This study explores the use of search query data to determine the prevalence of stroke and assess how patterns of digital engagement have evolved. 

With the rise of digital health analytics, search engine query data has emerged as a powerful tool for real-time disease monitoring. Previous studies have demonstrated its effectiveness in tracking both infectious diseases and chronic conditions. As more individuals turn to the internet for health-related information, analyzing search trends may provide valuable insights into public interest and disease burden.

Google Trends was accessed to analyze anonymized search engine query data from January 1, 2019 to December 31, 2024. The search strategy of “stroke signs + stroke symptoms + mini stroke – heat” was applied to retrieve engine interest data for all 50 states and the District of Columbia. 

The interest level was highest in Mississippi and Kentucky (100 and 98, respectively) and lowest in Rhode Island and District of Columbia (62 and 47, respectively). The Pearson correlation coefficient (r) was calculated to be 0.64 (p < 0.00001, 2-tailed).

Compared to previous data from 2005-2010 (r = 0.47 and p = 0.0005, 2-tailed), our analysis shows a stronger correlation between stroke-related search interest and stroke prevalence. Analyzing search engine query data enables the assessment of relative stroke prevalence and the identification of regional disparities in stroke burden. The geographic distribution of highest search interest has shifted, reflecting changes in stroke incidence, public health campaigns, healthcare access, and digital engagement. These insights can guide the allocation of public health resources for stroke prevention and education in the most affected regions.