Frequency and seasonal variation of ophthalmology-related internet searches

Leffler, Christopher T.; Davenport, Byrd; Chan, Dana C.

doi:10.3129/i10-022

Cited by 31 publications

(24 citation statements)

References 20 publications

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“…We found that Google searches for pink eye and related terms in the United States followed the seasonality seen in prior studies. 27 Previous studies have also found that allergic rhinitis ( which is related to allergic conjunctivitis), assessed through Google Trends, peaked in the spring, similar to our findings for allergic conjunctivitis. 28 This finding suggests some overlap of allergic conjunctivitis and allergic rhinitis in social media data and clinical diagnoses.…”

Section: Discussionsupporting

confidence: 90%

Surveillance Tools Emerging From Search Engines and Social Media Data for Determining Eye Disease Patterns

et al. 2016

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IMPORTANCE Internet-based search engine and social media data may provide a novel complementary source for better understanding the epidemiologic factors of infectious eye diseases, which could better inform eye health care and disease prevention.OBJECTIVE To assess whether data from internet-based social media and search engines are associated with objective clinic-based diagnoses of conjunctivitis.DESIGN, SETTING, AND PARTICIPANTS Data from encounters of 4143 patients diagnosed with conjunctivitis from June 3, 2012, to April 26, 2014, at the University of California San Francisco (UCSF) Medical Center, were analyzed using Spearman rank correlation of each weekly observation to compare demographics and seasonality of nonallergic conjunctivitis with allergic conjunctivitis. Data for patient encounters with diagnoses for glaucoma and influenza were also obtained for the same period and compared with conjunctivitis. Temporal patterns of Twitter and Google web search data, geolocated to the United States and associated with these clinical diagnoses, were compared with the clinical encounters. The a priori hypothesis was that weekly internet-based searches and social media posts about conjunctivitis may reflect the true weekly clinical occurrence of conjunctivitis.MAIN OUTCOMES AND MEASURES Weekly total clinical diagnoses at UCSF of nonallergic conjunctivitis, allergic conjunctivitis, glaucoma, and influenza were compared using Spearman rank correlation with equivalent weekly data on Tweets related to disease or disease-related keyword searches obtained from Google Trends.RESULTS Seasonality of clinical diagnoses of nonallergic conjunctivitis among the 4143 patients (2364 females [57.1%] and 1776 males [42.9%]) with 5816 conjunctivitis encounters at UCSF correlated strongly with results of Google searches in the United States for the term pink eye (ρ, 0.68 [95% CI, 0.52 to 0.78]; P < .001) and correlated moderately with Twitter results about pink eye (ρ, 0.38 [95% CI, 0.16 to 0.56]; P < .001) and with clinical diagnosis of influenza (ρ, 0.33 [95% CI, 0.12 to 0.49]; P < .001), but did not significantly correlate with seasonality of clinical diagnoses of allergic conjunctivitis diagnosis at UCSF (ρ, 0.21 [95% CI, −0.02 to 0.42]; P = .06) or with results of Google searches in the United States for the term eye allergy (ρ, 0.13 [95% CI, −0.06 to 0.32]; P = .19). Seasonality of clinical diagnoses of allergic conjunctivitis at UCSF correlated strongly with results of Google searches in the United States for the term eye allergy (ρ, 0.44 [95% CI, 0.24 to 0.60]; P < .001) and eye drops (ρ, 0.47 [95% CI, 0.27 to 0.62]; P < .001). CONCLUSIONS AND RELEVANCEInternet-based search engine and social media data may reflect the occurrence of clinically diagnosed conjunctivitis, suggesting that these data sources can be leveraged to better understand the epidemiologic factors of conjunctivitis.

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Section: Discussionsupporting

confidence: 90%

Surveillance Tools Emerging From Search Engines and Social Media Data for Determining Eye Disease Patterns

et al. 2016

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“…Books reflect only one aspect of culture and do not cover other written work, nonwritten media, and the spoken word. For instance, eye twitching is a common Internet search 73 but was not commonly used by authors, whose emphasis might differ from that of patients. The Oxford English Dictionary contained 95% of the ophthalmic 1-grams.…”

Section: Discussionmentioning

confidence: 99%

Evolution and Impact of Eye and Vision Terms in Written English

et al. 2013

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“…Several investigations, although unrelated to AR, have already been published on the use of these modern, Internet-based source data from Google Insights for Search or Google Trends [12–16]. …”

Section: Introductionmentioning

confidence: 99%

A Model for the Determination of Pollen Count Using Google Search Queries for Patients Suffering from Allergic Rhinitis

König

Mösges

2014

Journal of Allergy

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Background. The transregional increase in pollen-associated allergies and their diversity have been scientifically proven. However, patchy pollen count measurement in many regions is a worldwide problem with few exceptions. Methods. This paper used data gathered from pollen count stations in Germany, Google queries using relevant allergological/biological keywords, and patient data from three German study centres collected in a prospective, double-blind, randomised, placebo-controlled, multicentre immunotherapy study to analyse a possible correlation between these data pools. Results. Overall, correlations between the patient-based, combined symptom medication score and Google data were stronger than those with the regionally measured pollen count data. The correlation of the Google data was especially strong in the groups of severe allergy sufferers. The results of the three-centre analyses show moderate to strong correlations with the Google keywords (up to >0.8 cross-correlation coefficient, P < 0.001) in 10 out of 11 groups (three averaged patient cohorts and eight subgroups of severe allergy sufferers: high IgE class, high combined symptom medication score, and asthma). Conclusion. For countries with a good Internet infrastructure but no dense network of pollen traps, this could represent an alternative for determining pollen levels and, forecasting the pollen count for the next day.

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Frequency and seasonal variation of ophthalmology-related internet searches

Cited by 31 publications

References 20 publications

Surveillance Tools Emerging From Search Engines and Social Media Data for Determining Eye Disease Patterns

Surveillance Tools Emerging From Search Engines and Social Media Data for Determining Eye Disease Patterns

Evolution and Impact of Eye and Vision Terms in Written English

A Model for the Determination of Pollen Count Using Google Search Queries for Patients Suffering from Allergic Rhinitis

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