higher in the younger population. Hence, refractive errors could be confounding to these already-proven risk factors.

Association with systemic illnesses

Dry eye has been associated with systemic conditions, including arthritis, diabetes, and thyroid disease. In the present study, 14.4% of the total 640 DED patients were found to have associated illnesses such as Type 2 DM, thyroid disease, rheumatoid arthritis, and Sjogren’s syndrome. Elevated levels of circulating proinflammatory cytokines and inflammatory cell infiltration of lacrimal glands and associated MGD are responsible for ocular involvement in systemic illness. Many studies reported an association between DED and similar risk factors [15, 23, 24, 25].

Risk factors

DED has a multifactorial etiopathogenesis. Cigarette smoking, ocular allergy, and contact lens usage were identified as significant risk factors for DED. There was no significant association between severe DED and a history of previous ocular surgery, post-menopausal females, alcohol consumption, topical medications, post-viral keratitis, pterygium and systemic illness.

Contact lens usage may cause dry eye or aggravate preexisting DED. Contact lenses cause meibomian gland dysfunction, corneal hypoxia and hyposensitivity, which results in decreased tear production, increased evaporation, and subsequent tear film hyperosmolarity [15,26,27].

Smoking may affect the tear film stability and ocular surface sensitivity and act as a direct irritant to the eyes [15]. A significant association has been reported between smoking and DED in many other studies, too [2, 4, 15, 28].

The Tear Film and Ocular Surface Society Dry Eye Workshop II recently included allergic conjunctivitis among the ‘probable’ risk factors for DED [8, 29]. Recent advances in the DED pathogenic mechanisms provide a strong rationale for considering ocular allergy as a risk factor for DED [30]. Ocular allergy is associated with inflammatory mediators in tears, which can damage surface epithelium and cause tear film instability.

This is the first large sample study from a tertiary eye care centre to ascertain epidemiological patterns and risk factors for DED. It incorporates subjective tools with objective clinical tests to determine dry eye prevalence and risk factors. Most epidemiological studies conducted earlier included the elderly population (> 40 years of age), which falls short of representing the actual disease prevalence in the referenced population. With the increasing use of electronic gadgets by the younger age group, there is a need for more epidemiological studies that widen their inclusion criteria to ascertain precise estimates of the prevalence of DED. This study was designed and conducted to bridge this significant gap in knowledge effectively. As this study is a hospital-based cross-sectional study, there are limitations in extrapolating the results to the general population of this region. Females, the elderly and the rural population living in far areas are less likely to visit hospitals due to the sociocultural environment. This could be a potential cause of decreased prevalence. The study's cross-sectional design precludes us from knowing the antecedent-consequent relationship between risk factors and the endpoint.

This pioneer documentation analyses the epidemiology and aetiology of DED in central India and provides new insights into the magnitude, risk factors, and causes of DED across all age groups. Extensive data evaluation supported by adequate sample size renders this representative study to reflect the prevalence of DED, estimated to be 25%, with the most commonly affected age group of 51–70 years. Age and occupation of study subjects were significantly correlated with the occurrence of DED. Maximum prevalence of DED was found in household workers, students and Govt. employees. Uncorrected refractive status was significantly associated with the prevalence of DED. We did not observe any significant association between age, gender and place of residence with the occurrence of DED. Concerning risk factors, cigarette smoking, ocular allergy and contact lens usage were associated with increasing odds of developing DED.

The study predicts the burden of DED to increase further, particularly in young adults, due to an increase in VDT usage, systemic diseases, environmental concerns and lifestyle factors, Requiring a directed approach toward health education and awareness activities. Eye care facilities should adopt an integrated approach, including therapeutic and preventive measures, to mitigate the increasing prevalence and associated complications.

Competent Interest

The authors have no relevant financial or non-financial interests to disclose.

Author Contributions

All authors contributed to the study’s conception and design. Material preparation, data collection and analysis were performed by Sunita Sabarwal, Priyanka, Bruttendu Moharana, Rituka Gupta, Bhavana Sharma. The first draft of the manuscript was written by Bhavana Sharma and Sunita Sabarwal, and all authors contributed to new versions of the manuscript. All authors read and approved the final manuscript.

Funding:

No funding

Chao W, Belmonte C, Benitez Del Castillo JM, Bron AJ, Dua HS, Nichols KK, et al. Report of the Inaugural Meeting of the TFOS i(2) = initiating innovation Series: Targeting the Unmet Need for Dry Eye Treatment. Ocul Surf. 2016;14(2):264–316.
Sahai A, Malik P. Dry eye: prevalence and attributable risk factors in a hospital-based population. Indian J Ophthalmol. 2005 Jun;53(2):87–91.
Gupta N, Prasad I, Jain R, D’Souza P. Estimating the prevalence of dry eye among Indian patients attending a tertiary ophthalmology clinic. Ann Trop Med Parasitol. 2010 Apr;104(3):247–55.
Titiyal JS, Falera RC, Kaur M, Sharma V, Sharma N. Prevalence and risk factors of dry eye disease in North India: Ocular surface disease index-based cross-sectional hospital study. Indian J Ophthalmol. 2018 Feb;66(2):207–11.
Shah S, Jani H. Prevalence and associated factors of dry eye: Our experience in patients above 40 years of age at a tertiary care center Oman J Ophthalmol. 2015;8:151–6
Basak SK, Pal PP, Basak S, Bandyopadhyay A, Choudhury S, Sar S, et al Prevalence of dry eye diseases in hospital-based population in West Bengal, Eastern India J Indian Med Assoc. 2012;110:789–94
Schiffman RM, Christianson MD, Jacobsen G, Hirsch JD, Reis BL. Reliability and validity of the Ocular Surface Disease Index. Arch Ophthalmol Chic Ill 1960. 2000 May;118(5):615–21.
The definition and classification of dry eye disease: report of the Definition and Classification Subcommittee of the International Dry Eye WorkShop (2007). Ocul Surf. 2007 Apr;5(2):75–92.
Shilpy N, Patel DB. Prevalence of Dry Eye Disease in Western India. 2019 ;6(7):G10-G12.
Attri S, Dwivedi J, Mithal S, Gupta A, Singh LK. Dry Eye-Study of Prevalence, Associated Risk Factors and Frequency of Symptoms in Meerut District. Journal of Evolution of Medical and Dental Sciences. 2019 Nov 11;8(45):3382-7.
Donthineni PR, Kammari P, Shanbhag SS, Singh V, Das AV, Basu S. Incidence, demographics, types and risk The ocular factors of dry eye disease in India: Electronic medical records driven big data analytics report I. surface. 2019 Apr 1;17(2):250-6.
Mathers WD, Lane JA, Zimmerman MB. Tear film changes with normal aging. Cornea 1996; 15:229-35.
Adlakha N, Tirkey ER, Lakhtakia S. To assess the prevalence of dry eye disease in postmenopausal females in a tertiary care centre in Central India. J Med Sci Clin Res. 2017;5(10):29012-7.
Sherry A, Aridi M, Ghach W. Prevalence and risk factors of symptomatic dry eye disease in Lebanon. Contact Lens and Anterior Eye. 2020 Aug 1;43(4):355-8.
Moss SE, Klein R, Klein BE. Prevalence of and risk factors for dry eye syndrome. Arch Ophthalmol Chic Ill 1960. 2000 Sep;118(9):1264–8.
Bakkar MM, Shihadeh WA, Haddad MF, Khader YS. Epidemiology of symptoms of dry eye disease (DED) in Jordan: A cross-sectional non-clinical population-based study. Contact Lens Anterior Eye J Br Contact Lens Assoc. 2016 Jun;39(3):197–202.
Um SB, Kim NH, Lee HK, Song JS, Kim HC. Spatial epidemiology of dry eye disease: findings from South Korea. International journal of health geographics. 2014 Dec 1;13(1):31.
Moon JH, Kim KW, Moon NJ. Smartphone use is a risk factor for pediatric dry eye disease according to region and age: a case control study. BMC ophthalmology. 2016 Dec 1;16(1):188.
Alshamrani AA, Almousa AS, Almulhim AA, Alafaleq AA, Alosaimi MB, Alqahtani AM, Almulhem AM, Alshamrani MA, Alhallafi AH, Alqahtani IZ, Alshehri AA. Prevalence and risk factors of dry eye symptoms in a Saudi Arabian population. Middle East African journal of ophthalmology. 2017 Apr;24(2):67.
20. Bazeer S, Jansonius N, Snieder H, Hammond C, Vehof J. The relationship between occupation and dry eye. The Ocular Surface. 2019 Jul 1;17(3):484-90.
Lee JH, Lee W, Yoon JH, Seok H, Roh J, Won JU. Relationship between symptoms of dry eye syndrome and occupational characteristics: the Korean National Health and Nutrition Examination Survey 2010–2012. BMC Ophthalmol 2015;15:147.
Rahman ZA, Sanip S. P2-493 computer vision syndrome: The association with ergonomic factors. J Epidemiol Community Health. 2011;65 Suppl1:A357.
Khurana G, Khurana D, Jain R. Dry Eye in Patients With Diabetic Retinopathy: A Clinical Study. The Official Scientific Journal of Delhi Ophthalmological Society. 2016 Dec 24;27(3):190-3.
Kumar A, Vyas M. Dry Eye and Corneal Sensitivity in Patients with Type 2 Diabetes Mellitus. 2018.
Lee CY, Chen HC, Sun CC, Lin HY, Lu KH, Huang JY, Yeh CB, Yang SF. Gout as a Risk Factor for Dry Eye Disease: A Population-Based Cohort Study. Journal of clinical medicine. 2019 Jan;8(1):62.
Chen SP, Massaro-Giordano G, Pistilli M, Schreiber CA, Bunya VY. Tear osmolarity and dry eye symptoms in women using oral contraception and contact lenses. Cornea. 2013 Apr;32(4):423
Paulsen AJ, Cruickshanks KJ, Fischer ME, Huang G-H, Klein BEK, Klein R, et al. Dry eye in the beaver dam offspring study: prevalence, risk factors, and health-related quality of life. Am J Ophthalmol. 2014 Apr;157(4):799–806.
Xu L, Zhang W, Zhu XY, Suo T, Fan XQ, Fu Y, et al. Smoking and the risk of dry eye: A meta-analysis. Int J Ophthalmol. 2016;9:1480–6
Stapleton F, Alves M, Bunya VY, Jalbert I, Lekhanont K, Malet F, Na KS, Schaumberg D, Uchino M, Vehof J, Viso E. Tfos dews ii epidemiology report. The ocular surface. 2017 Jul 1;15(3):334-65.
Villani E, Rabbiolo G, Nucci P. Ocular allergy as a risk factor for dry eye in adults and children. Current Opinion in Allergy and Clinical Immunology. 2018 Oct 1;18(5):398-403.

No competing interests reported.

Download PDF

Editor assigned by journal
12 Jul, 2024
Submission checks completed at journal
12 Jul, 2024
First submitted to journal
11 Jul, 2024

You are reading this latest preprint version

Prevalence, pattern and associated risk factors of Dry eye disease: Results of a prospective patient database from a tertiary eye care centre in Central India

Status:

Version 1

Abstract

Purpose:

Methods:

Results:

Conclusion:

Figures

Introduction

Methods

Statistical analysis

Results

Discussion

Association with age

Association with Gender

Association with locality

Association with occupation

Association with refractive status

Association with systemic illnesses

Risk factors

Conclusion

Declarations

Competent Interest

Funding:

References

Additional Declarations

Status:

Version 1