A Novel Epidemiological Approach to Geographically Mapping Population Dry Eye Disease in the United States Through Google Trends

Abstract

Purpose: Our study fills the spatiotemporal gaps in dry eye disease (DED) epidemiology by using Google Trends as a novel epidemiological tool for geographically mapping DED in relation to environmental risk factors. Methods: We used Google Trends to extract DED-related queries estimating users' intent from 2004 to 2019 in the United States. We incorporated national climate data to generate heat maps comparing geographic, temporal, and environmental relationships of DED. Multivariable regression models were constructed to generate quadratic forecasts predicting DED and control searches. Results: Our results illustrated the upward trend, seasonal pattern, environmental influence, and spatial relationship of DED search volume across the US geography. Localized patches of DED interest were visualized in urban areas. There was no significant difference in DED queries across the US census regions (P = 0.3543). Regression model 1 predicted DED queries per state (R2 = 0.61), with the significant predictor being urban population [r = 0.56, adjusted (adj.) P < 0.001, n = 50]; model 2 predicted DED searches over time (R2 = 0.97), with significant predictors being control queries (r = 0.85, adj. P = 0.0169, n = 190), time (r = 0.96, adj. P < 0.001, n = 190), time2 (r = 0.97, adj. P < 0.001, n = 190), and seasonality (winter r = −0.04, adj. P = 0.0196, n = 190; spring r = 0.10, adj. P < 0.001, n = 190). Conclusions: Our study used Google Trends as a novel epidemiologic approach to geographically mapping the US DED. Importantly, urban population and seasonality were stronger risk factors of DED searches than temperature, humidity, sunshine, pollution, or region. Our work paves the way for future exploration of geographic information systems for locating DED and other diseases through online population metrics.

Systemic, environmental and lifestyle risk factors for dry eye disease in a mediterranean caucasian population

“…Environmental factors such as air pollution, wind, low humidity, use of central heating or air conditioning have been suggested to impact the integrity of the ocular surface [2,24,[40][41][42]. In the present study, air conditioning was identified as a potential risk factor for DED, but it did not show a statistically significant relationship with DED in the multivariate analysis.…”

Oculofacial plastic surgery-related online search trends including the impact of the COVID-19 pandemic

“…According to previously published methods, the authors included in all analyses control term data to account for fluctuations in search volume ascribed to general changes in internet traffic. 12,13 To compare the mean search volume during the COVID-19 pandemic stay-at-home period (March 1 2020, until May 31 2020) with the same time period in the prior 2 years, an unpaired t-test was performed. Multivariable regression analysis was utilized to examine the relationship between online interest for COVID-19 and interest for oculofacial plastic surgery from January 1 2020 until July 20 2020.…”

“…[9][10][11] The following methods were designed according to previously published methodologies. 12,13 The search query was performed with the following filters: "United States," "1/1/2004" through "7/20/2020," "All categories," "Web search." A "Health" filter was used to exclude ambiguous terms.…”

Utilizing Big Data From Google Trends to Map Population Depression in the United States: Exploratory Infodemiology Study

“…Data sets were downloaded for symptoms and terms listed by the American Psychiatric Association for major depressive disorder: “feeling sad,” “depressed,” “depression,” “empty,” “insomnia,” “fatigue,” “guilty,” “feeling guilty,” and “suicide” [ 21 ]. To account for random variance and overall increases in search queries, data sets were also downloaded across similar time periods for control terms based on previously published studies and popular internet search terms: “sports,” “news,” “google,” “youtube,” “facebook,” and “netflix” [ 18 , 19 , 22 ]. The values of depression search intent were summed and normalized relative to the control terms for the given region and time and are represented on a scale of 0 to 100 arbitrary units (AU).…”