title: Internet search trends analysis tools can provide real-time data on kidney stone disease in the United States.
abstract: OBJECTIVE
  To evaluate the utility of using Internet search trends data to estimate kidney stone occurrence and understand the priorities of patients with kidney stones. Internet search trends data represent a unique resource for monitoring population self-reported illness and health information-seeking behavior.
  
  
  METHODS
  The Google Insights for Search analysis tool was used to study searches related to kidney stones, with each search term returning a search volume index (SVI) according to the search frequency relative to the total search volume. SVIs for the term, "kidney stones," were compiled by location and time parameters and compared with the published weather and stone prevalence data. Linear regression analysis was performed to determine the association of the search interest score with known epidemiologic variations in kidney stone disease, including latitude, temperature, season, and state. The frequency of the related search terms was categorized by theme and qualitatively analyzed.
  
  
  RESULTS
  The SVI correlated significantly with established kidney stone epidemiologic predictors. The SVI correlated with the state latitude (R-squared=0.25; P<.001), the state mean annual temperature (R-squared=0.24; P<.001), and state combined sex prevalence (R-squared=0.25; P<.001). Female prevalence correlated more strongly than did male prevalence (R-squared=0.37; P<.001, and R-squared=0.17; P=.003, respectively). The national SVI correlated strongly with the average U.S. temperature by month (R-squared=0.54; P=.007). The search term ranking suggested that Internet users are most interested in the diagnosis, followed by etiology, infections, and treatment.
  
  
  CONCLUSIONS
  Geographic and temporal variability in kidney stone disease appear to be accurately reflected in Internet search trends data. Internet search trends data might have broader applications for epidemiologic and urologic research.
authors.affiliation: Division of Urology, University of Arizona College of Medicine, Tucson, Arizona 85724, USA.
authors.forename: Scott D
authors.initials: SD
authors.lastname: Willard
authors.forename: Mike M
authors.initials: MM
authors.lastname: Nguyen
mesh_terms: D003627:Data Interpretation, Statistical; D005260:Female; D005843:Geography; D006801:Humans; D057226:Information Seeking Behavior; D020407:Internet / Q000706:statistics & numerical data / Q000639:trends*; D007669:Kidney Calculi / Q000175:diagnosis / Q000453:epidemiology* / Q000209:etiology / Q000628:therapy; D016014:Linear Models; D008297:Male; D015995:Prevalence; D012621:Seasons; D012737:Sex Factors; D013696:Temperature; D014481:United States / Q000453:epidemiology