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.
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.
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 (R = 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 (R = 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), time (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).
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.