Literature DB >> 30802671

Incidence, demographics, types and risk factors of dry eye disease in India: Electronic medical records driven big data analytics report I.

Pragnya Rao Donthineni1, Priyanka Kammari2, Swapna S Shanbhag3, Vivek Singh4, Anthony Vipin Das2, Sayan Basu5.   

Abstract

PURPOSE: To describe the incidence, demographics, types and risk-factors of dry eye disease (DED) in patients presenting to a multi-tier ophthalmology hospital network in India.
METHODS: This was an observational hospital-based study of 1,458,830 new patients presenting between 2010 and 2018. Patients with recent onset of both symptoms and signs, as defined by the tear film and ocular surface society dry eye work shop (TFOS DEWS) II guidelines, were considered as DED subjects. The data was prospectively collected using an electronic medical record system. Multiple logistic regression with odds ratio (OR) estimation was performed to identify the high risk-factors of DED.
RESULTS: Overall, 21,290 (1.46%) patients were diagnosed with recent-onset DED. The incidence of DED was 2688 and 16,482 per million population in children and adults, respectively (p < 0.0001). While incidence was significantly greater in males in 3rd, 4th, 9th and 10th decade (p < 0.03), it was greater in females in 5th and 6th decade (p < 0.0001) of life. Classified etiologically 35.5%, 20.6% and 39.9% of patients had evaporative, aqueous deficient and mixed type of DED, respectively. Age (OR 3.7-13.5), urban residence (OR 1.6), professional work (OR 1.5); homemaking (OR 1.42), retirement/unemployment (OR 1.24) and socio-economic affluence (OR 1.6-3.2) were identified as high risk-factors for developing DED.
CONCLUSION: The study results indicate that age, sex, residence, occupation, and socio-economic status have significant impact on development of DED. Since India is an emerging economy with a growing middle-class, increasing urban-migration and large aging population, the country is on the brink of a DED epidemic.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Big data; Dry eye disease; Electronic medical records; Epidemiology

Mesh:

Year:  2019        PMID: 30802671     DOI: 10.1016/j.jtos.2019.02.007

Source DB:  PubMed          Journal:  Ocul Surf        ISSN: 1542-0124            Impact factor:   5.033


  21 in total

1.  Non-invasive Tear Film Assessment in Normal Population: Effect of Age, Sex, and Interparametric Relationship.

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3.  Demography and Clinical Features of Chalazion Among Patients Seen at a Multi-Tier Eye Care Network in India: An Electronic Medical Records Driven Big Data Analysis Report.

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5.  Commentary: Electronic medical record system - should complement but not replace traditional health care.

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6.  Big data and the eyeSmart electronic medical record system - An 8-year experience from a three-tier eye care network in India.

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Review 8.  An Evidence-Based Strategic Approach to Prevention and Treatment of Dry Eye Disease, a Modern Global Epidemic.

Authors:  Pragnya R Donthineni; Swapna S Shanbhag; Sayan Basu
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9.  Ocular and Periocular Tumors in India: An EyeSmart Electronic Medical Record Analysis of 9633 Cases from a Referral Center.

Authors:  Swathi Kaliki; Anthony V Das
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10.  Ophthatome™: an integrated knowledgebase of ophthalmic diseases for translating vision research into the clinic.

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Journal:  BMC Ophthalmol       Date:  2020-11-10       Impact factor: 2.209

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