Poster 1

by in  Poster Session 1

Linear and Periodic Trends in Ophthalmology-Related Internet Search Patterns of the US Population

Isdin Oke; Ankoor Shah
Boston Children’s Hospital
Boston

Introduction:  Patients are increasingly reliant on web-based searches to self assess eye-related symptoms, understand diagnoses, and decide on treatments. We hypothesize that search query analysis will provide insight into current presentation and treatment patterns of eye disease.

Methods:  We performed a retrospective search query analysis using the Google Trends database for ophthalmology terms subdivided into symptoms, diagnoses and treatments. Queries were restricted to the USA within 5-year interval (01/2012 – 01/2017). Matlab Curve-Fitting Toolbox was used to perform linear and nonlinear regression analysis, and goodness-of-fit was assessed by adjusted coefficients of determination (r2).

Results:  Query frequencies for symptoms (N=20): linear increase observed for ‘dry eye’ (r2=0.67), ‘eye pain’ (r2=0.62) and periodic trends observed for ‘pink eye’  (r2=0.76), ‘itchy eye’ (r2=0.67), ‘floaters’ (r2=0.59) and ‘eye discharge’ (r2=0.56). For diagnoses (N=30): linear increase observed for ‘diabetic eye’ (r2=0.44) and periodic trends observed for ‘stye’ (r2=0.81), ‘conjunctivitis’ (r2=0.63), ‘chalazion’ (r2=0.46), ‘corneal abrasion’ (r2=0.30). For treatments (N=10): linear increase observed for ‘cataract surgery’ (r2=0.74) and periodic trends observed for ‘eye drops’ (r2=0.82).

Discussion:  We demonstrate both linear and periodic trends in ophthalmology related queries (N=60). We propose a tool to interpolate the period and amplitude of seasonal variations in query frequency of terms such as ‘stye’, ‘conjunctivitis’, ‘chalazion’ and ‘corneal abrasions’.

Conclusion:  Search query analysis can supplement understanding of epidemiologic factors of eye-related symptoms and diagnose and may potentially serve as a powerful tool for predictive modelling of future trends in ophthalmology.

References:  1. Brownstein JS, Freifeld CC, Madoff LC. Digital Disease Detection – Harnessing the Web for Public Health Surveillance. N Engl J Med. 2009;360(21):2153-2157. doi:10.1056/NEJMp0900702.
2. Deiner MS, Lietman TM, McLeod SD, Chodosh J, Porco TC. Surveillance Tools Emerging From Search Engines and Social Media Data for Determining Eye Disease Patterns. JAMA Ophthalmol. 2016;134(9):1024. doi:10.1001/jamaophthalmol.2016.2267.
3. Nuti S V., Wayda B, Ranasinghe I, et al. The Use of Google Trends in Health Care Research: A Systematic Review. Voracek M, ed. PLoS One. 2014;9(10):e109583. doi:10.1371/journal.pone.0109583.

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