Temporal and geographic patterns of breast cancer-related google search activity and its association with CDC-reported incidence in the United States (2005-2023).

BACKGROUND: Breast cancer remains the most commonly diagnosed cancer among women worldwide and a leading cause of cancer-related mortality. With increasing reliance on digital platforms for health information, Google Trends has emerged as a potential tool for digital surveillance. This study aims to determine whether Google Trends search activity correlates with Centers for Disease Control (CDC)-reported breast cancer burden and to assess its viability as a complementary digital surveillance signal for public health monitoring. METHODS: This study utilized Google Trends data to analyze prevalent breast cancer-related search terms from 2005 to 2023 in the United States. The latest CDC-reported breast cancer incidence rates for 2023 were compared with State-level relative search volume (RSV) to identify patterns. Pearson correlation and linear regression analyses were performed to evaluate the association between state-level RSV and breast cancer incidence rates. RESULTS: Google Trends data showed a 21% increase in average annual breast cancer-related searches in the United States from 2005 to 2023. Linear regression analysis showed a statistically significant upward trend (β = 2.107 RSV units per year, P = 0.001), with only 46.2% of the variance in search activity (R 2 = 0.462). At the state level, West Virginia had the highest nineteen-year average for RSV of 28.59. However, Pearson correlation analysis revealed a weak and non-significant correlation between RSV and incidence rates (r = 0.328, P = 0.355), with only 10.8% of the variance explained (R 2 = 0.108). CONCLUSION: Although national digital search interest in breast cancer has increased significantly over time, Google Trends data demonstrated weak geographic concordance with state-level incidence rates. These findings suggest that while digital search data may reflect evolving public engagement, they should be interpreted cautiously when used as a proxy for geographic disease burden in digital surveillance frameworks.