Evaluating Online Search Behavior for Early Symptoms of Pediatric Acute Leukemia
Objectives: To examine U.S. search trends from the past 5 years related to early symptoms of childhood acute leukemia and compare them with clinical symptoms to improve awareness for early identification of pediatric cancers. Background: Acute leukemia accounts for 31% of all pediatric cancer cases worldwide. Early recognition of related signs promotes not only timely diagnosis but also the development of the most effective treatment plans, which are crucial for improving outcomes and survival rates when started early. While prior research explored the disease’s clinical presentation, few studies focused on how the public seeks information about these symptoms online. Hence, studying symptom search trends can reveal under-recognized warning signs and guide more impactful education strategies for parents and caregivers. Methods: To analyze the search trends, we entered five childhood acute leukemia-related symptoms into Google Trends: child easy bruising, child joint pain, child swollen lymph nodes, child red spots, and child bloating. We then limited the search parameters to Web Search from June 28, 2020, to June 29, 2025, in the United States. After gathering the trend results, we downloaded the relative interest over time data as a CSV file for further analysis. Results: Upon reviewing the Google Trends search interest score– which reflects the popularity of a search term relative to all Google searches within a given time and location–the results grouped into high interest and low interest terms. High interest terms individually averaged above 20, including child joint pain (22), child swollen lymph nodes (36), and child red spots (32). The remaining low interest terms, child easy bruising (0) and child bloated (2), scored below 20. Conclusions: The contrasting high and low search interest scores for the pediatric acute leukemia symptoms suggest varying levels of public awareness on early signs of the disease. Public health campaigns could prioritize symptoms with low search interest to explore strategies that better educate the public, parents, and caregivers.
Institutions
- University of California, Los Angeles (US)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-08-03
- DOI
- https://doi.org/10.5281/zenodo.16792235
- Primary Topic
- Data-Driven Disease Surveillance
- Type
- article
- Field-Weighted Citation Impact
- 0.00