Seasonal and Geographic Patterns in Seeking Cardiovascular Health Information: An Analysis of the Online Search Trends

Mayo Clin Proc. 2018 Sep;93(9):1185-1190. doi: 10.1016/j.mayocp.2018.07.011.


Objective: To ascertain whether temporal and geographic interest in seeking cardiovascular disease (CVD) information online follows seasonal and geographic patterns similar to those observed in real-world data.

Methods: We searched Google Trends for popular search terms relating to CVD. Relative search volumes (RSVs) were obtained for the period January 4, 2004, to April 19, 2014, for the United States and Australia. We compared average RSVs by month and season and used cosinor analysis to test for seasonal variation in RSVs. We also assessed correlations between state-level RSVs and CVD burden using an ecological correlational design.

Results: RSVs were 15% higher in the United States and 45% higher in Australia for winter compared with summer (P<.001 for difference for both). In the United States, RSVs were 36% higher in February compared with August, while in Australia, RSVs were 75% higher in August compared with January. On cosinor analysis, we found a significant seasonal variability in RSVs, with winter peaks and summer troughs for both the United States and Australia (P<.001 for zero amplitude test for both). We found a significant correlation between state-level RSVs and mortality from CVD (r=0.62; P<.001), heart disease (r=0.58; P<.001), coronary heart disease (r=0.48; P<.001), heart failure (r=0.51; P<.001), and stroke (r=0.60; P<.001).

Conclusion: Google search query volumes related to CVD follow strong seasonal patterns with winter peaks and summer troughs. There is moderate to strong positive correlation between state-level search query volumes and burden of CVD mortality.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Australia
  • Cardiovascular Diseases*
  • Consumer Health Information*
  • Geography
  • Humans
  • Information Seeking Behavior*
  • Internet / trends*
  • Linear Models
  • Search Engine / trends*
  • Seasons
  • United States